Mehdi Moayed Mohseni | Machine Learning | Best Researcher Award

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Assist. Prof. Dr. Mehdi Moayed Mohseni | Machine Learning | Best Researcher Award

Islamic Azad University Science and Research Branch | Iran

Assist. Prof. Dr. Mehdi Moayed Mohseni is a distinguished chemical engineer at Islamic Azad University, Tehran, Iran, with expertise in non-Newtonian fluid mechanics, convective heat transfer, viscoelastic fluids, rheology, and exergy analysis. He earned his Ph.D. in Chemical Engineering from Amirkabir University of Technology, Iran, focusing on hydrodynamic and heat transfer modeling and entropy analysis of viscoelastic fluids in centric and eccentric annuli, followed by an M.Sc. on heat transfer of Giesekus viscoelastic fluids and a B.Sc. on biological natural gas sweetening processes. His research integrates analytical and semi-analytical methods (HPM, perturbation, homotopy) with computational fluid dynamics (CFD) and mass/heat transfer studies. Assist. Prof. Dr. Mehdi Moayed Mohseni has authored 17 publications, including studies on thermal and rheological performance of nanofluids, contributing to 229 citations and an h-index of 8. He actively participates in conferences, such as the National Iranian Chemical Engineering Congress, and his work demonstrates a strong commitment to advancing understanding of complex fluid behavior and transport phenomena.

Profile: Scopus | Google Scholar

Featured Publications

Mohseni, M. M., Jouyandeh, M., Sajadi, S. M., Hejna, A., Habibzadeh, S., … (2022). Metal-organic frameworks (MOF) based heat transfer: A comprehensive review. Chemical Engineering Journal, 449, 137700.

Montazeri, N., Salahshoori, I., Feyzishendi, P., Miri, F. S., Mohseni, M. M., … (2023). pH-sensitive adsorption of gastrointestinal drugs (famotidine and pantoprazole) as pharmaceutical pollutants by using the Au-doped@ ZIF-90-glycerol adsorbent: Insights from … Journal of Materials Chemistry A, 11(47), 26127–26151.

Salahshoori, I., Vaziri, A., Jahanmardi, R., Mohseni, M. M., Khonakdar, H. A. (2024). Molecular simulation studies of pharmaceutical pollutant removal (rosuvastatin and simvastatin) using novel modified-MOF nanostructures (UIO-66, UIO-66/chitosan, and UIO-66 …). ACS Applied Materials & Interfaces, 16(20), 26685–26712.

Mohseni, M. M., & Rashidi, F. (2010). Viscoelastic fluid behavior in annulus using Giesekus model. Journal of Non-Newtonian Fluid Mechanics, 165(21-22), 1550–1553.

Bateni, A., Salahshoori, I., Jorabchi, M. N., Mohseni, M. M., Asadabadi, M. R., … (2025). Molecular simulation-based assessing of a novel metal-organic framework modified with alginate and chitosan biopolymers for anionic reactive black 5 and cationic crystal violet … Separation and Purification Technology, 354, 128986.

Kachi Anvesh | Machine Learning | Best Researcher Award

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Mr. Kachi Anvesh | Machine Learning | Best Researcher Award

Vardhaman College of Engineering | India

Mr. Kachi Anvesh is an Assistant Professor in the Department of Information Technology at Vardhaman College of Engineering, Hyderabad, with over 12 years of teaching and research experience. He is currently pursuing a Ph.D. in Computer Science at Visvesvaraya Technological University, Belagavi, and holds an M.Tech in Software Engineering with distinction and a B.Tech in Information Technology. His research focuses on medical image processing, deep learning, machine learning, and intelligent systems, with notable contributions including the detection of tessellated retinal disease, hypertensive retinopathy, glaucoma, cataract, and wheat head detection using advanced AI models. He has published in reputed journals and conferences such as JIKM, TSP-CMES, and Journal of Autonomous Intelligence, accumulating 13 citations and an h-index of 2. Mr. Anvesh has led innovative projects including bone age detection from X-ray images, facial expression recognition, emotion detection, foreign object debris detection, and predictive analytics systems, and holds certifications in AI and deep learning from IIT Ropar and other platforms, reflecting his strong contribution to engineering and AI research.

