Keyword: machine learning

2024 (1)
Advanced Computer Science Applications: Recent Trends in AI, Machine Learning, and Network Security. Singh, K., Banda, L., & Manjul, M. Apple Academic Press Inc., Boca Raton, FL, 2024.
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2023 (10)
Is It Worth It? Comparing Six Deep and Classical Methods for Unsupervised Anomaly Detection in Time Series. Rewicki, F., Denzler, J., & Niebling, J. Applied Sciences, 13(3):1778, January, 2023. Number: 3 Publisher: Multidisciplinary Digital Publishing Institute
Is It Worth It? Comparing Six Deep and Classical Methods for Unsupervised Anomaly Detection in Time Series [link]Paper  doi  abstract   bibtex   
Knowledge Discovery: Methods from data mining and machine learning. Shu, X. & Ye, Y. Social Science Research, 110:102817, 2023.
Knowledge Discovery: Methods from data mining and machine learning [link]Paper  doi  abstract   bibtex   
Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-based Guidance. Khattar, V. & Jin, M. In AAAI Conference on Artificial Intelligence (AAAI) AI for Social Impact Track, 2023.
Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-based Guidance [link]Arxiv  Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-based Guidance [pdf]Pdf  abstract   bibtex   25 downloads  
ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models. Wang, S., Zhao, Z., Ouyang, X., Wang, Q., & Shen, D. arXiv.org, February, 2023. Place: Ithaca Publisher: Cornell University Library, arXiv.org
ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models [link]Paper  abstract   bibtex   
FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures For Medical Imaging Segmentation. Celaya, A., Riviere, B., & Fuentes, D. Neurips 2023, 2023.
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Distinguishing Human-Written and ChatGPT-Generated Text Using Machine Learning. Alamleh, H., Alqahtani, A., & Elsaid, A. In pages 154–158, 2023.
Distinguishing Human-Written and ChatGPT-Generated Text Using Machine Learning [link]Paper  doi  abstract   bibtex   
Cloud-Free Global Maps of Essential Vegetation Traits Processed from the TOA Sentinel-3 Catalogue in Google Earth Engine. Kovács, D. D., Reyes-Muñoz, P., Salinero-Delgado, M., Mészáros, V. I., Berger, K., & Verrelst, J. Remote Sensing, 15(13):3404, January, 2023. Number: 13 Publisher: Multidisciplinary Digital Publishing Institute
Cloud-Free Global Maps of Essential Vegetation Traits Processed from the TOA Sentinel-3 Catalogue in Google Earth Engine [link]Paper  doi  abstract   bibtex   
A Human-on-the-Loop Optimization Autoformalism Approach for Sustainability. Jin, M., Sel, B., Hardeep, F., & Yin, W. In Preprint, 2023.
A Human-on-the-Loop Optimization Autoformalism Approach for Sustainability [link]Arxiv  bibtex   13 downloads  
From ChatGPT to CatGPT: The Implications of Artificial Intelligence on Library Cataloging. Brzustowicz, R. Information Technology and Libraries, September, 2023. Number: 3
From ChatGPT to CatGPT: The Implications of Artificial Intelligence on Library Cataloging [link]Paper  doi  abstract   bibtex   
GEORGIA: A Graph Neural Network Based EmulatOR for Glacial Isostatic Adjustment. Lin, Y., Whitehouse, P. L., Valentine, A. P., & Woodroffe, S. A. Geophysical Research Letters, 50(18):e2023GL103672, 2023. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2023GL103672
GEORGIA: A Graph Neural Network Based EmulatOR for Glacial Isostatic Adjustment [link]Paper  doi  abstract   bibtex   
2022 (11)
A Survey of Open Source Automation Tools for Data Science Predictions. Hoell, N. August, 2022. arXiv:2208.11792 [cs]
A Survey of Open Source Automation Tools for Data Science Predictions [link]Paper  doi  abstract   bibtex   
A Machine Learning Tutorial for Operational Meteorology. Part I: Traditional Machine Learning. Chase, R. J., Harrison, D. R., Burke, A., Lackmann, G. M., & McGovern, A. Weather and Forecasting, 37(8):1509–1529, August, 2022.
