Conference Dates
October 12-14, 2026
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Conference Venue
Berlin, Germany
October 12-14, 2026
Berlin, Germany
Welcome to the Artificial Intelligence, Machine Learning & Data Science Conference, organized by Innovatex Conferences and scheduled for 12–14 October 2026 in Berlin, Germany. Guided by the theme “AI, Machine Learning & Data Science: Driving the Next Wave of Digital Transformation,” this event brings together global experts, researchers, and industry leaders to share insights and explore emerging innovations. The conference offers nearly thirty focused scientific sessions covering the latest developments in AI, ML, deep learning, automation, and data-driven technologies. Participants will engage in keynote lectures, interactive presentations, and dedicated poster sessions showcasing promising young researchers. Set in the vibrant tech hub of Berlin, the event provides an excellent platform for learning, networking, and discovering the advancements transforming the future of intelligent systems.
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An early outline of the planned sessions and activities, subject to final updates before the conference.
A concise document providing key information about the conference, including its theme, schedule, speakers, and participation details.
Discover the diverse scientific sessions designed to share cutting-edge research and innovations.
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Slovakia
Slovakia
university of zilina
Malaysia
Malaysia
Sunway University
M. A. Hannan is a Distinguished Professor of Intelligent Systems in Energy and Power Applications at Sunway University, Malaysia. He is a Coordinating Lead Author (CLA) for the UN IPCC WG III AR7 and serves as Adjunct Professor at Korea University and Visiting Professor at Universiti Tenaga Nasional. His research focuses on intelligent energy systems, renewables integration, energy storage, smart grids, energy management, and power electronics. He is a Clarivate Highly Cited Researcher (2022 & 2023) in Engineering, with over 400 publications, 25,000+ Scopus citations (H-index 74) and 33,000+ Google Scholar citations (H-index 87). He has secured AUD 8.2M in research funding and supervised numerous postgraduate researchers. Prof. Hannan actively leads national and international research collaborations and serves in editorial and professional roles within IEEE and other organizations.
USA
USA
Pittsburg State University
Ram Gupta is an Associate Vice President for Research and Support and a Professor of Chemistry at Pittsburg State University. Stanford University has recently named Gupta as being among the top 2% of research scientists worldwide. Before joining Pittsburg State University, he worked as an Assistant Research Professor at Missouri State University, Springfield, MO then as a Senior Research Scientist at North Carolina A&T State University, Greensboro, NC. Dr. Gupta’s research spans a range of subjects critical to current and future societal needs including: semiconducting materials & devices, biopolymers, flame-retardant polymers, green energy production & storage using nanostructured materials & conducting polymers, electrocatalysts, optoelectronics & photovoltaics devices, organic-inorganic heterojunctions for sensors, nanomagnetism, biocompatible nanofibers for tissue regeneration, scaffold & antibacterial applications, and bio-degradable metallic implants. Dr. Gupta has mentored 10 PhD./Postdoc scholars, 76 MS students, and 58 undergraduate/high school students. Dr. Gupta has published over 380 peer-reviewed journal articles (13,400+ citations, 63 h-index, 280 i10-index), made over 500 national/international/regional presentations, chaired/organized many sessions at national/international meetings, wrote several book chapters (120+), worked as Editor for many books (50+) for American Chemical Society, CRC, Springer, Elsevier, etc. and received several million dollars for research and educational activities from external agencies. He is also serving as Editor, Associate Editor, Guest Editor, and editorial board member for various journals.