Profile: Scopus | Orcid | Google Scholar

Featured Publications

Anvesh, K., Prasad, S., Laxman, V. V. S. R., & Narayana, B. S. (2019). Automatic student analysis and placement prediction using advanced machine learning algorithms. International Journal of Innovative Technology and Exploring Engineering, 8, 9.

Suma, K., Sunitha, G., Karnati, R., Aruna, E. R., Anvesh, K., Kale, N., & Kishore, P. K. (2024). CETR: CenterNet-Vision transformer model for wheat head detection. Journal of Autonomous Intelligence, 7(3), 6.

Venkatesh, M., Dhanalakshmi, C., Adapa, A., Manzoor, M., & Anvesh, K. (2023). Criminal face detection system.

Anvesh, K., Srilatha, M., Raghunadha Reddy, T., Gopi Chand, M., & Jyothi, G. D. (2018). Improving student academic performance using an attribute selection algorithm. Proceedings of the First International Conference on Artificial Intelligence and Cognitive…, 3.

Rajendar, B., Bhavana, K., Divya, C., Swarna, M., & Anvesh, K. (2017). Evaluation of cardiac tonic activity of methanolic leaf extract of Moringa oleifera. International Journal of Pharma Sciences and Research, 8(6), 152–156.

Alper Mitincik | Artificial Intelligence | Best Researcher Award

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Mr. Alper Mitincik | Artificial Intelligence | Best Researcher Award

Galatasaray University | Turkey

Mr. Alper Mitincik is an accomplished software engineer and researcher with extensive expertise in Java, Python, SQL, and scalable data-driven applications. He has led significant projects, including a national cloud storage system and one of the largest Turkish-language crawling-based search engines, demonstrating exceptional skills in search engine architecture, Elasticsearch optimization, ranking algorithms, and large-scale data pipelines. Alper has published research on information retrieval and deep learning, notably “Text-Based Image Retrieval System Using Semantic Visual Content for Re-Ranking” in Engineering Applications of Artificial Intelligence (2025), and his M.Sc. thesis focused on semantic search frameworks. Currently pursuing a Ph.D. in Computer Engineering, his research emphasizes advanced recommendation systems, transformers, and graph attention networks. With experience mentoring engineers, implementing best practices, and designing robust software architectures, Alper combines industrial impact with academic innovation. Recognized with awards such as Turkcell’s CXO Award and holding certifications in machine learning,

Profile: Google Scholar

Featured Publications

Parlak, İ. B., & Mıtıncık, A. (2022). Designing an information framework for semantic search. Avrupa Bilim ve Teknoloji Dergisi, 682–689.

Topcu, B., Mıtıncık, A., Erdem, M. G., & Yanikoglu, B. (2025). Text-based image retrieval system using semantic visual content for re-ranking. Engineering Applications of Artificial Intelligence, 160, 111770.

Xiongjun Zhao | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Xiongjun Zhao | Artificial Intelligence | Best Researcher Award

Assist. Prof. Dr. Xiongjun Zhao, Hunan University, China

Assist. Prof. Dr. Xiongjun Zhao is a biomedical AI researcher currently serving as an Assistant Researcher at the School of Information Science and Engineering, Hunan University. He holds a Ph.D. in Computer Science and Technology and a bachelor’s degree in Software Engineering, both from Hunan University, where he was recognized as an Outstanding Graduate and Student Leader. His research focuses on biomedical big data analysis, multi-modal learning, and intelligent medical systems. Dr. Zhao has authored five significant papers, including a CCF-A ACM MM conference paper and three SCI-indexed journal articles. He has also secured four patents in medical AI technologies and leads multiple funded research projects backed by prominent Chinese institutions. An awardee of national honors in mathematics and programming competitions, he brings strong technical expertise in Python, C++, and deep learning frameworks. Dr. Zhao actively mentors students and contributes to international conferences such as IEEE BIBM and ACM MM.

