Prof. Dr. Maria Pia Fanti IEEE Fellow Polytechnic University of Bari, Italy |
Biography:Maria Pia Fanti is full professor of System and Control Engineering at the Department of Electrical and Information Engineering of the Polytechnic of Bari (Italy). She received the Laurea degree in Electronic Engineering from the University of Pisa (Italy), in 1983. She was a visiting researcher at the Rensselaer Polytechnic Institute of Troy, New York, in 1999. Since 1983 she has been with the Department of Electrical and Electronic Engineering of the Polytechnic of Bari (Italy), where she was Assistant Professor from 1990 till 1998 and Associate Professor from 1990 till April 2012. Maria Pia Fanti is IEEE fellow for contributions to modeling and control of discrete event systems. Her research interests include Discrete event systems, Petri net, consensus algorithms, networked and control systems, management and modeling of logistic systems, automated manufacturing systems, automatic guided vehicle systems, traffic networks, and healthcare systems. Maria Pia Fanti is author of 2 books and 280+ papers including 85 journal papers including, 11 book chapters and many conference proceeding papers. Title: Artificial Intelligence Approaches for Cooperative, Connected and Automated Mobility Abstract: Cooperative, Connected and Automated Mobility (CCAM) is expected to reshape the way of travelling and moving in Europe and around the world. By the CCAM, the automated vehicles have to be integrated into the mobility and transport system by designing and implementing infrastructures, new services, platforms, cooperation and governance models. The smart traffic management and the deployment of CCAM innovative technologies and services is increasing efficiency and reducing congestion. This talk presents the approaches that are used to accelerate the implementation of innovative CCAM technologies and systems. The proposed methodology is based on the implementation and integration of enhanced Physical, Digital and Operational Infrastructures to enrich CCAM services and increase safety and traffic efficiency. The proposed actions will help to develop new mobility concepts, leading to healthier, safer, more accessible, sustainable, cost-effective and demand-responsive transport. Some solutions will be presented using Artificial Intelligence techniques and optimization strategies applied for a full integration of CCAVs in the real traffic for transportation. |
Prof. Shahid Hussain Jiangsu University, China |
Biography:Prof. Dr. Shahid Hussain by national: Pakistan, is currently working as Professor (Full) at School of Materials Science and Engineering, Jiangsu University, China. He completed his PhD degree in Chongqing University, 2015, after started Post-Doctoral research fellowship 2015-2017. He joined Jiangsu University as Associate Professor in July 2017 and based on his outstanding achievements and experiences, he was promoted as Full Professor in July, 2020, and was approved by the state, Govt of China. He is life time memebr for FIAAM (Fellow of International Association of Advanced Materials) since DEC 2024. Research foundation and working conditions At present, Dr. Shahid Hussain and the project team have executed a lot of work in the field of metal oxide, sulfides, MXenes and metal-organic based gas sensors, supercapacitors and LiS Batteries. He has published high quality research articles, and also has a wealth of experience, which laid a solid foundation for the project related research. Dr Shahid Hussain has excellent working experience on gas sensors and has been working on sensor device fabrication since 2011. He has published more than 330+ SCI indexed journal research articles with h-Index is 56 and i-10 index 215 in Google Scholar with 11000 citations (~ January 2025) and 11 book Chapters including Nano Energy, Chemical Engineering Journal, Journal of Hazardous Materials, Applied Materials & Interfaces, Journal of Materials Chemistry A, Sensors and Actuators B, Chemosphere, Inorganic Chemistry, Journal of Cleaner Production, Applied Surface Science, Electrochemica Acta, Materials Science and Engineering, etc and has impact factor ~ IF>2550. He is also working as an Editor for 17 SCI indexed journals (Elsevier, Springer, Frontiers, Hindawi, American Scientific Publishers and MDPI). Moreover, he has appeared as Keynote speaker in 28 international conferences and as Conference Chair in 3 international conferences. He also has an honorserving as a Coordinator for Materials Science Society of Pakistan (MSSP). He is also member for International Think Tank Institute of Peace and Development (INSPAD-UNO) since August 2020. Title: Low-Temperature Gas-Sensing System Based on Metal–Organic Framework-Derived In2O3 Structures and Advanced Machine Learning Techniques Abstract: In the bustling metropolis of tomorrow, where pollution levels are a constant concern, a team of innovative researchers embarked on a quest to revolutionize air quality monitoring. In pursuit of this objective, this study embarked on the synthesis of indium oxide materials via a straightforward solvothermal method purposely for gas detection. Through meticulous analysis of their gas-sensing capabilities, a remarkable discovery came to light. Moreover, machine learning techniques were utilized to predict the response characteristics of the sensing materials to various environmental conditions, concentrations of target gases, and operational parameters. This predictive capability can guide the design of more efficient and robust gas sensors, ultimately contributing to improved safety and environmental monitoring. As the demand for efficient, portable, and eco-friendly electronics continues to grow, these findings contribute to the development of sustainable and high-performance materials that can meet the needs of modern technology. |
Prof. Yaguan Qian Zhejiang University of Science and Technology, China |
Biography:Yaguan Qian, Professor, Master's and Doctoral Supervisor. He received his Ph.D. in Computer Science from Zhejiang University in 2014. Currently, he serves as the Vice Dean of the Big Data College at Zhejiang University of Science and Technology, the Head of the Big Data Discipline, the Director of the Hikvision Edge Intelligent Security Joint Laboratory, and the Leader of the Big Data and Artificial Intelligence Security Team. He is a member of the Big Data Security and Privacy Computing Committee of the Chinese Information Processing Society of China and a specially appointed researcher at the Provincial Key Laboratory of Intelligent IoT Network and Data Security. He acts as a review expert for the Wu Wenjun Artificial Intelligence Science and Technology Award, the Executive Chair of the International Symposium on Sensor Technology and Control (ISSTC), a program committee member of the top artificial intelligence conference AAAI, and a reviewer for several CCF-recommended journals and top international conferences. His main research directions include AI model security, multimodal deep learning, and optimization algorithms in machine learning. Over the past five years, he has published more than 30 papers in top conferences such as ICCV, ECV, and AAAI, and in important CCF-recommended journals such as IEEE TIFS, IEEE TNNLS, ACM TOPS, ACM TKDD, Journal of Software, Journal of Computer Research and Development, and Acta Electronica Sinica. He has been granted 7 invention patents and has led several national and provincial projects, including an innovation special zone project of the Central Military Commission's Science and Technology Committee (national level), a general project of the National Natural Science Foundation of China, a key project of the Zhejiang Provincial Natural Science Foundation, and a general project of the Zhejiang Provincial Natural Science Foundation. He has diligently guided and trained 18 postgraduate students, 4 of whom have continued to pursue doctoral studies. Title: Visual Pollution: Misleading Attacks Targeting Artificial Intelligence Systems Abstract: With the rapid advancement of deep learning technologies, artificial intelligence systems have become increasingly prevalent, ranging from license plate recognition and facial recognition to autonomous driving. However, misleading attacks targeting AI systems pose a real-world threat. By introducing artificial interference, these attacks can cause AI systems to make incorrect judgments, leading to erroneous decisions and potentially severe accidents. This presentation will introduce the principles and research progress of such attacks, as well as the latest research achievements from our team. |