var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"jsonp\":\"1\",\"host\":\"bibbase.org\",\"ssl\":false},\n      data: [{\"title\":\"Enhanced Convolutional-Neural-Network Architecture for Crop Classification\",\"type\":\"article\",\"year\":\"2021\",\"pages\":\"1-23\",\"websites\":\"https://www.mdpi.com/2076-3417/11/9/4292\",\"id\":\"24a0c385-d592-3a29-a7e6-33a2d377a118\",\"created\":\"2022-02-02T06:07:46.014Z\",\"file_attached\":\"true\",\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"aea1f410-b6a0-3014-afb8-bc5b4ad73524\",\"last_modified\":\"2022-02-02T06:07:47.387Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Automatic crop identification and monitoring is a key element in enhancing food production processes as well as diminishing the related environmental impact. Although several efficient deep learning techniques have emerged in the field of multispectral imagery analysis, the crop classification problem still needs more accurate solutions. This work introduces a competitive methodology for crop classification from multispectral satellite imagery mainly using an enhanced 2D convolutional neural network (2D-CNN) designed at a smaller-scale architecture, as well as a novel post-processing step. The proposed methodology contains four steps: image stacking, patch extraction, classification model design (based on a 2D-CNN architecture), and post-processing. First, the images are stacked to increase the number of features. Second, the input images are split into patches and fed into the 2D-CNN model. Then, the 2D-CNN model is constructed within a small-scale framework, and properly trained to recognize 10 different types of crops. Finally, a post-processing step is performed in order to reduce the classification error caused by lower-spatial-resolution images. Experiments were carried over the so-named Campo Verde database, which consists of a set of satellite images captured by Landsat and Sentinel satellites from the municipality of Campo Verde, Brazil. In contrast to the maximum accuracy values reached by remarkable works reported in the literature (amounting to an overall accuracy of about 81%, a f1 score of 75.89%, and average accuracy of 73.35%), the proposed methodology achieves a competitive overall accuracy of 81.20%, a f1 score of 75.89%, and an average accuracy of 88.72% when classifying 10 different crops, while ensuring an adequate trade-off between the number of multiply-accumulate operations (MACs) and accuracy. Furthermore, given its ability to effectively classify patches from two image sequences, this methodology may result appealing for other real-world applications, such as the classification of urban materials.\",\"bibtype\":\"article\",\"author\":\"Moreno-revelo, Mónica Y and Guachi-guachi, Lorena and Gómez-mendoza, Juan Bernardo and Revelo-fuelagán, Javier and Peluffo-ordóñez, Diego H\",\"journal\":\"Applied Sciences\",\"bibtex\":\"@article{\\n title = {Enhanced Convolutional-Neural-Network Architecture for Crop Classification},\\n type = {article},\\n year = {2021},\\n pages = {1-23},\\n websites = {https://www.mdpi.com/2076-3417/11/9/4292},\\n id = {24a0c385-d592-3a29-a7e6-33a2d377a118},\\n created = {2022-02-02T06:07:46.014Z},\\n file_attached = {true},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\\n last_modified = {2022-02-02T06:07:47.387Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Automatic crop identification and monitoring is a key element in enhancing food production processes as well as diminishing the related environmental impact. Although several efficient deep learning techniques have emerged in the field of multispectral imagery analysis, the crop classification problem still needs more accurate solutions. This work introduces a competitive methodology for crop classification from multispectral satellite imagery mainly using an enhanced 2D convolutional neural network (2D-CNN) designed at a smaller-scale architecture, as well as a novel post-processing step. The proposed methodology contains four steps: image stacking, patch extraction, classification model design (based on a 2D-CNN architecture), and post-processing. First, the images are stacked to increase the number of features. Second, the input images are split into patches and fed into the 2D-CNN model. Then, the 2D-CNN model is constructed within a small-scale framework, and properly trained to recognize 10 different types of crops. Finally, a post-processing step is performed in order to reduce the classification error caused by lower-spatial-resolution images. Experiments were carried over the so-named Campo Verde database, which consists of a set of satellite images captured by Landsat and Sentinel satellites from the municipality of Campo Verde, Brazil. In contrast to the maximum accuracy values reached by remarkable works reported in the literature (amounting to an overall accuracy of about 81%, a f1 score of 75.89%, and average accuracy of 73.35%), the proposed methodology achieves a competitive overall accuracy of 81.20%, a f1 score of 75.89%, and an average accuracy of 88.72% when classifying 10 different crops, while ensuring an adequate trade-off between the number of multiply-accumulate operations (MACs) and accuracy. Furthermore, given its ability to effectively classify patches from two image sequences, this methodology may result appealing for other real-world applications, such as the classification of urban materials.},\\n bibtype = {article},\\n author = {Moreno-revelo, Mónica Y and Guachi-guachi, Lorena and Gómez-mendoza, Juan Bernardo and Revelo-fuelagán, Javier and Peluffo-ordóñez, Diego H},\\n journal = {Applied Sciences}\\n}\",\"author_short\":[\"Moreno-revelo,  M., Y.\",\"Guachi-guachi,  L.\",\"Gómez-mendoza,  J., B.\",\"Revelo-fuelagán,  J.\",\"Peluffo-ordóñez,  D., H.\"],\"urls\":{\"Paper\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/file/2fc18dfd-d588-0a55-0dfa-18c79fa2f527/2021_AppliedSciences_Enhanced_CNN.pdf.pdf\",\"Website\":\"https://www.mdpi.com/2076-3417/11/9/4292\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"morenorevelo-guachiguachi-gmezmendoza-revelofuelagn-peluffoordez-enhancedconvolutionalneuralnetworkarchitectureforcropclassification-2021\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Comparison of current deep convolutional neural networks for the segmentation of breast masses in mammograms\",\"type\":\"article\",\"year\":\"2021\",\"websites\":\"https://ieeexplore.ieee.org/document/9614200\",\"id\":\"c9a3316f-2543-320d-b8bb-5ed45ce86aab\",\"created\":\"2022-02-02T06:07:46.249Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"aea1f410-b6a0-3014-afb8-bc5b4ad73524\",\"last_modified\":\"2022-02-02T06:07:46.249Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"9614200\",\"source_type\":\"article\",\"private_publication\":false,\"abstract\":\"Breast cancer causes approximately 684,996 deaths worldwide, making it the leading cause of female cancer mortality. However, these figures can be reduced with early diagnosis through mammographic imaging, allowing for the timely and effective treatment of this disease. To establish the best tools for contributing to the automatic diagnosis of breast cancer, different deep learning (DL) architectures were compared in terms of breast lesion segmentation, lesion type classification, and degree of suspicion of malignancy tests. The tasks were completed with state-of-the-art architectures and backbones. Initially, during segmentation, the base UNet, Visual Geometry Group 19 (VGG19), InceptionResNetV2, EfficientNet, MobileNetv2, ResNet, ResNeXt, MultiResUNet, linkNet-VGG19, DenseNet, SEResNet and SeResNeXt architectures were compared, where “Res” denotes a residual network. In addition, training was performed with 5 of the most advanced loss functions and validated by the Dice coefficient, sensitivity, and specificity. The proposed models achieved Dice values above 90%, with the EfficientNet architecture achieving 94.75% and 99% accuracy on the two tasks. Subsequently, classification was addressed with the ResNet50V2, VGG19, InceptionResNetV2, DenseNet121, InceptionV3, Xception and EfficientNetB7 networks. The proposed models achieved 96.97% and 97.73% accuracy through the VGG19 and ResNet50V2 networks on the lesion classification and degree of suspicion tasks, respectively. All three tasks were addressed with open-access databases, including the Digital Database for Screening Mammography (DDSM), the Mammographic Image Analysis Society (MIAS) database, and INbreast.\",\"bibtype\":\"article\",\"author\":\"Anaya-Isaza, Andrés and Mera-Jiménez, Leonel and Cabrera-Chavarro, Johan and Guachi-Guachi, Lorena and Peluffo-Ordóñez, Diego and Rios-Patiño, Jorge\",\"doi\":\"10.1109/ACCESS.2021.3127862\",\"journal\":\"IEEE Access\",\"bibtex\":\"@article{\\n title = {Comparison of current deep convolutional neural networks for the segmentation of breast masses in mammograms},\\n type = {article},\\n year = {2021},\\n websites = {https://ieeexplore.ieee.org/document/9614200},\\n id = {c9a3316f-2543-320d-b8bb-5ed45ce86aab},\\n created = {2022-02-02T06:07:46.249Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\\n last_modified = {2022-02-02T06:07:46.249Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {9614200},\\n source_type = {article},\\n private_publication = {false},\\n abstract = {Breast cancer causes approximately 684,996 deaths worldwide, making it the leading cause of female cancer mortality. However, these figures can be reduced with early diagnosis through mammographic imaging, allowing for the timely and effective treatment of this disease. To establish the best tools for contributing to the automatic diagnosis of breast cancer, different deep learning (DL) architectures were compared in terms of breast lesion segmentation, lesion type classification, and degree of suspicion of malignancy tests. The tasks were completed with state-of-the-art architectures and backbones. Initially, during segmentation, the base UNet, Visual Geometry Group 19 (VGG19), InceptionResNetV2, EfficientNet, MobileNetv2, ResNet, ResNeXt, MultiResUNet, linkNet-VGG19, DenseNet, SEResNet and SeResNeXt architectures were compared, where “Res” denotes a residual network. In addition, training was performed with 5 of the most advanced loss functions and validated by the Dice coefficient, sensitivity, and specificity. The proposed models achieved Dice values above 90%, with the EfficientNet architecture achieving 94.75% and 99% accuracy on the two tasks. Subsequently, classification was addressed with the ResNet50V2, VGG19, InceptionResNetV2, DenseNet121, InceptionV3, Xception and EfficientNetB7 networks. The proposed models achieved 96.97% and 97.73% accuracy through the VGG19 and ResNet50V2 networks on the lesion classification and degree of suspicion tasks, respectively. All three tasks were addressed with open-access databases, including the Digital Database for Screening Mammography (DDSM), the Mammographic Image Analysis Society (MIAS) database, and INbreast.},\\n bibtype = {article},\\n author = {Anaya-Isaza, Andrés and Mera-Jiménez, Leonel and Cabrera-Chavarro, Johan and Guachi-Guachi, Lorena and Peluffo-Ordóñez, Diego and Rios-Patiño, Jorge},\\n doi = {10.1109/ACCESS.2021.3127862},\\n journal = {IEEE Access}\\n}\",\"author_short\":[\"Anaya-Isaza,  A.\",\"Mera-Jiménez,  L.\",\"Cabrera-Chavarro,  J.\",\"Guachi-Guachi,  L.\",\"Peluffo-Ordóñez,  D.\",\"Rios-Patiño,  J.\"],\"urls\":{\"Website\":\"https://ieeexplore.ieee.org/document/9614200\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"anayaisaza-merajimnez-cabrerachavarro-guachiguachi-peluffoordez-riospatio-comparisonofcurrentdeepconvolutionalneuralnetworksforthesegmentationofbreastmassesinmammograms-2021\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"type\":\"inbook\",\"year\":\"2019\",\"keywords\":\"Artificial neural networks,Long-term demand forecasting,Small business\",\"pages\":\"362-373\",\"websites\":\"http://link.springer.com/10.1007/978-3-030-20518-8_31\",\"id\":\"d0ee1709-f877-3515-a99a-f05f24797555\",\"created\":\"2022-02-02T06:07:46.500Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"aea1f410-b6a0-3014-afb8-bc5b4ad73524\",\"last_modified\":\"2022-02-02T06:07:46.500Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"Herrera-Granda2019a\",\"private_publication\":false,\"abstract\":\"This paper shows a neural networks-based demand forecasting model designed for a small manufacturer of bottled water in Ecuador, which currently doesn’t have adequate demand forecast methodologies, causing problems of customer orders non-compliance, inventory excess and economic losses. However, by working with accurate predictions, the manufacturer will have an anticipated vision of future needs in order to satisfy the demand for manufactured products, in other words, to guarantee on time and reasonable use of the resources. To solve the problems that this small manufacturer has to face a historic demand data acquisition process was done through the last 36 months costumer order records. In the construction of the historical time series, that was analyzed, demand dates and volumes were established as input variables. Then the design of forecast models was done, based on classical methods and multi-layer neural networks, which were evaluated by means of quantitative error indicators. The application of these methods was done through the R programming language. After this, a stage of training and improvement of the network is included, it was evaluated against the results of the classic forecasting methods, and the next 12 months were predicted by means of the best obtained model. Finally, the feasibility of the use of neural networks in the forecast of demand for purified water bottles, is demonstrated.\",\"bibtype\":\"inbook\",\"author\":\"Herrera-Granda, Israel D. and Chicaiza-Ipiales, Joselyn A. and Herrera-Granda, Erick P. and Lorente-Leyva, Leandro L. and Caraguay-Procel, Jorge A. and García-Santillán, Iván D. and Peluffo-Ordóñez, Diego H.\",\"doi\":\"10.1007/978-3-030-20518-8_31\",\"chapter\":\"Artificial Neural Networks for Bottled Water Demand Forecasting: A Small Business Case Study\",\"title\":\"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)\",\"bibtex\":\"@inbook{\\n type = {inbook},\\n year = {2019},\\n keywords = {Artificial neural networks,Long-term demand forecasting,Small business},\\n pages = {362-373},\\n websites = {http://link.springer.com/10.1007/978-3-030-20518-8_31},\\n id = {d0ee1709-f877-3515-a99a-f05f24797555},\\n created = {2022-02-02T06:07:46.500Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\\n last_modified = {2022-02-02T06:07:46.500Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {Herrera-Granda2019a},\\n private_publication = {false},\\n abstract = {This paper shows a neural networks-based demand forecasting model designed for a small manufacturer of bottled water in Ecuador, which currently doesn’t have adequate demand forecast methodologies, causing problems of customer orders non-compliance, inventory excess and economic losses. However, by working with accurate predictions, the manufacturer will have an anticipated vision of future needs in order to satisfy the demand for manufactured products, in other words, to guarantee on time and reasonable use of the resources. To solve the problems that this small manufacturer has to face a historic demand data acquisition process was done through the last 36 months costumer order records. In the construction of the historical time series, that was analyzed, demand dates and volumes were established as input variables. Then the design of forecast models was done, based on classical methods and multi-layer neural networks, which were evaluated by means of quantitative error indicators. The application of these methods was done through the R programming language. After this, a stage of training and improvement of the network is included, it was evaluated against the results of the classic forecasting methods, and the next 12 months were predicted by means of the best obtained model. Finally, the feasibility of the use of neural networks in the forecast of demand for purified water bottles, is demonstrated.},\\n bibtype = {inbook},\\n author = {Herrera-Granda, Israel D. and Chicaiza-Ipiales, Joselyn A. and Herrera-Granda, Erick P. and Lorente-Leyva, Leandro L. and Caraguay-Procel, Jorge A. and García-Santillán, Iván D. and Peluffo-Ordóñez, Diego H.},\\n doi = {10.1007/978-3-030-20518-8_31},\\n chapter = {Artificial Neural Networks for Bottled Water Demand Forecasting: A Small Business Case Study},\\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\\n}\",\"author_short\":[\"Herrera-Granda,  I., D.\",\"Chicaiza-Ipiales,  J., A.\",\"Herrera-Granda,  E., P.\",\"Lorente-Leyva,  L., L.\",\"Caraguay-Procel,  J., A.\",\"García-Santillán,  I., D.\",\"Peluffo-Ordóñez,  D., H.\"],\"urls\":{\"Website\":\"http://link.springer.com/10.1007/978-3-030-20518-8_31\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"herreragranda-chicaizaipiales-herreragranda-lorenteleyva-caraguayprocel-garcasantilln-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019\",\"role\":\"author\",\"keyword\":[\"Artificial neural networks\",\"Long-term demand forecasting\",\"Small business\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Artificial Neural Networks for Urban Water Demand Forecasting: A Case Study\",\"type\":\"article\",\"year\":\"2019\",\"pages\":\"012004\",\"volume\":\"1284\",\"websites\":\"https://iopscience.iop.org/article/10.1088/1742-6596/1284/1/012004\",\"month\":\"8\",\"id\":\"d25efbb9-d760-3246-ad23-47aedb6830b6\",\"created\":\"2022-02-02T06:07:46.757Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"aea1f410-b6a0-3014-afb8-bc5b4ad73524\",\"last_modified\":\"2022-02-02T06:07:46.757Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"Lorente-Leyva2019a\",\"private_publication\":false,\"abstract\":\"This paper presents an application of an artificial neural network model in forecasting urban water demand using MATLAB software. Considering that in any planning process, the demand forecast plays a fundamental role, being one of the premises to organize and control a set of activities or processes. The versatility of the short, medium and long-term prediction that is provided to the company that offers the water distribution service to determine the supply capacity, maintenance activities, and system improvements as a strategic planning tool. Shown to improve network performance by using time series water demand data, the model can provide excellent fit and forecast without relying on the explicit inclusion of climatic factors and number of consumers. The excellent accuracy of the model indicates the effectiveness of forecasting over different time horizons. Finally, the results obtained from the Artificial Neural Network are compared with traditional statistical models.\",\"bibtype\":\"article\",\"author\":\"Lorente-Leyva, Leandro L. and Pavón-Valencia, Jairo F. and Montero-Santos, Yakcleem and Herrera-Granda, Israel D and Herrera-Granda, Erick P. and Peluffo-Ordóñez, Diego H.\",\"doi\":\"10.1088/1742-6596/1284/1/012004\",\"journal\":\"Journal of Physics: Conference Series\",\"bibtex\":\"@article{\\n title = {Artificial Neural Networks for Urban Water Demand Forecasting: A Case Study},\\n type = {article},\\n year = {2019},\\n pages = {012004},\\n volume = {1284},\\n websites = {https://iopscience.iop.org/article/10.1088/1742-6596/1284/1/012004},\\n month = {8},\\n id = {d25efbb9-d760-3246-ad23-47aedb6830b6},\\n created = {2022-02-02T06:07:46.757Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\\n last_modified = {2022-02-02T06:07:46.757Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {Lorente-Leyva2019a},\\n private_publication = {false},\\n abstract = {This paper presents an application of an artificial neural network model in forecasting urban water demand using MATLAB software. Considering that in any planning process, the demand forecast plays a fundamental role, being one of the premises to organize and control a set of activities or processes. The versatility of the short, medium and long-term prediction that is provided to the company that offers the water distribution service to determine the supply capacity, maintenance activities, and system improvements as a strategic planning tool. Shown to improve network performance by using time series water demand data, the model can provide excellent fit and forecast without relying on the explicit inclusion of climatic factors and number of consumers. The excellent accuracy of the model indicates the effectiveness of forecasting over different time horizons. Finally, the results obtained from the Artificial Neural Network are compared with traditional statistical models.},\\n bibtype = {article},\\n author = {Lorente-Leyva, Leandro L. and Pavón-Valencia, Jairo F. and Montero-Santos, Yakcleem and Herrera-Granda, Israel D and Herrera-Granda, Erick P. and Peluffo-Ordóñez, Diego H.},\\n doi = {10.1088/1742-6596/1284/1/012004},\\n journal = {Journal of Physics: Conference Series}\\n}\",\"author_short\":[\"Lorente-Leyva,  L., L.\",\"Pavón-Valencia,  J., F.\",\"Montero-Santos,  Y.\",\"Herrera-Granda,  I., D.\",\"Herrera-Granda,  E., P.\",\"Peluffo-Ordóñez,  D., H.\"],\"urls\":{\"Website\":\"https://iopscience.iop.org/article/10.1088/1742-6596/1284/1/012004\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"lorenteleyva-pavnvalencia-monterosantos-herreragranda-herreragranda-peluffoordez-artificialneuralnetworksforurbanwaterdemandforecastingacasestudy-2019\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Artificial Neural Networks in the Demand Forecasting of a Metal-Mechanical Industry\",\"type\":\"article\",\"year\":\"2019\",\"pages\":\"81-87\",\"volume\":\"15\",\"websites\":\"http://www.medwelljournals.com/abstract/?doi=jeasci.2020.81.87\",\"month\":\"10\",\"day\":\"25\",\"id\":\"d2a33562-d740-37c2-a6df-cc39c178a563\",\"created\":\"2022-02-02T06:07:46.991Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"aea1f410-b6a0-3014-afb8-bc5b4ad73524\",\"last_modified\":\"2022-02-02T06:07:46.991Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"L.Lorente-Leyva2019\",\"private_publication\":false,\"abstract\":\"This research presents an application of artificial neural networks in demand forecasting by using MATLAB Software. Keeping in mind that in any planning process forecasts play a fundamental role, being one of the bases for; planning, organizing and controlling production. It gives priority to the most critical nodes and their key activities, so that, the decisions made about them will generate the greatest possible positive impact. The methodology applied demonstrates the quality of the solutions found which are compared with traditional statistical methods to demonstrate the value of the solution proposed. When the results show that the minimum quadratic error is reached with the application of artificial neural networks, a better performance is obtained. Therefore, a suitable horizon is established for the planification and decision making in the metal-mechanical industry for the use of artificial intelligence in the production processes.