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Special Session

Intelligent approaches for data mining applications    View

Real-Time Computational and Intelligent Techniques: Challenges and Advancements    View

Computational Intelligence in Healthcare: Advances and Challenges    View

Invited Speakers

Dr. Khan Muhammad,
Sungkyunkwan University, South Korea

Title: Intelligent Fire Scene Analysis using Efficient Convolutional Neural Networks
Abstract: In today’s era, surveillance cameras are playing a major role in the detection of abnormal events such as fire, accidents, and violence. Among these events, fire is the most critical one, needing instance detection to minimize the overall damage to human lives and properties. For early detection of fire, several traditional and vision-based methods exist with a set of advantages and drawbacks. This talk will briefly discuss about the currently available approaches for early fire detection and will highlight some of their major drawbacks. Next, a few representative vision-based fire detection, segmentation, analysis methods will be discussed along with the available fire datasets. The talk will be concluded with major challenges in this area and a few directions for further research.
Biography: Khan Muhammad (S’16–M’18, SM’22) received his Ph.D. in Digital Contents from Sejong University, Republic of Korea in February 2019. He was an Assistant Professor in the Department of Software, Sejong University, from March 2019 to February 2022. He is currently the director of the Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab) and an Assistant Professor (Tenure-Track) with the Department of Applied AI, School of Convergence, College of Computing and Informatics, Sungkyunkwan University, Seoul, Republic of Korea. His research interests include intelligent video surveillance, information security, video summarization, and smart cities. He has registered 10 patents and contributed 220+ papers in peer-reviewed journals and conference proceedings in his research areas. His contributions have received 11,500+ citations to date, with an H-index of 58. He is an Associate Editor/Editorial Board Member for more than 14 journals. He is among the most highly cited researchers in 2021, according to the Web of Science.

Prof. Utpal Garain
Computer Vision & Pattern Recognition [CVPR] Unit
Indian Statistical Institute, Kolkata

Title: ..
Abstract: ..
Biography: Utpal Garain received his bachelor and master degrees in computer science and engineering in 1994 and 1997,respectively from Jadavpur University, India and Ph.D. degree from Indian Statistical Institute in 2005. He received post doctoral fellowship from CNRS, France and studied in Univ. of Rouen, France for one year in 2004. After starting his career in software industries like Larsen & Toubro Ltd. Mumbai, Dun & Dradstreet Satyam Software, Chennai, etc. later on, he joined Indian Statistical Institute where he is, at present, serving as a Professor. His research interest is now focused on exploring deep learning methods for language, image and video analysis including NLP tools, OCRs, handwriting analysis, computational forensics, and like. His research is additionally influenced by the TDIL need (technology development in Indian languages). Dr. Garain is one of the associate editors of Int. J. on Document Analysis and Recognition (IJDAR). At present, he is the Chair for IAPR Technical Committee (TC-6) on Computational Forensics. Prof. Garain has been serving as program committee member for several international conferences including ICPR, ICDAR, ACM-SAC, etc. Moreover, he has been regularly reviewing papers for several international journals including IEEE PAMI, SMC, EvC, IJDAR, PR, PRL, Image & Vision Computing, etc. For his significant contribution in pattern recognition and its applications for language engineering, Prof. Garain received the Young Engineer Award in 2006 from the Indian National Academy of Engineering (INAE). In 2011, he received the prestigious Indo-US Research Fellowship (IUSSTF) in the field of Engineering Sciences for conducting research on information retrieval in the Univ. of Maryland, College Park, MD. In 2016, Prof. Garain received JSPS Invitational Fellowship for Research in Osaka University, Japan.

Dr. Dmitrii I Kaplun
Electrotechnical University "LETI", Saint Petersburg

Title: Video-based tracking and model-parametric analysis of animal movement patterns in biomedical applications
Abstract: Rapid advancement in computer vision technologies provides increasing opportunities for the quantitative characterization of animal behavior, although reduction of their analysis to several scalar metrics appears a common limitation for the representation of complex behavioral patterns. We suggest an alternative approach to the quantitative assessment of animal behavioral patterns by parameterization of a generalized scalable model based on fractional Brownian motion with using detrended fluctuation analysis and detrended partial cross-correlation analysis of the observational movement trajectories. Our approach is based on the analysis of video recordings from different animal-involved tests. We show explicitly that the proposed approach and models on its basis can be used for a robust estimation of interpretable scalar metrics commonly used in behavioral analysis leading to the emphasized differences between experimental groups. We believe that this approach, due to its universality, robustness and clear physical interpretation, is a perspective tool for the analysis of animal behaviour complexity under various experimental and natural conditions in different biomedical applications.
Biography: KAPLUN DMITRII I. PhD (2009), Associate Professor (2015), lead researcher at Saint Petersburg Electrotechnical University “LETI” (Saint Petersburg, Russia). In 2009 he defended his PhD thesis in digital signal processing at Saint Petersburg Electrotechnical University “LETI”. The current research and academic work are related with digital signal and image processing, embedded and reconfigurable systems, computer vision and machine learning. D. Kaplun regularly takes part in various interdisciplinary projects related to the use of computer vision and machine learning for biomedical data processing. The most substantial results are in the fields of digital signal and image processing, embedded systems and machine learning. Author of more than 100 papers in journals, including leading journals, and conference proceedings. He is an Associate/Guest Editor/Editorial Board Member such journals as Frontiers in Neuroinformatics, Industrial Artificial Intelligence, Scientific Reports.