A Machine Learning Tutorial for Operational Meteorology. Part I: Traditional Machine Learning [link]Paper  doi  abstract   bibtex   
Reinforcement Learning in Manufacturing Control: Baselines, challenges and ways forward. Samsonov, V., Ben Hicham, K., & Meisen, T. Engineering Applications of Artificial Intelligence, 112:104868, June, 2022.
Reinforcement Learning in Manufacturing Control: Baselines, challenges and ways forward [link]Paper  doi  bibtex   
SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learning. Alnuqaydan, A., Gleyzer, S., & Prosper, H. June, 2022. arXiv:2206.08901 [hep-ph]
SYMBA: Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learning [link]Paper  abstract   bibtex   
We need to talk about random seeds. Bethard, S. October, 2022.
We need to talk about random seeds [link]Paper  doi  bibtex   5 downloads  
Symmetry Group Equivariant Architectures for Physics. Bogatskiy, A., Ganguly, S., Kipf, T., Kondor, R., Miller, D. W., Murnane, D., Offermann, J. T., Pettee, M., Shanahan, P., Shimmin, C., & Thais, S. arXiv:2203.06153 [astro-ph, physics:hep-ex, physics:hep-ph], March, 2022. arXiv: 2203.06153
Symmetry Group Equivariant Architectures for Physics [link]Paper  abstract   bibtex   
The Prediction and Influential Factors of Violence in Male Schizophrenia Patients With Machine Learning Algorithms. Yu, T., Zhang, X., Liu, X., Xu, C., & Deng, C. Frontiers in Psychiatry, 13(79989):9, 2022.
The Prediction and Influential Factors of Violence in Male Schizophrenia Patients With Machine Learning Algorithms [link]Paper  doi  abstract   bibtex   
Curating Cyberbullying Datasets: a Human-AI Collaborative Approach. Gomez, C. E., Sztainberg, M. O., & Trana, R. E. International Journal of Bullying Prevention, 4(1):35–46, 2022.
Curating Cyberbullying Datasets: a Human-AI Collaborative Approach [link]Paper  doi  abstract   bibtex   
Development of a Machine Learning-based Soft Sensor for an Oil Refinery’s Distillation Column. Ferreira, J., Pedemonte, M., & Torres, A. I. Computers & Chemical Engineering, March, 2022.
Development of a Machine Learning-based Soft Sensor for an Oil Refinery’s Distillation Column [link]Paper  doi  abstract   bibtex   
Analysis of Common Prediction Models for a Fuzzy Connected Source Target Production Based on Time Dependent Significance. Soller, S., Hoelzl, G., Greiler, T., & Kranz, M. In Proceedings of the 2022 International Conference on Embedded Wireless Systems and Networks, of EWSN '22, pages 226–231, USA, 2022. Junction Publishing.
Analysis of Common Prediction Models for a Fuzzy Connected Source Target Production Based on Time Dependent Significance [pdf]Paper  abstract   bibtex   
Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging. Benkarim, O., Paquola, C., Park, B., Kebets, V., Hong, S., Wael, R. V. d., Zhang, S., Yeo, B. T. T., Eickenberg, M., Ge, T., Poline, J., Bernhardt, B. C., & Bzdok, D. PLOS Biology, 20(4):e3001627, April, 2022. Publisher: Public Library of Science
Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging [link]Paper  doi  abstract   bibtex   
2021 (9)
A reinforcement learning iterated local search for makespan minimization in additive manufacturing machine scheduling problems. Alicastro, M., Ferone, D., Festa, P., Fugaro, S., & Pastore, T. Computers and Operations Research, 131:105272, Pergamon, jul, 2021.
