Zaid Allal | Machine Learning | Best Researcher Award

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Dr. Zaid Allal | Machine Learning | Best Researcher Award

Dr. Zaid Allal | LISTIC (Laboratory of Computer Science, Systems, Information and Knowledge Processing) | Morocco

Zaid Allal is a Moroccan researcher and doctoral candidate in computer science specializing in artificial intelligence applications for energy systems. With a solid foundation in mathematics and computing, he has built his academic and professional journey through a blend of education, research, and teaching. His work integrates machine learning with renewable energy systems, focusing on optimizing hydrogen energy technologies. Currently affiliated with the University of Savoie Mont Blanc and the LISTIC Laboratory in France, his research explores intelligent solutions for predictive maintenance, fault detection, and system stability. His dedication lies in bridging sustainable energy with advanced AI technologies.

Publication Profile

Scopus

ORCID

Google Scholar

Education Background

Zaid Allal holds a Master’s degree in Advanced Information Technology and Computing Applications from the University of Franche-Comté in France, graduating with distinction and honors. He earned a Bachelor’s degree in Mathematics and IT Systems from Mohammed First University in Oujda. Before his higher education, he received his Baccalaureate in Physical Sciences and Chemistry with honors. Additionally, he completed a certified training in Mathematics Education, coordinated with the Moroccan Ministry of Education. His strong academic background in both theoretical and applied domains provides a firm base for his research in AI and renewable energy integration.

Professional Experience

Zaid has over seven years of experience in mathematics education under the Moroccan Ministry of Education. Transitioning into research, he engaged in machine learning projects focused on renewable energy systems and hydrogen technologies at the University of Franche-Comté. Currently, he is a Ph.D. researcher at the University of Savoie Mont Blanc and contributes to the LISTIC Laboratory. His projects span predictive analytics, power consumption forecasting, and anomaly detection in smart grids. His work integrates theoretical AI models with practical energy sector challenges, contributing to research publications, international conferences, and innovative academic-industrial collaborations.

Awards and Honors

Zaid Allal has consistently demonstrated academic excellence throughout his career, receiving distinction and honors during both his undergraduate and postgraduate studies. His Master’s program recognized his outstanding performance with academic distinction. In addition to his formal qualifications, he has participated in several high-impact training initiatives, including NASA Space Apps competitions and AI ambassador programs. These accolades reflect his commitment to excellence in education, innovation, and technological advancement, highlighting his dedication to exploring and applying cutting-edge artificial intelligence methods within the energy and environmental sectors.

Research Focus

Zaid’s research centers on applying machine learning and deep learning techniques to address challenges in renewable energy systems and the hydrogen value chain. He focuses on areas such as predictive maintenance, fault and anomaly detection, power forecasting, and system optimization. His expertise extends to smart grids, hydrogen storage systems, and photovoltaic energy solutions. He employs explainable AI and reinforcement learning to develop sustainable, efficient, and interpretable models. By combining theoretical AI approaches with real-world energy applications, he aims to contribute to the advancement of intelligent and sustainable energy infrastructures.

Top  Publications

Explainable AI of Tree-Based Algorithms for Fault Detection and Diagnosis in Grid-Connected PV Systems
Published Year: 2025
Citation: 14

Review on ML Applications in Hydrogen Energy Systems
Published Year: 2025
Citation: 11

Power Consumption Prediction in Warehouses Using Variational Autoencoders and Tree-Based Regression Models
Published Year: 2024
Citation: 9

Efficient Health Indicators for RUL Prediction of PEM Fuel Cells
Published Year: 2024
Citation: 7

Machine Learning Algorithms for Solar Irradiance Prediction: A Comparative Study
Published Year: 2024
Citation: 6

Conclusion

Zaid Allal exemplifies the fusion of academic excellence, professional dedication, and research-driven innovation. With a strong foundation in mathematics and computing, he has evolved into a researcher committed to applying artificial intelligence in solving pressing energy challenges. His work across renewable energy, hydrogen systems, and smart grid technologies positions him as a valuable contributor to the evolving energy-tech landscape. Through ongoing research, publication, and collaboration, he continues to push the boundaries of sustainable innovation, striving to create data-driven and explainable solutions for the future of energy management and system optimization.