Publication Profile

Orcid

Work Experience

Assist. Prof. Dr. Xiongjun Zhao currently holds the position of Assistant Researcher at the School of Information Science and Engineering, Hunan University, a role he began in July 2025. In this capacity, he is deeply involved in cutting-edge research and development focused on biomedical big data analysis, multi-modal learning, and intelligent medical systems. His responsibilities include leading and contributing to a range of scientific research projects, many of which involve national and provincial collaborations. Dr. Zhao plays a pivotal role in advancing academic innovation through his contributions to interdisciplinary projects and by leveraging artificial intelligence in healthcare-related research. In addition to his research activities, he actively mentors students, fostering a dynamic and innovative research environment in the laboratory. His work exemplifies a commitment to technological advancement and academic leadership, positioning him as a key contributor to the development of intelligent medical solutions through data-driven methodologies.

Educational Background

Assist. Prof. Dr. Xiongjun Zhao received his comprehensive academic training from Hunan University, a prestigious institution recognized under China’s Double First-Class Initiative. He pursued a Direct Ph.D. Program in Computer Science and Technology from September 2020 to June 2025, under the mentorship of Prof. Ying Jianguo, a distinguished Changjiang Scholar. His doctoral studies focused on advanced topics in biomedical data science and artificial intelligence. Prior to that, Dr. Zhao completed his Bachelor’s degree in Software Engineering at the same university between September 2016 and June 2020. During his undergraduate years, he was honored as an Outstanding Graduate and recognized for his leadership and community involvement with accolades such as Outstanding Student Leader and Outstanding Volunteer. His solid educational foundation, shaped by rigorous training and guided mentorship, has laid the groundwork for his successful academic and research career in the fields of machine learning and intelligent medical systems.

Research Experience

Assist. Prof. Dr. Xiongjun Zhao has a strong and focused research background in biomedical big data analysis, multi-modal learning, and intelligent medical models. His work integrates advanced machine learning techniques with complex medical datasets to develop deep learning frameworks—particularly Transformer-based models—for clinical diagnostics and decision support systems. His notable academic contributions include the publication of one top-tier conference paper and three SCI-indexed journal articles, along with the successful registration of four patents in the medical AI domain. Dr. Zhao has led and participated in several high-impact research projects, notably those funded by the Hunan Provincial Department of Health and the China Association for Science and Technology. His role as a core researcher in these projects highlights his leadership in interdisciplinary and applied medical research. He has also presented at major international conferences such as ACM MM and IEEE BIBM, reflecting his active engagement in the global scientific community.

Awards and Recognitions

Assist. Prof. Dr. Xiongjun Zhao has received several prestigious awards that reflect his academic excellence and problem-solving capabilities in both theoretical and applied domains. He was awarded the National Second Prize in the National University Student Mathematics Competition (ASC), showcasing his strong analytical and mathematical skills. In competitive programming, he earned the Regional Second Prize in the ACM-ICPC International Collegiate Programming Contest, a globally recognized event for algorithmic proficiency. His team received an Honorable Mention (Top 15%) in the Mathematical Contest in Modeling (MCM/ICPC), further emphasizing his strength in interdisciplinary problem-solving. Additionally, he secured the National Third Prize in the China Graduate Mathematical Modeling Competition and the Provincial Third Prize in the Hunan “Internet+” Innovation and Entrepreneurship Competition. These accolades collectively highlight Dr. Zhao’s commitment to academic rigor, computational excellence, and innovation, and they underscore his readiness to tackle real-world challenges through collaborative and research-driven approaches.