A reinforcement learning iterated local search for makespan minimization in additive manufacturing machine scheduling problems [link]Paper  doi  abstract   bibtex   
Deep learning for physics research. Erdmann, M. World Scientific, New Jersey, 2021.
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Use of Electronic Health Data for Disease Prediction: A Comprehensive Literature Review. Hossain, M. E., Khan, A., Moni, M. A., & Uddin, S. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(2):745–758, March, 2021. Conference Name: IEEE/ACM Transactions on Computational Biology and Bioinformatics
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Recurrent localization networks applied to the Lippmann-Schwinger equation. Kelly, C. & Kalidindi, S. R. Computational Materials Science, 2021.
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Can deep learning beat numerical weather prediction?. Schultz, M. G., Betancourt, C., Gong, B., Kleinert, F., Langguth, M., Leufen, L. H., Mozaffari, A., & Stadtler, S. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379(2194):20200097, April, 2021.
Can deep learning beat numerical weather prediction? [link]Paper  doi  abstract   bibtex   
Semantization of Machine Learning and Data Science (a Project Idea). Alexiev, V. & Boytcheva, S. presentation, September, 2021.
Semantization of Machine Learning and Data Science (a Project Idea) [link]Paper  abstract   bibtex   
Fault Detection in DC Microgrids Using Short-Time Fourier Transform. Grcić, I., Pandžić, H., & Novosel, D. Energies, 14(2):14, 2021. IF:3.252; Q3
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A Systematic Review of Artificial Intelligence Public Datasets for Railway Applications. Pappaterra, M. J., Flammini, F., Vittorini, V., & Bešinović, N. Infrastructures, 6(10):136, October, 2021. Number: 10 Publisher: Multidisciplinary Digital Publishing Institute
A Systematic Review of Artificial Intelligence Public Datasets for Railway Applications [link]Paper  doi  abstract   bibtex   
BridgeClassics. Künstliche Intelligenz für die Klassische Philologie. Beyer, A., Schulz, K., & Cordes, L. May, 2021.
BridgeClassics. Künstliche Intelligenz für die Klassische Philologie [link]Paper  doi  abstract   bibtex   5 downloads  
2020 (9)
Prediction of physical violence in schizophrenia with machine learning algorithms. Wang, K. Z., Bani-Fatemi, A., Adanty, C., Harripaul, R., Griffiths, J., Kolla, N., Gerretsen, P., Graff, A., & De Luca, V. Psychiatry Research, 289:11296, 2020.
Prediction of physical violence in schizophrenia with machine learning algorithms [link]Paper  doi  abstract   bibtex   
Finding warning markers: Leveraging natural language processing and machine learning technologies to detect risk of school violence. Ni, Y., Barzman, D., Bachtel, A., Griffey, M., Osborn, A., & Sorter, M. International Journal of Medical Informatics, 139(10413):7, 2020.
Finding warning markers: Leveraging natural language processing and machine learning technologies to detect risk of school violence [link]Paper  doi  abstract   bibtex   
Neural Identification for Control. Saha, P. & Mukhopadhyay, S. arXiv:2009.11782 [cs, eess], September, 2020. arXiv: 2009.11782
Neural Identification for Control [link]Paper  abstract   bibtex   
An enhanced deep learning approach for brain cancer MRI images classification using residual networks. Ismael, S. A. [., Mohammed, A., & Hefny, H. Artificial Intelligence in Medicine, 102:101779, 2020.
An enhanced deep learning approach for brain cancer MRI images classification using residual networks [link]Paper  doi  abstract   bibtex   
Computer-inspired Quantum Experiments. Krenn, M., Erhard, M., & Zeilinger, A. arXiv:2002.09970 [quant-ph], February, 2020. arXiv: 2002.09970 repo: https://github.com/XuemeiGu/MelvinPython/
Computer-inspired Quantum Experiments [link]Paper  abstract   bibtex   
Thermodynamics-based Artificial Neural Networks for constitutive modeling. Masi, F., Stefanou, I., Vannucci, P., & Maffi-Berthier, V. arXiv:2005.12183 [physics, stat], May, 2020. arXiv: 2005.12183
Thermodynamics-based Artificial Neural Networks for constitutive modeling [link]Paper  abstract   bibtex   
Not All Roads Lead to Rome: Pitch Representation and Model Architecture for Automatic Harmonic Analysis. Micchi, G., Gotham, M., & Giraud, M. Transactions of the International Society for Music Information Retrieval, 3(1):42–54, 2020.
