Connectionists: FLAIRS-35 May 15-18, 2022, Jensen Beach, Florida Call for Papers

[David B]

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:    January 17, 2022

Submission Due Date:  January 24, 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 at umbc.edu, Steven Gutstein at s.m.gutstein at gmail.com, or Bill
Eberle at weberle at tntech.edu.