Keynotes
Bio
Prof. Ammar Muthanna is a Professor at Saint Petersburg State University of Telecommunications and a Senior Member of IEEE. He received his B.Sc., M.Sc., Ph.D. in Computer Sciences/Telecommunication Systems, and Doctor of Sciences (Dr.Sc.) degrees from Saint Petersburg State University of Telecommunications. He served as a Postdoctoral Researcher from 2017 to 2019 and has directed the Scientific Center for Future Wireless Networks Modeling at RUDN University since 2020. His international experience includes participation in the Erasmus Student Program at the University of Ljubljana and serving as a Visiting Researcher at Tampere University, Finland. He is an IEEE Senior Member and ACM Professional Member and has contributed extensively to international conferences and scientific journals as a Chair, Co-Chair, and editor. He is actively involved in the International Telecommunication Union (ITU), including ITU-T Study Groups 11, 13, and 16, and serves as Chair of SG13RG-EECAT. He has also contributed to the ITU AI-5G Challenge as an expert and organizer. His research interests include wireless communications, 5G/6G cellular systems, artificial intelligence, telepresence applications, edge/fog computing, and software-defined networking.
Abstract
How can we create technologies to help us reflect on and potentially change our behavior, as well as improve our health and overall wellbeing both at work and at home? In this talk, I will briefly describe the last several years of work our research team has been doing in this area. We have developed wearable technology to help families manage tense situations with their children, mobile phone-based applications for handling stress and depression, as well as automatic stress sensing systems plus interventions to help users just in time. The overarching goal in all of this research is to develop intelligent systems that work with and adapt to the user so that they can maximize their personal health goals and improve their wellbeing.
Dr. Ammar Muthanna
Professor, Department of Communication Networks and Data Transmission, Saint Petersburg State University of Telecommunications
Dr. S. Dhanasekaran
Associate Professor, Department of Electronics and Communication Engineering Sri Eshwar Engineering College, Coimbatore, India.
Bio
Dr. S. Dhanasekaran is currently working as Associate Professor in the Department of Electronics and Communication Engineering, at Sri Eshwar College of Engineering, Coimbatore. He has around 16 years of teaching experience in various institutes. He received his BE degree in Electronics and Communication Engineering in 2008 from Sri Balaji Chock lingam Engineering College, Arani. He completed his ME in Communication Systems in 2010 from PSG College of Technology, Coimbatore. He completed his PhD in the year 2022 from Anna University, Chennai in the area of Communication systems. His areas of Expertise include Wireless Communication, Deep Learning, Biomedical Signal Processing and Wireless Networks and Artificial Intelligence. He has received many awards and recognition that includes top 2% scientist in the year of 2026 by Stanford University and Elsevier, best Researcher award 2025 and Top 5% scientist 2025 at institute level for his innovative, collaborative and ground-breaking research activities. He is a life time member of IEEE and ISTE. He has published more than 30 articles in reputed SCI indexed and scopus journals with a good citation score.
Abstract
The last years saw a steep increase in the number of wearable sensors and systems, mhealth and uhealth apps both in the clinical settings and in everyday life. Further large amounts of data both in the clinical settings (imaging, biochemical, medication, electronic health records, -omics), in the community (behavioral, social media, mental state, genetic tests, wearable driven bio-parameters and biosignals) as well as environmental stressors and data (air quality, water pollution etc.) have been produced, and made available to the scientific and medical community, powering the new AI/DL/ML based analytics for the identification of new digital biomarkers leading to new diagnostic pathways, updated clinical and treatment guidelines, and a better and more intuitive interaction medium between the citizen and the health care system.
Thus, the concept of connected and translational health has started evolving steadily, connecting pervasive health systems, using new predictive models, new approaches in biological systems modeling and simulation, as well as fusing data and information from different pipelines for more efficient diagnosis and disease management.
In this talk, we will present the current state-of-the-art in personalized health care by presenting cases from COVID-19 and COPD patients using advanced wearable vests and new technology sensors including lung sound and EIT, new outcome prediction models in COVID-19 ICU patients fusing X-Rays, lung sounds, and ICU parameters transformed via AI/ML/DL pipelines, new approaches fusing environmental stressors with -omics analytics for chronic disease management, and finally new ML/AI-driven methodologies for predicting mental health diseases including suicidality, anxiety, and depression.