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Pathological test type and chemical detection using deep neural networks: a case study using ELISA and LFA assays. Hoque Tania, M., Kaiser, M. S., Abu-Hassan, K., & Hossain, M. A. Journal of Enterprise Information Management, January, 2020.
Pathological test type and chemical detection using deep neural networks: a case study using ELISA and LFA assays [link]Paper  doi  abstract   bibtex   
Comparison of Time Series Clustering Algorithms for Machine State Detection. Hennig, M., Grafinger, M., Gerhard, D., Dumss, S., & Rosenberger, P. Procedia CIRP, 93:1352–1357, January, 2020.
Comparison of Time Series Clustering Algorithms for Machine State Detection [link]Paper  doi  abstract   bibtex   
2019 (16)
Identification of Caveolin-1 Domain Signatures via Graphlet Analysis of Single Molecule Super-Resolution Data. Khater, I. M., Meng, F., Nabi, I. R., & Hamarneh, G. Bioinformatics, 35(18):3468-3475, 2019.
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Applications of machine learning in real-life digital health interventions: Review of the literature. Triantafyllidis, A., K. & Tsanas, A. Journal of Medical Internet Research, 21(4):1-9, 2019.
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Next Generation Mapping: Combining Deep Learning, Cloud Computing, and Big Remote Sensing Data. Parente, L., Taquary, E., Silva, A. P., Souza, C., & Ferreira, L. Remote Sensing, 11(23):2881, January, 2019.
Next Generation Mapping: Combining Deep Learning, Cloud Computing, and Big Remote Sensing Data [link]Paper  doi  abstract   bibtex   
Deep Learning at Scale. Viviani, P., Drocco, M., Baccega, D., Colonnelli, I., & Aldinucci, M. In Proc. of 27th Euromicro Intl. Conference on Parallel Distributed and network-based Processing (PDP), pages 124–131, Pavia, Italy, 2019. IEEE.
Deep Learning at Scale [pdf]Paper  doi  abstract   bibtex   
Generalizable Feature Learning in the Presence of Data Bias and Domain Class Imbalance with Application to Skin Lesion Classification. Yoon, C., Hamarneh, G., & Abugharbieh, R. In Lecture Notes in Computer Science, Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 11767, pages 365-373, 2019.
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Aggregated outputs by linear models: An application on marine litter beaching prediction. Hernández-González, J., Inza, I., Granado, I., Basurko, O., C., Fernandes, J., A., & Lozano, J., A. Information Sciences, 481:381-393, 2019.
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Online Machine Learning Audiometry. Barbour, D. L., Howard, R. T., Song, X. D., Metzger, N., Sukesan, K. A., DiLorenzo, J. C., Snyder, B. R. D., Chen, J. Y., Degen, E. A., Buchbinder, J. M., & Heisey, K. L. Ear and Hearing, 40(4):918–926, August, 2019.
Online Machine Learning Audiometry [link]Paper  doi  abstract   bibtex   
From open access to perpetual access: archiving web-published scholarship. Bailey, J. & Praetzellis, M. June, 2019.
From open access to perpetual access: archiving web-published scholarship [link]Paper  bibtex   
Machine learning discovery of longitudinal patterns of depression and suicidal ideation. Gong, J., Simon, G. E., & Liu, S. PLOS ONE, 14(9):e0222665, September, 2019.