Research Focus

Assist. Prof. Dr. Xiongjun Zhao’s research is primarily centered on biomedical big data analysis, intelligent medical systems, and multi-modal machine learning for clinical decision-making. His work integrates advanced deep learning techniques—such as Transformers, Graph Neural Networks (GNNs), and multimodal prompt learning—into healthcare applications, particularly in medical image analysis, electronic health record (EHR) modeling, and medication guidance systems. His studies include developing continual learning models for EHRs (TransEHR), large language models for drug recommendations (ShennongMGS), and novel diagnostic approaches using multimodal X-ray interpretation and ECG analysis (ECGNN). He also explores multitask and multi-view learning frameworks to enhance predictive accuracy in various clinical settings. His research lies at the intersection of artificial intelligence, medical informatics, and computational biology, aiming to improve diagnostic accuracy, clinical workflow, and personalized medicine. Through high-impact publications and patents, Dr. Zhao contributes significantly to the advancement of AI-driven healthcare technologies and smart medical diagnostics.

Publication Top Notes

  • TransEHR: Alignment-Free Electronic Health Records Continual Learning Across Feature Spaces, Expert Systems with Applications, 2025, DOI: 10.1016/j.eswa.2025.129020

  • ShennongMGS: An LLM-based Chinese Medication Guidance System, ACM Transactions on Management Information Systems, 2025, DOI: 10.1145/3658451

  • Report-Concept Textual-Prompt Learning for Enhancing X-ray Diagnosis, ACM MM (Conference), 2024, DOI: 10.1145/3664647.3681568

  • ECGNN: Enhancing Abnormal Recognition in 12-Lead ECG with Graph Neural Network, IEEE BIBM, 2022, DOI: 10.1109/BIBM55620.2022.9995419

  • UniMed: Multimodal Multitask Learning for Medical Predictions, IEEE BIBM, 2022, DOI: 10.1109/BIBM55620.2022.9995044

  • A Knowledge-aware Machine Reading Comprehension Framework for Dialogue Symptom Diagnosis, IEEE BIBM, 2021, DOI: 10.1109/bibm52615.2021.9669717

  • Multi-View Weighted Feature Fusion Using CNN for Pneumonia Detection on Chest X-Rays, IEEE HEALTHCOM, 2021, DOI: 10.1109/healthcom49281.2021.9399029

Eugene Levner | Artificial Intelligence | Best Researcher Award

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Prof. Eugene Levner | Artificial Intelligence | Best Researcher Award

Professor at Holon Institute of Technology, Israel

Prof. Eugene Levner is a renowned expert in computational mathematics, operations research, and artificial intelligence, with a career spanning over five decades. He earned his Ph.D. from the Central Economic-Mathematical Institute of the USSR Academy of Sciences, focusing on graph models and scheduling problems. He has held prominent academic positions in Russia and Israel, including Holon Institute of Technology, Bar Ilan University, and The Hebrew University of Jerusalem. Prof. Levner has authored numerous influential publications in top-tier journals and received multiple Best Paper and Excellence in Teaching awards. His research spans scheduling theory, robotics, fuzzy logic, and digital medicine, with over 1,500 citations highlighting his global impact. He has been a guest lecturer at institutions across Europe, North America, and Asia and has served on editorial boards of leading journals. His work continues to influence the fields of algorithm design, risk management, and smart manufacturing systems.

Professional Profile

Google Scholar

Academic Background

Prof. Eugene Levner holds an exceptional academic background in computational mathematics and systems science. He earned his B.S. and M.S. degrees in Computational Mathematics from Moscow State Lomonosov University between 1963 and 1968, where he developed a strong foundation in algorithmic thinking and mathematical modeling. He went on to complete his Ph.D. in Computer and Systems Science at the Central Economic-Mathematical Institute of the USSR Academy of Sciences from 1969 to 1973. His doctoral research focused on the design of graph models and methods for solving scheduling problems, laying the groundwork for a lifelong career in optimization and operations research. Prof. Levner was mentored by distinguished scholars, including Prof. Boris T. Polyak and Prof. David B. Yudin, both influential figures in applied mathematics. His education equipped him with advanced skills in mathematical programming, which he later applied across multiple disciplines such as artificial intelligence, robotics, and digital medicine.