Dr. Saikat Gochhait | Artificial Intelligence | Best Researcher Award

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Dr. Saikat Gochhait | Artificial Intelligence | Best Researcher Award

Assistant Professor, Symbiosis International (Deemed to be University), India

Dr. Saikat Gochhait is an accomplished Indian academic, researcher, and innovator, currently serving as an Assistant Professor at Symbiosis International Deemed University, Pune. With a strong background in management, information technology, and behavioral sciences, he also contributes as a Research Team Member at the Symbiosis Centre for Behavioral Sciences and Adjunct Faculty at the Neuroscience Research Institute, Samara State Medical University, Russia. He is a prolific inventor with several published patents and has been recognized for his contributions to interdisciplinary research in artificial intelligence, neuroscience, and optimization algorithms.

Publication Profile

🎓 Education Background

Dr. Gochhait earned his Doctor of Philosophy (Ph.D.) in Management from Sambalpur University in 2014 🧠, a Master’s in Business Management from the same university in 2009 📊, and a Master’s in Information Technology from Sikkim Manipal University in 2017 💻. His diverse academic training has laid a multidisciplinary foundation that supports his cross-functional research across business, IT, and neuroscience domains.

💼 Professional Experience

With over two decades of experience spanning academia and industry, Dr. Gochhait has held key roles such as Assistant Professor at ASBM University, Khalikote University, and HOD at Sambhram Institute of Technology. His industry experience includes strategic roles at IFGL Refractories Ltd. and Tata Krosaki Refractories Ltd. Currently, at Symbiosis International University, he mentors postgraduate and doctoral students, manages AI-centric research projects, and continues collaborative ventures with prestigious institutions including IIT Roorkee and international universities 🌏.

🏆 Awards and Honors

Dr. Gochhait has been honored as a Senior Member of IEEE in 2019 and recognized by the Alpha Network of the Federation of European Neuroscience Societies in 2024 🌟. His academic excellence has earned him international research fellowships from leading institutions, including the Natural Sciences and Engineering Research Council of Canada, Samara State Medical University (Russia), National Dong Hwa University (Taiwan), and the University of Deusto (Spain), with total grants exceeding USD 20,000 💰.

🔬 Research Focus

Dr. Gochhait’s research is rooted in artificial intelligence, behavioral science, energy prediction, bio-inspired optimization algorithms, and neuroscience-enhanced technology applications 🧬. He is a principal investigator of high-impact government-funded projects such as AI-based load forecasting for dispatch centers and BCI-integrated neurofeedback games. His innovations also extend to smart agriculture and transport systems, reflecting his dedication to societal improvement through technology 🤖🌱.

✅ Conclusion

Blending visionary academic pursuit with innovative problem-solving, Dr. Saikat Gochhait continues to drive global research collaborations, mentor emerging scholars, and contribute meaningful technological solutions to real-world challenges 📚🌍. His evolving body of work bridges disciplines, industries, and nations, making him a respected figure in AI, management, and neuroscience research.

📚 Top Publications

Pufferfish Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems
Biomimetics, 2024 – Indexed in Scopus/WoS
Cited by: 12 articles

Dollmaker Optimization Algorithm: A Novel Human-Inspired Optimizer for Solving Optimization Problems
International Journal of Intelligent Engineering and Systems, 2024 – Indexed in Scopus
Cited by: 9 articles

Addax Optimization Algorithm: A Novel Nature-Inspired Optimizer for Solving Engineering Applications
International Journal of Intelligent Engineering and Systems, 2024 – Indexed in Scopus
Cited by: 7 articles

Enhancing Household Energy Consumption Predictions Through Explainable AI Frameworks
IEEE Access, 2024 – Indexed in Scopus/WoS
Cited by: 15 articles

URL Shortener for Web Consumption: An Extensive and Impressive Security Algorithm
 Indonesian Journal of Electrical Engineering and Computer Science, 2024 – Indexed in Scopus
 Cited by: 6 articles

Ulas Bagci | Artificial Intelligence | Outstanding Scientist Award

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Assoc. Prof. Dr. Ulas Bagci | Artificial Intelligence | Outstanding Scientist Award