Machine learning discovery of longitudinal patterns of depression and suicidal ideation [link]Paper  doi  bibtex   
Machine Learning Prediction of Defect Types for Electroluminescence Images of Photovoltaic Panels. Mantel, C., Villebro, F., Benatto, G., Parikh, H., Wendlandt, S., Hossain, K., Poulsen, P., Spataru, S., Sera, D., & Forchhammer, S. In Proceedings of SPIE, volume 11139, of Proceedings of SPIE, the International Society for Optical Engineering, 2019. SPIE - International Society for Optical Engineering. 14th International Conference on Solid State Lighting and LED-based Illumination Systems
: SPIE Optical Engineering + Applications ; Conference date: 09-08-2015 Through 13-08-2015

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Data-Driven Chemical Reaction Classification, Fingerprinting and Clustering using Attention-Based Neural Networks. Philippe Schwaller, Daniel Probst, Alain C. Vaucher, Vishnu H Nair, Teodoro Laino, & Jean-Louis Reymond ChemRxiv, December, 2019.
Data-Driven Chemical Reaction Classification, Fingerprinting and Clustering using Attention-Based Neural Networks [link]Paper  doi  abstract   bibtex   
Multi-dimensional Evaluation of Temporal Neural Networks on Solar Irradiance Forecasting. Song, Z. & Brown, L. E. In 2019 IEEE Power Energy Society Innovative Smart Grid Technologies Conference (ISGT)-Asia, Chengdu, China, May, 2019.
doi  bibtex   1 download  
SnapCode - A Snapshot Based Approach to Code Stylometry. Sarnot, S. A. P., Rinke, S., Raimalwalla, R., Joshi, R., Khengare, R., & Goel, P. In 2019 International Conference on Information Technology (ICIT), pages 337–341, December, 2019.
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Learning Everywhere: Pervasive Machine Learning for Effective High-Performance Computation. Fox, G., Glazier, J. A., Kadupitiya, J., Jadhao, V., Kim, M., Qiu, J., Sluka, J. P., Somogyi, E., Marathe, M., Adiga, A., Chen, J., Beckstein, O., & Jha, S. In 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pages 422–429, May, 2019. ISSN: null
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Deep Learning Based Plant Disease Detection for Smart Agriculture. Ale, L., Sheta, A., Li, L., Wang, Y., & Zhang, N. In 2019 IEEE Globecom Workshops (GC Wkshps), pages 1–6, December, 2019.
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Statistical representation models for mutation information within genomic data. Özcan ŞImŞek, N., Ö., Özgür, A., & Gürgen, F. BMC Bioinformatics, BioMed Central Ltd., 6, 2019.
Statistical representation models for mutation information within genomic data [pdf]Paper  bibtex   
2018 (10)
Learning Fast and Slow: A Unified Batch/Stream Framework. Montiel, J., Bifet, A., Losing, V., Read, J., & Abdessalem, T. In 2018 IEEE International Conference on Big Data (Big Data), pages 1065–1072, December, 2018.
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Multiobjective Optimization for Stiffness and Position Control in a Soft Robot Arm Module. Ansari, Y., Manti, M., Falotico, E., Cianchetti, M., & Laschi, C. IEEE Robotics and Automation Letters, 3(1):108–115, January, 2018.
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Detecting Anomalous Behavior of Black-Box Services Modeled with Distance-Based Online Clustering. Gulenko, A., Schmidt, F., Acker, A., Wallschläger, M., Kao, O., & Liu, F. In 2018 IEEE 11th International Conference on Cloud Computing (CLOUD), pages 912–915, July, 2018. ISSN: 2159-6190
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Machine-learning-based Dynamic IR Drop Prediction for ECO. Fang, Y., Lin, H., Su, M., Li, C., & Fang, E. J. In Proceedings of the International Conference on Computer-Aided Design, of ICCAD '18, pages 17:1–17:7, New York, NY, USA, 2018. ACM. event-place: San Diego, California
Machine-learning-based Dynamic IR Drop Prediction for ECO [link]Paper  doi  abstract   bibtex   
A review of machine learning in obesity. DeGregory, K., Kuiper, P., DeSilvio, T., Pleuss, J., Miller, R., Roginski, J., Fisher, C., Harness, D., Viswanath, S., Heymsfield, S., Dungan, I., & Thomas, D. Obesity Reviews, 2018.