\",\"bibtype\":\"article\",\"author\":\"L. Lorente-Leyva, Leandro and R. Patino-Alarcon, Delio and Montero-Santos, Yakcleem and D. Herrera-Granda, Israel and H. Peluffo-Ordonez, Diego and M. Lastre-Aleaga, Arlys and Cordoves-Garcia, Alexis\",\"doi\":\"10.36478/jeasci.2020.81.87\",\"journal\":\"Journal of Engineering and Applied Sciences\",\"number\":\"1\",\"bibtex\":\"@article{\\n title = {Artificial Neural Networks in the Demand Forecasting of a Metal-Mechanical Industry},\\n type = {article},\\n year = {2019},\\n pages = {81-87},\\n volume = {15},\\n websites = {http://www.medwelljournals.com/abstract/?doi=jeasci.2020.81.87},\\n month = {10},\\n day = {25},\\n id = {d2a33562-d740-37c2-a6df-cc39c178a563},\\n created = {2022-02-02T06:07:46.991Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\\n last_modified = {2022-02-02T06:07:46.991Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {L.Lorente-Leyva2019},\\n private_publication = {false},\\n abstract = {This research presents an application of artificial neural networks in demand forecasting by using MATLAB Software. Keeping in mind that in any planning process forecasts play a fundamental role, being one of the bases for; planning, organizing and controlling production. It gives priority to the most critical nodes and their key activities, so that, the decisions made about them will generate the greatest possible positive impact. The methodology applied demonstrates the quality of the solutions found which are compared with traditional statistical methods to demonstrate the value of the solution proposed. When the results show that the minimum quadratic error is reached with the application of artificial neural networks, a better performance is obtained. Therefore, a suitable horizon is established for the planification and decision making in the metal-mechanical industry for the use of artificial intelligence in the production processes.},\\n bibtype = {article},\\n author = {L. Lorente-Leyva, Leandro and R. Patino-Alarcon, Delio and Montero-Santos, Yakcleem and D. Herrera-Granda, Israel and H. Peluffo-Ordonez, Diego and M. Lastre-Aleaga, Arlys and Cordoves-Garcia, Alexis},\\n doi = {10.36478/jeasci.2020.81.87},\\n journal = {Journal of Engineering and Applied Sciences},\\n number = {1}\\n}\",\"author_short\":[\"L. Lorente-Leyva,  L.\",\"R. Patino-Alarcon,  D.\",\"Montero-Santos,  Y.\",\"D. Herrera-Granda,  I.\",\"H. Peluffo-Ordonez,  D.\",\"M. Lastre-Aleaga,  A.\",\"Cordoves-Garcia,  A.\"],\"urls\":{\"Website\":\"http://www.medwelljournals.com/abstract/?doi=jeasci.2020.81.87\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"llorenteleyva-rpatinoalarcon-monterosantos-dherreragranda-hpeluffoordonez-mlastrealeaga-cordovesgarcia-artificialneuralnetworksinthedemandforecastingofametalmechanicalindustry-2019\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Instance Selection on CNNs for Alzheimer’s Disease Classification from MRI\",\"type\":\"inproceedings\",\"year\":\"2022\",\"pages\":\"330-337\",\"websites\":\"https://www.scitepress.org/Link.aspx?doi=10.5220/0010900100003122\",\"publisher\":\"SciTePress\",\"institution\":\"INSTICC\",\"id\":\"b1289f2e-9cba-3e3f-b692-0db0c4a8e9f2\",\"created\":\"2022-02-22T01:00:12.425Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"aea1f410-b6a0-3014-afb8-bc5b4ad73524\",\"last_modified\":\"2022-02-22T01:00:12.425Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"icpram22\",\"source_type\":\"conference\",\"private_publication\":false,\"bibtype\":\"inproceedings\",\"author\":\"Castro-Silva., J and Moreno-García., M and Guachi-Guachi., Lorena and Peluffo-Ordóñez., D\",\"doi\":\"10.5220/0010900100003122\",\"booktitle\":\"Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM,\",\"bibtex\":\"@inproceedings{\\n title = {Instance Selection on CNNs for Alzheimer’s Disease Classification from MRI},\\n type = {inproceedings},\\n year = {2022},\\n pages = {330-337},\\n websites = {https://www.scitepress.org/Link.aspx?doi=10.5220/0010900100003122},\\n publisher = {SciTePress},\\n institution = {INSTICC},\\n id = {b1289f2e-9cba-3e3f-b692-0db0c4a8e9f2},\\n created = {2022-02-22T01:00:12.425Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\\n last_modified = {2022-02-22T01:00:12.425Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {icpram22},\\n source_type = {conference},\\n private_publication = {false},\\n bibtype = {inproceedings},\\n author = {Castro-Silva., J and Moreno-García., M and Guachi-Guachi., Lorena and Peluffo-Ordóñez., D},\\n doi = {10.5220/0010900100003122},\\n booktitle = {Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM,}\\n}\",\"author_short\":[\"Castro-Silva.,  J.\",\"Moreno-García.,  M.\",\"Guachi-Guachi.,  L.\",\"Peluffo-Ordóñez.,  D.\"],\"urls\":{\"Website\":\"https://www.scitepress.org/Link.aspx?doi=10.5220/0010900100003122\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"castrosilva-morenogarca-guachiguachi-peluffoordez-instanceselectiononcnnsforalzheimersdiseaseclassificationfrommri-2022\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"ECG-Based Heartbeat Classification for Arrhythmia Detection Using Artificial Neural Networks\",\"type\":\"inproceedings\",\"year\":\"2022\",\"pages\":\"247-259\",\"websites\":\"https://link.springer.com/chapter/10.1007/978-3-031-10450-3_20\",\"publisher\":\"Springer International Publishing\",\"city\":\"Cham\",\"id\":\"fe0bb748-7f2d-396d-be6a-7337af3ccdc3\",\"created\":\"2022-07-16T03:47:10.517Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"aea1f410-b6a0-3014-afb8-bc5b4ad73524\",\"last_modified\":\"2022-07-16T03:47:10.517Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"10.1007/978-3-031-10450-3_20\",\"source_type\":\"inproceedings\",\"private_publication\":false,\"abstract\":\"Cardiovascular disease (CVD) has quickly grown in prevalence over the previous decade, becoming the major cause of human morbidity on a global scale. Due to the massive number of ECG data, manual analysis is regarded as a time-consuming, costly and prone to human error task. In the other hand, computational systems based on biomedical signal processing and machine learning techniques might be suited for supporting arrhythmia diagnostic processes, while solving some of those issues. In general, such systems involve five stages: acquisition, preprocessing, segmentation, characterization, and classification. Yet numerous fundamental aspects remain unresolved, including sensitivity to signal fluctuation, accuracy, computing cost, generalizability, and interpretability. In this context, the present study offers a comparative analysis of ECG signal classification using two artificial neural networks created by different machine learning frameworks. The neural nets were built into a pipeline that aims to strike an appropriate balance among signal robustness, variability, and accuracy. The proposed approach reaches up to 99\\\\% of overall accuracy for each register while keeping the computational cost low.\",\"bibtype\":\"inproceedings\",\"author\":\"Cepeda, Eduardo and Sánchez-Pozo, Nadia N and Peluffo-Ordóñez, Diego H and González-Vergara, Juan and Almeida-Galárraga, Diego\",\"editor\":\"Gervasi, Osvaldo and Murgante, Beniamino and Hendrix, Eligius M T and Taniar, David and Apduhan, Bernady O\",\"booktitle\":\"Computational Science and Its Applications -- ICCSA 2022\",\"bibtex\":\"@inproceedings{\\n title = {ECG-Based Heartbeat Classification for Arrhythmia Detection Using Artificial Neural Networks},\\n type = {inproceedings},\\n year = {2022},\\n pages = {247-259},\\n websites = {https://link.springer.com/chapter/10.1007/978-3-031-10450-3_20},\\n publisher = {Springer International Publishing},\\n city = {Cham},\\n id = {fe0bb748-7f2d-396d-be6a-7337af3ccdc3},\\n created = {2022-07-16T03:47:10.517Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\\n last_modified = {2022-07-16T03:47:10.517Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {10.1007/978-3-031-10450-3_20},\\n source_type = {inproceedings},\\n private_publication = {false},\\n abstract = {Cardiovascular disease (CVD) has quickly grown in prevalence over the previous decade, becoming the major cause of human morbidity on a global scale. Due to the massive number of ECG data, manual analysis is regarded as a time-consuming, costly and prone to human error task. In the other hand, computational systems based on biomedical signal processing and machine learning techniques might be suited for supporting arrhythmia diagnostic processes, while solving some of those issues. In general, such systems involve five stages: acquisition, preprocessing, segmentation, characterization, and classification. Yet numerous fundamental aspects remain unresolved, including sensitivity to signal fluctuation, accuracy, computing cost, generalizability, and interpretability. In this context, the present study offers a comparative analysis of ECG signal classification using two artificial neural networks created by different machine learning frameworks. The neural nets were built into a pipeline that aims to strike an appropriate balance among signal robustness, variability, and accuracy. The proposed approach reaches up to 99\\\\% of overall accuracy for each register while keeping the computational cost low.},\\n bibtype = {inproceedings},\\n author = {Cepeda, Eduardo and Sánchez-Pozo, Nadia N and Peluffo-Ordóñez, Diego H and González-Vergara, Juan and Almeida-Galárraga, Diego},\\n editor = {Gervasi, Osvaldo and Murgante, Beniamino and Hendrix, Eligius M T and Taniar, David and Apduhan, Bernady O},\\n booktitle = {Computational Science and Its Applications -- ICCSA 2022}\\n}\",\"author_short\":[\"Cepeda,  E.\",\"Sánchez-Pozo,  N., N.\",\"Peluffo-Ordóñez,  D., H.\",\"González-Vergara,  J.\",\"Almeida-Galárraga,  D.\"],\"editor_short\":[\"Gervasi,  O.\",\"Murgante,  B.\",\"Hendrix,  E., M., T.\",\"Taniar,  D.\",\"Apduhan,  B., O.\"],\"urls\":{\"Website\":\"https://link.springer.com/chapter/10.1007/978-3-031-10450-3_20\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"cepeda-snchezpozo-peluffoordez-gonzlezvergara-almeidagalrraga-ecgbasedheartbeatclassificationforarrhythmiadetectionusingartificialneuralnetworks-2022\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":4,\"html\":\"\"},{\"title\":\"A Computer Vision Model to Identify the Incorrect Use of Face Masks for COVID-19 Awareness\",\"type\":\"article\",\"year\":\"2022\",\"volume\":\"12\",\"websites\":\"https://www.mdpi.com/2076-3417/12/14/6924\",\"id\":\"70d7f700-db7a-39e4-8146-84cf0ff26274\",\"created\":\"2022-07-24T01:29:08.758Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"aea1f410-b6a0-3014-afb8-bc5b4ad73524\",\"last_modified\":\"2022-07-24T01:29:08.758Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"app12146924\",\"source_type\":\"article\",\"private_publication\":false,\"abstract\":\"Face mask detection has become a great challenge in computer vision, demanding the coalition of technology with COVID-19 awareness. Researchers have proposed deep learning models to detect the use of face masks. However, the incorrect use of a face mask can be as harmful as not wearing any protection at all. In this paper, we propose a compound convolutional neural network (CNN) architecture based on two computer vision tasks: object localization to discover faces in images/videos, followed by an image classification CNN to categorize the faces and show if someone is using a face mask correctly, incorrectly, or not at all. The first CNN is built upon RetinaFace, a model to detect faces in images, whereas the second CNN uses a ResNet-18 architecture as a classification backbone. Our model enables an accurate identification of people who are not correctly following the COVID-19 healthcare recommendations on face mask use. To enable further global use of our technology, we have released both the dataset used to train the classification model and our proposed computer vision pipeline to the public, and optimized it for embedded systems deployment.\",\"bibtype\":\"article\",\"author\":\"Crespo, Fabricio and Crespo, Anthony and Sierra-Martínez, Luz Marina and Peluffo-Ordóñez, Diego Hernán and Morocho-Cayamcela, Manuel Eugenio\",\"doi\":\"10.3390/app12146924\",\"journal\":\"Applied Sciences\",\"number\":\"14\",\"bibtex\":\"@article{\\n title = {A Computer Vision Model to Identify the Incorrect Use of Face Masks for COVID-19 Awareness},\\n type = {article},\\n year = {2022},\\n volume = {12},\\n websites = {https://www.mdpi.com/2076-3417/12/14/6924},\\n id = {70d7f700-db7a-39e4-8146-84cf0ff26274},\\n created = {2022-07-24T01:29:08.758Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\\n last_modified = {2022-07-24T01:29:08.758Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {app12146924},\\n source_type = {article},\\n private_publication = {false},\\n abstract = {Face mask detection has become a great challenge in computer vision, demanding the coalition of technology with COVID-19 awareness. Researchers have proposed deep learning models to detect the use of face masks. However, the incorrect use of a face mask can be as harmful as not wearing any protection at all. In this paper, we propose a compound convolutional neural network (CNN) architecture based on two computer vision tasks: object localization to discover faces in images/videos, followed by an image classification CNN to categorize the faces and show if someone is using a face mask correctly, incorrectly, or not at all. The first CNN is built upon RetinaFace, a model to detect faces in images, whereas the second CNN uses a ResNet-18 architecture as a classification backbone. Our model enables an accurate identification of people who are not correctly following the COVID-19 healthcare recommendations on face mask use. To enable further global use of our technology, we have released both the dataset used to train the classification model and our proposed computer vision pipeline to the public, and optimized it for embedded systems deployment.},\\n bibtype = {article},\\n author = {Crespo, Fabricio and Crespo, Anthony and Sierra-Martínez, Luz Marina and Peluffo-Ordóñez, Diego Hernán and Morocho-Cayamcela, Manuel Eugenio},\\n doi = {10.3390/app12146924},\\n journal = {Applied Sciences},\\n number = {14}\\n}\",\"author_short\":[\"Crespo,  F.\",\"Crespo,  A.\",\"Sierra-Martínez,  L., M.\",\"Peluffo-Ordóñez,  D., H.\",\"Morocho-Cayamcela,  M., E.\"],\"urls\":{\"Website\":\"https://www.mdpi.com/2076-3417/12/14/6924\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"crespo-crespo-sierramartnez-peluffoordez-morochocayamcela-acomputervisionmodeltoidentifytheincorrectuseoffacemasksforcovid19awareness-2022\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Neural Networks on Noninvasive Electrocardiographic Imaging Reconstructions: Preliminary Results\",\"type\":\"inproceedings\",\"year\":\"2023\",\"pages\":\"55-63\",\"websites\":\"https://link.springer.com/chapter/10.1007/978-3-031-25942-5_5\",\"publisher\":\"Springer Nature Switzerland\",\"city\":\"Cham\",\"id\":\"e947ed90-061d-30f3-be14-0374b37b4f4f\",\"created\":\"2023-02-13T23:39:20.520Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"aea1f410-b6a0-3014-afb8-bc5b4ad73524\",\"last_modified\":\"2023-02-13T23:39:20.520Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"10.1007/978-3-031-25942-5_5\",\"source_type\":\"inproceedings\",\"private_publication\":false,\"abstract\":\"In the reverse electrocardiography (ECG) problem, the objective is to reconstruct the heart's electrical activity from a set of body surface potentials by solving the direct model and the geometry of the torso. Over the years, researchers have used various approaches to solve this problem, from direct, iterative, probabilistic, and those based on deep learning. The interest of the latter, among the wide range of techniques, is because the complexity of the problem can be significantly reduced while increasing the precision of the estimation. In this article, we evaluate the performance of a deep learning-based neural network compared to the Tikhonov method of zero order (ZOT), first (FOT), and second (SOT). Preliminary results show an improvement in performance over real data when Pearson's correlation coefficient (CC) and (RMSE) are calculated. The CC's mean value and standard deviation for the proposed method were 0.960 (0.065), well above ZOT, which was 0.864 (0.047).\",\"bibtype\":\"inproceedings\",\"author\":\"Mayorca-Torres, Dagoberto and León-Salas, Alejandro José and Peluffo-Ordoñez, Diego Hernán\",\"editor\":\"Botto-Tobar, Miguel and Gómez, Omar S and Rosero Miranda, Raul and Díaz Cadena, Angela and Luna-Encalada, Washington\",\"booktitle\":\"Trends in Artificial Intelligence and Computer Engineering\",\"bibtex\":\"@inproceedings{\\n title = {Neural Networks on Noninvasive Electrocardiographic Imaging Reconstructions: Preliminary Results},\\n type = {inproceedings},\\n year = {2023},\\n pages = {55-63},\\n websites = {https://link.springer.com/chapter/10.1007/978-3-031-25942-5_5},\\n publisher = {Springer Nature Switzerland},\\n city = {Cham},\\n id = {e947ed90-061d-30f3-be14-0374b37b4f4f},\\n created = {2023-02-13T23:39:20.520Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\\n last_modified = {2023-02-13T23:39:20.520Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {10.1007/978-3-031-25942-5_5},\\n source_type = {inproceedings},\\n private_publication = {false},\\n abstract = {In the reverse electrocardiography (ECG) problem, the objective is to reconstruct the heart's electrical activity from a set of body surface potentials by solving the direct model and the geometry of the torso. Over the years, researchers have used various approaches to solve this problem, from direct, iterative, probabilistic, and those based on deep learning. The interest of the latter, among the wide range of techniques, is because the complexity of the problem can be significantly reduced while increasing the precision of the estimation. In this article, we evaluate the performance of a deep learning-based neural network compared to the Tikhonov method of zero order (ZOT), first (FOT), and second (SOT). Preliminary results show an improvement in performance over real data when Pearson's correlation coefficient (CC) and (RMSE) are calculated. The CC's mean value and standard deviation for the proposed method were 0.960 (0.065), well above ZOT, which was 0.864 (0.047).},\\n bibtype = {inproceedings},\\n author = {Mayorca-Torres, Dagoberto and León-Salas, Alejandro José and Peluffo-Ordoñez, Diego Hernán},\\n editor = {Botto-Tobar, Miguel and Gómez, Omar S and Rosero Miranda, Raul and Díaz Cadena, Angela and Luna-Encalada, Washington},\\n booktitle = {Trends in Artificial Intelligence and Computer Engineering}\\n}\",\"author_short\":[\"Mayorca-Torres,  D.\",\"León-Salas,  A., J.\",\"Peluffo-Ordoñez,  D., H.\"],\"editor_short\":[\"Botto-Tobar,  M.\",\"Gómez,  O., S.\",\"Rosero Miranda,  R.\",\"Díaz Cadena,  A.\",\"Luna-Encalada,  W.\"],\"urls\":{\"Website\":\"https://link.springer.com/chapter/10.1007/978-3-031-25942-5_5\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"mayorcatorres-lensalas-peluffoordoez-neuralnetworksonnoninvasiveelectrocardiographicimagingreconstructionspreliminaryresults-2023\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Crop Classification Using Deep Learning: A Quick Comparative Study of Modern Approaches\",\"type\":\"inproceedings\",\"year\":\"2022\",\"pages\":\"31-44\",\"websites\":\"https://link.springer.com/chapter/10.1007/978-3-031-19647-8_3\",\"publisher\":\"Springer International Publishing\",\"city\":\"Cham\",\"id\":\"d0ea8cbb-e8dd-3b80-8451-9b858e90db31\",\"created\":\"2023-03-01T20:18:51.423Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"aea1f410-b6a0-3014-afb8-bc5b4ad73524\",\"last_modified\":\"2023-03-01T20:18:51.423Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"10.1007/978-3-031-19647-8_3\",\"source_type\":\"inproceedings\",\"private_publication\":false,\"abstract\":\"Automatic crop classification using new technologies is recognized as one of the most important assets in today's smart farming improvement. Investments in technology and innovation are key issues for shaping agricultural productivity as well as the inclusiveness and sustainability of the global agricultural transformation. Digital image processing (DIP) has been widely adopted in this field, by merging Unmanned Aerial Vehicle (UAV) based remote sensing and deep learning (DL) as a powerful tool for crop classification. Despite the wide range of alternatives, the proper selection of a DL approach is still an open and challenging issue. In this work, we carry out an exhaustive performance evaluation of three remarkable and lightweight DL approaches, namely: Visual Geometry Group (VGG), Residual Neural Network (ResNet) and Inception V3, tested on high resolution agriculture crop images dataset. Experimental results show that InceptionV3 outperforms VGG and ResNet in terms of precision (0,92), accuracy (0,97), recall (0,91), AUC (0,98), PCR (0,97), and F1 (0,91).\",\"bibtype\":\"inproceedings\",\"author\":\"Raki, Hind and González-Vergara, Juan and Aalaila, Yahya and Elhamdi, Mouad and Bamansour, Sami and Guachi-Guachi, Lorena and Peluffo-Ordoñez, Diego H\",\"editor\":\"Florez, Hector and Gomez, Henry\",\"booktitle\":\"Applied Informatics\",\"bibtex\":\"@inproceedings{\\n title = {Crop Classification Using Deep Learning: A Quick Comparative Study of Modern Approaches},\\n type = {inproceedings},\\n year = {2022},\\n pages = {31-44},\\n websites = {https://link.springer.com/chapter/10.1007/978-3-031-19647-8_3},\\n publisher = {Springer International Publishing},\\n city = {Cham},\\n id = {d0ea8cbb-e8dd-3b80-8451-9b858e90db31},\\n created = {2023-03-01T20:18:51.423Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\\n last_modified = {2023-03-01T20:18:51.423Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {10.1007/978-3-031-19647-8_3},\\n source_type = {inproceedings},\\n private_publication = {false},\\n abstract = {Automatic crop classification using new technologies is recognized as one of the most important assets in today's smart farming improvement. Investments in technology and innovation are key issues for shaping agricultural productivity as well as the inclusiveness and sustainability of the global agricultural transformation. Digital image processing (DIP) has been widely adopted in this field, by merging Unmanned Aerial Vehicle (UAV) based remote sensing and deep learning (DL) as a powerful tool for crop classification. Despite the wide range of alternatives, the proper selection of a DL approach is still an open and challenging issue. In this work, we carry out an exhaustive performance evaluation of three remarkable and lightweight DL approaches, namely: Visual Geometry Group (VGG), Residual Neural Network (ResNet) and Inception V3, tested on high resolution agriculture crop images dataset. Experimental results show that InceptionV3 outperforms VGG and ResNet in terms of precision (0,92), accuracy (0,97), recall (0,91), AUC (0,98), PCR (0,97), and F1 (0,91).