| Assoc. Prof. Ata Jahangir MoshayediRARL_ Jiangix University of Scince and Technology, China Jiangxi University of Science and Technology, China (IEEE Senior Member) Dr. Ata Jahangir Moshayedi is an Associate Professor at Jiangxi University of Science and Technology. He holds a Ph.D. in Electronic Science from Savitribai Phule Pune University (formerly the University of Pune), India. He is a Senior Member of IEEE, a Professional Member of ACM, and a Life Member of the Instrument Society of India.Dr. Moshayedi has published over 100 research papers, authored 4 books, contributed to 4 book chapters, and holds 2 patents and 16 copyrights. He actively serves as a technical program committee member and session chair for numerous international conferences. His current research focuses on robotics, particularly the development of autonomous guided vehicles (AGVs) for applications such as smart farming, food delivery and elderly care. Speech Title: Pharyngeal Phonetics as Breaking Free from Spirometry Abstract: Traditional spirometry, while essential for diagnosing respiratory diseases, is constrained by its invasiveness and dependence on patient exertion, rendering it unsuitable for many high-risk individuals. This work presents a paradigm-shifting alternative: a non-contact lung function assessment that leverages pharyngeal phonetics and machine learning. By analyzing respiratory acoustics without forceful exhalation, our method eliminates key risks and discomforts. It unlocks new possibilities for telemedicine and decentralized monitoring, allows for high-frequency testing, and employs advanced analytics to detect incipient lung impairment. Ultimately, this technology promises to democratize respiratory care by offering a safer, more scalable, and readily deployable solution through standard consumer devices. |
| Assoc. Prof. Pavel LoskotZhejiang University - University of Illinois at Urbana-Champaign Institute, China (IEEE Senior Member) Pavel Loskot has nearly 30 years of experience in design, analysis, implementation and deployment of telecommunication systems through numerous academic and industrial collaborative projects and consultancy contracts. Expert level knowledge of digital and statistical signal processing, algorithms and methods. Solid background in applied probability and statistics. Avid Linux programmer and user since 1996. In 2014/2015, as a Visiting Researcher at CSRC of the Chinese Academy of Engineering Physics started working on computational molecular biology. In 1999-2001, Research Scientist and Project Manager at CWC, Oulu, Finland. A Fellow of the Higher Education Academy of the UK, and the Recognised Research Supervisor of the UK Council for Graduate Education. A Senior Member of the IEEE, since 2013. Elected IARIA Fellow in December 2025. Speech Title: Adaptive Filtering Strategies in Signal Processing Abstract: Deep learning methods can offer a robust performance in filtering random processes, since they are trained on a very large corpus of data. The downside is that they require long training, and also the inferences are computationally expensive. An alternative is to resort to more traditional model-based methods that are numerically much cheaper, however, they are only effective for a given class of random processes. Generally, the key consideration in adaptive filter designs is what prior knowledge is available, and what knowledge can and should be learned from data. This leads to a number of different supervised and unsupervised filtering methods. In this talk, I will discuss different strategies for linear and non-linear adaptive filtering of stochastic processes that has been proposed many decades ago, so they can be considered to be the predecessors of more recent machine learning methods. In addition to classical Kalman and particle filters, I will explain the supervised and unsupervised filtering methods, and also mention less common filters such as information filters, Golay-Sawitz filters and other sliding-window regression filters. |
 | Assoc. Prof. Azhar Imran MudassirBeijing University of Technology, China (IEEE Senior Member) Dr. Azhar Imran is an Associate Professor at the Department of Computer Science, Beijing University of Technology, China, specializing in Artificial Intelligence, Data Science, and Machine Learning. With over 13 years of academic experience, he has made significant contributions in Natural Language Processing (NLP), Generative Adversarial Networks (GANs), Computer Vision, and Cyber Intelligence. Dr. Imran has published 85 research articles and delivered keynote speeches at prestigious conferences, including ICBDDM-24, ICCTEC, CCET25, CVIT-24, ICDSP24, and CVIT-23. He is a Senior Member of IEEE and serves on the editorial boards of multiple high-impact journals. Dr. Imran has received numerous awards, including the Outstanding Graduate Award and Best Researcher Award from Beijing University of Technology, as well as the Embassy Honored Award from the Pakistan Embassy in Beijing. His work continues to bridge the gap between academic research and real-world AI applications. Speech Title: From Data to Decision-Making: Transforming Information into Impact Abstract: In today’s data-rich environment, the availability of information does not automatically lead to effective decision-making. This keynote explores the transformation from data to decision-making, focusing on how raw, heterogeneous, and large-scale data can be converted into meaningful insights that generate real-world impact. The talk presents a high-level view of the data-to-decision pipeline, encompassing data acquisition, analytics, learning, and intelligent reasoning, while highlighting the role of artificial intelligence, machine learning, and data-driven models in enabling informed and timely decisions. Beyond technical advances, it addresses critical challenges such as data quality, uncertainty, interpretability, and the integration of human expertise with automated systems. Through selected applications and emerging research trends, the keynote emphasizes a shift from prediction-centric approaches toward explainable, responsible, and impact-oriented decision-making frameworks. The talk concludes by outlining future research directions and opportunities for designing intelligent systems that are robust, trustworthy, and aligned with societal and organizational needs. |