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RouteNet: Routability Prediction for Mixed-size Designs Using Convolutional Neural Network. Xie, Z., Huang, Y., Fang, G., Ren, H., Fang, S., Chen, Y., & Corporation, N. In Proceedings of the International Conference on Computer-Aided Design, of ICCAD '18, pages 80:1–80:8, New York, NY, USA, 2018. ACM. event-place: San Diego, California
RouteNet: Routability Prediction for Mixed-size Designs Using Convolutional Neural Network [link]Paper  doi  abstract   bibtex   
SENATUS: An Experimental SDN/NFV Orchestrator. Troia, S., Rodriguez, A., Alvizu, R., & Maier, G. In 2018 IEEE Conference on Network Function Virtualization and Software Defined Networks, NFV-SDN 2018, 2018.
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Online ensemble learning with abstaining classifiers for drifting and noisy data streams. Krawczyk, B. & Cano, A. Applied Soft Computing, 68:677–692, July, 2018.
Online ensemble learning with abstaining classifiers for drifting and noisy data streams [link]Paper  doi  abstract   bibtex   
Gaze and the Control of Foot Placement When Walking in Natural Terrain. Matthis, J. S., Yates, J. L., & Hayhoe, M. M. Current Biology, April, 2018.
Gaze and the Control of Foot Placement When Walking in Natural Terrain [link]Paper  doi  bibtex   
Sleep Duration and Physical Activity Profiles Associated With Self-Reported Stroke in the United States: Application of Bayesian Belief Network Modeling Techniques. Seixas, A., A., Henclewood, D., A., Williams, S., K., Jagannathan, R., Ramos, A., Zizi, F., & Jean-Louis, G. Frontiers in Neurology, 9:534, Frontiers, 7, 2018.
Sleep Duration and Physical Activity Profiles Associated With Self-Reported Stroke in the United States: Application of Bayesian Belief Network Modeling Techniques [link]Website  doi  abstract   bibtex   
2017 (5)
Deep Learning in Medical Imaging: General Overview. Lee, J., Jun, S., Cho, Y., Lee, H., Kim, G. B., Seo, J. B., & Kim, N. Korean journal of radiology, 18(4):570–584, August, 2017. Place: Korea (South)
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BrainNetCNN: Artificial Convolutional Neural Networks for Connectomes. Kawahara, J., Brown, C. J., Miller, S., Booth, B. G., Chau, V., Grunau, R., Zwicker, J., & Hamarneh, G. In 2nd Annual Health Technology Symposium, Vancouver, Canada, pages 1, 2017.
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Deep learning for computational chemistry. Goh, G. B, Hodas, N. O, & Vishnu, A. Journal of Computational Chemistry, 38(16):1291–1307, 2017.
Deep learning for computational chemistry [link]Paper  doi  bibtex   
The Matter Simulation (R)evolution. Aspuru-Guzik, A., Lindh, R., & Reiher, M. November, 2017.
The Matter Simulation (R)evolution [link]Paper  doi  abstract   bibtex   
Training an Actor-Critic Reinforcement Learning Controller for Arm Movement Using Human-Generated Rewards. Jagodnik, K. M., Thomas, P. S., van den Bogert, A. J., Branicky, M. S., & Kirsch, R. F. IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society, 25(10):1892–1905, 2017.
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2016 (7)
S/HIC: Robust Identification of Soft and Hard Sweeps Using Machine Learning. Schrider, D. R. & Kern, A. D. PLOS Genetics, 12(3):e1005928, March, 2016.
S/HIC: Robust Identification of Soft and Hard Sweeps Using Machine Learning [link]Paper  doi  abstract   bibtex   
Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks. Geitgey, A. June, 2016.