India
India
Vishwakarma Institute of Technology
Parikshit is a senior member IEEE and is Professor, Dean Academics at Vishwakarma Institute of Technology, Pune, India. Prior to this, he worked as a Dean - Research and Development at VIT, Dean - Research and Development and Head - Department of Artificial Intelligence and Data Science at Vishwakarma Institute of Information Technology, Pune, India and Professor, Head, Department of Computer Engineering at Sinhgad Institutes. He completed his Ph. D from Aalborg University, Denmark in 2013 and completed his Post Doctoral Research at CMI, Copenhagen, Denmark. He is also Post Doctoral Fellow at university of maría Auxiliadora. He has 25 years of teaching and research experience. He is an ex-member of the Board of Studies in Computer Engineering, Ex-Chairman Information Technology, Savitribai Phule Pune University, Member – BoS and academic council member at more than 30 Universities and autonomous colleges across India. He has 117 patents, 440+ research publications (Google Scholar citations-4580 plus, H index-30 and Scopus Citations are 2400 plus with H index -23, Web of Science citations are 625 with H index - 12) and authored/edited 82 books with Springer, CRC Press, Cambridge University Press, etc. He is editor in chief for Research Journal of Computer Systems and Engineering (RJCSE), Associate Editor for IGI Global - Journal of Affective Computing and Human Interfaces (JACHI), member-Editorial Review Board for IGI Global – International Journal of Ambient Computing and Intelligence and reviewer for various transactions, journals and conferences of the repute. His research interests are Machine Learning, Data Science, Algorithms, Internet of Things, Identity Management and Security. He is guiding 8 PhD students in IoT and machine learning and EIGHT students have successfully defended their PhD under his supervision from SPPU and Three students completed Postdoc under his mentorship from NTU, Taiwan. He is also the recipient of “Best Faculty Award” by Sinhgad Institutes and Cognizant Technologies Solutions, International Level S4DS distinguished Researcher of the Year 2023 and State Level Meritorious Teacher Award and Distinguished Research Guide Award at IEEE ICTBIG 2024, organised by Symbiosis University of Applied Sciences (SUAS), Indore. He has delivered 400 plus lectures at national and international level. His book on Design and Analysis of algorithms is referred as Textbook in IIITs and NITs and his book on Data Analysis on Pandemic by CRC press has received two international awards in 2020. His edited title, ‘Data Science: Techniques and Intelligent Applications’, has been awarded the prestigious Choice Outstanding Academic Titles Award for 2024. He is also Certified ISO 27001:2022 Lead Auditor. He has also worked as an Invited Guest faculty at several international universities like UMA, Lima Peru in South America, National Taipei University Taiwan etc. He visited 24 countries till date for various academic and research collaborations.
Portugal
Portugal
Faculty of Engineering - University of Porto
João Manuel R. S. Tavares graduated in Mechanical Engineering from the Universidade do Porto, Portugal, in 1992. He also obtained his M.Sc. degree and Ph.D. degree in Electrical and Computer Engineering from the same university in 1995 and 2001, respectively, and received his Habilitation in Mechanical Engineering in 2015. He is a senior researcher at the Institute of Science and Innovation in Mechanical and Industrial Engineering (INEGI) and a Full Professor at the Department of Mechanical Engineering (DEMec) of the Faculdade de Engenharia da Universidade do Porto (FEUP). Since June 2023, he has been serving as the Head of DEMec. João Tavares has made significant contributions to his field, serving as co-editor of over 90 books and co-authoring more than 50 book chapters and 650 articles in international and national journals and conferences. He also holds 3 international and 3 national patents. His editorial roles are extensive; he is a committee member for numerous international and national journals and conferences, co-founder and co-editor of the "Lecture Notes in Computational Vision and Biomechanics" series published by Springer, founder and Editor-in-Chief of "Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization" published by Taylor & Francis, and Editor-in-Chief of "Computer Methods in Biomechanics and Biomedical Engineering," also published by Taylor & Francis. Furthermore, he is a co-founder and co-chair of several international conference series, including the International Symposium on Computational Modeling of Objects Presented in Images, the ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing, the International Conference on Computational and Experimental Biomedical Sciences, and the International Conference on Biodental Engineering. João Tavares has also (co-)supervised numerous MSc and PhD theses and supervised several post-doctoral projects, participating in many scientific projects as both a researcher and scientific coordinator. His research focuses on computational vision, medical imaging, biomechanics, biomedical engineering, and new product development.
Taiwan
Taiwan
National Cheng Kung University
Mu-Yen Chen is a Distinguished Professor in the Department of Engineering Science at National Cheng Kung University (NCKU), Taiwan. Recognized in the World Top 2% Scientists (Stanford List), he is an influential researcher in AI, machine learning, soft computing, data mining, and intelligent systems. He has published over 200 scientific papers and serves as Associate Editor for Computers in Human Behavior – Artificial Humans, IEEE Access, and Applied Soft Computing, as well as Department Editor for IEEE Transactions on Engineering Management. Prof. Chen is widely known for his contributions to intelligent computing and continues to play a key role in advancing research and international collaboration.
UK
UK
Sheffield University Management School
Yichuan Wang is a Senior Lecturer/Associate Professor in Digital Marketing at Sheffield University Management School. Prior to joining SUMS, he worked for Newcastle University Business School and Auburn University (USA). He is a multi-disciplinary researcher, working mostly across marketing, information systems and healthcare. Yichuan is the author or co-author of over 100 publications, attracting in excess of 6,000 citations (Google Scholar). His research has appeared in ABS 4-indexed journals including British Journal of Management, Social Science & Medicine, International Journal of Operations & Production Management.