},\\n bibtype = {inproceedings},\\n author = {Raki, Hind and González-Vergara, Juan and Aalaila, Yahya and Elhamdi, Mouad and Bamansour, Sami and Guachi-Guachi, Lorena and Peluffo-Ordoñez, Diego H},\\n editor = {Florez, Hector and Gomez, Henry},\\n booktitle = {Applied Informatics}\\n}\",\"author_short\":[\"Raki,  H.\",\"González-Vergara,  J.\",\"Aalaila,  Y.\",\"Elhamdi,  M.\",\"Bamansour,  S.\",\"Guachi-Guachi,  L.\",\"Peluffo-Ordoñez,  D., H.\"],\"editor_short\":[\"Florez,  H.\",\"Gomez,  H.\"],\"urls\":{\"Website\":\"https://link.springer.com/chapter/10.1007/978-3-031-19647-8_3\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"raki-gonzlezvergara-aalaila-elhamdi-bamansour-guachiguachi-peluffoordoez-cropclassificationusingdeeplearningaquickcomparativestudyofmodernapproaches-2022\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Instance Selection Framework for Alzheimer’s Disease Classification Using Multiple Regions of Interest and Atlas Integration\",\"type\":\"inproceedings\",\"year\":\"2024\",\"pages\":\"453-460\",\"websites\":\"https://www.scitepress.org/Link.aspx?doi=10.5220/0012469600003654\",\"publisher\":\"SciTePress\",\"institution\":\"INSTICC\",\"id\":\"979ed068-0e34-3d58-a432-a1b8d2140266\",\"created\":\"2024-03-06T14:15:39.886Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"aea1f410-b6a0-3014-afb8-bc5b4ad73524\",\"last_modified\":\"2024-03-06T14:15:39.886Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"icpram24\",\"source_type\":\"conference\",\"private_publication\":false,\"abstract\":\"Optimal selection of informative instances from a dataset is critical for constructing accurate predictive models. As databases expand, leveraging instance selection techniques becomes imperative to condense data into a more manageable size. This research unveils a novel framework designed to strategically identify and choose the most informative 2D brain image slices for Alzheimer’s disease classification. Such a framework integrates annotations from multiple regions of interest across multiple atlases. The proposed framework consists of six core components: 1) Atlas merging for ROI annotation and hemisphere separation. 2) Image preprocessing to extract informative slices. 3) Dataset construction to prevent data leakage, select subjects, and split data. 4) Data generation for memory-efficient batches. 5) Model construction for diverse classification training and testing. 6) Weighted ensemble for combining predictions from multiple models with a single learning algorithm. Our instanc e selection framework was applied to construct Transformer-based classification models, demonstrating an overall accuracy of approximately 98.33% in distinguishing between Cognitively Normal and Alzheimer’s cases at the subject level. It exhibited enhancements of 3.68%, 3.01%, 3.62% for sagittal, coronal, and axial planes respectively in comparison with the percentile technique.\",\"bibtype\":\"inproceedings\",\"author\":\"Castro-Silva., Juan A. and Moreno-García., Maria and Guachi-Guachi., Lorena and Peluffo-Ordóñez., Diego H.\",\"doi\":\"10.5220/0012469600003654\",\"booktitle\":\"Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM\",\"bibtex\":\"@inproceedings{\\n title = {Instance Selection Framework for Alzheimer’s Disease Classification Using Multiple Regions of Interest and Atlas Integration},\\n type = {inproceedings},\\n year = {2024},\\n pages = {453-460},\\n websites = {https://www.scitepress.org/Link.aspx?doi=10.5220/0012469600003654},\\n publisher = {SciTePress},\\n institution = {INSTICC},\\n id = {979ed068-0e34-3d58-a432-a1b8d2140266},\\n created = {2024-03-06T14:15:39.886Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\\n last_modified = {2024-03-06T14:15:39.886Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {icpram24},\\n source_type = {conference},\\n private_publication = {false},\\n abstract = {Optimal selection of informative instances from a dataset is critical for constructing accurate predictive models. As databases expand, leveraging instance selection techniques becomes imperative to condense data into a more manageable size. This research unveils a novel framework designed to strategically identify and choose the most informative 2D brain image slices for Alzheimer’s disease classification. Such a framework integrates annotations from multiple regions of interest across multiple atlases. The proposed framework consists of six core components: 1) Atlas merging for ROI annotation and hemisphere separation. 2) Image preprocessing to extract informative slices. 3) Dataset construction to prevent data leakage, select subjects, and split data. 4) Data generation for memory-efficient batches. 5) Model construction for diverse classification training and testing. 6) Weighted ensemble for combining predictions from multiple models with a single learning algorithm. Our instanc e selection framework was applied to construct Transformer-based classification models, demonstrating an overall accuracy of approximately 98.33% in distinguishing between Cognitively Normal and Alzheimer’s cases at the subject level. It exhibited enhancements of 3.68%, 3.01%, 3.62% for sagittal, coronal, and axial planes respectively in comparison with the percentile technique.},\\n bibtype = {inproceedings},\\n author = {Castro-Silva., Juan A. and Moreno-García., Maria and Guachi-Guachi., Lorena and Peluffo-Ordóñez., Diego H.},\\n doi = {10.5220/0012469600003654},\\n booktitle = {Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM}\\n}\",\"author_short\":[\"Castro-Silva.,  J., A.\",\"Moreno-García.,  M.\",\"Guachi-Guachi.,  L.\",\"Peluffo-Ordóñez.,  D., H.\"],\"urls\":{\"Website\":\"https://www.scitepress.org/Link.aspx?doi=10.5220/0012469600003654\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"castrosilva-morenogarca-guachiguachi-peluffoordez-instanceselectionframeworkforalzheimersdiseaseclassificationusingmultipleregionsofinterestandatlasintegration-2024\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Multiple Inputs and Mixed Data for Alzheimer’s Disease Classification Based on 3D Vision Transformer\",\"type\":\"article\",\"year\":\"2024\",\"pages\":\"2720\",\"volume\":\"12\",\"websites\":\"https://www.mdpi.com/2227-7390/12/17/2720\",\"publisher\":\"MDPI\",\"id\":\"67e1abb8-ae6d-3e39-bc38-f3a6afe5febf\",\"created\":\"2024-09-04T16:45:13.004Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"aea1f410-b6a0-3014-afb8-bc5b4ad73524\",\"last_modified\":\"2024-09-04T16:45:13.004Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"castro2024multiple\",\"source_type\":\"article\",\"private_publication\":false,\"abstract\":\"The current methods for diagnosing Alzheimer’s Disease using Magnetic Resonance Imaging (MRI) have significant limitations. Many previous studies used 2D Transformers to analyze individual brain slices independently, potentially losing critical 3D contextual information. Region of interest-based models often focus on only a few brain regions despite Alzheimer’s affecting multiple areas. Additionally, most classification models rely on a single test, whereas diagnosing Alzheimer’s requires a multifaceted approach integrating diverse data sources for a more accurate assessment. This study introduces a novel methodology called the Multiple Inputs and Mixed Data 3D Vision Transformer (MIMD-3DVT). This method processes consecutive slices together to capture the feature dimensions and spatial information, fuses multiple 3D ROI imaging data inputs, and integrates mixed data from demographic factors, cognitive assessments, and brain imaging. The proposed methodology was experimentally evaluated using a combined dataset that included the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Australian Imaging, Biomarker, and Lifestyle Flagship Study of Ageing (AIBL), and the Open Access Series of Imaging Studies (OASIS). Our MIMD-3DVT, utilizing single or multiple ROIs, achieved an accuracy of 97.14%, outperforming the state-of-the-art methods in distinguishing between Normal Cognition and Alzheimer’s Disease.\",\"bibtype\":\"article\",\"author\":\"Castro-Silva, Juan A and Moreno-García, María N and Peluffo-Ordóñez, Diego H\",\"journal\":\"Mathematics\",\"number\":\"17\",\"bibtex\":\"@article{\\n title = {Multiple Inputs and Mixed Data for Alzheimer’s Disease Classification Based on 3D Vision Transformer},\\n type = {article},\\n year = {2024},\\n pages = {2720},\\n volume = {12},\\n websites = {https://www.mdpi.com/2227-7390/12/17/2720},\\n publisher = {MDPI},\\n id = {67e1abb8-ae6d-3e39-bc38-f3a6afe5febf},\\n created = {2024-09-04T16:45:13.004Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\\n last_modified = {2024-09-04T16:45:13.004Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {castro2024multiple},\\n source_type = {article},\\n private_publication = {false},\\n abstract = {The current methods for diagnosing Alzheimer’s Disease using Magnetic Resonance Imaging (MRI) have significant limitations. Many previous studies used 2D Transformers to analyze individual brain slices independently, potentially losing critical 3D contextual information. Region of interest-based models often focus on only a few brain regions despite Alzheimer’s affecting multiple areas. Additionally, most classification models rely on a single test, whereas diagnosing Alzheimer’s requires a multifaceted approach integrating diverse data sources for a more accurate assessment. This study introduces a novel methodology called the Multiple Inputs and Mixed Data 3D Vision Transformer (MIMD-3DVT). This method processes consecutive slices together to capture the feature dimensions and spatial information, fuses multiple 3D ROI imaging data inputs, and integrates mixed data from demographic factors, cognitive assessments, and brain imaging. The proposed methodology was experimentally evaluated using a combined dataset that included the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Australian Imaging, Biomarker, and Lifestyle Flagship Study of Ageing (AIBL), and the Open Access Series of Imaging Studies (OASIS). Our MIMD-3DVT, utilizing single or multiple ROIs, achieved an accuracy of 97.14%, outperforming the state-of-the-art methods in distinguishing between Normal Cognition and Alzheimer’s Disease.},\\n bibtype = {article},\\n author = {Castro-Silva, Juan A and Moreno-García, María N and Peluffo-Ordóñez, Diego H},\\n journal = {Mathematics},\\n number = {17}\\n}\",\"author_short\":[\"Castro-Silva,  J., A.\",\"Moreno-García,  M., N.\",\"Peluffo-Ordóñez,  D., H.\"],\"urls\":{\"Website\":\"https://www.mdpi.com/2227-7390/12/17/2720\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"castrosilva-morenogarca-peluffoordez-multipleinputsandmixeddataforalzheimersdiseaseclassificationbasedon3dvisiontransformer-2024\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Novel hippocampus-centered methodology for informative instance selection in Alzheimer's disease data\",\"type\":\"article\",\"year\":\"2024\",\"keywords\":\"Alzheimer's disease,Deep learning,Hippocampus,Instance selection\",\"pages\":\"e37552\",\"volume\":\"10\",\"id\":\"b2d13133-eb4d-3fe0-bc3e-23479c4e0c95\",\"created\":\"2024-09-25T22:33:25.171Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"aea1f410-b6a0-3014-afb8-bc5b4ad73524\",\"last_modified\":\"2024-09-25T22:33:25.171Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"CASTROSILVA2024e37552\",\"source_type\":\"article\",\"private_publication\":false,\"bibtype\":\"article\",\"author\":\"Castro-Silva, Juan A and Moreno-García, María N and Guachi-Guachi, Lorena and Peluffo-Ordóñez, Diego H\",\"doi\":\"https://doi.org/10.1016/j.heliyon.2024.e37552\",\"journal\":\"Heliyon\",\"number\":\"19\",\"bibtex\":\"@article{\\n title = {Novel hippocampus-centered methodology for informative instance selection in Alzheimer's disease data},\\n type = {article},\\n year = {2024},\\n keywords = {Alzheimer's disease,Deep learning,Hippocampus,Instance selection},\\n pages = {e37552},\\n volume = {10},\\n id = {b2d13133-eb4d-3fe0-bc3e-23479c4e0c95},\\n created = {2024-09-25T22:33:25.171Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\\n last_modified = {2024-09-25T22:33:25.171Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {CASTROSILVA2024e37552},\\n source_type = {article},\\n private_publication = {false},\\n bibtype = {article},\\n author = {Castro-Silva, Juan A and Moreno-García, María N and Guachi-Guachi, Lorena and Peluffo-Ordóñez, Diego H},\\n doi = {https://doi.org/10.1016/j.heliyon.2024.e37552},\\n journal = {Heliyon},\\n number = {19}\\n}\",\"author_short\":[\"Castro-Silva,  J., A.\",\"Moreno-García,  M., N.\",\"Guachi-Guachi,  L.\",\"Peluffo-Ordóñez,  D., H.\"],\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"castrosilva-morenogarca-guachiguachi-peluffoordez-novelhippocampuscenteredmethodologyforinformativeinstanceselectioninalzheimersdiseasedata-2024\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"Alzheimer's disease\",\"Deep learning\",\"Hippocampus\",\"Instance selection\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Automatic Identification of Forest Areas in the ``Carolina'' Park Using ResNet50, EfficientNetB0 and VGG16: A Case Study\",\"type\":\"inproceedings\",\"year\":\"2025\",\"pages\":\"31-42\",\"websites\":\"https://link.springer.com/chapter/10.1007/978-3-031-75144-8_3\",\"publisher\":\"Springer Nature Switzerland\",\"city\":\"Cham\",\"id\":\"0feb5a1a-f84d-3920-9540-8f59bb2f4515\",\"created\":\"2024-11-12T17:54:48.400Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"aea1f410-b6a0-3014-afb8-bc5b4ad73524\",\"last_modified\":\"2024-11-12T17:54:48.400Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"10.1007/978-3-031-75144-8_3\",\"source_type\":\"inproceedings\",\"private_publication\":false,\"abstract\":\"This study explores the challenges of identifying forest areas in the ``Carolina'' Park in Quito, Ecuador, using Convolutional Neural Networks (CNN) and aerial imagery to support sustainable urban expansion plans. A dataset was constructed using 32\\\\$\\\\$\\\\backslash,\\\\backslashtimes \\\\backslash,\\\\$\\\\$\\\\texttimes32\\\\$\\\\$\\\\backslash,\\\\backslashtimes \\\\backslash,\\\\$\\\\$\\\\texttimes3 pixel patches extracted from 230 aerial images obtained from several videos captured by drones over the park. Three CNN models (ResNet50, EfficientNetB0 and VGG16) were trained to differentiate between forested, non-forested and hybrid areas. The methodology involved manual classification of 2100 patches into these three categories. The results showed that ResNet50 performed the best overall, with an accuracy of 76.66\\\\% \\\\textpm 8\\\\%, followed closely by VGG16, while EfficientNetB0 showed inferior performance on this specific dataset. Qualitative analysis of predictions on test images confirmed the effective identification of forest areas. These findings suggest that ResNet50 may be a suitable model for this task, demonstrating a high ability to learn and recognize patterns in forested areas through patch-based analysis, even with relatively small datasets derived from aerial drone imagery.\",\"bibtype\":\"inproceedings\",\"author\":\"Guapaz, Julian and Jervis, Juan Pablo and Haro, Diego and Padilla, Jefferson and Guachi, Robinson and Peluffo-Ordóñez, D H and Guachi-Guachi, Lorena\",\"editor\":\"Florez, Hector and Astudillo, Hernán\",\"booktitle\":\"Applied Informatics\",\"bibtex\":\"@inproceedings{\\n title = {Automatic Identification of Forest Areas in the ``Carolina'' Park Using ResNet50, EfficientNetB0 and VGG16: A Case Study},\\n type = {inproceedings},\\n year = {2025},\\n pages = {31-42},\\n websites = {https://link.springer.com/chapter/10.1007/978-3-031-75144-8_3},\\n publisher = {Springer Nature Switzerland},\\n city = {Cham},\\n id = {0feb5a1a-f84d-3920-9540-8f59bb2f4515},\\n created = {2024-11-12T17:54:48.400Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\\n last_modified = {2024-11-12T17:54:48.400Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {10.1007/978-3-031-75144-8_3},\\n source_type = {inproceedings},\\n private_publication = {false},\\n abstract = {This study explores the challenges of identifying forest areas in the ``Carolina'' Park in Quito, Ecuador, using Convolutional Neural Networks (CNN) and aerial imagery to support sustainable urban expansion plans. A dataset was constructed using 32\\\\$\\\\$\\\\backslash,\\\\backslashtimes \\\\backslash,\\\\$\\\\$\\\\texttimes32\\\\$\\\\$\\\\backslash,\\\\backslashtimes \\\\backslash,\\\\$\\\\$\\\\texttimes3 pixel patches extracted from 230 aerial images obtained from several videos captured by drones over the park. Three CNN models (ResNet50, EfficientNetB0 and VGG16) were trained to differentiate between forested, non-forested and hybrid areas. The methodology involved manual classification of 2100 patches into these three categories. The results showed that ResNet50 performed the best overall, with an accuracy of 76.66\\\\% \\\\textpm 8\\\\%, followed closely by VGG16, while EfficientNetB0 showed inferior performance on this specific dataset. Qualitative analysis of predictions on test images confirmed the effective identification of forest areas. These findings suggest that ResNet50 may be a suitable model for this task, demonstrating a high ability to learn and recognize patterns in forested areas through patch-based analysis, even with relatively small datasets derived from aerial drone imagery.},\\n bibtype = {inproceedings},\\n author = {Guapaz, Julian and Jervis, Juan Pablo and Haro, Diego and Padilla, Jefferson and Guachi, Robinson and Peluffo-Ordóñez, D H and Guachi-Guachi, Lorena},\\n editor = {Florez, Hector and Astudillo, Hernán},\\n booktitle = {Applied Informatics}\\n}\",\"author_short\":[\"Guapaz,  J.\",\"Jervis,  J., P.\",\"Haro,  D.\",\"Padilla,  J.\",\"Guachi,  R.\",\"Peluffo-Ordóñez,  D., H.\",\"Guachi-Guachi,  L.\"],\"editor_short\":[\"Florez,  H.\",\"Astudillo,  H.\"],\"urls\":{\"Website\":\"https://link.springer.com/chapter/10.1007/978-3-031-75144-8_3\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"guapaz-jervis-haro-padilla-guachi-peluffoordez-guachiguachi-automaticidentificationofforestareasinthecarolinaparkusingresnet50efficientnetb0andvgg16acasestudy-2025\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Automatic Differentiation Between Coriander and Parsley Using MobileNetV2\",\"type\":\"inproceedings\",\"year\":\"2025\",\"pages\":\"18-30\",\"websites\":\"https://link.springer.com/chapter/10.1007/978-3-031-75144-8_2\",\"publisher\":\"Springer Nature Switzerland\",\"city\":\"Cham\",\"id\":\"295f4a04-2d1d-36e7-b290-da33944d17a2\",\"created\":\"2024-11-12T18:03:35.797Z\",\"file_attached\":false,\"profile_id\":\"aba9653c-d139-3f95-aad8-969c487ed2f3\",\"group_id\":\"aea1f410-b6a0-3014-afb8-bc5b4ad73524\",\"last_modified\":\"2024-11-12T18:03:35.797Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"citation_key\":\"10.1007/978-3-031-75144-8_2\",\"source_type\":\"inproceedings\",\"private_publication\":false,\"abstract\":\"Ecuadorian cuisine is very varied, with typical dishes from various regions often using fresh herbs. Among them, coriander and parsley are frequently used but often confused because of their similar appearance. This confusion can result in inexperienced or visually impaired people using the wrong herb, thus altering the flavor and unique qualities of the dishes. To address this problem, in this work, we present an image classification model capable of distinguishing between coriander and parsley leaves. Three architectures (MobileNetV2, EfficientNetB0 and InceptionV3) were evaluated to determine which offered the highest accuracy. MobileNetV2 proved to be the most efficient model, demonstrating superior stability and achieving greater than 99\\\\% accuracy.\",\"bibtype\":\"inproceedings\",\"author\":\"Páez, Ian and Arévalo, José and Martinez, Mateo and Molina, Martin and Guachi, Robinson and Peluffo-Ordóñez, D H and Guachi-Guachi, Lorena\",\"editor\":\"Florez, Hector and Astudillo, Hernán\",\"booktitle\":\"Applied Informatics\",\"bibtex\":\"@inproceedings{\\n title = {Automatic Differentiation Between Coriander and Parsley Using MobileNetV2},\\n type = {inproceedings},\\n year = {2025},\\n pages = {18-30},\\n websites = {https://link.springer.com/chapter/10.1007/978-3-031-75144-8_2},\\n publisher = {Springer Nature Switzerland},\\n city = {Cham},\\n id = {295f4a04-2d1d-36e7-b290-da33944d17a2},\\n created = {2024-11-12T18:03:35.797Z},\\n file_attached = {false},\\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\\n last_modified = {2024-11-12T18:03:35.797Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n citation_key = {10.1007/978-3-031-75144-8_2},\\n source_type = {inproceedings},\\n private_publication = {false},\\n abstract = {Ecuadorian cuisine is very varied, with typical dishes from various regions often using fresh herbs. Among them, coriander and parsley are frequently used but often confused because of their similar appearance. This confusion can result in inexperienced or visually impaired people using the wrong herb, thus altering the flavor and unique qualities of the dishes. To address this problem, in this work, we present an image classification model capable of distinguishing between coriander and parsley leaves. Three architectures (MobileNetV2, EfficientNetB0 and InceptionV3) were evaluated to determine which offered the highest accuracy. MobileNetV2 proved to be the most efficient model, demonstrating superior stability and achieving greater than 99\\\\% accuracy.},\\n bibtype = {inproceedings},\\n author = {Páez, Ian and Arévalo, José and Martinez, Mateo and Molina, Martin and Guachi, Robinson and Peluffo-Ordóñez, D H and Guachi-Guachi, Lorena},\\n editor = {Florez, Hector and Astudillo, Hernán},\\n booktitle = {Applied Informatics}\\n}\",\"author_short\":[\"Páez,  I.\",\"Arévalo,  J.\",\"Martinez,  M.\",\"Molina,  M.\",\"Guachi,  R.\",\"Peluffo-Ordóñez,  D., H.\",\"Guachi-Guachi,  L.\"],\"editor_short\":[\"Florez,  H.\",\"Astudillo,  H.