Keynotes

Keynote Speakers

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Invited Speakers

Prof. Daniel Molina-Pérez, ESCOM, National Polytechnic Institute, Mexico

Speech Title: Noise: The Nightmare of Contrast Enhancement — Classical Techniques, Nature-Inspired Optimization, and Learning-Based Approaches

Abstract: Enhancing medical images is not only about improving contrast, but also about dealing with noise. In practice, increasing contrast often comes at the cost of amplifying noise, which can directly affect diagnostic reliability. Early methods improve contrast without explicitly accounting for this issue, while sequential approaches attempt to reduce noise and enhance contrast in separate steps. More advanced techniques introduce local, noise-aware adjustments during the enhancement process, achieving better results, although still relying on parameter tuning and limited transformation models.
Optimization-based approaches, particularly those inspired by nature, provide a way to explicitly control this trade-off, while deep learning methods learn it from data, often achieving strong performance but raising concerns such as limited controllability, hallucinations, and bias. Despite significant progress, balancing contrast and noise in a reliable and interpretable way remains a challenging problem.

Biography: DANIEL MOLINA-PÉREZ received the Ph.D. degree in Robotic and Mechatronic Systems from the Centro de Innovación y Desarrollo Tecnológico en Cómputo (CIDETEC), Instituto Politécnico Nacional, Mexico City, in 2024. He is currently a Professor-Researcher with the Escuela Superior de Cómputo (ESCOM), Instituto Politécnico Nacional. He is a member of the Artificial Intelligence and Data Science Network of the Instituto Politécnico Nacional. His research interests include evolutionary computation, multi-objective optimization, mixed-integer nonlinear programming, image enhancement, and hybrid optimization methods for solving complex engineering problems. Dr. Molina-Pérez has authored and co-authored multiple articles in indexed journals, including Swarm and Evolutionary Computation, PeerJ Computer Science, and Mathematical and Computational Applications, and has presented his work at leading international conferences such as the IEEE Congress on Evolutionary Computation (CEC). He received the Best Paper Award at the Mexican International Conference on Artificial Intelligence in 2022 and was recognized with the Outstanding Academic Performance Award at the doctoral level, reflecting his academic excellence and research contributions.

 

Prof. Anuranjan Misra, Noida International University, India

Speech Title: AI-Driven Digital Twin for Predictive Semiconductor Design using Hardware-in-the-Loop Learning

Abstract: The semiconductor systems we use today are very complicated which makes it really hard to predict how power they will use how well they will work and how long they will last when we are still designing them. The old tools we use to design these systems rely much on simple models that do not take into account how all the different parts of the system interact with each other and how they will work in the real world.
This is about a way of designing semiconductor systems using a digital twin that is controlled by artificial intelligence. This digital twin uses a kind of computer chip called an FPGA to test the system in real time and get data on how much power it uses how well it works and how the different parts of the system are working together. We use this data to train machine learning models that can learn how the design of the system affects how it works so we can predict what the system will be like before we even build it.
The digital twin is always getting better because it uses a loop where the artificial intelligence makes predictions we test them against data from the hardware and then the artificial intelligence makes new predictions based on what it has learned. This system uses a few kinds of machine learning including supervised learning to make predictions Bayesian methods to figure out how sure we are of our predictions and reinforcement learning to make the design better. By including rules about how the physical world works the system makes sure its predictions are realistic and stable.

Biography: Dr Anuranjan Misra is Professor & Dean at Greater Noida Institute of Technology(GNIOT), Greater Noida. He holds a Ph.D. in Computer Science & Engineering and possesses three Master’s degrees in Computer Science and Engineering, Law, and Mathematics, demonstrating his multidisciplinary approach to education and research. He is EX -Chairman of Computer society of India, Ghaziabad Chapter. He is Head MSME Business Incubation-GNIOT Centre(an Initiative of Ministry of MSME, Govt. of India), Head MSME Design Centre-GNIOT Center(an Initiative of Ministry of MSME, Govt. of India), .President Institution's Innovation Council -GNIOT Centre(an Initiative of Ministry of Education, Govt. of India), Chair Unnat Bharat Abhiyan- GNIOT (an Initiative of Ministry of Education, Govt. of India), and Chair Smart Campus Cloud Network- GNIOT (an Initiative of TERRI & AICTE , New Delhi) . He has 25 years of rich experience in academics, research and industry. He has delivered more than 25 expert talks around the world. He has more than 150 publications. He has handled research funding of 6+ crores. He has Senior Member of ACM, IEEE, CSI, IACSIT, IACNG, IRACST, CSTA, ISOC, ICE, AEE, IFETS, ISMCDM, SIGSE. His research is in AI, Big Data, Cloud Computing, Data Science, and Algorithms. He is passionate about quality of higher education in India.