var bibbase_data = {"data":"\"Loading..\"\n\n
\n\n \n\n \n\n \n \n\n \n\n \n \n\n \n\n \n
\n generated by\n \n \"bibbase.org\"\n\n \n
\n \n\n
\n\n \n\n\n
\n\n Excellent! Next you can\n create a new website with this list, or\n embed it in an existing web page by copying & pasting\n any of the following snippets.\n\n
\n JavaScript\n (easiest)\n
\n \n <script src=\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/group/c75f4111-ee1e-34d4-80ba-c2a281986acc?jsonp=1&jsonp=1\"></script>\n \n
\n\n PHP\n
\n \n <?php\n $contents = file_get_contents(\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/group/c75f4111-ee1e-34d4-80ba-c2a281986acc?jsonp=1\");\n print_r($contents);\n ?>\n \n
\n\n iFrame\n (not recommended)\n
\n \n <iframe src=\"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3/group/c75f4111-ee1e-34d4-80ba-c2a281986acc?jsonp=1\"></iframe>\n \n
\n\n

\n For more details see the documention.\n

\n
\n
\n\n
\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 create a free account. 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
\n\n
\n\n

To the site owner:

\n\n

Action required! Mendeley is changing its\n API. In order to keep using Mendeley with BibBase past April\n 14th, you need to:\n

    \n
  1. renew the authorization for BibBase on Mendeley, and
    2. \n
  2. update the BibBase URL\n in your page the same way you did when you initially set up\n this page.\n
    3. \n
\n