\"],\"urls\":{\"Website\":\"https://link.springer.com/chapter/10.1007/978-3-031-75144-8_2\"},\"biburl\":\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3\",\"bibbaseid\":\"pez-arvalo-martinez-molina-guachi-peluffoordez-guachiguachi-automaticdifferentiationbetweencorianderandparsleyusingmobilenetv2-2025\",\"role\":\"author\",\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"}],\n      metadata: {\"authorlinks\":{}},\n      ownerID: undefined\n    };\n  </script>\n\n  <script type=\"text/javascript\">\n  var site$;\n  if (typeof $ != 'undefined') {\n    console.log('existing jquery: storing and then restoring');\n    site$ = $;\n    $ = null;\n  }\n  </script>\n\n  <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/2.2.4/jquery.min.js\">\n  </script>\n  <script type=\"text/javascript\">\n  if (site$) {\n    console.log('existing jquery: avoiding conflict and restoring');\n    bb$ = $.noConflict(true);\n    $ = site$;\n  } else {\n    bb$ = $;\n  }\n  </script>\n\n  <script src=\"//bibbase.org/js/bibbase.min.js\"\n          type=\"text/javascript\">\n  </script>\n\n  <script type=\"text/javascript\"\n          src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML\">\n  </script>\n  <script type=\"text/x-mathjax-config\">\n    MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\\\(','\\\\)']]},\n                        skipTags: [\"body\"],\n                        processClass: \"bibbase_paper\"});\n  </script>\n\n  <style>\n  @media print {\n    .dontprint {\n        display: none !important;\n    }\n\n  .bibbase_group {\n    background-color: #E0E0E0;\n    font-style: italic;\n    font-size: larger;\n    text-shadow: 0px 0px 3px #FFFFFF;\n    border-radius: 4px 4px 4px 4px;\n    -moz-border-radius: 4px 4px 4px 4px;\n    -webkit-border-radius: 4px;\n    padding: 5px 40px 5px;\n    margin-top: 10px;\n    margin-bottom: 5px;\n    cursor: pointer;\n  }\n\n  .bibbase_paper {\n    margin-bottom: 5px;\n  }\n\n  }\n  </style>\n\n  \n  <div style=\"float: right; margin-right: 10px; margin-top: 10px; text-align: right; font-size: 12px\">\n    generated by\n    <a href=\"//bibbase.org\"\n       onclick=\"trackOutboundLink('bibbase', 'logo clicked', window.location.href, '//bibbase.org'); return false;\">\n      <img src=\"//bibbase.org/img/logo.svg\"\n           style=\"vertical-align: middle; margin-left: 3px; height: 16px;\"/\n           alt=\"bibbase.org\"\n           title=\"BibBase – The easiest way to maintain your publications page.\"\n        ></a>\n    \n  </div>\n  \n\n  <div id=\"bibbase_header\" class=\"dontprint\" style=\"\">\n\n    <ul class=\"nav nav-pills\" style=\"margin: 0px;\">\n\n      <li class=\"dropdown\" id=\"groupby_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\">\n          Group by\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li class=\"groupby year\"><a onclick=\"groupby('year')\">\n            Year</a>\n        </li>\n        <li class=\"groupby author\"><a onclick=\"groupby('author_short')\">\n            Author</a>\n        </li>\n        <li class=\"groupby type\"><a onclick=\"groupby('type')\">\n            Type</a>\n        </li>\n        <li class=\"groupby keyword\"><a onclick=\"groupby('keyword')\">\n            Keyword</a>\n        </li>\n        <li class=\"groupby downloads\"><a onclick=\"groupby('downloads')\">\n            Downloads</a>\n        </li>\n      </ul>\n      </li>\n\n\n      <li class=\"dropdown\" id=\"menu_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\" alt=\"controls\">\n          <i class=\"fa fa-reorder\" alt=\"controls\"></i>\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li>\n          <a onclick=\"toggleAll()\">\n            <i class=\"fa fa-chevron-down fa-fw\"></i> Expand/Collapse All\n          </a>\n        </li>\n        <li>\n          <a download=\"all.bib\" href=\"data:text/bibtex;base64,@article{
 title = {Enhanced Convolutional-Neural-Network Architecture for Crop Classification},
 type = {article},
 year = {2021},
 pages = {1-23},
 websites = {https://www.mdpi.com/2076-3417/11/9/4292},
 id = {24a0c385-d592-3a29-a7e6-33a2d377a118},
 created = {2022-02-02T06:07:46.014Z},
 file_attached = {true},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},
 last_modified = {2022-02-02T06:07:47.387Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Automatic crop identification and monitoring is a key element in enhancing food production processes as well as diminishing the related environmental impact. Although several efficient deep learning techniques have emerged in the field of multispectral imagery analysis, the crop classification problem still needs more accurate solutions. This work introduces a competitive methodology for crop classification from multispectral satellite imagery mainly using an enhanced 2D convolutional neural network (2D-CNN) designed at a smaller-scale architecture, as well as a novel post-processing step. The proposed methodology contains four steps: image stacking, patch extraction, classification model design (based on a 2D-CNN architecture), and post-processing. First, the images are stacked to increase the number of features. Second, the input images are split into patches and fed into the 2D-CNN model. Then, the 2D-CNN model is constructed within a small-scale framework, and properly trained to recognize 10 different types of crops. Finally, a post-processing step is performed in order to reduce the classification error caused by lower-spatial-resolution images. Experiments were carried over the so-named Campo Verde database, which consists of a set of satellite images captured by Landsat and Sentinel satellites from the municipality of Campo Verde, Brazil. In contrast to the maximum accuracy values reached by remarkable works reported in the literature (amounting to an overall accuracy of about 81%, a f1 score of 75.89%, and average accuracy of 73.35%), the proposed methodology achieves a competitive overall accuracy of 81.20%, a f1 score of 75.89%, and an average accuracy of 88.72% when classifying 10 different crops, while ensuring an adequate trade-off between the number of multiply-accumulate operations (MACs) and accuracy. Furthermore, given its ability to effectively classify patches from two image sequences, this methodology may result appealing for other real-world applications, such as the classification of urban materials.},
 bibtype = {article},
 author = {Moreno-revelo, Mónica Y and Guachi-guachi, Lorena and Gómez-mendoza, Juan Bernardo and Revelo-fuelagán, Javier and Peluffo-ordóñez, Diego H},
 journal = {Applied Sciences}
}

@article{
 title = {Comparison of current deep convolutional neural networks for the segmentation of breast masses in mammograms},
 type = {article},
 year = {2021},
 websites = {https://ieeexplore.ieee.org/document/9614200},
 id = {c9a3316f-2543-320d-b8bb-5ed45ce86aab},
 created = {2022-02-02T06:07:46.249Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},
 last_modified = {2022-02-02T06:07:46.249Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {9614200},
 source_type = {article},
 private_publication = {false},
 abstract = {Breast cancer causes approximately 684,996 deaths worldwide, making it the leading cause of female cancer mortality. However, these figures can be reduced with early diagnosis through mammographic imaging, allowing for the timely and effective treatment of this disease. To establish the best tools for contributing to the automatic diagnosis of breast cancer, different deep learning (DL) architectures were compared in terms of breast lesion segmentation, lesion type classification, and degree of suspicion of malignancy tests. The tasks were completed with state-of-the-art architectures and backbones. Initially, during segmentation, the base UNet, Visual Geometry Group 19 (VGG19), InceptionResNetV2, EfficientNet, MobileNetv2, ResNet, ResNeXt, MultiResUNet, linkNet-VGG19, DenseNet, SEResNet and SeResNeXt architectures were compared, where “Res” denotes a residual network. In addition, training was performed with 5 of the most advanced loss functions and validated by the Dice coefficient, sensitivity, and specificity. The proposed models achieved Dice values above 90%, with the EfficientNet architecture achieving 94.75% and 99% accuracy on the two tasks. Subsequently, classification was addressed with the ResNet50V2, VGG19, InceptionResNetV2, DenseNet121, InceptionV3, Xception and EfficientNetB7 networks. The proposed models achieved 96.97% and 97.73% accuracy through the VGG19 and ResNet50V2 networks on the lesion classification and degree of suspicion tasks, respectively. All three tasks were addressed with open-access databases, including the Digital Database for Screening Mammography (DDSM), the Mammographic Image Analysis Society (MIAS) database, and INbreast.},
 bibtype = {article},
 author = {Anaya-Isaza, Andrés and Mera-Jiménez, Leonel and Cabrera-Chavarro, Johan and Guachi-Guachi, Lorena and Peluffo-Ordóñez, Diego and Rios-Patiño, Jorge},
 doi = {10.1109/ACCESS.2021.3127862},
 journal = {IEEE Access}
}

@inbook{
 type = {inbook},
 year = {2019},
 keywords = {Artificial neural networks,Long-term demand forecasting,Small business},
 pages = {362-373},
 websites = {http://link.springer.com/10.1007/978-3-030-20518-8_31},
 id = {d0ee1709-f877-3515-a99a-f05f24797555},
 created = {2022-02-02T06:07:46.500Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},
 last_modified = {2022-02-02T06:07:46.500Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Herrera-Granda2019a},
 private_publication = {false},
 abstract = {This paper shows a neural networks-based demand forecasting model designed for a small manufacturer of bottled water in Ecuador, which currently doesn’t have adequate demand forecast methodologies, causing problems of customer orders non-compliance, inventory excess and economic losses. However, by working with accurate predictions, the manufacturer will have an anticipated vision of future needs in order to satisfy the demand for manufactured products, in other words, to guarantee on time and reasonable use of the resources. To solve the problems that this small manufacturer has to face a historic demand data acquisition process was done through the last 36 months costumer order records. In the construction of the historical time series, that was analyzed, demand dates and volumes were established as input variables. Then the design of forecast models was done, based on classical methods and multi-layer neural networks, which were evaluated by means of quantitative error indicators. The application of these methods was done through the R programming language. After this, a stage of training and improvement of the network is included, it was evaluated against the results of the classic forecasting methods, and the next 12 months were predicted by means of the best obtained model. Finally, the feasibility of the use of neural networks in the forecast of demand for purified water bottles, is demonstrated.},
 bibtype = {inbook},
 author = {Herrera-Granda, Israel D. and Chicaiza-Ipiales, Joselyn A. and Herrera-Granda, Erick P. and Lorente-Leyva, Leandro L. and Caraguay-Procel, Jorge A. and García-Santillán, Iván D. and Peluffo-Ordóñez, Diego H.},
 doi = {10.1007/978-3-030-20518-8_31},
 chapter = {Artificial Neural Networks for Bottled Water Demand Forecasting: A Small Business Case Study},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

@article{
 title = {Artificial Neural Networks for Urban Water Demand Forecasting: A Case Study},
 type = {article},
 year = {2019},
 pages = {012004},
 volume = {1284},
 websites = {https://iopscience.iop.org/article/10.1088/1742-6596/1284/1/012004},
 month = {8},
 id = {d25efbb9-d760-3246-ad23-47aedb6830b6},
 created = {2022-02-02T06:07:46.757Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},
 last_modified = {2022-02-02T06:07:46.757Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Lorente-Leyva2019a},
 private_publication = {false},
 abstract = {This paper presents an application of an artificial neural network model in forecasting urban water demand using MATLAB software. Considering that in any planning process, the demand forecast plays a fundamental role, being one of the premises to organize and control a set of activities or processes. The versatility of the short, medium and long-term prediction that is provided to the company that offers the water distribution service to determine the supply capacity, maintenance activities, and system improvements as a strategic planning tool. Shown to improve network performance by using time series water demand data, the model can provide excellent fit and forecast without relying on the explicit inclusion of climatic factors and number of consumers. The excellent accuracy of the model indicates the effectiveness of forecasting over different time horizons. Finally, the results obtained from the Artificial Neural Network are compared with traditional statistical models.},
 bibtype = {article},
 author = {Lorente-Leyva, Leandro L. and Pavón-Valencia, Jairo F. and Montero-Santos, Yakcleem and Herrera-Granda, Israel D and Herrera-Granda, Erick P. and Peluffo-Ordóñez, Diego H.},
 doi = {10.1088/1742-6596/1284/1/012004},
 journal = {Journal of Physics: Conference Series}
}

@article{
 title = {Artificial Neural Networks in the Demand Forecasting of a Metal-Mechanical Industry},
 type = {article},
 year = {2019},
 pages = {81-87},
 volume = {15},
 websites = {http://www.medwelljournals.com/abstract/?doi=jeasci.2020.81.87},
 month = {10},
 day = {25},
 id = {d2a33562-d740-37c2-a6df-cc39c178a563},
 created = {2022-02-02T06:07:46.991Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},
 last_modified = {2022-02-02T06:07:46.991Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {L.Lorente-Leyva2019},
 private_publication = {false},
 abstract = {This research presents an application of artificial neural networks in demand forecasting by using MATLAB Software. Keeping in mind that in any planning process forecasts play a fundamental role, being one of the bases for; planning, organizing and controlling production. It gives priority to the most critical nodes and their key activities, so that, the decisions made about them will generate the greatest possible positive impact. The methodology applied demonstrates the quality of the solutions found which are compared with traditional statistical methods to demonstrate the value of the solution proposed. When the results show that the minimum quadratic error is reached with the application of artificial neural networks, a better performance is obtained. Therefore, a suitable horizon is established for the planification and decision making in the metal-mechanical industry for the use of artificial intelligence in the production processes.},
 bibtype = {article},
 author = {L. Lorente-Leyva, Leandro and R. Patino-Alarcon, Delio and Montero-Santos, Yakcleem and D. Herrera-Granda, Israel and H. Peluffo-Ordonez, Diego and M. Lastre-Aleaga, Arlys and Cordoves-Garcia, Alexis},
 doi = {10.36478/jeasci.2020.81.87},
 journal = {Journal of Engineering and Applied Sciences},
 number = {1}
}

@inproceedings{
 title = {Instance Selection on CNNs for Alzheimer’s Disease Classification from MRI},
 type = {inproceedings},
 year = {2022},
 pages = {330-337},
 websites = {https://www.scitepress.org/Link.aspx?doi=10.5220/0010900100003122},
 publisher = {SciTePress},
 institution = {INSTICC},
 id = {b1289f2e-9cba-3e3f-b692-0db0c4a8e9f2},
 created = {2022-02-22T01:00:12.425Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},
 last_modified = {2022-02-22T01:00:12.425Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {icpram22},
 source_type = {conference},
 private_publication = {false},
 bibtype = {inproceedings},
 author = {Castro-Silva., J and Moreno-García., M and Guachi-Guachi., Lorena and Peluffo-Ordóñez., D},
 doi = {10.5220/0010900100003122},
 booktitle = {Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM,}
}

@inproceedings{
 title = {ECG-Based Heartbeat Classification for Arrhythmia Detection Using Artificial Neural Networks},
 type = {inproceedings},
 year = {2022},
 pages = {247-259},
 websites = {https://link.springer.com/chapter/10.1007/978-3-031-10450-3_20},
 publisher = {Springer International Publishing},
 city = {Cham},
 id = {fe0bb748-7f2d-396d-be6a-7337af3ccdc3},
 created = {2022-07-16T03:47:10.517Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},
 last_modified = {2022-07-16T03:47:10.517Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {10.1007/978-3-031-10450-3_20},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {Cardiovascular disease (CVD) has quickly grown in prevalence over the previous decade, becoming the major cause of human morbidity on a global scale. Due to the massive number of ECG data, manual analysis is regarded as a time-consuming, costly and prone to human error task. In the other hand, computational systems based on biomedical signal processing and machine learning techniques might be suited for supporting arrhythmia diagnostic processes, while solving some of those issues. In general, such systems involve five stages: acquisition, preprocessing, segmentation, characterization, and classification. Yet numerous fundamental aspects remain unresolved, including sensitivity to signal fluctuation, accuracy, computing cost, generalizability, and interpretability. In this context, the present study offers a comparative analysis of ECG signal classification using two artificial neural networks created by different machine learning frameworks. The neural nets were built into a pipeline that aims to strike an appropriate balance among signal robustness, variability, and accuracy. The proposed approach reaches up to 99\% of overall accuracy for each register while keeping the computational cost low.},
 bibtype = {inproceedings},
 author = {Cepeda, Eduardo and Sánchez-Pozo, Nadia N and Peluffo-Ordóñez, Diego H and González-Vergara, Juan and Almeida-Galárraga, Diego},
 editor = {Gervasi, Osvaldo and Murgante, Beniamino and Hendrix, Eligius M T and Taniar, David and Apduhan, Bernady O},
 booktitle = {Computational Science and Its Applications -- ICCSA 2022}
}

@article{
 title = {A Computer Vision Model to Identify the Incorrect Use of Face Masks for COVID-19 Awareness},
 type = {article},
 year = {2022},
 volume = {12},
 websites = {https://www.mdpi.com/2076-3417/12/14/6924},
 id = {70d7f700-db7a-39e4-8146-84cf0ff26274},
 created = {2022-07-24T01:29:08.758Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},
 last_modified = {2022-07-24T01:29:08.758Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {app12146924},
 source_type = {article},
 private_publication = {false},
 abstract = {Face mask detection has become a great challenge in computer vision, demanding the coalition of technology with COVID-19 awareness. Researchers have proposed deep learning models to detect the use of face masks. However, the incorrect use of a face mask can be as harmful as not wearing any protection at all. In this paper, we propose a compound convolutional neural network (CNN) architecture based on two computer vision tasks: object localization to discover faces in images/videos, followed by an image classification CNN to categorize the faces and show if someone is using a face mask correctly, incorrectly, or not at all. The first CNN is built upon RetinaFace, a model to detect faces in images, whereas the second CNN uses a ResNet-18 architecture as a classification backbone. Our model enables an accurate identification of people who are not correctly following the COVID-19 healthcare recommendations on face mask use. To enable further global use of our technology, we have released both the dataset used to train the classification model and our proposed computer vision pipeline to the public, and optimized it for embedded systems deployment.},
 bibtype = {article},
 author = {Crespo, Fabricio and Crespo, Anthony and Sierra-Martínez, Luz Marina and Peluffo-Ordóñez, Diego Hernán and Morocho-Cayamcela, Manuel Eugenio},
 doi = {10.3390/app12146924},
 journal = {Applied Sciences},
 number = {14}
}

@inproceedings{
 title = {Neural Networks on Noninvasive Electrocardiographic Imaging Reconstructions: Preliminary Results},
 type = {inproceedings},
 year = {2023},
 pages = {55-63},
 websites = {https://link.springer.com/chapter/10.1007/978-3-031-25942-5_5},
 publisher = {Springer Nature Switzerland},
 city = {Cham},
 id = {e947ed90-061d-30f3-be14-0374b37b4f4f},
 created = {2023-02-13T23:39:20.520Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},
 last_modified = {2023-02-13T23:39:20.520Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {10.1007/978-3-031-25942-5_5},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {In the reverse electrocardiography (ECG) problem, the objective is to reconstruct the heart's electrical activity from a set of body surface potentials by solving the direct model and the geometry of the torso. Over the years, researchers have used various approaches to solve this problem, from direct, iterative, probabilistic, and those based on deep learning. The interest of the latter, among the wide range of techniques, is because the complexity of the problem can be significantly reduced while increasing the precision of the estimation. In this article, we evaluate the performance of a deep learning-based neural network compared to the Tikhonov method of zero order (ZOT), first (FOT), and second (SOT). Preliminary results show an improvement in performance over real data when Pearson's correlation coefficient (CC) and (RMSE) are calculated. The CC's mean value and standard deviation for the proposed method were 0.960 (0.065), well above ZOT, which was 0.864 (0.047).},
 bibtype = {inproceedings},
 author = {Mayorca-Torres, Dagoberto and León-Salas, Alejandro José and Peluffo-Ordoñez, Diego Hernán},
 editor = {Botto-Tobar, Miguel and Gómez, Omar S and Rosero Miranda, Raul and Díaz Cadena, Angela and Luna-Encalada, Washington},
 booktitle = {Trends in Artificial Intelligence and Computer Engineering}
}

@inproceedings{
 title = {Crop Classification Using Deep Learning: A Quick Comparative Study of Modern Approaches},
 type = {inproceedings},
 year = {2022},
 pages = {31-44},
 websites = {https://link.springer.com/chapter/10.1007/978-3-031-19647-8_3},
 publisher = {Springer International Publishing},
 city = {Cham},
 id = {d0ea8cbb-e8dd-3b80-8451-9b858e90db31},
 created = {2023-03-01T20:18:51.423Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},