Machine Learning is Fun! Part 3: Deep Learning and Convolutional Neural Networks [link]Paper  abstract   bibtex   
Discovering Biosignatures of Cav1 Domains: Computational Methods for Super-resolution Microscopy. Khater, I. M., Meng, F., Nabi, I. R., & Hamarneh, G. In LSI Imaging Super-resolution Mini-symposium, Vancouver, Canada, pages 1, 2016.
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Classifying smoking urges via machine learning. Dumortier, A., Beckjord, E., Shiffman, S., & Sejdić, E. Computer Methods and Programs in Biomedicine, 137:203-213, Elsevier Ireland Ltd, 12, 2016.
Classifying smoking urges via machine learning [link]Website  abstract   bibtex   
Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza. Allen, C., Tsou, M., Aslam, A., Nagel, A., & Gawron, J. PLOS ONE, 11(7):e0157734, July, 2016. 00000 Publisher: Public Library of Science
Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza [link]Paper  doi  abstract   bibtex   
Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry. Ioannidis, K., Chamberlain, S., R., Treder, M., S., Kiraly, F., Leppink, E., W., Redden, S., A., Stein, D., J., Lochner, C., & Grant, J., E. Journal of Psychiatric Research, 83:94-102, 2016.
Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry [link]Website  abstract   bibtex   
Clinically-Inspired Automatic Classification of Ovarian Carcinoma Subtypes. BenTaieb, A., Nosrati, M., Li-Chang, H., Huntsman, D., & Hamarneh, G. Journal of Pathology Informatics, 7(1):1-28, 2016.
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2015 (5)
Predicting students' happiness from physiology, phone, mobility, and behavioral data. Jaques, N., Taylor, S., Azaria, A., Ghandeharioun, A., Sano, A., & Picard, R. In 2015 International Conference on Affective Computing and Intelligent Interaction, ACII 2015, volume 2015, pages 222-228, 12, 2015. Institute of Electrical and Electronics Engineers Inc..
Predicting students' happiness from physiology, phone, mobility, and behavioral data [link]Website  abstract   bibtex   
Probabilistic event calculus for event recognition. Skarlatidis, A., Paliouras, G., Artikis, A., & Vouros, G. A. ACM Trans. Comput. Logic, 16(2):11:1--11:37, ACM, New York, NY, USA, feb, 2015.
Probabilistic event calculus for event recognition [link]Paper  doi  bibtex   
Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures. Bobb, J. F., Valeri, L., Claus Henn, B., Christiani, D. C., Wright, R. O., Mazumdar, M., Godleski, J. J., & Coull, B. A. Biostatistics (Oxford, England), 16(3):493–508, September, 2015. Publisher: Biostatistics
Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures [link]Paper  doi  abstract   bibtex   
Grand Challenge Veterinary Imaging: Technology, Science, and Communication. McEvoy, F., J. Frontiers in veterinary science, 2:38, 9, 2015.
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Metadata Dependent Mondrian Processes. Wang, Y., Li, B., Wang, Y., & Chen, F. In Proceedings of the 32Nd International Conference on International Conference on Machine Learning - Volume 37, of ICML'15, pages 1339--1347, 2015. JMLR.org.
Metadata Dependent Mondrian Processes [link]Paper  bibtex   
2014 (1)
ARTgrid: A Two-Level Learning Architecture Based on Adaptive Resonance Theory. Švaco, M. & Jerbić, B. Advances in Artificial Neural Systems, 2014:1–9, 2014.
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2013 (1)
An optimal control strategy with enhanced robustness for air-conditioning systems considering model and measurement uncertainties. Zhu, N., Shan, K., Wang, S., & Sun, Y. Energy and Buildings, 67:540–550, December, 2013.