\n\n

\n \n \n Fix it now\n

\n
\n\n
\n\n\n
\n \n \n
\n
\n  \n 2026\n \n \n (2)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Towards Machine Learning–Enhanced Ad Hoc Networks for Disaster Scenarios.\n \n \n \n \n\n\n \n Suárez-Zambrano Wilman\nand Astudillo-León, J., P., L., L., G., L., P., D., H.\n\n\n \n\n\n\n In Ferrández Vicente José Manuel\nand Val-Calvo, M., A., H., editor(s), Bioinspired Intelligent Systems: From Robotics and Computer Vision to Trustworthy Applications, pages 507-517, 2026. Springer Nature Switzerland\n \n\n\n\n
\n\n\n\n \n \n \"TowardsWebsite\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Towards Machine Learning–Enhanced Ad Hoc Networks for Disaster Scenarios},\n type = {inproceedings},\n year = {2026},\n pages = {507-517},\n websites = {https://link.springer.com/chapter/10.1007/978-3-032-27317-8_48},\n publisher = {Springer Nature Switzerland},\n city = {Cham},\n id = {1eae92fa-15a7-3826-8c4d-171880192076},\n created = {2026-09-09T16:52:24.956Z},\n accessed = {2026-05-30},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {c75f4111-ee1e-34d4-80ba-c2a281986acc},\n last_modified = {2026-09-09T16:52:24.956Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {InProceedings},\n private_publication = {false},\n abstract = {Reliable communication is one of the main challenges in disaster scenarios, where conventional infrastructure is often unavailable and mobile nodes exhibit highly dynamic behavior. This paper presents an artificial intelligence–based approach to enhance communication in wireless ad hoc networks under such conditions. A dataset was generated from scratch by integrating the ns-3 network simulator with BonnMotion to model realistic human mobility in disaster environments. From these simulations, two key features—channel utilization factor and queue packet size—were extracted and used to train a supervised learning model with the CatBoost algorithm. The model was validated with accuracy, precision, and F1-score, and then reintroduced into the simulation to support real-time decision-making. Experimental results show that the AI-enhanced strategy achieves substantial improvements in Packet Delivery Ratio (PDR), Throughput, and End-to-End Delay compared to a baseline without Quality of Service (QoS). These findings demonstrate the feasibility of integrating machine learning into communication layers to increase the resilience and efficiency of ad hoc networks for disaster response applications.},\n bibtype = {inproceedings},\n author = {Suárez-Zambrano Wilman\nand Astudillo-León, Juan Pablo\nand Lemus-Cárdenas Leticia\nand Guachi-Guachi Lorena\nand Peluffo-Ordóñez D H},\n editor = {Ferrández Vicente José Manuel\nand Val-Calvo, Mikel\nand Adeli Hojjat},\n booktitle = {Bioinspired Intelligent Systems: From Robotics and Computer Vision to Trustworthy Applications}\n}
\n
\n\n\n
\n Reliable communication is one of the main challenges in disaster scenarios, where conventional infrastructure is often unavailable and mobile nodes exhibit highly dynamic behavior. This paper presents an artificial intelligence–based approach to enhance communication in wireless ad hoc networks under such conditions. A dataset was generated from scratch by integrating the ns-3 network simulator with BonnMotion to model realistic human mobility in disaster environments. From these simulations, two key features—channel utilization factor and queue packet size—were extracted and used to train a supervised learning model with the CatBoost algorithm. The model was validated with accuracy, precision, and F1-score, and then reintroduced into the simulation to support real-time decision-making. Experimental results show that the AI-enhanced strategy achieves substantial improvements in Packet Delivery Ratio (PDR), Throughput, and End-to-End Delay compared to a baseline without Quality of Service (QoS). These findings demonstrate the feasibility of integrating machine learning into communication layers to increase the resilience and efficiency of ad hoc networks for disaster response applications.\n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Interpretable MobileNetV3 for Early and Late Blight Detection in Andean Potato Leaves.\n \n \n \n \n\n\n \n Guachi-Guachi Lorena\nand Gavilánez, E., G., J., O., V., G., R., S., W., P., D., H.\n\n\n \n\n\n\n In Ferrández Vicente José Manuel\nand Val-Calvo, M., A., H., editor(s), Bioinspired Intelligent Systems: From Robotics and Computer Vision to Trustworthy Applications, pages 391-399, 2026. Springer Nature Switzerland\n \n\n\n\n
\n\n\n\n \n \n \"InterpretableWebsite\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Interpretable MobileNetV3 for Early and Late Blight Detection in Andean Potato Leaves},\n type = {inproceedings},\n year = {2026},\n pages = {391-399},\n websites = {https://link.springer.com/chapter/10.1007/978-3-032-27317-8_37},\n publisher = {Springer Nature Switzerland},\n city = {Cham},\n id = {feef3662-1d8c-33b8-afb1-8cf62a10c4b4},\n created = {2026-09-09T16:52:29.940Z},\n accessed = {2026-05-30},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {c75f4111-ee1e-34d4-80ba-c2a281986acc},\n last_modified = {2026-09-09T16:52:29.940Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {InProceedings},\n private_publication = {false},\n abstract = {Potato leaf blight remains one of the most destructive leaf diseases affecting potato crops in the Andean region, posing a significant threat to food security and the livelihoods of smallholder farmers. This work presents a computer vision framework for the automated detection of early blight (Alternaria solani) and late blight (Phytophthora infestans) in native Andean potato varieties using RGB imagery. Two datasets were employed: a localized dataset (2,766 images) and an extended-localized dataset incorporating additional distractor images that closely resemble those from localized dataset (3,666 images). Three convolutional neural network (CNNs) architectures, a custom CNN, EfficientNetB0, and MobileNetV3, were evaluated for classification performance and interpretability. MobileNetV3 achieved 100% accuracy on the localized dataset and 98.67% on the extended dataset. Grad-CAM visualizations revealed that, under increased variability, MobileNetV3 maintained spatially distributed attention over leaf regions while minimizing reliance on background artifacts, outperforming compared architectures in robustness and interpretability. These results demonstrate that lightweight CNNs trained on localized data augmented with distractor images can effectively mitigate dataset bias and enable the development of efficient, deployable disease detection tools for small-scale agriculture.},\n bibtype = {inproceedings},\n author = {Guachi-Guachi Lorena\nand Gavilánez, Esteban\nand Guerrero Jeffrey\nand Osejo Victor\nand Guachi Robinson\nand Suárez-Zambrano Wilman\nand Peluffo-Ordóñez D H},\n editor = {Ferrández Vicente José Manuel\nand Val-Calvo, Mikel\nand Adeli Hojjat},\n booktitle = {Bioinspired Intelligent Systems: From Robotics and Computer Vision to Trustworthy Applications}\n}
\n
\n\n\n
\n Potato leaf blight remains one of the most destructive leaf diseases affecting potato crops in the Andean region, posing a significant threat to food security and the livelihoods of smallholder farmers. This work presents a computer vision framework for the automated detection of early blight (Alternaria solani) and late blight (Phytophthora infestans) in native Andean potato varieties using RGB imagery. Two datasets were employed: a localized dataset (2,766 images) and an extended-localized dataset incorporating additional distractor images that closely resemble those from localized dataset (3,666 images). Three convolutional neural network (CNNs) architectures, a custom CNN, EfficientNetB0, and MobileNetV3, were evaluated for classification performance and interpretability. MobileNetV3 achieved 100% accuracy on the localized dataset and 98.67% on the extended dataset. Grad-CAM visualizations revealed that, under increased variability, MobileNetV3 maintained spatially distributed attention over leaf regions while minimizing reliance on background artifacts, outperforming compared architectures in robustness and interpretability. These results demonstrate that lightweight CNNs trained on localized data augmented with distractor images can effectively mitigate dataset bias and enable the development of efficient, deployable disease detection tools for small-scale agriculture.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n\n\n\n
\n\n\n \n\n \n \n \n \n\n
\n"}; document.write(bibbase_data.data);