The International Conference on Recent Advancements in Computing in AI, IoT and Computer Engineering Technology (CICET 2026)
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Keynotes
Speaker I: Dr. Yeon Lee, Inha University, Incheon, South Korea
Title: From Intelligent Service Placement to Privacy-Aware AIoT: Emerging Strategies for Secure and Scalable Edge Intelligence
Abstract: As Artificial Intelligence of Things (AIoT) continues to integrate deep learning into distributed cloud-edge environments, the balance between computational efficiency and data privacy becomes increasingly critical. In this talk, we explore two converging research trajectories that reflect this dual challenge: service placement strategies for High-Performance Computing (HPC) cloud environments, and privacy-preserving deep learning for sensitive domains such as medical imaging. First, we discuss a fuzzy-based resource placement approach that dynamically allocates services in real time, optimizing for cost, latency, and performance in heterogeneous HPC cloud systems. This approach highlights the role of multi-criteria decision-making and feedback learning in managing data-intensive applications. We then transition to the domain of privacy in AIoT systems, focusing on MedErase, a lesion-aware federated unlearning framework tailored for privacy-preserving medical imaging. MedErase demonstrates how selective gradient masking and feature-level knowledge distillation can enable compliance with “rightto-be-forgotten” regulations such as GDPR, while preserving diagnostic utility and minimizing retraining overhead. By connecting these two lines of research, the talk offers a unified perspective on how AIoT systems can be made both intelligent and privacy-aware. We also provide an overview of current trends in federated unlearning, lightweight AI models at the edge, and legal compliance in data-driven systems. The session concludes with future research directions for integrating context-aware service management with secure, federated learning in scalable AIoT environments.
Biography: Dr. Yeon Lee received her B.S. degree from the School of Computer Science and Engineering at Chongqing University of Posts and Telecommunications, Chongqing, China, and her M.S. and Ph.D. degrees in Computer Science and Information Engineering from Inha University, Incheon, South Korea, completing her Ph.D. in 2017.
She is currently an Assistant Professor in the Department of Computer Science and Information Engineering at Inha University. Her research interests include indoor location-based services, personalized point-of-interest (POI) recommendation systems, geo-sensor data processing, data stream clustering, and spatial data management.
Dr. Lee has made significant contributions to the development of personalized indoor POI recommendation techniques and real-time geo-spatial data stream management systems. Her recent work focuses on enhancing the accuracy and scalability of location-aware services using innovative data analytics and sensor driven models.
In addition to her academic and research achievements, Dr. Lee actively contributes to the academic community. She serves as an editorial board member for two international journals: Human-centric Computing and Information Sciences (HCIS) and Journal of Information Processing Systems (JIPS). She also holds key leadership roles as a board member of the Korea Society for Next-Generation Computing (KSNGC), the Korea Information Processing Society (KIPS), and the Korea Industry Association. Through her research, teaching, and editorial responsibilities, Dr. Lee continues to play a pivotal role in advancing technologies for smart environments and intelligent computing systems. She is a sought-after speaker in the fields of indoor location computing and sensor-based data recommendation.
Speaker II: Prof. Meriem BENHADDI, Cadi Ayyad University, Faculty of Sciences and Technology, Marrakesh, Morocco
Title: Closing the Loop: How AI is Unleashing a New Wave of Process Innovation in the IoT World with Self-Optimizing Operations
Abstract: The Internet of Things promised a revolution of visibility. We installed sensors, connected assets, and built dazzling dashboards. But for many, this has created a new problem: data overload and analytical paralysis. We can see everything, but are we any smarter? It's time to move beyond dashboards and into the era of autonomous action. This talk will demonstrate how Artificial Intelligence is the catalytic force transforming IoT from a passive monitoring tool into an active engine for process innovation, shifting logistics from static to dynamic and from control to autonomy. We will explore how AI acts as the central nervous system for IoT, turning raw data streams into intelligent, self-optimizing workflows. The future lies not in watching processes, but in building processes that watch, learn, and improve themselves. In this talk, we will delve into how specific AI disciplines, from predictive and prescriptive analytics to generative AI and digital twins are creating a new class of self-optimizing systems..
Biography: Meriem Benhaddi holds a Dr. Eng. in software Engineering from ENSIAS school, Mohammed V University, Rabat, Morocco, and a Ph.D. in Computer Science from Cadi Ayyad University, Marrakesh, Morocco.
Dr. Meriem Benhaddi is currently an Associate Professor in the IT Department of Faculty of Sciences and Technology, Cadi Ayyad University of Marrakech. Her current work focuses on Machine Learning/Deep Learning algorithms and their applications in various fields such as healthcare and social sciences. She has dozens of publications in indexed international journals and conferences. She has also achieved many certifications including IBM Big Data, Deep Learning from MILA & University of Montreal, as well as pedagogical trainings at Strasbourg University in France and International University of Andalusia in Spain. She has over 18 years of teaching experience, as well as supervising Masters and PhD students in AI and Big data among other fields.
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