Professional Background

Prof. Eugene Levner has had a distinguished professional career marked by academic leadership and groundbreaking research in computer science, operations research, and artificial intelligence. Beginning as a researcher at the Institute of Automation and Remote Control in Moscow, he went on to serve at the Central Economic-Mathematical Institute of the USSR Academy of Sciences for over two decades. He later held academic positions at Moscow State University and The Hebrew University of Jerusalem. From 1994 to 2010, he was a professor at the Holon Institute of Technology in Israel, where he also received multiple excellence awards. He further contributed as a lecturer at Bar Ilan University and served as a full-time professor at Ashkelon Academic College. Prof. Levner has been a visiting lecturer at leading institutions across Europe, Asia, and North America. Currently, he serves as Emeritus Professor at the Holon Institute of Technology, continuing to mentor students and contribute to international research.

Awards and Honors

Prof. Eugene Levner has received numerous prestigious awards and honors in recognition of his outstanding contributions to research, teaching, and academic leadership. Early in his career, he was awarded the Silver Diploma by the USSR Institute of Control Problems in 1972 and received the Best Paper Award from the Moscow Government in 1981. His international recognition includes listings in Marquis’ Who’s Who in Science and Engineering and 2000 Outstanding Scientists of the 20th Century. He has earned multiple Best Paper Awards at international conferences in Russia, Mexico, and Israel, including INCOM-IFAC and MICAI. In addition to research excellence, he was honored with Excellence in Teaching and Research Awards at the Holon Institute of Technology between 2009 and 2021. He also received a special award from Shanghai Jiao Tong University in 2010 for his exceptional instruction in operations research. These accolades reflect his lasting global impact in applied mathematics and computer science.

Research Focus

Prof. Eugene Levner’s research spans several core areas in computational mathematics and applied computer science, with a primary focus on algorithm design, scheduling theory, and operations research. He has made significant contributions to the development of graph-based models and approximation algorithms for complex scheduling and optimization problems, particularly in manufacturing systems and robotics. His work integrates artificial intelligence techniques with digital medicine, risk management, and decision-making under uncertainty. Prof. Levner has also advanced research in fuzzy logic and its applications in intelligent systems and supply chain resilience. His recent studies explore adaptive scheduling, energy-efficient computing, and the ripple effects of environmental risks using entropy-based models. He has published extensively in high-impact journals, contributing to both theoretical foundations and real-world applications. Through multidisciplinary research and international collaborations, Prof. Levner continues to influence areas such as smart manufacturing, autonomous systems, and computational logistics, maintaining relevance in both academic and industrial research communities.

Publication Top Notes

Integer Programming and Flows in Networks
Year: 1974 | Cited by: 472

Fast Approximation Algorithm for Job Sequencing with Deadlines
Year: 1981 | Cited by: 121

Computational Complexity of Approximation Algorithms for Combinatorial Problems
Year: 1979 | Cited by: 124

An Improved Algorithm for Cyclic Flowshop Scheduling in a Robotic Cell
Year: 1997 | Cited by: 139

Cyclic Scheduling in Robotic Flowshops
Year: 2000 | Cited by: 280

Multiple-Part Cyclic Hoist Scheduling Using a Sieve Method
Year: 2002 | Cited by: 111

Adaptive Scheduling Server for Power-Aware Real-Time Tasks
Year: 2004 | Cited by: 130

Perishable Inventory Management with Dynamic Pricing Using Time–Temperature Indicators Linked to Automatic Detecting Devices
Year: 2014 | Cited by: 145

Complexity of Cyclic Scheduling Problems: A State-of-the-Art Survey
Year: 2010 | Cited by: 231

Entropy-Based Model for the Ripple Effect: Managing Environmental Risks in Supply Chains
Year: 2018 | Cited by: 110

