Hidden Markov Models and Applications

This book focuses on recent advances, approaches, theories, and applications related Hidden Markov Models (HMMs). In particular, the book presents recent inference frameworks and applications that consider HMMs. The authors discuss challenging problems that exist when considering HMMs for a specific task or application, such as estimation or selection, etc. The goal of this volume is to summarize the recent advances and modern approaches related to these problems. The book also reports advances on classic but difficult problems in HMMs such as inference and feature selection and describes real-world applications of HMMs from several domains. The book pertains to researchers and graduate students, who will gain a clear view of recent developments related to HMMs and their applications.

• 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 HMMsin 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.

Auteur

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.

Contenu

Chapter1. A Roadmap to Hidden Markov Models and A Review of its Application in Occupancy Estimation.- Chapter2. Bounded asymmetric Gaussian mixture-based hidden Markov models.- Chapter3. Using HMM to model neural dynamics and decode useful signals for neuroprosthetic control.- Chapter4. Fire Detection in Images with Discrete Hidden Markov Models.- Chapter5. Hidden Markov Models: Discrete Feature Selection in Activity Recognition.- Chapter6. Bayesian Inference of Hidden Markov Models using Dirichlet Mixtures.- Chapter7. Online learning of Inverted Beta-Liouville HMMs for Anomaly Detection in Crowd Scenes.- Chapter8. A Novel Continuous Hidden Markov Model for Modeling Positive Sequential Data.- Chapter9. Multivariate Beta-based Hidden Markov Models Applied to Human Activity Recognition.- Chapter10. Multivariate Beta-based Hierarchical Dirichlet Process Hidden Markov Models in Medical Applications.- Chapter11. Shifted-Scaled Dirichlet Based Hierarchical Dirichlet Process Hidden Markov Models with Variational Inference Learning.