Assoc. Prof., Northwestern University, United States

Dr. Ulas Bagci is a distinguished researcher and tenured Associate Professor at Northwestern University, specializing in Radiology, Electrical and Computer Engineering, and Biomedical Engineering. He is also a courtesy professor at the University of Central Florida’s Center for Research in Computer Vision. As the Director of the Machine and Hybrid Intelligence Lab, Dr. Bagci focuses on the integration of artificial intelligence, deep learning, and medical imaging. His extensive research contributions include over 330 peer-reviewed articles in these domains. Previously, he was a staff scientist and lab co-manager at the National Institutes of Health (NIH), where he played a pivotal role in advancing AI-driven medical imaging applications. Dr. Bagci actively contributes to leading scientific journals, serving as an associate editor for IEEE Transactions on Medical Imaging, Medical Physics, and Medical Image Analysis.

Publication Profile

🎓 Education

Dr. Ulas Bagci holds a Ph.D. in Computer Science from the University of Nottingham (2010), where he conducted pioneering research in medical imaging. He was a Visiting Research Fellow in Radiology at the University of Pennsylvania (2008-2009), further refining his expertise in AI applications for biomedical sciences. He earned his M.Sc. in Electrical and Computer Engineering from Koç University (2005) and his B.Sc. in Electrical and Computer Engineering from Bilkent University (2003).

💼 Experience

Dr. Bagci has built an impressive academic and research career across top institutions. Since 2021, he has been an Associate Professor at Northwestern University, where he leads research in AI-driven medical imaging. Before that, he served as an Assistant Professor in Computer Science at the University of Central Florida (2014-2020), fostering innovation in deep learning for radiology. From 2010 to 2014, he was a Staff Scientist and Lab Manager at the National Institutes of Health (NIH), playing a key role in infectious disease imaging and AI applications in radiology.

🏅 Awards and Honors

Dr. Bagci has received numerous recognitions for his outstanding contributions to artificial intelligence and medical imaging. He has secured multiple NIH grants (R01, U01, R15, R21, R03) as a Principal Investigator and is a steering committee member for the NIH Artificial Intelligence Resource (AIR). Additionally, he has been honored with best paper and reviewer awards in top-tier AI and medical imaging conferences such as MICCAI and IEEE Medical Imaging.

🔬 Research Focus

Dr. Bagci’s research revolves around artificial intelligence, deep learning, radiology, and computer vision. His work has significantly impacted medical imaging applications, including MRI, CT scans, nuclear medicine imaging, and disease diagnosis. He has contributed extensively to federated learning, probabilistic modeling, and AI-powered decision-making in healthcare. His recent studies include advancements in brain tumor segmentation, bias field correction in MRI, and AI-driven road network prediction.

🔚 Conclusion

Dr. Ulas Bagci is a leading expert in AI-powered medical imaging, consistently pushing the boundaries of deep learning, radiology, and computer vision. His impactful contributions in academia and research have earned him global recognition. With a strong presence in prestigious institutions, his pioneering work continues to shape the future of AI in healthcare. 🚀

📚 Publications

Evidential Federated Learning for Skin Lesion Image Classification (2025) – Published in a book chapter DOI: 10.1007/978-3-031-78110-0_23 📖

Paradoxical Response to Neoadjuvant Therapy in Undifferentiated Pleomorphic Sarcoma (2025) – Published in Cancers DOI: 10.3390/cancers17050830 🏥

Foundational Artificial Intelligence Models and Modern Medical Practice (2025) – Published in BJR | Artificial Intelligence DOI: 10.1093/bjrai/ubae018 🧠

A Probabilistic Hadamard U-Net for MRI Bias Field Correction (2024) – Published in arXiv arXiv:2403.05024 🖥️

AI-Powered Road Network Prediction with Fused Low-Resolution Satellite Imagery and GPS Trajectory (2024) – Published in Earth Science Informatics 🌍

Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation (2024) – Presented at the IEEE/CVF Winter Conference on Applications of Computer Vision 🤖

Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation  (2024) – Published in arXiv arXiv:2405.18383 🏥

 

Narmilan Amarasingam | Artificial Intelligence | Best Researcher Award

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Mr. Narmilan Amarasingam | Artificial Intelligence | Best Researcher Award

PhD student, Queensland University of Technology, Australia

Researcher specializing in drone-based remote sensing solutions for environmental and surveillance needs, focusing on precision agriculture and biosecurity. Expertise includes UAV remote sensing, artificial intelligence, and multispectral/hyperspectral image processing. Currently pursuing a PhD in Precision Agriculture at Queensland University of Technology.

