FLAIRS-35 Special Track on Neural Networks and Data Mining The Florida Artificial Intelligence Research Symposium (FLAIRS) is an interdisciplinary conference which features double-blind reviewing, free tutorials, and a warm and sunny venue. Abstract Due Date: February 7, 2022 Submission Due Date: February 14, 2022 Conference: May 15-18, 2022 Jensen Beach, Florida Website: https://sites.google.com/view/flairs-35-nn-dm-track/home URL: https://www.flairs-35.info/call-for-papers Papers are being solicited for a special track on Neural Networks and Data Mining at the 35th International FLAIRS Conference (https://www.flairs-35.info/home). This special track will be devoted to neural networks and data mining with the aim of presenting new and important contributions in these areas. Papers and contributions are encouraged for any work related to neural networks, data mining, or the intersection thereof. Topics of interest may include (but are in no way limited to): applications such as Pattern Recognition, Control and Process Monitoring, Biomedical Applications, Robotics, Text Mining, Diagnostic Problems, Telecommunications, Power Systems, Signal Processing; Intelligence analysis, medical and health applications, text, video, and multi-media mining, E-commerce and web data, financial data analysis, cyber security, remote sensing, earth sciences, bioinformatics, and astronomy; algorithms such as new developments in Back Propagation, RBF, SVM, Deep Learning, Ensemble Methods, Kernel Approaches; hybrid approaches such as Neural Networks/Genetic Algorithms, Neural Network/Expert Systems, Causal Nets trained with Backpropagation, and Neural Network/Fuzzy Logic applications such as Intelligence analysis, medical and health applications, text, video, and multi-media mining, E-commerce and web data, financial data analysis, cyber security, remote sensing, earth sciences, bioinformatics, and astronomy; modeling algorithms such as hidden Markov models, decision trees, neural networks, statistical methods, or probabilistic methods; case studies in areas of application, or over different algorithms and approaches; graph modeling, pattern discovery, and anomaly detection; feature extraction and selection; post-processing techniques such as visualization, summarization, or trending; preprocessing and data reduction; and knowledge engineering or warehousing. Questions regarding the track should be addressed to: David Bisant at bisant@umbc.edu, Steven Gutstein at s.m.gutstein@gmail.com, or Bill Eberle at weberle@tntech.edu.