Conclusion

Prof. Eugene Levner is a distinguished scholar with a lifelong dedication to advancing computational mathematics, operations research, and artificial intelligence. With a Ph.D. from the Central Economic-Mathematical Institute of the USSR Academy of Sciences and mentorship under world-renowned experts, his foundational work in graph models, scheduling, and optimization has had lasting global impact. He has published extensively in high-impact journals, with several highly cited papers influencing both theoretical and applied research. Prof. Levner has held senior academic positions in leading institutions across Russia and Israel and delivered invited lectures worldwide. His pioneering research in scheduling theory, robotics, fuzzy logic, and digital medicine, combined with multiple international awards and recognition for both teaching and research excellence, solidifies his reputation as a leader in his field. Through mentoring, interdisciplinary innovation, and global collaboration, Prof. Levner’s work continues to shape contemporary science and technology, making him an exceptional and highly deserving recipient of the “Best Researcher Award.”

 

 

Temitayo Fagbola | Machine Learning | Best Researcher Award

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Dr. Temitayo Fagbola | Machine Learning | Best Researcher Award

Dr. Temitayo Fagbola, University of Hull, England, United Kingdom

Dr. Temitayo Matthew Fagbola is a Teaching Fellow at the University of Hull, UK, specializing in Applied Artificial Intelligence, with research interests in generative AI, medical imaging, NLP, and ethical AI systems. He holds a PhD in Computer Science from LAUTECH, Nigeria, and has extensive academic experience in Nigeria, South Africa, and the UK. A Fellow of the Higher Education Academy (FHEA), he has earned multiple research grants and awards, including excellence in feedback and teaching. Dr. Fagbola has over 480 citations and serves on several editorial boards and technical committees.

Publication Profile

Scopus

Google Scholar

🎓 Educational Background

Dr. Temitayo Fagbola possesses a strong academic foundation in Computer Science. He recently completed a Postgraduate Certificate in Academic Practice at the University of Hull, UK (2023–2024) 🎓. He earned his Ph.D. in Computer Science from Ladoke Akintola University of Technology, Nigeria (2012–2015) 🧠, following an M.Sc. in Computer Science from the University of Ibadan (2009–2011) 💻. His academic journey began with a B.Tech. (Hons) in Computer Science from LAUTECH (2002–2007) 📘. This diverse educational background underpins his expertise in AI, data science, and academic teaching and research.

💼 Professional Experience

Dr. Temitayo Fagbola is currently a Teaching Fellow at the Centre of Excellence in Data Science, AI, and Modelling, University of Hull, UK (Oct. 2022–Present) 🇬🇧. He has served as a Senior Lecturer at FUOYE, Nigeria (2021–2022) and held research roles at Durban University of Technology, South Africa 🇿🇦. His academic journey includes roles as Lecturer and Assistant Lecturer at FUOYE (2012–2018) 👨‍🏫. His work focuses on Applied AI in Health 🧠, with expertise in CNNs, LLMs, denoising autoencoders, transfer learning, computer vision, NLP, and AI ethics

🏅 Honours, Awards

Dr. Temitayo Fagbola was awarded the prestigious Fellowship of the Higher Education Academy (FHEA), UK 🇬🇧 in June 2024. He won the Excellence in Feedback award and was a finalist for Excellence in Teaching at the University of Hull 🏆. His accolades include travel grants to NeurIPS 2019 in Canada 🇨🇦, FAT* Conference in the USA 🇺🇸, and Deep Learning events in South Africa 🇿🇦. He held a Postdoctoral Fellowship at Durban University of Technology and received a Best Paper Award in 2014 📝. His recognitions span academia, teaching excellence, and global AI forums