An optimal control strategy with enhanced robustness for air-conditioning systems considering model and measurement uncertainties [link]Paper  doi  abstract   bibtex   
2012 (4)
Locally-Adaptive Similarity Metric for Deformable Medical Image Registration. Tang, L. Y. W., Hero, A. O, & Hamarneh, G. In IEEE International Symposium on Biomedical Imaging (IEEE ISBI), pages 728-731, 2012.
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CrossCheck: Combining Crawling and Differencing to Better Detect Cross-browser Incompatibilities in Web Applications. Choudhary, S. R., Prasad, M. R., & Orso, A. In Proceedings of the 2012 IEEE Fifth International Conference on Software Testing, Verification and Validation, pages 171–180, Washington, DC, USA, 2012. IEEE Computer Society.
CrossCheck: Combining Crawling and Differencing to Better Detect Cross-browser Incompatibilities in Web Applications [link]Paper  doi  bibtex   
Transfer learning for cross-company software defect prediction. Ma, Y., Luo, G., Zeng, X., & Chen, A. Information and Software Technology, 54(3):248--256, 2012.
Transfer learning for cross-company software defect prediction [link]Paper  doi  abstract   bibtex   
Triaging incoming change requests: Bug or commit history, or code authorship?. Linares-Vásquez, M., Hossen, K., Dang, H., Kagdi, H., Gethers, M., & Poshyvanyk, D. In Software Maintenance (ICSM), 2012 28th IEEE International Conference on, pages 451–460, September, 2012.
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2011 (2)
Reducing the Effort of Bug Report Triage: Recommenders for Development-oriented Decisions. Anvik, J. & Murphy, G. C. ACM Trans. Softw. Eng. Methodol., 20(3):10:1–10:35, August, 2011.
Reducing the Effort of Bug Report Triage: Recommenders for Development-oriented Decisions [link]Paper  doi  bibtex   
Online Support Vector Regression With Varying Parameters for Time-Dependent Data. Omitaomu, O. A., Jeong, M. K., & Badiru, A. B. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 41(1):191–197, January, 2011. Conference Name: IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans
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2010 (1)
Lexicons and Grammars for Named Entity Annotation in the National Corpus of Polish. Savary, A. & Piskorski, J. Information Systems Journal, 2010.
Lexicons and Grammars for Named Entity Annotation in the National Corpus of Polish [pdf]Website  abstract   bibtex   
2008 (2)
3D Bicipital Groove Shape Analysis and Relationship to Tendopathy. Ward, A., Hamarneh, G., & Schweitzer, M. Journal of Digital Imaging, 21(2):219-234, 2008.
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Comparison of classification accuracy using Cohen’s Weighted Kappa. Ben-David, A. Expert Systems with Applications, 34(2):825–832, February, 2008.
Comparison of classification accuracy using Cohen’s Weighted Kappa [link]Paper  doi  abstract   bibtex   
2006 (1)
Annotation guidelines for machine learning-based named entity recognition in microbiology. Nédellec, C., Bessieres, P., Bossy, R., Kptoujanksy, A., & Manine, A., P. Machine Learning, Citeseer, 2006.
Annotation guidelines for machine learning-based named entity recognition in microbiology [link]Website  abstract   bibtex   
2001 (1)
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties. Fan, J. & Li, R. Journal of the American Statistical Association, 96(456):1348–1360, December, 2001.
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties [link]Paper  doi  bibtex   
1999 (1)
An overview of statistical learning theory. Vapnik, V. N. IEEE Trans Neural Netw, 10(5):988–999, 1999.
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1994 (1)
On a learnability question associated to neural networks with continuous activations (extended abstract). DasGupta, B., Siegelmann, H. T., & Sontag, E. In COLT '94: Proceedings of the seventh annual conference on Computational learning theory, pages 47–56, New York, NY, USA, 1994. ACM Press.
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Thoughtful Machine Learning with Python: A Test-Driven Approach. Kirk, M. O'Reilly, First edition edition. OCLC: ocn908375399
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Exploring Reinforcement Learning for Mobile Percussive Collaboration. Derbinsky, N. & Essl, G. Technical Report
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