 last_modified = {2023-03-01T20:18:51.423Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {10.1007/978-3-031-19647-8_3},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {Automatic crop classification using new technologies is recognized as one of the most important assets in today's smart farming improvement. Investments in technology and innovation are key issues for shaping agricultural productivity as well as the inclusiveness and sustainability of the global agricultural transformation. Digital image processing (DIP) has been widely adopted in this field, by merging Unmanned Aerial Vehicle (UAV) based remote sensing and deep learning (DL) as a powerful tool for crop classification. Despite the wide range of alternatives, the proper selection of a DL approach is still an open and challenging issue. In this work, we carry out an exhaustive performance evaluation of three remarkable and lightweight DL approaches, namely: Visual Geometry Group (VGG), Residual Neural Network (ResNet) and Inception V3, tested on high resolution agriculture crop images dataset. Experimental results show that InceptionV3 outperforms VGG and ResNet in terms of precision (0,92), accuracy (0,97), recall (0,91), AUC (0,98), PCR (0,97), and F1 (0,91).},
 bibtype = {inproceedings},
 author = {Raki, Hind and González-Vergara, Juan and Aalaila, Yahya and Elhamdi, Mouad and Bamansour, Sami and Guachi-Guachi, Lorena and Peluffo-Ordoñez, Diego H},
 editor = {Florez, Hector and Gomez, Henry},
 booktitle = {Applied Informatics}
}

@inproceedings{
 title = {Instance Selection Framework for Alzheimer’s Disease Classification Using Multiple Regions of Interest and Atlas Integration},
 type = {inproceedings},
 year = {2024},
 pages = {453-460},
 websites = {https://www.scitepress.org/Link.aspx?doi=10.5220/0012469600003654},
 publisher = {SciTePress},
 institution = {INSTICC},
 id = {979ed068-0e34-3d58-a432-a1b8d2140266},
 created = {2024-03-06T14:15:39.886Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},
 last_modified = {2024-03-06T14:15:39.886Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {icpram24},
 source_type = {conference},
 private_publication = {false},
 abstract = {Optimal selection of informative instances from a dataset is critical for constructing accurate predictive models. As databases expand, leveraging instance selection techniques becomes imperative to condense data into a more manageable size. This research unveils a novel framework designed to strategically identify and choose the most informative 2D brain image slices for Alzheimer’s disease classification. Such a framework integrates annotations from multiple regions of interest across multiple atlases. The proposed framework consists of six core components: 1) Atlas merging for ROI annotation and hemisphere separation. 2) Image preprocessing to extract informative slices. 3) Dataset construction to prevent data leakage, select subjects, and split data. 4) Data generation for memory-efficient batches. 5) Model construction for diverse classification training and testing. 6) Weighted ensemble for combining predictions from multiple models with a single learning algorithm. Our instanc e selection framework was applied to construct Transformer-based classification models, demonstrating an overall accuracy of approximately 98.33% in distinguishing between Cognitively Normal and Alzheimer’s cases at the subject level. It exhibited enhancements of 3.68%, 3.01%, 3.62% for sagittal, coronal, and axial planes respectively in comparison with the percentile technique.},
 bibtype = {inproceedings},
 author = {Castro-Silva., Juan A. and Moreno-García., Maria and Guachi-Guachi., Lorena and Peluffo-Ordóñez., Diego H.},
 doi = {10.5220/0012469600003654},
 booktitle = {Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM}
}

@article{
 title = {Multiple Inputs and Mixed Data for Alzheimer’s Disease Classification Based on 3D Vision Transformer},
 type = {article},
 year = {2024},
 pages = {2720},
 volume = {12},
 websites = {https://www.mdpi.com/2227-7390/12/17/2720},
 publisher = {MDPI},
 id = {67e1abb8-ae6d-3e39-bc38-f3a6afe5febf},
 created = {2024-09-04T16:45:13.004Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},
 last_modified = {2024-09-04T16:45:13.004Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {castro2024multiple},
 source_type = {article},
 private_publication = {false},
 abstract = {The current methods for diagnosing Alzheimer’s Disease using Magnetic Resonance Imaging (MRI) have significant limitations. Many previous studies used 2D Transformers to analyze individual brain slices independently, potentially losing critical 3D contextual information. Region of interest-based models often focus on only a few brain regions despite Alzheimer’s affecting multiple areas. Additionally, most classification models rely on a single test, whereas diagnosing Alzheimer’s requires a multifaceted approach integrating diverse data sources for a more accurate assessment. This study introduces a novel methodology called the Multiple Inputs and Mixed Data 3D Vision Transformer (MIMD-3DVT). This method processes consecutive slices together to capture the feature dimensions and spatial information, fuses multiple 3D ROI imaging data inputs, and integrates mixed data from demographic factors, cognitive assessments, and brain imaging. The proposed methodology was experimentally evaluated using a combined dataset that included the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Australian Imaging, Biomarker, and Lifestyle Flagship Study of Ageing (AIBL), and the Open Access Series of Imaging Studies (OASIS). Our MIMD-3DVT, utilizing single or multiple ROIs, achieved an accuracy of 97.14%, outperforming the state-of-the-art methods in distinguishing between Normal Cognition and Alzheimer’s Disease.},
 bibtype = {article},
 author = {Castro-Silva, Juan A and Moreno-García, María N and Peluffo-Ordóñez, Diego H},
 journal = {Mathematics},
 number = {17}
}

@article{
 title = {Novel hippocampus-centered methodology for informative instance selection in Alzheimer's disease data},
 type = {article},
 year = {2024},
 keywords = {Alzheimer's disease,Deep learning,Hippocampus,Instance selection},
 pages = {e37552},
 volume = {10},
 id = {b2d13133-eb4d-3fe0-bc3e-23479c4e0c95},
 created = {2024-09-25T22:33:25.171Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},
 last_modified = {2024-09-25T22:33:25.171Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {CASTROSILVA2024e37552},
 source_type = {article},
 private_publication = {false},
 bibtype = {article},
 author = {Castro-Silva, Juan A and Moreno-García, María N and Guachi-Guachi, Lorena and Peluffo-Ordóñez, Diego H},
 doi = {https://doi.org/10.1016/j.heliyon.2024.e37552},
 journal = {Heliyon},
 number = {19}
}

@inproceedings{
 title = {Automatic Identification of Forest Areas in the ``Carolina'' Park Using ResNet50, EfficientNetB0 and VGG16: A Case Study},
 type = {inproceedings},
 year = {2025},
 pages = {31-42},
 websites = {https://link.springer.com/chapter/10.1007/978-3-031-75144-8_3},
 publisher = {Springer Nature Switzerland},
 city = {Cham},
 id = {0feb5a1a-f84d-3920-9540-8f59bb2f4515},
 created = {2024-11-12T17:54:48.400Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},
 last_modified = {2024-11-12T17:54:48.400Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {10.1007/978-3-031-75144-8_3},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {This study explores the challenges of identifying forest areas in the ``Carolina'' Park in Quito, Ecuador, using Convolutional Neural Networks (CNN) and aerial imagery to support sustainable urban expansion plans. A dataset was constructed using 32\$\$\backslash,\backslashtimes \backslash,\$\$\texttimes32\$\$\backslash,\backslashtimes \backslash,\$\$\texttimes3 pixel patches extracted from 230 aerial images obtained from several videos captured by drones over the park. Three CNN models (ResNet50, EfficientNetB0 and VGG16) were trained to differentiate between forested, non-forested and hybrid areas. The methodology involved manual classification of 2100 patches into these three categories. The results showed that ResNet50 performed the best overall, with an accuracy of 76.66\% \textpm 8\%, followed closely by VGG16, while EfficientNetB0 showed inferior performance on this specific dataset. Qualitative analysis of predictions on test images confirmed the effective identification of forest areas. These findings suggest that ResNet50 may be a suitable model for this task, demonstrating a high ability to learn and recognize patterns in forested areas through patch-based analysis, even with relatively small datasets derived from aerial drone imagery.},
 bibtype = {inproceedings},
 author = {Guapaz, Julian and Jervis, Juan Pablo and Haro, Diego and Padilla, Jefferson and Guachi, Robinson and Peluffo-Ordóñez, D H and Guachi-Guachi, Lorena},
 editor = {Florez, Hector and Astudillo, Hernán},
 booktitle = {Applied Informatics}
}

@inproceedings{
 title = {Automatic Differentiation Between Coriander and Parsley Using MobileNetV2},
 type = {inproceedings},
 year = {2025},
 pages = {18-30},
 websites = {https://link.springer.com/chapter/10.1007/978-3-031-75144-8_2},
 publisher = {Springer Nature Switzerland},
 city = {Cham},
 id = {295f4a04-2d1d-36e7-b290-da33944d17a2},
 created = {2024-11-12T18:03:35.797Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},
 last_modified = {2024-11-12T18:03:35.797Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {10.1007/978-3-031-75144-8_2},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {Ecuadorian cuisine is very varied, with typical dishes from various regions often using fresh herbs. Among them, coriander and parsley are frequently used but often confused because of their similar appearance. This confusion can result in inexperienced or visually impaired people using the wrong herb, thus altering the flavor and unique qualities of the dishes. To address this problem, in this work, we present an image classification model capable of distinguishing between coriander and parsley leaves. Three architectures (MobileNetV2, EfficientNetB0 and InceptionV3) were evaluated to determine which offered the highest accuracy. MobileNetV2 proved to be the most efficient model, demonstrating superior stability and achieving greater than 99\% accuracy.},
 bibtype = {inproceedings},
 author = {Páez, Ian and Arévalo, José and Martinez, Mateo and Molina, Martin and Guachi, Robinson and Peluffo-Ordóñez, D H and Guachi-Guachi, Lorena},
 editor = {Florez, Hector and Astudillo, Hernán},
 booktitle = {Applied Informatics}
}\">\n            <i class=\"fa fa-download fa-fw\"></i> Download BibTeX\n          </a>\n        </li>\n        <li>\n          <a href=\"//bibbase.org/rss?bib=\">\n            <i class=\"fa fa-rss fa-fw\"></i> RSS Feed\n          </a>\n          </li>\n      </ul>\n      </li>\n\n      \n\n\n      \n    </ul>\n\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_embed\"\n         style=\"display: none;\">\n\n      Excellent! Next you can\n      <a href=\"/network/register?next=/network/newSiteFromTemplate%3Fbiburl=\"\n      >create a new website with this list</a>, or\n      embed it in an existing web page by copying &amp; pasting\n      any of the following snippets.\n\n      <div style=\"text-align: left; margin-top: 1em;\">\n        <strong>JavaScript</strong>\n        <span class=\"comment recommended\">(easiest)</span>\n        <div class=\"well well-small\">\n          <code>\n            &lt;script src=\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/group/aea1f410-b6a0-3014-afb8-bc5b4ad73524?jsonp=1&amp;jsonp=1\"&gt;&lt;/script&gt;\n          </code>\n        </div>\n\n        <strong>PHP</strong>\n        <div class=\"well well-small\">\n          <code>\n            &lt;?php\n            $contents = file_get_contents(\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/group/aea1f410-b6a0-3014-afb8-bc5b4ad73524?jsonp=1\");\n            print_r($contents);\n            ?&gt;\n          </code>\n        </div>\n\n        <strong>iFrame</strong>\n        <span class=\"comment\">(not recommended)</span>\n        <div class=\"well well-small\">\n          <code>\n            &lt;iframe src=\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/group/aea1f410-b6a0-3014-afb8-bc5b4ad73524?jsonp=1\"&gt;&lt;/iframe&gt;\n          </code>\n        </div>\n\n        <p>\n          For more details see the <a href=\"//bibbase.org/help\">documention</a>.\n        </p>\n      </div>\n    </div>\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_preview\"\n         style=\"display: none;\">\n\n      This is a preview! To use this list on your own web site\n      or create a new web site from it,\n      <a href=\"/network/register?next=/network/newAccountWithFile%3FfileId=\"\n      >create a free account</a>. The file will be added\n      and you will be able to edit it in the File Manager.\n      We will show you instructions once you've created your account.\n    </div>\n\n    <div class=\"alert alert-warning bibbase_msg\" id=\"bibbase_msg_mendeley1\"\n         style=\"display: none;\">\n\n      <p>To the site owner:</p>\n\n      <p><strong>Action required!</strong> Mendeley is changing its\n        API. In order to keep using Mendeley with BibBase past April\n        14th, you need to:\n        <ol>\n          <li>renew the authorization for BibBase on Mendeley, and</li>\n          <li>update the BibBase URL\n            in your page the same way you did when you initially set up\n            this page.\n          </li>\n        </ol>\n      </p>\n\n      <p>\n        <a href=\"http://bibbase.org/cgi-bin/mendeley2/get.py\">\n          <i class=\"fa fa-angle-double-right\"></i>\n          Fix it now</a>\n      </p>\n    </div>\n\n  </div>\n\n\n  <div id=\"bibbase_body\">\n    \n  \n  <div class=\"bibbase_group_whole\" id=\"group_2025\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2025')\"\n      onkeypress=\"toggleGroup('group_2025')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2025</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"guapaz-jervis-haro-padilla-guachi-peluffoordez-guachiguachi-automaticidentificationofforestareasinthecarolinaparkusingresnet50efficientnetb0andvgg16acasestudy-2025\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-3-031-75144-8_3\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'guapaz-jervis-haro-padilla-guachi-peluffoordez-guachiguachi-automaticidentificationofforestareasinthecarolinaparkusingresnet50efficientnetb0andvgg16acasestudy-2025', 'https://link.springer.com/chapter/10.1007/978-3-031-75144-8_3', event);\">\n    Automatic Identification of Forest Areas in the ``Carolina'' Park Using ResNet50, EfficientNetB0 and VGG16: A Case Study.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Guapaz,  J.; Jervis,  J., P.; Haro,  D.; Padilla,  J.; Guachi,  R.; Peluffo-Ordóñez,  D., H.;  and Guachi-Guachi,  L.\n</span>\n\n  \n\n</span>\n\n  In Florez,  H.;  and Astudillo,  H., editor(s), <i>Applied Informatics</i>, pages 31-42,  2025. Springer Nature Switzerland\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-3-031-75144-8_3\"\n     onclick=\"log_download('guapaz-jervis-haro-padilla-guachi-peluffoordez-guachiguachi-automaticidentificationofforestareasinthecarolinaparkusingresnet50efficientnetb0andvgg16acasestudy-2025', 'https://link.springer.com/chapter/10.1007/978-3-031-75144-8_3', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Automatic Identification of Forest Areas in the &#x60;&#x60;Carolina&#x27;&#x27; Park Using ResNet50, EfficientNetB0 and VGG16: A Case Study [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/guapaz-jervis-haro-padilla-guachi-peluffoordez-guachiguachi-automaticidentificationofforestareasinthecarolinaparkusingresnet50efficientnetb0andvgg16acasestudy-2025\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('guapaz-jervis-haro-padilla-guachi-peluffoordez-guachiguachi-automaticidentificationofforestareasinthecarolinaparkusingresnet50efficientnetb0andvgg16acasestudy-2025')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Automatic Identification of Forest Areas in the &#x60;&#x60;Carolina&#x27;&#x27; Park Using ResNet50, EfficientNetB0 and VGG16: A Case Study},\n type = {inproceedings},\n year = {2025},\n pages = {31-42},\n websites = {https://link.springer.com/chapter/10.1007/978-3-031-75144-8_3},\n publisher = {Springer Nature Switzerland},\n city = {Cham},\n id = {0feb5a1a-f84d-3920-9540-8f59bb2f4515},\n created = {2024-11-12T17:54:48.400Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\n last_modified = {2024-11-12T17:54:48.400Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {10.1007/978-3-031-75144-8_3},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {This study explores the challenges of identifying forest areas in the &#x60;&#x60;Carolina&#x27;&#x27; Park in Quito, Ecuador, using Convolutional Neural Networks (CNN) and aerial imagery to support sustainable urban expansion plans. A dataset was constructed using 32\\$\\$\\backslash,\\backslashtimes \\backslash,\\$\\$\\texttimes32\\$\\$\\backslash,\\backslashtimes \\backslash,\\$\\$\\texttimes3 pixel patches extracted from 230 aerial images obtained from several videos captured by drones over the park. Three CNN models (ResNet50, EfficientNetB0 and VGG16) were trained to differentiate between forested, non-forested and hybrid areas. The methodology involved manual classification of 2100 patches into these three categories. The results showed that ResNet50 performed the best overall, with an accuracy of 76.66\\% \\textpm 8\\%, followed closely by VGG16, while EfficientNetB0 showed inferior performance on this specific dataset. Qualitative analysis of predictions on test images confirmed the effective identification of forest areas. These findings suggest that ResNet50 may be a suitable model for this task, demonstrating a high ability to learn and recognize patterns in forested areas through patch-based analysis, even with relatively small datasets derived from aerial drone imagery.},\n bibtype = {inproceedings},\n author = {Guapaz, Julian and Jervis, Juan Pablo and Haro, Diego and Padilla, Jefferson and Guachi, Robinson and Peluffo-Ordóñez, D H and Guachi-Guachi, Lorena},\n editor = {Florez, Hector and Astudillo, Hernán},\n booktitle = {Applied Informatics}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This study explores the challenges of identifying forest areas in the ``Carolina'' Park in Quito, Ecuador, using Convolutional Neural Networks (CNN) and aerial imagery to support sustainable urban expansion plans. A dataset was constructed using 32\\$\\$\\backslash,\\backslashtimes \\backslash,\\$\\$\\texttimes32\\$\\$\\backslash,\\backslashtimes \\backslash,\\$\\$\\texttimes3 pixel patches extracted from 230 aerial images obtained from several videos captured by drones over the park. Three CNN models (ResNet50, EfficientNetB0 and VGG16) were trained to differentiate between forested, non-forested and hybrid areas. The methodology involved manual classification of 2100 patches into these three categories. The results showed that ResNet50 performed the best overall, with an accuracy of 76.66\\% \\textpm 8\\%, followed closely by VGG16, while EfficientNetB0 showed inferior performance on this specific dataset. Qualitative analysis of predictions on test images confirmed the effective identification of forest areas. These findings suggest that ResNet50 may be a suitable model for this task, demonstrating a high ability to learn and recognize patterns in forested areas through patch-based analysis, even with relatively small datasets derived from aerial drone imagery.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"pez-arvalo-martinez-molina-guachi-peluffoordez-guachiguachi-automaticdifferentiationbetweencorianderandparsleyusingmobilenetv2-2025\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-3-031-75144-8_2\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'pez-arvalo-martinez-molina-guachi-peluffoordez-guachiguachi-automaticdifferentiationbetweencorianderandparsleyusingmobilenetv2-2025', 'https://link.springer.com/chapter/10.1007/978-3-031-75144-8_2', event);\">\n    Automatic Differentiation Between Coriander and Parsley Using MobileNetV2.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Páez,  I.; Arévalo,  J.; Martinez,  M.; Molina,  M.; Guachi,  R.; Peluffo-Ordóñez,  D., H.;  and Guachi-Guachi,  L.\n</span>\n\n  \n\n</span>\n\n  In Florez,  H.;  and Astudillo,  H., editor(s), <i>Applied Informatics</i>, pages 18-30,  2025. Springer Nature Switzerland\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-3-031-75144-8_2\"\n     onclick=\"log_download('pez-arvalo-martinez-molina-guachi-peluffoordez-guachiguachi-automaticdifferentiationbetweencorianderandparsleyusingmobilenetv2-2025', 'https://link.springer.com/chapter/10.1007/978-3-031-75144-8_2', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Automatic Differentiation Between Coriander and Parsley Using MobileNetV2 [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pez-arvalo-martinez-molina-guachi-peluffoordez-guachiguachi-automaticdifferentiationbetweencorianderandparsleyusingmobilenetv2-2025\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pez-arvalo-martinez-molina-guachi-peluffoordez-guachiguachi-automaticdifferentiationbetweencorianderandparsleyusingmobilenetv2-2025')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Automatic Differentiation Between Coriander and Parsley Using MobileNetV2},\n type = {inproceedings},\n year = {2025},\n pages = {18-30},\n websites = {https://link.springer.com/chapter/10.1007/978-3-031-75144-8_2},\n publisher = {Springer Nature Switzerland},\n city = {Cham},\n id = {295f4a04-2d1d-36e7-b290-da33944d17a2},\n created = {2024-11-12T18:03:35.797Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\n last_modified = {2024-11-12T18:03:35.797Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {10.1007/978-3-031-75144-8_2},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Ecuadorian cuisine is very varied, with typical dishes from various regions often using fresh herbs. Among them, coriander and parsley are frequently used but often confused because of their similar appearance. This confusion can result in inexperienced or visually impaired people using the wrong herb, thus altering the flavor and unique qualities of the dishes. To address this problem, in this work, we present an image classification model capable of distinguishing between coriander and parsley leaves. Three architectures (MobileNetV2, EfficientNetB0 and InceptionV3) were evaluated to determine which offered the highest accuracy. MobileNetV2 proved to be the most efficient model, demonstrating superior stability and achieving greater than 99\\% accuracy.},\n bibtype = {inproceedings},\n author = {Páez, Ian and Arévalo, José and Martinez, Mateo and Molina, Martin and Guachi, Robinson and Peluffo-Ordóñez, D H and Guachi-Guachi, Lorena},\n editor = {Florez, Hector and Astudillo, Hernán},\n booktitle = {Applied Informatics}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Ecuadorian cuisine is very varied, with typical dishes from various regions often using fresh herbs. Among them, coriander and parsley are frequently used but often confused because of their similar appearance. This confusion can result in inexperienced or visually impaired people using the wrong herb, thus altering the flavor and unique qualities of the dishes. To address this problem, in this work, we present an image classification model capable of distinguishing between coriander and parsley leaves. Three architectures (MobileNetV2, EfficientNetB0 and InceptionV3) were evaluated to determine which offered the highest accuracy. MobileNetV2 proved to be the most efficient model, demonstrating superior stability and achieving greater than 99\\% accuracy.