📜 Professional Certifications

Dr. Temitayo Fagbola holds multiple certifications including Aviatrix Multicloud Network Associate 🌐, Machine Learning Applications from Global AI Hub 🤖, and two Huawei ICT Associate credentials in Big Data and Routing & Switching 📊📡. He actively contributes to academic service as a reviewer on the FoSE Research Ethics Committee 🧪 and a member of the Recognised Teacher Status Working Group at the University of Hull 🇬🇧. As a module leader and lecturer in Applied AI 📘, he has co-supervised seven MSc dissertations and one PhD thesis, nurturing the next generation of AI and CS researchers

🔍 Research Focus

Dr. Temitayo Fagbola’s research lies at the intersection of Artificial Intelligence 🤖, Machine Learning 📈, and Cloud Computing ☁️, with impactful work in email classification ✉️, timetabling optimization 📅, and AI ethics ⚖️. His contributions span Natural Language Processing 🗣️, Computer Vision 🖼️, and human-centered AI systems 👥, often integrating metaheuristic algorithms and deep learning for real-world challenges. He’s also active in educational technology 🎓, COVID-19 smart health solutions 😷, and AI-powered predictive systems, showing a strong commitment to applied AI in public services and education sectors 🌍. His publications are widely cited, reflecting global scholarly influence

Conclusion

Dr. Temitayo Fagbola’s innovative research, international recognition, publication impact, and commitment to academic excellence, he is an excellent candidate for the Best Researcher Award. His work addresses real-world problems through advanced AI methods, making him not only a researcher of merit but a contributor to the global AI and data science community.

Publication Top Notes

📘 Computer-based test (CBT) system for university academic enterprise examination – 108 citations – 📅 2013
☁️ The Impact and Challenges of Cloud Computing Adoption on Public Universities – 93 citations – 📅 2014
📩 Hybrid GA-SVM for efficient feature selection in e-mail classification – 51 citations – 📅 2012
📚 Cloud Computing: Concepts, Architecture & Applications – 37 citations – 📅 2019
😷 Smart face masks for COVID-19 management – 21 citations – 📅 2022
🧠 Towards AI-based systems: Human-centered requirements – 20 citations – 📅 2019
🧮 Hybrid Metaheuristic Feature Extraction for Timetabling – 19 citations – 📅 2012
📱 Mobile ML Models for Student Performance Prediction – 15 citations – 📅 2018
📧 Optimized Feature Selection for Email Classification – 15 citations – 📅 2014
🎓 Transformational Roles of Edge Intelligence (Special Issue) – 12 citations – 📅 2024
🚀 Survey on Mobile Agent Migration Process – 12 citations – 📅 2016
🏥 ERP Implementation in Hospital Systems – 11 citations – 📅 2023

Arshad Muhammad | Machine Learning | Best Researcher Award

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Mr. Arshad Muhammad | Machine Learning | Best Researcher Award

Mr. Arshad Muhammad, Chongqing University, China

A goal-oriented and multi-skilled IT professional with extensive experience in managing IT infrastructure, software implementations, system administration, and research. Currently pursuing a PhD at Chongqing University, China, Mr. Arshad has previously worked as a Research Assistant and Lecturer at various institutions, including Muhammad Nawaz Sharif University and Chenab College. He holds multiple degrees in Computer Science and Information Technology. His research interests include machine learning, intrusion detection systems, and medical imaging. He has published in top journals, contributing to fields such as IoMT security and healthcare networks. 🌍📊

Publication Profile

Orcid

Professional & Educator 💻📚

Mr. Arshad Muhammad is an experienced IT professional with a strong background in research, education, and system administration. Currently pursuing his PhD at Chongqing University, China, he has served as a Research Assistant, where he conducts literature reviews, designs research projects, and mentors undergraduates. He has also lectured at Muhammad Nawaz Sharif University and Chenab College, focusing on computer science and student development. Previously, as a Network Administrator at Al-Khair University, he managed IT infrastructure, system security, and student records. His expertise spans machine learning, data analysis, and education. 🌍🔐