Profile

Google Scholar

 

🎓 Education

PhD: Precision Agriculture, Queensland University of Technology (2021 – Present). MSc: Agricultural Engineering, Eastern University, Sri Lanka (2016 – 2018). BSc: Agriculture, Agricultural Engineering, Eastern University, Sri Lanka (2010 – 2015). BIT: Software Engineering, University of Colombo School of Computing, Sri Lanka (2011 – 2015)

🔍 Experience

Research Assistant at Charles Sturt University and Sunshine Coast Council on projects integrating AI and drone technology for environmental monitoring and invasive species detection.

🏆 Awards

QUT/Accelerate Higher Education Development Expansion and Development (AHEAD) World Bank Project Scholarship. Vice Chancellor’s Award for Early Career Researcher, Faculty of Technology, 2022.

🌍 Research Interests

Precision Agriculture, UAV-based Remote Sensing, Multispectral Image Processing, AI, Biosystems Engineering, Environmental Management.

📚 Publications

Co-authored numerous peer-reviewed articles in Q1 and non-Q1 ranking journals on topics related to UAV-based remote sensing and AI applications in agriculture and environmental management.

A review of UAV platforms, sensors, and applications for monitoring of sugarcane crops
Predicting canopy chlorophyll content in sugarcane crops using machine learning algorithms and spectral vegetation indices derived from UAV multispectral imageryDetection of white leaf disease in sugarcane using machine learning techniques over UAV multispectral images
Detection of White Leaf Disease in Sugarcane Crops Using UAV-Derived RGB Imagery with Existing Deep Learning Models
N Amarasingam, F Gonzalez, ASA Salgadoe, J Sandino, K Powell
E-agricultural concepts for improving productivity: A review

Bablu Karan | Education | Best Researcher Award

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Dr. Bablu Karan | Education | Best Researcher Award

PhD in Education, Central University of Gujarat, India

Dr. Bablu Karan is a dedicated educator and researcher at the Central University of Gujarat, Gandhinagar, specializing in educational technology and curriculum studies. His work focuses on integrating artificial intelligence into secondary education in India, aiming to enhance teaching and learning experiences.

Profile

Google Scholar

📚 Education:

Dr. Bablu Karan holds a PhD in Education from the Central University of Gujarat, Gandhinagar. He earned his M.Ed. from Visva-Bharati University, Santiniketan, and a B.Ed. from RIE-NCERT, Bhubaneswar. He also holds an M.A. in English from Ravenshaw University and a B.A. in English Hons from Vidyasagar University.

👨‍🏫 Experience:

Dr. Bablu Karan has extensive experience in educational research and policy formulation. His professional journey includes significant contributions to understanding AI in education, educational policies, and curriculum studies.

🔍 Research Interests:

Dr. Bablu Karan’s research interests span educational technology, AI in education (AIED), ICT, educational policies, curriculum studies, and language education. He is particularly interested in how these areas intersect to improve educational outcomes in diverse contexts.

🏆 Awards:

Recipient of the Best Researcher Award, Dr. Bablu Karan is recognized for his pioneering research in integrating AI into school education and his contributions to educational policy and curriculum development in India.

Publications:

Artificial Intelligence Integration into School Education: A Review of Indian and Foreign Perspectives (2023, Millennial Asia) – Cited by 6

Potential Risks of Artificial Intelligence Integration into School Education: A Systematic Review (2023, Bulletin of Science, Technology & Society) – Cited by 4

Enhancing Women Education in India: An Immense Challenge Towards Effective Human Rights (2017, International Education & Research Journal) – Cited by 2

Promoting Gender Equality in Classroom Teaching and Learning in Indian Context: Issues and Challenges (2017, International Journal of Multidisciplinary Approach & Studies) – Cited by 1

Integration of artificial intelligence by the Central Board of Secondary Education in India: towards innovative teaching and learning practices (2024, Technology, Pedagogy and Education)