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2024\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2024')\"\n      onkeypress=\"toggleGroup('group_2024')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2024</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"castrosilva-morenogarca-guachiguachi-peluffoordez-instanceselectionframeworkforalzheimersdiseaseclassificationusingmultipleregionsofinterestandatlasintegration-2024\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.scitepress.org/Link.aspx?doi=10.5220/0012469600003654\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'castrosilva-morenogarca-guachiguachi-peluffoordez-instanceselectionframeworkforalzheimersdiseaseclassificationusingmultipleregionsofinterestandatlasintegration-2024', 'https://www.scitepress.org/Link.aspx?doi=10.5220/0012469600003654', event);\">\n    Instance Selection Framework for Alzheimer’s Disease Classification Using Multiple Regions of Interest and Atlas Integration.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Castro-Silva.,  J., A.; Moreno-García.,  M.; Guachi-Guachi.,  L.;  and Peluffo-Ordóñez.,  D., H.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM</i>, pages 453-460,  2024. SciTePress\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.scitepress.org/Link.aspx?doi=10.5220/0012469600003654\"\n     onclick=\"log_download('castrosilva-morenogarca-guachiguachi-peluffoordez-instanceselectionframeworkforalzheimersdiseaseclassificationusingmultipleregionsofinterestandatlasintegration-2024', 'https://www.scitepress.org/Link.aspx?doi=10.5220/0012469600003654', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Instance Selection Framework for Alzheimer’s Disease Classification Using Multiple Regions of Interest and Atlas Integration [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.5220/0012469600003654\"\n     onclick=\"log_download('castrosilva-morenogarca-guachiguachi-peluffoordez-instanceselectionframeworkforalzheimersdiseaseclassificationusingmultipleregionsofinterestandatlasintegration-2024', 'http://doi.org/10.5220/0012469600003654', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/castrosilva-morenogarca-guachiguachi-peluffoordez-instanceselectionframeworkforalzheimersdiseaseclassificationusingmultipleregionsofinterestandatlasintegration-2024\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('castrosilva-morenogarca-guachiguachi-peluffoordez-instanceselectionframeworkforalzheimersdiseaseclassificationusingmultipleregionsofinterestandatlasintegration-2024')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Instance Selection Framework for Alzheimer’s Disease Classification Using Multiple Regions of Interest and Atlas Integration},\n type = {inproceedings},\n year = {2024},\n pages = {453-460},\n websites = {https://www.scitepress.org/Link.aspx?doi=10.5220/0012469600003654},\n publisher = {SciTePress},\n institution = {INSTICC},\n id = {979ed068-0e34-3d58-a432-a1b8d2140266},\n created = {2024-03-06T14:15:39.886Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\n last_modified = {2024-03-06T14:15:39.886Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {icpram24},\n source_type = {conference},\n private_publication = {false},\n abstract = {Optimal selection of informative instances from a dataset is critical for constructing accurate predictive models. As databases expand, leveraging instance selection techniques becomes imperative to condense data into a more manageable size. This research unveils a novel framework designed to strategically identify and choose the most informative 2D brain image slices for Alzheimer’s disease classification. Such a framework integrates annotations from multiple regions of interest across multiple atlases. The proposed framework consists of six core components: 1) Atlas merging for ROI annotation and hemisphere separation. 2) Image preprocessing to extract informative slices. 3) Dataset construction to prevent data leakage, select subjects, and split data. 4) Data generation for memory-efficient batches. 5) Model construction for diverse classification training and testing. 6) Weighted ensemble for combining predictions from multiple models with a single learning algorithm. Our instanc e selection framework was applied to construct Transformer-based classification models, demonstrating an overall accuracy of approximately 98.33% in distinguishing between Cognitively Normal and Alzheimer’s cases at the subject level. It exhibited enhancements of 3.68%, 3.01%, 3.62% for sagittal, coronal, and axial planes respectively in comparison with the percentile technique.},\n bibtype = {inproceedings},\n author = {Castro-Silva., Juan A. and Moreno-García., Maria and Guachi-Guachi., Lorena and Peluffo-Ordóñez., Diego H.},\n doi = {10.5220/0012469600003654},\n booktitle = {Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Optimal selection of informative instances from a dataset is critical for constructing accurate predictive models. As databases expand, leveraging instance selection techniques becomes imperative to condense data into a more manageable size. This research unveils a novel framework designed to strategically identify and choose the most informative 2D brain image slices for Alzheimer’s disease classification. Such a framework integrates annotations from multiple regions of interest across multiple atlases. The proposed framework consists of six core components: 1) Atlas merging for ROI annotation and hemisphere separation. 2) Image preprocessing to extract informative slices. 3) Dataset construction to prevent data leakage, select subjects, and split data. 4) Data generation for memory-efficient batches. 5) Model construction for diverse classification training and testing. 6) Weighted ensemble for combining predictions from multiple models with a single learning algorithm. Our instanc e selection framework was applied to construct Transformer-based classification models, demonstrating an overall accuracy of approximately 98.33% in distinguishing between Cognitively Normal and Alzheimer’s cases at the subject level. It exhibited enhancements of 3.68%, 3.01%, 3.62% for sagittal, coronal, and axial planes respectively in comparison with the percentile technique.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"castrosilva-morenogarca-peluffoordez-multipleinputsandmixeddataforalzheimersdiseaseclassificationbasedon3dvisiontransformer-2024\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.mdpi.com/2227-7390/12/17/2720\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'castrosilva-morenogarca-peluffoordez-multipleinputsandmixeddataforalzheimersdiseaseclassificationbasedon3dvisiontransformer-2024', 'https://www.mdpi.com/2227-7390/12/17/2720', event);\">\n    Multiple Inputs and Mixed Data for Alzheimer’s Disease Classification Based on 3D Vision Transformer.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Castro-Silva,  J., A.; Moreno-García,  M., N.;  and Peluffo-Ordóñez,  D., H.\n</span>\n\n  \n\n</span>\n\n  <i>Mathematics</i>, 12(17): 2720.  2024.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.mdpi.com/2227-7390/12/17/2720\"\n     onclick=\"log_download('castrosilva-morenogarca-peluffoordez-multipleinputsandmixeddataforalzheimersdiseaseclassificationbasedon3dvisiontransformer-2024', 'https://www.mdpi.com/2227-7390/12/17/2720', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Multiple Inputs and Mixed Data for Alzheimer’s Disease Classification Based on 3D Vision Transformer [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/castrosilva-morenogarca-peluffoordez-multipleinputsandmixeddataforalzheimersdiseaseclassificationbasedon3dvisiontransformer-2024\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('castrosilva-morenogarca-peluffoordez-multipleinputsandmixeddataforalzheimersdiseaseclassificationbasedon3dvisiontransformer-2024')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Multiple Inputs and Mixed Data for Alzheimer’s Disease Classification Based on 3D Vision Transformer},\n type = {article},\n year = {2024},\n pages = {2720},\n volume = {12},\n websites = {https://www.mdpi.com/2227-7390/12/17/2720},\n publisher = {MDPI},\n id = {67e1abb8-ae6d-3e39-bc38-f3a6afe5febf},\n created = {2024-09-04T16:45:13.004Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\n last_modified = {2024-09-04T16:45:13.004Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {castro2024multiple},\n source_type = {article},\n private_publication = {false},\n abstract = {The current methods for diagnosing Alzheimer’s Disease using Magnetic Resonance Imaging (MRI) have significant limitations. Many previous studies used 2D Transformers to analyze individual brain slices independently, potentially losing critical 3D contextual information. Region of interest-based models often focus on only a few brain regions despite Alzheimer’s affecting multiple areas. Additionally, most classification models rely on a single test, whereas diagnosing Alzheimer’s requires a multifaceted approach integrating diverse data sources for a more accurate assessment. This study introduces a novel methodology called the Multiple Inputs and Mixed Data 3D Vision Transformer (MIMD-3DVT). This method processes consecutive slices together to capture the feature dimensions and spatial information, fuses multiple 3D ROI imaging data inputs, and integrates mixed data from demographic factors, cognitive assessments, and brain imaging. The proposed methodology was experimentally evaluated using a combined dataset that included the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Australian Imaging, Biomarker, and Lifestyle Flagship Study of Ageing (AIBL), and the Open Access Series of Imaging Studies (OASIS). Our MIMD-3DVT, utilizing single or multiple ROIs, achieved an accuracy of 97.14%, outperforming the state-of-the-art methods in distinguishing between Normal Cognition and Alzheimer’s Disease.},\n bibtype = {article},\n author = {Castro-Silva, Juan A and Moreno-García, María N and Peluffo-Ordóñez, Diego H},\n journal = {Mathematics},\n number = {17}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The current methods for diagnosing Alzheimer’s Disease using Magnetic Resonance Imaging (MRI) have significant limitations. Many previous studies used 2D Transformers to analyze individual brain slices independently, potentially losing critical 3D contextual information. Region of interest-based models often focus on only a few brain regions despite Alzheimer’s affecting multiple areas. Additionally, most classification models rely on a single test, whereas diagnosing Alzheimer’s requires a multifaceted approach integrating diverse data sources for a more accurate assessment. This study introduces a novel methodology called the Multiple Inputs and Mixed Data 3D Vision Transformer (MIMD-3DVT). This method processes consecutive slices together to capture the feature dimensions and spatial information, fuses multiple 3D ROI imaging data inputs, and integrates mixed data from demographic factors, cognitive assessments, and brain imaging. The proposed methodology was experimentally evaluated using a combined dataset that included the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Australian Imaging, Biomarker, and Lifestyle Flagship Study of Ageing (AIBL), and the Open Access Series of Imaging Studies (OASIS). Our MIMD-3DVT, utilizing single or multiple ROIs, achieved an accuracy of 97.14%, outperforming the state-of-the-art methods in distinguishing between Normal Cognition and Alzheimer’s Disease.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"castrosilva-morenogarca-guachiguachi-peluffoordez-novelhippocampuscenteredmethodologyforinformativeinstanceselectioninalzheimersdiseasedata-2024\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Novel hippocampus-centered methodology for informative instance selection in Alzheimer's disease data.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Castro-Silva,  J., A.; Moreno-García,  M., N.; Guachi-Guachi,  L.;  and Peluffo-Ordóñez,  D., H.\n</span>\n\n  \n\n</span>\n\n  <i>Heliyon</i>, 10(19): e37552.  2024.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/https://doi.org/10.1016/j.heliyon.2024.e37552\"\n     onclick=\"log_download('castrosilva-morenogarca-guachiguachi-peluffoordez-novelhippocampuscenteredmethodologyforinformativeinstanceselectioninalzheimersdiseasedata-2024', 'http://doi.org/https://doi.org/10.1016/j.heliyon.2024.e37552', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/castrosilva-morenogarca-guachiguachi-peluffoordez-novelhippocampuscenteredmethodologyforinformativeinstanceselectioninalzheimersdiseasedata-2024\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('castrosilva-morenogarca-guachiguachi-peluffoordez-novelhippocampuscenteredmethodologyforinformativeinstanceselectioninalzheimersdiseasedata-2024')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Novel hippocampus-centered methodology for informative instance selection in Alzheimer&#x27;s disease data},\n type = {article},\n year = {2024},\n keywords = {Alzheimer&#x27;s disease,Deep learning,Hippocampus,Instance selection},\n pages = {e37552},\n volume = {10},\n id = {b2d13133-eb4d-3fe0-bc3e-23479c4e0c95},\n created = {2024-09-25T22:33:25.171Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\n last_modified = {2024-09-25T22:33:25.171Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {CASTROSILVA2024e37552},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Castro-Silva, Juan A and Moreno-García, María N and Guachi-Guachi, Lorena and Peluffo-Ordóñez, Diego H},\n doi = {https://doi.org/10.1016/j.heliyon.2024.e37552},\n journal = {Heliyon},\n number = {19}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2023\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2023')\"\n      onkeypress=\"toggleGroup('group_2023')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2023</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"mayorcatorres-lensalas-peluffoordoez-neuralnetworksonnoninvasiveelectrocardiographicimagingreconstructionspreliminaryresults-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-3-031-25942-5_5\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'mayorcatorres-lensalas-peluffoordoez-neuralnetworksonnoninvasiveelectrocardiographicimagingreconstructionspreliminaryresults-2023', 'https://link.springer.com/chapter/10.1007/978-3-031-25942-5_5', event);\">\n    Neural Networks on Noninvasive Electrocardiographic Imaging Reconstructions: Preliminary Results.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Mayorca-Torres,  D.; León-Salas,  A., J.;  and Peluffo-Ordoñez,  D., H.\n</span>\n\n  \n\n</span>\n\n  In Botto-Tobar,  M.; Gómez,  O., S.; Rosero Miranda,  R.; Díaz Cadena,  A.;  and Luna-Encalada,  W., editor(s), <i>Trends in Artificial Intelligence and Computer Engineering</i>, pages 55-63,  2023. Springer Nature Switzerland\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-3-031-25942-5_5\"\n     onclick=\"log_download('mayorcatorres-lensalas-peluffoordoez-neuralnetworksonnoninvasiveelectrocardiographicimagingreconstructionspreliminaryresults-2023', 'https://link.springer.com/chapter/10.1007/978-3-031-25942-5_5', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Neural Networks on Noninvasive Electrocardiographic Imaging Reconstructions: Preliminary Results [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/mayorcatorres-lensalas-peluffoordoez-neuralnetworksonnoninvasiveelectrocardiographicimagingreconstructionspreliminaryresults-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('mayorcatorres-lensalas-peluffoordoez-neuralnetworksonnoninvasiveelectrocardiographicimagingreconstructionspreliminaryresults-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Neural Networks on Noninvasive Electrocardiographic Imaging Reconstructions: Preliminary Results},\n type = {inproceedings},\n year = {2023},\n pages = {55-63},\n websites = {https://link.springer.com/chapter/10.1007/978-3-031-25942-5_5},\n publisher = {Springer Nature Switzerland},\n city = {Cham},\n id = {e947ed90-061d-30f3-be14-0374b37b4f4f},\n created = {2023-02-13T23:39:20.520Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\n last_modified = {2023-02-13T23:39:20.520Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {10.1007/978-3-031-25942-5_5},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {In the reverse electrocardiography (ECG) problem, the objective is to reconstruct the heart&#x27;s electrical activity from a set of body surface potentials by solving the direct model and the geometry of the torso. Over the years, researchers have used various approaches to solve this problem, from direct, iterative, probabilistic, and those based on deep learning. The interest of the latter, among the wide range of techniques, is because the complexity of the problem can be significantly reduced while increasing the precision of the estimation. In this article, we evaluate the performance of a deep learning-based neural network compared to the Tikhonov method of zero order (ZOT), first (FOT), and second (SOT). Preliminary results show an improvement in performance over real data when Pearson&#x27;s correlation coefficient (CC) and (RMSE) are calculated. The CC&#x27;s mean value and standard deviation for the proposed method were 0.960 (0.065), well above ZOT, which was 0.864 (0.047).},\n bibtype = {inproceedings},\n author = {Mayorca-Torres, Dagoberto and León-Salas, Alejandro José and Peluffo-Ordoñez, Diego Hernán},\n editor = {Botto-Tobar, Miguel and Gómez, Omar S and Rosero Miranda, Raul and Díaz Cadena, Angela and Luna-Encalada, Washington},\n booktitle = {Trends in Artificial Intelligence and Computer Engineering}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In the reverse electrocardiography (ECG) problem, the objective is to reconstruct the heart's electrical activity from a set of body surface potentials by solving the direct model and the geometry of the torso. Over the years, researchers have used various approaches to solve this problem, from direct, iterative, probabilistic, and those based on deep learning. The interest of the latter, among the wide range of techniques, is because the complexity of the problem can be significantly reduced while increasing the precision of the estimation. In this article, we evaluate the performance of a deep learning-based neural network compared to the Tikhonov method of zero order (ZOT), first (FOT), and second (SOT). Preliminary results show an improvement in performance over real data when Pearson's correlation coefficient (CC) and (RMSE) are calculated. The CC's mean value and standard deviation for the proposed method were 0.960 (0.065), well above ZOT, which was 0.864 (0.047).\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2022\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2022')\"\n      onkeypress=\"toggleGroup('group_2022')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2022</span>\n      <span class=\"bibbase_group_count\">\n        \n        (4)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"castrosilva-morenogarca-guachiguachi-peluffoordez-instanceselectiononcnnsforalzheimersdiseaseclassificationfrommri-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.scitepress.org/Link.aspx?doi=10.5220/0010900100003122\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'castrosilva-morenogarca-guachiguachi-peluffoordez-instanceselectiononcnnsforalzheimersdiseaseclassificationfrommri-2022', 'https://www.scitepress.org/Link.aspx?doi=10.5220/0010900100003122', event);\">\n    Instance Selection on CNNs for Alzheimer’s Disease Classification from MRI.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Castro-Silva.,  J.; Moreno-García.,  M.; Guachi-Guachi.,  L.;  and Peluffo-Ordóñez.,  D.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM,</i>, pages 330-337,  2022. SciTePress\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.scitepress.org/Link.aspx?doi=10.5220/0010900100003122\"\n     onclick=\"log_download('castrosilva-morenogarca-guachiguachi-peluffoordez-instanceselectiononcnnsforalzheimersdiseaseclassificationfrommri-2022', 'https://www.scitepress.org/Link.aspx?doi=10.5220/0010900100003122', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Instance Selection on CNNs for Alzheimer’s Disease Classification from MRI [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.5220/0010900100003122\"\n     onclick=\"log_download('castrosilva-morenogarca-guachiguachi-peluffoordez-instanceselectiononcnnsforalzheimersdiseaseclassificationfrommri-2022', 'http://doi.org/10.5220/0010900100003122', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/castrosilva-morenogarca-guachiguachi-peluffoordez-instanceselectiononcnnsforalzheimersdiseaseclassificationfrommri-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('castrosilva-morenogarca-guachiguachi-peluffoordez-instanceselectiononcnnsforalzheimersdiseaseclassificationfrommri-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Instance Selection on CNNs for Alzheimer’s Disease Classification from MRI},\n type = {inproceedings},\n year = {2022},\n pages = {330-337},\n websites = {https://www.scitepress.org/Link.aspx?doi=10.5220/0010900100003122},\n publisher = {SciTePress},\n institution = {INSTICC},\n id = {b1289f2e-9cba-3e3f-b692-0db0c4a8e9f2},\n created = {2022-02-22T01:00:12.425Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\n last_modified = {2022-02-22T01:00:12.425Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {icpram22},\n source_type = {conference},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Castro-Silva., J and Moreno-García., M and Guachi-Guachi., Lorena and Peluffo-Ordóñez., D},\n doi = {10.5220/0010900100003122},\n booktitle = {Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - ICPRAM,}\n}</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"cepeda-snchezpozo-peluffoordez-gonzlezvergara-almeidagalrraga-ecgbasedheartbeatclassificationforarrhythmiadetectionusingartificialneuralnetworks-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-3-031-10450-3_20\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'cepeda-snchezpozo-peluffoordez-gonzlezvergara-almeidagalrraga-ecgbasedheartbeatclassificationforarrhythmiadetectionusingartificialneuralnetworks-2022', 'https://link.springer.com/chapter/10.1007/978-3-031-10450-3_20', event);\">\n    ECG-Based Heartbeat Classification for Arrhythmia Detection Using Artificial Neural Networks.