Oman
Oman
Sultan Qaboos University
Saud M. Al-Jufaili is an Omani fisheries scientist and academic leader at Sultan Qaboos University (SQU) in Muscat, Oman. He serves as Associate Professor and Head of the Department of Marine Science and Fisheries in the College of Agricultural and Marine Sciences. His research focuses on the assessment, management, and biology of Omani fisheries, including studies of freshwater fishes, coastal sardine fisheries, and postharvest handling practices. Dr. Al-Jufaili has more than 15 years of teaching and research experience and has published numerous scientific articles on fisheries science. He holds a Ph.D. in Fisheries Science from Oregon State University, USA, and his work contributes to both academic knowledge and sustainable fisheries practices in Oman.
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Meet the distinguished speakers who will share their expertise and insights during the conference.
Malaysia
Malaysia
University of Technology Malaysia
Title
Instabilities A Hybrid CFD-Neural Network Framework for The Early Prediction of Thermo-Acoustic in Hydrogen-Fueled Gas Turbine Combustors
Abstract
The transition to low-carbon energy systems has intensified interest in hydrogen as a carbon-free fuel for gas turbine power generation. However, hydrogen’s high flame speed, diffusivity, and reactivity significantly increase its susceptibility to thermo-acoustic instabilities (TAIs), which result from the coupling between unsteady heat release and acoustic pressure oscillations. These instabilities can cause excessive vibrations, structural damage, efficiency losses, and operational failure. Early detection remains challenging because high-fidelity Computational Fluid Dynamics (CFD) simulations are computationally expensive and unsuitable for real-time applications. This study presents a hybrid Computational Fluid Dynamics–Neural Network (CFD–NN) framework for early prediction of longitudinal thermo-acoustic instabilities in hydrogen-fueled combustors. A two-dimensional high-fidelity CFD model is developed to simulate unsteady hydrogen combustion. Time-resolved pressure and heat-release data are collected from six monitoring probes, and the Local Rayleigh Index (LRI) is used to identify instability-driving regions. Results show strong spatial variation, with one probe exhibiting dominant instability while others demonstrate damping or transitional behavior. CFD-derived features are used to train a feedforward neural network for early instability prediction. The model achieves excellent accuracy (R² = 0.9998) with significantly reduced computation time, delivering predictions hundreds of times faster than CFD. The proposed framework enables real-time monitoring and intelligent combustion control for hydrogen gas turbines.
Brazil
Brazil
São Carlos Federal University
Title: Estimating H I Mass Fraction in Galaxies with Bayesian Neural Networks
Abstract
Neutral atomic hydrogen (H I) regulates galaxy growth and quenching, but direct 21 cm measurements remain observationally expensive and affected by selection biases. We develop Bayesian neural networks (BNNs)—a type of neural model that returns both a prediction and an associated uncertainty—to infer the H I mass, log10(MHI), from widely available optical properties (e.g., stellar mass, apparent magnitudes, and diagnostic colors) and simple structural parameters. For continuity with the photometric gas fraction (PGF) literature, we also report the gas-to-stellar-mass ratio, log10(G/S), where explicitly noted. Our dataset is a reproducible cross-match of SDSS DR12, the MPA–JHU value-added catalogs, and the 100% ALFALFA release, resulting in 31,501 galaxies after quality controls. To ensure fair evaluation, we adopt fixed train/validation/test partitions and an additional sky-holdout region to probe domain shift, i.e., how well the model extrapolates to sky regions that were not used for training. We also audit features to avoid information leakage and benchmark the BNNs against deterministic models, including a feed-forward neural network baseline and gradient-boosted trees (GBTs, a standard tree-based ensemble method in machine learning). Performance is assessed using mean absolute error (MAE), root-mean-square error (RMSE), and probabilistic diagnostics such as the negative log-likelihood (NLL, a loss that rewards models that assign high probability to the observed H I masses), reliability diagrams (plots comparing predicted probabilities to observed frequencies), and empirical 68%/95% coverage. The Bayesian models achieve point accuracy comparable to the deterministic baselines while additionally providing calibrated prediction intervals that adapt to stellar mass, surface density, and color. This enables galaxy-by-galaxy uncertainty estimation and prioritization for 21 cm follow-up that explicitly accounts for predicted uncertainties (“risk-aware” target selection). Overall, the results demonstrate that uncertainty-aware machine-learning methods offer a scalable and reproducible route to inferring galactic H I content from widely available optical data.
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