Overview
- Includes new advances on finite and infinite Hidden Markov Models (HMMs) and their applications from different disciplines
- Tackles recent challenges related to the deployment of HMMs in real-life applications (e.g., big data, multimodal data, etc.)
- Presents new applications of HMMs by considering advancements with respect to inference techniques and recent technological advancements
Part of the book series: Unsupervised and Semi-Supervised Learning (UNSESUL)
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Table of contents (11 chapters)
Editors and Affiliations
About the editors
Nizar Bouguila received the engineer degree from the University of Tunis, Tunis, Tunisia, in 2000, and the M.Sc. and Ph.D. degrees in computer science from Sherbrooke University, Sherbrooke, QC, Canada, in 2002 and 2006, respectively. He is currently a Professor with the Concordia Institute for Information Systems Engineering (CIISE) at Concordia University, Montreal, Quebec, Canada. His research interests include image processing, machine learning, data mining,, computer vision, and pattern recognition. Prof. Bouguila received the best Ph.D Thesis Award in Engineering and Natural Sciences from Sherbrooke University in 2007. He was awarded the prestigious Prix d’excellence de l’association des doyens des etudes superieures au Quebec (best Ph.D Thesis Award in Engineering and Natural Sciences in Quebec), and was a runner-up for the prestigious NSERC doctoral prize. He was the holder of a Concordia University research Chair Tier 2 from 2014 to 2019 and was named Concordia University research Fellow in 2020. He is the author or co-author of more than 400 publications in several prestigious journals and conferences. He is a regular reviewer for many international journals and serving as associate editor for several journals such as Pattern Recognition journal and Engineering Applications of Artificial Intelligence, etc. Dr. Bouguila is a licensed Professional Engineer registered in Ontario, and a Senior Member of the IEEE.
Wentao Fan received his M.Sc. and Ph.D. degrees in Electrical and Computer Engineering from Concordia University, Montreal, Quebec, Canada, in 2009 and 2014, respectively. He is currently a Professor in the Department of Computer Science and Technology, Huaqiao University, Xiamen, China. His research interests include machine learning, computer vision, deep learning and pattern recognition.
Manar Amayri received the bachelor's degree in power engineering from Damascus University, Damascus, Syria, in 2006, the master's degree in electrical power systems from the Power Department, Damascus University, in 2014, the master's degree in smart grids and buildings from ENES3, INP-Grenoble (Institute National Polytechnique de Grenoble), Grenoble, France, in 2014, and the Ph.D. degree in energy smart-buildings from Grenoble Institute of Technology, Grenoble, in 2017. She was a Post-Doctoral Researcher with INP- Grenoble and then Concordia University, Montreal, QC, Canada, from 2017 to 2020. She is currently an Associate Professor with ENES3, INP-Grenoble, G-SCOP Laboratory (Sciences pour la conception, l’Optimisation et la Production). Her research interests include data mining, machine learning, explainable artificial intelligence (AI), energy, and smart buildings.Bibliographic Information
Book Title: Hidden Markov Models and Applications
Editors: Nizar Bouguila, Wentao Fan, Manar Amayri
Series Title: Unsupervised and Semi-Supervised Learning
DOI: https://doi.org/10.1007/978-3-030-99142-5
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-030-99141-8Published: 20 May 2022
Softcover ISBN: 978-3-030-99144-9Published: 20 May 2023
eBook ISBN: 978-3-030-99142-5Published: 19 May 2022
Series ISSN: 2522-848X
Series E-ISSN: 2522-8498
Edition Number: 1
Number of Pages: X, 298
Number of Illustrations: 8 b/w illustrations, 149 illustrations in colour
Topics: Signal, Image and Speech Processing, Probability and Statistics in Computer Science, Statistics and Computing/Statistics Programs