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Cepeda,  E.; Sánchez-Pozo,  N., N.; Peluffo-Ordóñez,  D., H.; González-Vergara,  J.;  and Almeida-Galárraga,  D.\n</span>\n\n  \n\n</span>\n\n  In Gervasi,  O.; Murgante,  B.; Hendrix,  E., M., T.; Taniar,  D.;  and Apduhan,  B., O., editor(s), <i>Computational Science and Its Applications -- ICCSA 2022</i>, pages 247-259,  2022. Springer International Publishing\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-3-031-10450-3_20\"\n     onclick=\"log_download('cepeda-snchezpozo-peluffoordez-gonzlezvergara-almeidagalrraga-ecgbasedheartbeatclassificationforarrhythmiadetectionusingartificialneuralnetworks-2022', 'https://link.springer.com/chapter/10.1007/978-3-031-10450-3_20', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"ECG-Based Heartbeat Classification for Arrhythmia Detection Using Artificial Neural Networks [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/cepeda-snchezpozo-peluffoordez-gonzlezvergara-almeidagalrraga-ecgbasedheartbeatclassificationforarrhythmiadetectionusingartificialneuralnetworks-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>4 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('cepeda-snchezpozo-peluffoordez-gonzlezvergara-almeidagalrraga-ecgbasedheartbeatclassificationforarrhythmiadetectionusingartificialneuralnetworks-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {ECG-Based Heartbeat Classification for Arrhythmia Detection Using Artificial Neural Networks},\n type = {inproceedings},\n year = {2022},\n pages = {247-259},\n websites = {https://link.springer.com/chapter/10.1007/978-3-031-10450-3_20},\n publisher = {Springer International Publishing},\n city = {Cham},\n id = {fe0bb748-7f2d-396d-be6a-7337af3ccdc3},\n created = {2022-07-16T03:47:10.517Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\n last_modified = {2022-07-16T03:47:10.517Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {10.1007/978-3-031-10450-3_20},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Cardiovascular disease (CVD) has quickly grown in prevalence over the previous decade, becoming the major cause of human morbidity on a global scale. Due to the massive number of ECG data, manual analysis is regarded as a time-consuming, costly and prone to human error task. In the other hand, computational systems based on biomedical signal processing and machine learning techniques might be suited for supporting arrhythmia diagnostic processes, while solving some of those issues. In general, such systems involve five stages: acquisition, preprocessing, segmentation, characterization, and classification. Yet numerous fundamental aspects remain unresolved, including sensitivity to signal fluctuation, accuracy, computing cost, generalizability, and interpretability. In this context, the present study offers a comparative analysis of ECG signal classification using two artificial neural networks created by different machine learning frameworks. The neural nets were built into a pipeline that aims to strike an appropriate balance among signal robustness, variability, and accuracy. The proposed approach reaches up to 99\\% of overall accuracy for each register while keeping the computational cost low.},\n bibtype = {inproceedings},\n author = {Cepeda, Eduardo and Sánchez-Pozo, Nadia N and Peluffo-Ordóñez, Diego H and González-Vergara, Juan and Almeida-Galárraga, Diego},\n editor = {Gervasi, Osvaldo and Murgante, Beniamino and Hendrix, Eligius M T and Taniar, David and Apduhan, Bernady O},\n booktitle = {Computational Science and Its Applications -- ICCSA 2022}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Cardiovascular disease (CVD) has quickly grown in prevalence over the previous decade, becoming the major cause of human morbidity on a global scale. Due to the massive number of ECG data, manual analysis is regarded as a time-consuming, costly and prone to human error task. In the other hand, computational systems based on biomedical signal processing and machine learning techniques might be suited for supporting arrhythmia diagnostic processes, while solving some of those issues. In general, such systems involve five stages: acquisition, preprocessing, segmentation, characterization, and classification. Yet numerous fundamental aspects remain unresolved, including sensitivity to signal fluctuation, accuracy, computing cost, generalizability, and interpretability. In this context, the present study offers a comparative analysis of ECG signal classification using two artificial neural networks created by different machine learning frameworks. The neural nets were built into a pipeline that aims to strike an appropriate balance among signal robustness, variability, and accuracy. The proposed approach reaches up to 99\\% of overall accuracy for each register while keeping the computational cost low.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"crespo-crespo-sierramartnez-peluffoordez-morochocayamcela-acomputervisionmodeltoidentifytheincorrectuseoffacemasksforcovid19awareness-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.mdpi.com/2076-3417/12/14/6924\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'crespo-crespo-sierramartnez-peluffoordez-morochocayamcela-acomputervisionmodeltoidentifytheincorrectuseoffacemasksforcovid19awareness-2022', 'https://www.mdpi.com/2076-3417/12/14/6924', event);\">\n    A Computer Vision Model to Identify the Incorrect Use of Face Masks for COVID-19 Awareness.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Crespo,  F.; Crespo,  A.; Sierra-Martínez,  L., M.; Peluffo-Ordóñez,  D., H.;  and Morocho-Cayamcela,  M., E.\n</span>\n\n  \n\n</span>\n\n  <i>Applied Sciences</i>, 12(14).  2022.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.mdpi.com/2076-3417/12/14/6924\"\n     onclick=\"log_download('crespo-crespo-sierramartnez-peluffoordez-morochocayamcela-acomputervisionmodeltoidentifytheincorrectuseoffacemasksforcovid19awareness-2022', 'https://www.mdpi.com/2076-3417/12/14/6924', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A Computer Vision Model to Identify the Incorrect Use of Face Masks for COVID-19 Awareness [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.3390/app12146924\"\n     onclick=\"log_download('crespo-crespo-sierramartnez-peluffoordez-morochocayamcela-acomputervisionmodeltoidentifytheincorrectuseoffacemasksforcovid19awareness-2022', 'http://doi.org/10.3390/app12146924', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/crespo-crespo-sierramartnez-peluffoordez-morochocayamcela-acomputervisionmodeltoidentifytheincorrectuseoffacemasksforcovid19awareness-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('crespo-crespo-sierramartnez-peluffoordez-morochocayamcela-acomputervisionmodeltoidentifytheincorrectuseoffacemasksforcovid19awareness-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {A Computer Vision Model to Identify the Incorrect Use of Face Masks for COVID-19 Awareness},\n type = {article},\n year = {2022},\n volume = {12},\n websites = {https://www.mdpi.com/2076-3417/12/14/6924},\n id = {70d7f700-db7a-39e4-8146-84cf0ff26274},\n created = {2022-07-24T01:29:08.758Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\n last_modified = {2022-07-24T01:29:08.758Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {app12146924},\n source_type = {article},\n private_publication = {false},\n abstract = {Face mask detection has become a great challenge in computer vision, demanding the coalition of technology with COVID-19 awareness. Researchers have proposed deep learning models to detect the use of face masks. However, the incorrect use of a face mask can be as harmful as not wearing any protection at all. In this paper, we propose a compound convolutional neural network (CNN) architecture based on two computer vision tasks: object localization to discover faces in images/videos, followed by an image classification CNN to categorize the faces and show if someone is using a face mask correctly, incorrectly, or not at all. The first CNN is built upon RetinaFace, a model to detect faces in images, whereas the second CNN uses a ResNet-18 architecture as a classification backbone. Our model enables an accurate identification of people who are not correctly following the COVID-19 healthcare recommendations on face mask use. To enable further global use of our technology, we have released both the dataset used to train the classification model and our proposed computer vision pipeline to the public, and optimized it for embedded systems deployment.},\n bibtype = {article},\n author = {Crespo, Fabricio and Crespo, Anthony and Sierra-Martínez, Luz Marina and Peluffo-Ordóñez, Diego Hernán and Morocho-Cayamcela, Manuel Eugenio},\n doi = {10.3390/app12146924},\n journal = {Applied Sciences},\n number = {14}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Face mask detection has become a great challenge in computer vision, demanding the coalition of technology with COVID-19 awareness. Researchers have proposed deep learning models to detect the use of face masks. However, the incorrect use of a face mask can be as harmful as not wearing any protection at all. In this paper, we propose a compound convolutional neural network (CNN) architecture based on two computer vision tasks: object localization to discover faces in images/videos, followed by an image classification CNN to categorize the faces and show if someone is using a face mask correctly, incorrectly, or not at all. The first CNN is built upon RetinaFace, a model to detect faces in images, whereas the second CNN uses a ResNet-18 architecture as a classification backbone. Our model enables an accurate identification of people who are not correctly following the COVID-19 healthcare recommendations on face mask use. To enable further global use of our technology, we have released both the dataset used to train the classification model and our proposed computer vision pipeline to the public, and optimized it for embedded systems deployment.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"raki-gonzlezvergara-aalaila-elhamdi-bamansour-guachiguachi-peluffoordoez-cropclassificationusingdeeplearningaquickcomparativestudyofmodernapproaches-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-3-031-19647-8_3\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'raki-gonzlezvergara-aalaila-elhamdi-bamansour-guachiguachi-peluffoordoez-cropclassificationusingdeeplearningaquickcomparativestudyofmodernapproaches-2022', 'https://link.springer.com/chapter/10.1007/978-3-031-19647-8_3', event);\">\n    Crop Classification Using Deep Learning: A Quick Comparative Study of Modern Approaches.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Raki,  H.; González-Vergara,  J.; Aalaila,  Y.; Elhamdi,  M.; Bamansour,  S.; Guachi-Guachi,  L.;  and Peluffo-Ordoñez,  D., H.\n</span>\n\n  \n\n</span>\n\n  In Florez,  H.;  and Gomez,  H., editor(s), <i>Applied Informatics</i>, pages 31-44,  2022. Springer International Publishing\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-3-031-19647-8_3\"\n     onclick=\"log_download('raki-gonzlezvergara-aalaila-elhamdi-bamansour-guachiguachi-peluffoordoez-cropclassificationusingdeeplearningaquickcomparativestudyofmodernapproaches-2022', 'https://link.springer.com/chapter/10.1007/978-3-031-19647-8_3', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Crop Classification Using Deep Learning: A Quick Comparative Study of Modern Approaches [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/raki-gonzlezvergara-aalaila-elhamdi-bamansour-guachiguachi-peluffoordoez-cropclassificationusingdeeplearningaquickcomparativestudyofmodernapproaches-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('raki-gonzlezvergara-aalaila-elhamdi-bamansour-guachiguachi-peluffoordoez-cropclassificationusingdeeplearningaquickcomparativestudyofmodernapproaches-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{\n title = {Crop Classification Using Deep Learning: A Quick Comparative Study of Modern Approaches},\n type = {inproceedings},\n year = {2022},\n pages = {31-44},\n websites = {https://link.springer.com/chapter/10.1007/978-3-031-19647-8_3},\n publisher = {Springer International Publishing},\n city = {Cham},\n id = {d0ea8cbb-e8dd-3b80-8451-9b858e90db31},\n created = {2023-03-01T20:18:51.423Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\n last_modified = {2023-03-01T20:18:51.423Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {10.1007/978-3-031-19647-8_3},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Automatic crop classification using new technologies is recognized as one of the most important assets in today&#x27;s smart farming improvement. Investments in technology and innovation are key issues for shaping agricultural productivity as well as the inclusiveness and sustainability of the global agricultural transformation. Digital image processing (DIP) has been widely adopted in this field, by merging Unmanned Aerial Vehicle (UAV) based remote sensing and deep learning (DL) as a powerful tool for crop classification. Despite the wide range of alternatives, the proper selection of a DL approach is still an open and challenging issue. In this work, we carry out an exhaustive performance evaluation of three remarkable and lightweight DL approaches, namely: Visual Geometry Group (VGG), Residual Neural Network (ResNet) and Inception V3, tested on high resolution agriculture crop images dataset. Experimental results show that InceptionV3 outperforms VGG and ResNet in terms of precision (0,92), accuracy (0,97), recall (0,91), AUC (0,98), PCR (0,97), and F1 (0,91).},\n bibtype = {inproceedings},\n author = {Raki, Hind and González-Vergara, Juan and Aalaila, Yahya and Elhamdi, Mouad and Bamansour, Sami and Guachi-Guachi, Lorena and Peluffo-Ordoñez, Diego H},\n editor = {Florez, Hector and Gomez, Henry},\n booktitle = {Applied Informatics}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Automatic crop classification using new technologies is recognized as one of the most important assets in today's smart farming improvement. Investments in technology and innovation are key issues for shaping agricultural productivity as well as the inclusiveness and sustainability of the global agricultural transformation. Digital image processing (DIP) has been widely adopted in this field, by merging Unmanned Aerial Vehicle (UAV) based remote sensing and deep learning (DL) as a powerful tool for crop classification. Despite the wide range of alternatives, the proper selection of a DL approach is still an open and challenging issue. In this work, we carry out an exhaustive performance evaluation of three remarkable and lightweight DL approaches, namely: Visual Geometry Group (VGG), Residual Neural Network (ResNet) and Inception V3, tested on high resolution agriculture crop images dataset. Experimental results show that InceptionV3 outperforms VGG and ResNet in terms of precision (0,92), accuracy (0,97), recall (0,91), AUC (0,98), PCR (0,97), and F1 (0,91).\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2021\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2021')\"\n      onkeypress=\"toggleGroup('group_2021')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2021</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"morenorevelo-guachiguachi-gmezmendoza-revelofuelagn-peluffoordez-enhancedconvolutionalneuralnetworkarchitectureforcropclassification-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/file/2fc18dfd-d588-0a55-0dfa-18c79fa2f527/2021_AppliedSciences_Enhanced_CNN.pdf.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'morenorevelo-guachiguachi-gmezmendoza-revelofuelagn-peluffoordez-enhancedconvolutionalneuralnetworkarchitectureforcropclassification-2021', 'https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/file/2fc18dfd-d588-0a55-0dfa-18c79fa2f527/2021_AppliedSciences_Enhanced_CNN.pdf.pdf', event);\">\n    Enhanced Convolutional-Neural-Network Architecture for Crop Classification.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Moreno-revelo,  M., Y.; Guachi-guachi,  L.; Gómez-mendoza,  J., B.; Revelo-fuelagán,  J.;  and Peluffo-ordóñez,  D., H.\n</span>\n\n  \n\n</span>\n\n  <i>Applied Sciences</i>,1-23.  2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/file/2fc18dfd-d588-0a55-0dfa-18c79fa2f527/2021_AppliedSciences_Enhanced_CNN.pdf.pdf\"\n     onclick=\"log_download('morenorevelo-guachiguachi-gmezmendoza-revelofuelagn-peluffoordez-enhancedconvolutionalneuralnetworkarchitectureforcropclassification-2021', 'https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/file/2fc18dfd-d588-0a55-0dfa-18c79fa2f527/2021_AppliedSciences_Enhanced_CNN.pdf.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Enhanced Convolutional-Neural-Network Architecture for Crop Classification [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n  <a href=\"https://www.mdpi.com/2076-3417/11/9/4292\"\n     onclick=\"log_download('morenorevelo-guachiguachi-gmezmendoza-revelofuelagn-peluffoordez-enhancedconvolutionalneuralnetworkarchitectureforcropclassification-2021', 'https://www.mdpi.com/2076-3417/11/9/4292', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Enhanced Convolutional-Neural-Network Architecture for Crop Classification [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/morenorevelo-guachiguachi-gmezmendoza-revelofuelagn-peluffoordez-enhancedconvolutionalneuralnetworkarchitectureforcropclassification-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('morenorevelo-guachiguachi-gmezmendoza-revelofuelagn-peluffoordez-enhancedconvolutionalneuralnetworkarchitectureforcropclassification-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Enhanced Convolutional-Neural-Network Architecture for Crop Classification},\n type = {article},\n year = {2021},\n pages = {1-23},\n websites = {https://www.mdpi.com/2076-3417/11/9/4292},\n id = {24a0c385-d592-3a29-a7e6-33a2d377a118},\n created = {2022-02-02T06:07:46.014Z},\n file_attached = {true},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\n last_modified = {2022-02-02T06:07:47.387Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Automatic crop identification and monitoring is a key element in enhancing food production processes as well as diminishing the related environmental impact. Although several efficient deep learning techniques have emerged in the field of multispectral imagery analysis, the crop classification problem still needs more accurate solutions. This work introduces a competitive methodology for crop classification from multispectral satellite imagery mainly using an enhanced 2D convolutional neural network (2D-CNN) designed at a smaller-scale architecture, as well as a novel post-processing step. The proposed methodology contains four steps: image stacking, patch extraction, classification model design (based on a 2D-CNN architecture), and post-processing. First, the images are stacked to increase the number of features. Second, the input images are split into patches and fed into the 2D-CNN model. Then, the 2D-CNN model is constructed within a small-scale framework, and properly trained to recognize 10 different types of crops. Finally, a post-processing step is performed in order to reduce the classification error caused by lower-spatial-resolution images. Experiments were carried over the so-named Campo Verde database, which consists of a set of satellite images captured by Landsat and Sentinel satellites from the municipality of Campo Verde, Brazil. In contrast to the maximum accuracy values reached by remarkable works reported in the literature (amounting to an overall accuracy of about 81%, a f1 score of 75.89%, and average accuracy of 73.35%), the proposed methodology achieves a competitive overall accuracy of 81.20%, a f1 score of 75.89%, and an average accuracy of 88.72% when classifying 10 different crops, while ensuring an adequate trade-off between the number of multiply-accumulate operations (MACs) and accuracy. Furthermore, given its ability to effectively classify patches from two image sequences, this methodology may result appealing for other real-world applications, such as the classification of urban materials.},\n bibtype = {article},\n author = {Moreno-revelo, Mónica Y and Guachi-guachi, Lorena and Gómez-mendoza, Juan Bernardo and Revelo-fuelagán, Javier and Peluffo-ordóñez, Diego H},\n journal = {Applied Sciences}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Automatic crop identification and monitoring is a key element in enhancing food production processes as well as diminishing the related environmental impact. Although several efficient deep learning techniques have emerged in the field of multispectral imagery analysis, the crop classification problem still needs more accurate solutions. This work introduces a competitive methodology for crop classification from multispectral satellite imagery mainly using an enhanced 2D convolutional neural network (2D-CNN) designed at a smaller-scale architecture, as well as a novel post-processing step. The proposed methodology contains four steps: image stacking, patch extraction, classification model design (based on a 2D-CNN architecture), and post-processing. First, the images are stacked to increase the number of features. Second, the input images are split into patches and fed into the 2D-CNN model. Then, the 2D-CNN model is constructed within a small-scale framework, and properly trained to recognize 10 different types of crops. Finally, a post-processing step is performed in order to reduce the classification error caused by lower-spatial-resolution images. Experiments were carried over the so-named Campo Verde database, which consists of a set of satellite images captured by Landsat and Sentinel satellites from the municipality of Campo Verde, Brazil. In contrast to the maximum accuracy values reached by remarkable works reported in the literature (amounting to an overall accuracy of about 81%, a f1 score of 75.89%, and average accuracy of 73.35%), the proposed methodology achieves a competitive overall accuracy of 81.20%, a f1 score of 75.89%, and an average accuracy of 88.72% when classifying 10 different crops, while ensuring an adequate trade-off between the number of multiply-accumulate operations (MACs) and accuracy. Furthermore, given its ability to effectively classify patches from two image sequences, this methodology may result appealing for other real-world applications, such as the classification of urban materials.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"anayaisaza-merajimnez-cabrerachavarro-guachiguachi-peluffoordez-riospatio-comparisonofcurrentdeepconvolutionalneuralnetworksforthesegmentationofbreastmassesinmammograms-2021\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ieeexplore.ieee.org/document/9614200\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'anayaisaza-merajimnez-cabrerachavarro-guachiguachi-peluffoordez-riospatio-comparisonofcurrentdeepconvolutionalneuralnetworksforthesegmentationofbreastmassesinmammograms-2021', 'https://ieeexplore.ieee.org/document/9614200', event);\">\n    Comparison of current deep convolutional neural networks for the segmentation of breast masses in mammograms.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Anaya-Isaza,  A.; Mera-Jiménez,  L.; Cabrera-Chavarro,  J.; Guachi-Guachi,  L.; Peluffo-Ordóñez,  D.;  and Rios-Patiño,  J.\n</span>\n\n  \n\n</span>\n\n  <i>IEEE Access</i>.  2021.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://ieeexplore.ieee.org/document/9614200\"\n     onclick=\"log_download('anayaisaza-merajimnez-cabrerachavarro-guachiguachi-peluffoordez-riospatio-comparisonofcurrentdeepconvolutionalneuralnetworksforthesegmentationofbreastmassesinmammograms-2021', 'https://ieeexplore.ieee.org/document/9614200', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Comparison of current deep convolutional neural networks for the segmentation of breast masses in mammograms [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1109/ACCESS.2021.3127862\"\n     onclick=\"log_download('anayaisaza-merajimnez-cabrerachavarro-guachiguachi-peluffoordez-riospatio-comparisonofcurrentdeepconvolutionalneuralnetworksforthesegmentationofbreastmassesinmammograms-2021', 'http://doi.org/10.1109/ACCESS.2021.3127862', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/anayaisaza-merajimnez-cabrerachavarro-guachiguachi-peluffoordez-riospatio-comparisonofcurrentdeepconvolutionalneuralnetworksforthesegmentationofbreastmassesinmammograms-2021\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('anayaisaza-merajimnez-cabrerachavarro-guachiguachi-peluffoordez-riospatio-comparisonofcurrentdeepconvolutionalneuralnetworksforthesegmentationofbreastmassesinmammograms-2021')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Comparison of current deep convolutional neural networks for the segmentation of breast masses in mammograms},\n type = {article},\n year = {2021},\n websites = {https://ieeexplore.ieee.org/document/9614200},\n id = {c9a3316f-2543-320d-b8bb-5ed45ce86aab},\n created = {2022-02-02T06:07:46.249Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\n last_modified = {2022-02-02T06:07:46.249Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {9614200},\n source_type = {article},\n private_publication = {false},\n abstract = {Breast cancer causes approximately 684,996 deaths worldwide, making it the leading cause of female cancer mortality. However, these figures can be reduced with early diagnosis through mammographic imaging, allowing for the timely and effective treatment of this disease. To establish the best tools for contributing to the automatic diagnosis of breast cancer, different deep learning (DL) architectures were compared in terms of breast lesion segmentation, lesion type classification, and degree of suspicion of malignancy tests. The tasks were completed with state-of-the-art architectures and backbones. Initially, during segmentation, the base UNet, Visual Geometry Group 19 (VGG19), InceptionResNetV2, EfficientNet, MobileNetv2, ResNet, ResNeXt, MultiResUNet, linkNet-VGG19, DenseNet, SEResNet and SeResNeXt architectures were compared, where “Res” denotes a residual network. In addition, training was performed with 5 of the most advanced loss functions and validated by the Dice coefficient, sensitivity, and specificity. The proposed models achieved Dice values above 90%, with the EfficientNet architecture achieving 94.75% and 99% accuracy on the two tasks. Subsequently, classification was addressed with the ResNet50V2, VGG19, InceptionResNetV2, DenseNet121, InceptionV3, Xception and EfficientNetB7 networks. The proposed models achieved 96.97% and 97.73% accuracy through the VGG19 and ResNet50V2 networks on the lesion classification and degree of suspicion tasks, respectively. All three tasks were addressed with open-access databases, including the Digital Database for Screening Mammography (DDSM), the Mammographic Image Analysis Society (MIAS) database, and INbreast.},\n bibtype = {article},\n author = {Anaya-Isaza, Andrés and Mera-Jiménez, Leonel and Cabrera-Chavarro, Johan and Guachi-Guachi, Lorena and Peluffo-Ordóñez, Diego and Rios-Patiño, Jorge},\n doi = {10.1109/ACCESS.2021.3127862},\n journal = {IEEE Access}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Breast cancer causes approximately 684,996 deaths worldwide, making it the leading cause of female cancer mortality. However, these figures can be reduced with early diagnosis through mammographic imaging, allowing for the timely and effective treatment of this disease. To establish the best tools for contributing to the automatic diagnosis of breast cancer, different deep learning (DL) architectures were compared in terms of breast lesion segmentation, lesion type classification, and degree of suspicion of malignancy tests. The tasks were completed with state-of-the-art architectures and backbones. Initially, during segmentation, the base UNet, Visual Geometry Group 19 (VGG19), InceptionResNetV2, EfficientNet, MobileNetv2, ResNet, ResNeXt, MultiResUNet, linkNet-VGG19, DenseNet, SEResNet and SeResNeXt architectures were compared, where “Res” denotes a residual network. In addition, training was performed with 5 of the most advanced loss functions and validated by the Dice coefficient, sensitivity, and specificity. The proposed models achieved Dice values above 90%, with the EfficientNet architecture achieving 94.75% and 99% accuracy on the two tasks. Subsequently, classification was addressed with the ResNet50V2, VGG19, InceptionResNetV2, DenseNet121, InceptionV3, Xception and EfficientNetB7 networks. The proposed models achieved 96.97% and 97.73% accuracy through the VGG19 and ResNet50V2 networks on the lesion classification and degree of suspicion tasks, respectively. All three tasks were addressed with open-access databases, including the Digital Database for Screening Mammography (DDSM), the Mammographic Image Analysis Society (MIAS) database, and INbreast.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2019\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2019')\"\n      onkeypress=\"toggleGroup('group_2019')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2019</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"herreragranda-chicaizaipiales-herreragranda-lorenteleyva-caraguayprocel-garcasantilln-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://link.springer.com/10.1007/978-3-030-20518-8_31\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'herreragranda-chicaizaipiales-herreragranda-lorenteleyva-caraguayprocel-garcasantilln-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019', 'http://link.springer.com/10.1007/978-3-030-20518-8_31', event);\">\n    Artificial Neural Networks for Bottled Water Demand Forecasting: A Small Business Case Study.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Herrera-Granda,  I., D.; Chicaiza-Ipiales,  J., A.; Herrera-Granda,  E., P.; Lorente-Leyva,  L., L.; Caraguay-Procel,  J., A.; García-Santillán,  I., D.;  and Peluffo-Ordóñez,  D., H.\n</span>\n\n  \n\n</span>\n\n  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pages 362-373.  2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://link.springer.com/10.1007/978-3-030-20518-8_31\"\n     onclick=\"log_download('herreragranda-chicaizaipiales-herreragranda-lorenteleyva-caraguayprocel-garcasantilln-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019', 'http://link.springer.com/10.1007/978-3-030-20518-8_31', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-030-20518-8_31\"\n     onclick=\"log_download('herreragranda-chicaizaipiales-herreragranda-lorenteleyva-caraguayprocel-garcasantilln-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019', 'http://doi.org/10.1007/978-3-030-20518-8_31', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/herreragranda-chicaizaipiales-herreragranda-lorenteleyva-caraguayprocel-garcasantilln-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('herreragranda-chicaizaipiales-herreragranda-lorenteleyva-caraguayprocel-garcasantilln-peluffoordez-lecturenotesincomputerscienceincludingsubserieslecturenotesinartificialintelligenceandlecturenotesinbioinformatics-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inbook{\n type = {inbook},\n year = {2019},\n keywords = {Artificial neural networks,Long-term demand forecasting,Small business},\n pages = {362-373},\n websites = {http://link.springer.com/10.1007/978-3-030-20518-8_31},\n id = {d0ee1709-f877-3515-a99a-f05f24797555},\n created = {2022-02-02T06:07:46.500Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\n last_modified = {2022-02-02T06:07:46.500Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Herrera-Granda2019a},\n private_publication = {false},\n abstract = {This paper shows a neural networks-based demand forecasting model designed for a small manufacturer of bottled water in Ecuador, which currently doesn’t have adequate demand forecast methodologies, causing problems of customer orders non-compliance, inventory excess and economic losses. However, by working with accurate predictions, the manufacturer will have an anticipated vision of future needs in order to satisfy the demand for manufactured products, in other words, to guarantee on time and reasonable use of the resources. To solve the problems that this small manufacturer has to face a historic demand data acquisition process was done through the last 36 months costumer order records. In the construction of the historical time series, that was analyzed, demand dates and volumes were established as input variables. Then the design of forecast models was done, based on classical methods and multi-layer neural networks, which were evaluated by means of quantitative error indicators. The application of these methods was done through the R programming language. After this, a stage of training and improvement of the network is included, it was evaluated against the results of the classic forecasting methods, and the next 12 months were predicted by means of the best obtained model. Finally, the feasibility of the use of neural networks in the forecast of demand for purified water bottles, is demonstrated.},\n bibtype = {inbook},\n author = {Herrera-Granda, Israel D. and Chicaiza-Ipiales, Joselyn A. and Herrera-Granda, Erick P. and Lorente-Leyva, Leandro L. and Caraguay-Procel, Jorge A. and García-Santillán, Iván D. and Peluffo-Ordóñez, Diego H.},\n doi = {10.1007/978-3-030-20518-8_31},\n chapter = {Artificial Neural Networks for Bottled Water Demand Forecasting: A Small Business Case Study},\n title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper shows a neural networks-based demand forecasting model designed for a small manufacturer of bottled water in Ecuador, which currently doesn’t have adequate demand forecast methodologies, causing problems of customer orders non-compliance, inventory excess and economic losses. However, by working with accurate predictions, the manufacturer will have an anticipated vision of future needs in order to satisfy the demand for manufactured products, in other words, to guarantee on time and reasonable use of the resources. To solve the problems that this small manufacturer has to face a historic demand data acquisition process was done through the last 36 months costumer order records. In the construction of the historical time series, that was analyzed, demand dates and volumes were established as input variables. Then the design of forecast models was done, based on classical methods and multi-layer neural networks, which were evaluated by means of quantitative error indicators. The application of these methods was done through the R programming language. After this, a stage of training and improvement of the network is included, it was evaluated against the results of the classic forecasting methods, and the next 12 months were predicted by means of the best obtained model. Finally, the feasibility of the use of neural networks in the forecast of demand for purified water bottles, is demonstrated.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"lorenteleyva-pavnvalencia-monterosantos-herreragranda-herreragranda-peluffoordez-artificialneuralnetworksforurbanwaterdemandforecastingacasestudy-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://iopscience.iop.org/article/10.1088/1742-6596/1284/1/012004\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'lorenteleyva-pavnvalencia-monterosantos-herreragranda-herreragranda-peluffoordez-artificialneuralnetworksforurbanwaterdemandforecastingacasestudy-2019', 'https://iopscience.iop.org/article/10.1088/1742-6596/1284/1/012004', event);\">\n    Artificial Neural Networks for Urban Water Demand Forecasting: A Case Study.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Lorente-Leyva,  L., L.; Pavón-Valencia,  J., F.; Montero-Santos,  Y.; Herrera-Granda,  I., D.; Herrera-Granda,  E., P.;  and Peluffo-Ordóñez,  D., H.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Physics: Conference Series</i>, 1284: 012004. 8 2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://iopscience.iop.org/article/10.1088/1742-6596/1284/1/012004\"\n     onclick=\"log_download('lorenteleyva-pavnvalencia-monterosantos-herreragranda-herreragranda-peluffoordez-artificialneuralnetworksforurbanwaterdemandforecastingacasestudy-2019', 'https://iopscience.iop.org/article/10.1088/1742-6596/1284/1/012004', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Artificial Neural Networks for Urban Water Demand Forecasting: A Case Study [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1088/1742-6596/1284/1/012004\"\n     onclick=\"log_download('lorenteleyva-pavnvalencia-monterosantos-herreragranda-herreragranda-peluffoordez-artificialneuralnetworksforurbanwaterdemandforecastingacasestudy-2019', 'http://doi.org/10.1088/1742-6596/1284/1/012004', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/lorenteleyva-pavnvalencia-monterosantos-herreragranda-herreragranda-peluffoordez-artificialneuralnetworksforurbanwaterdemandforecastingacasestudy-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('lorenteleyva-pavnvalencia-monterosantos-herreragranda-herreragranda-peluffoordez-artificialneuralnetworksforurbanwaterdemandforecastingacasestudy-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Artificial Neural Networks for Urban Water Demand Forecasting: A Case Study},\n type = {article},\n year = {2019},\n pages = {012004},\n volume = {1284},\n websites = {https://iopscience.iop.org/article/10.1088/1742-6596/1284/1/012004},\n month = {8},\n id = {d25efbb9-d760-3246-ad23-47aedb6830b6},\n created = {2022-02-02T06:07:46.757Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\n last_modified = {2022-02-02T06:07:46.757Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Lorente-Leyva2019a},\n private_publication = {false},\n abstract = {This paper presents an application of an artificial neural network model in forecasting urban water demand using MATLAB software. Considering that in any planning process, the demand forecast plays a fundamental role, being one of the premises to organize and control a set of activities or processes. The versatility of the short, medium and long-term prediction that is provided to the company that offers the water distribution service to determine the supply capacity, maintenance activities, and system improvements as a strategic planning tool. Shown to improve network performance by using time series water demand data, the model can provide excellent fit and forecast without relying on the explicit inclusion of climatic factors and number of consumers. The excellent accuracy of the model indicates the effectiveness of forecasting over different time horizons. Finally, the results obtained from the Artificial Neural Network are compared with traditional statistical models.},\n bibtype = {article},\n author = {Lorente-Leyva, Leandro L. and Pavón-Valencia, Jairo F. and Montero-Santos, Yakcleem and Herrera-Granda, Israel D and Herrera-Granda, Erick P. and Peluffo-Ordóñez, Diego H.},\n doi = {10.1088/1742-6596/1284/1/012004},\n journal = {Journal of Physics: Conference Series}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper presents an application of an artificial neural network model in forecasting urban water demand using MATLAB software. Considering that in any planning process, the demand forecast plays a fundamental role, being one of the premises to organize and control a set of activities or processes. The versatility of the short, medium and long-term prediction that is provided to the company that offers the water distribution service to determine the supply capacity, maintenance activities, and system improvements as a strategic planning tool. Shown to improve network performance by using time series water demand data, the model can provide excellent fit and forecast without relying on the explicit inclusion of climatic factors and number of consumers. The excellent accuracy of the model indicates the effectiveness of forecasting over different time horizons. Finally, the results obtained from the Artificial Neural Network are compared with traditional statistical models.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"llorenteleyva-rpatinoalarcon-monterosantos-dherreragranda-hpeluffoordonez-mlastrealeaga-cordovesgarcia-artificialneuralnetworksinthedemandforecastingofametalmechanicalindustry-2019\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.medwelljournals.com/abstract/?doi=jeasci.2020.81.87\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'llorenteleyva-rpatinoalarcon-monterosantos-dherreragranda-hpeluffoordonez-mlastrealeaga-cordovesgarcia-artificialneuralnetworksinthedemandforecastingofametalmechanicalindustry-2019', 'http://www.medwelljournals.com/abstract/?doi=jeasci.2020.81.87', event);\">\n    Artificial Neural Networks in the Demand Forecasting of a Metal-Mechanical Industry.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  L. Lorente-Leyva,  L.; R. Patino-Alarcon,  D.; Montero-Santos,  Y.; D. Herrera-Granda,  I.; H. Peluffo-Ordonez,  D.; M. Lastre-Aleaga,  A.;  and Cordoves-Garcia,  A.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Engineering and Applied Sciences</i>, 15(1): 81-87. 10 2019.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.medwelljournals.com/abstract/?doi=jeasci.2020.81.87\"\n     onclick=\"log_download('llorenteleyva-rpatinoalarcon-monterosantos-dherreragranda-hpeluffoordonez-mlastrealeaga-cordovesgarcia-artificialneuralnetworksinthedemandforecastingofametalmechanicalindustry-2019', 'http://www.medwelljournals.com/abstract/?doi=jeasci.2020.81.87', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Artificial Neural Networks in the Demand Forecasting of a Metal-Mechanical Industry [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.36478/jeasci.2020.81.87\"\n     onclick=\"log_download('llorenteleyva-rpatinoalarcon-monterosantos-dherreragranda-hpeluffoordonez-mlastrealeaga-cordovesgarcia-artificialneuralnetworksinthedemandforecastingofametalmechanicalindustry-2019', 'http://doi.org/10.36478/jeasci.2020.81.87', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/llorenteleyva-rpatinoalarcon-monterosantos-dherreragranda-hpeluffoordonez-mlastrealeaga-cordovesgarcia-artificialneuralnetworksinthedemandforecastingofametalmechanicalindustry-2019\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('llorenteleyva-rpatinoalarcon-monterosantos-dherreragranda-hpeluffoordonez-mlastrealeaga-cordovesgarcia-artificialneuralnetworksinthedemandforecastingofametalmechanicalindustry-2019')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Artificial Neural Networks in the Demand Forecasting of a Metal-Mechanical Industry},\n type = {article},\n year = {2019},\n pages = {81-87},\n volume = {15},\n websites = {http://www.medwelljournals.com/abstract/?doi=jeasci.2020.81.87},\n month = {10},\n day = {25},\n id = {d2a33562-d740-37c2-a6df-cc39c178a563},\n created = {2022-02-02T06:07:46.991Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {aea1f410-b6a0-3014-afb8-bc5b4ad73524},\n last_modified = {2022-02-02T06:07:46.991Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {L.Lorente-Leyva2019},\n private_publication = {false},\n abstract = {This research presents an application of artificial neural networks in demand forecasting by using MATLAB Software. Keeping in mind that in any planning process forecasts play a fundamental role, being one of the bases for; planning, organizing and controlling production. It gives priority to the most critical nodes and their key activities, so that, the decisions made about them will generate the greatest possible positive impact. The methodology applied demonstrates the quality of the solutions found which are compared with traditional statistical methods to demonstrate the value of the solution proposed. When the results show that the minimum quadratic error is reached with the application of artificial neural networks, a better performance is obtained. Therefore, a suitable horizon is established for the planification and decision making in the metal-mechanical industry for the use of artificial intelligence in the production processes.},\n bibtype = {article},\n author = {L. Lorente-Leyva, Leandro and R. Patino-Alarcon, Delio and Montero-Santos, Yakcleem and D. Herrera-Granda, Israel and H. Peluffo-Ordonez, Diego and M. Lastre-Aleaga, Arlys and Cordoves-Garcia, Alexis},\n doi = {10.36478/jeasci.2020.81.87},\n journal = {Journal of Engineering and Applied Sciences},\n number = {1}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This research presents an application of artificial neural networks in demand forecasting by using MATLAB Software. Keeping in mind that in any planning process forecasts play a fundamental role, being one of the bases for; planning, organizing and controlling production. It gives priority to the most critical nodes and their key activities, so that, the decisions made about them will generate the greatest possible positive impact. The methodology applied demonstrates the quality of the solutions found which are compared with traditional statistical methods to demonstrate the value of the solution proposed. When the results show that the minimum quadratic error is reached with the application of artificial neural networks, a better performance is obtained. Therefore, a suitable horizon is established for the planification and decision making in the metal-mechanical industry for the use of artificial intelligence in the production processes.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);