Academic Journey 🎓💡

Mr. Arshad Muhammad’s academic journey reflects his dedication to computer science and information technology. He began with a Secondary School Certificate in Science from the Board of Intermediate and Secondary Education, Multan. He continued his studies, earning a Higher Secondary School Certificate in Science. He then pursued a Bachelor’s degree in Computer Science from Islamia University Bahawalpur, followed by a Master’s in Computer Science (16 years) and a Master of Science in Information Technology (18 years) from Government College University Faisalabad. Currently, he is pursuing a PhD at Chongqing University, China, in the field of computer science and technology. 🌐📚

Research Focus

Mr. Arshad Muhammad’s research primarily focuses on cybersecurity in healthcare networks and intrusion detection systems (IDS) for the Internet of Medical Things (IoMT) 🏥🔒. His work includes developing deep reinforcement learning-based IDS to secure IoMT healthcare networks, as seen in his article “A Deep Reinforcement Learning-Based Robust Intrusion Detection System for Securing IoMT Healthcare Networks” published in Frontiers in Medicine 🔐. He also explores anomaly detection using hybrid machine learning techniques, with a special emphasis on real-time human activity detection and smart systems like cattle management using IoT technologies 🐄📡. His contributions bridge machine learning, cybersecurity, and healthcare innovation. 🌐💡

Conclusion 🏆

Mr. Arshad Muhammad stands out as a candidate for the Research for Best Researcher Award due to his strong academic background, significant research contributions, impressive publication record, and dedication to teaching and mentorship. His interdisciplinary expertise in machine learning, IoT, and healthcare security aligns well with the evolving demands of research in these fields. Moreover, his proactive involvement in projects and mentoring roles further solidifies his position as an impactful and influential researcher.

Publication Top Notes

  • A Deep Reinforcement Learning-Based Robust Intrusion Detection System for Securing IoMT Healthcare Networks – Frontiers in Medicine (2025) 🧠🔒 | DOI: 10.3389/fmed.2025.1524286 📅

  • FOID: A Feature-Optimized Intrusion Detection System for Securing IoMT Healthcare Networks – 18th International Conference on Open Source Systems and Technologies (ICOSST) (2024) 📊💻 | DOI: 10.1109/icosst64562.2024.10871156 📅

  • RCLNet: An Effective Anomaly-Based Intrusion Detection System for Securing the Internet of Medical Things – Frontiers in Digital Health (2024) 🏥📡 | DOI: 10.3389/fdgth.2024.1467241 📅

  • An E-Tag Based Smart Cattle Management and Diagnosis System – IEEE Xplore: 2023 IEEE 3rd International Conference on Computer Systems (ICCS) (2023) 🐄📱 | 📅

  • Hybrid Machine Learning Techniques to Detect Real-Time Human Activity Using UCI Dataset – EAI Endorsed Transactions on Internet of Things (EAI.EU) (2021) 🧠📊 | 📅

Yanchun Chen | Technology | Best Researcher Award

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Dr. Yanchun Chen | Technology | Best Researcher Award

Dr. Yanchun Chen, Communication University of China, China

Yanchun Chen is a Ph.D. student in Information Communication at the Communication University of China (CUC), specializing in digital public opinion. With a background in computational communication, she has contributed extensively to public opinion analysis and media convergence research. She has published in high-impact journals, including Cities and Ethics and Information Technology. Yanchun has presented her research at IAMCR, AEJMC, and ICA conferences, receiving the IAMCR Urban Communication Award. Her expertise lies in digital media ethics, risk communication, and the socio-political impact of emerging technologies.

Publication Profile

Orcid

🎓 Education

Yanchun Chen is pursuing her Ph.D. in Information Communication at CUC (2024–Present), focusing on digital public opinion. She holds an M.S. in Communication (Computational Communication) from the State Key Laboratory of Media Convergence and Communication, CUC (2022–2024), where she analyzed communication data to interpret public sentiment. She completed her B.S. in Tourism Management from Minjiang University (2017–2021), developing foundational insights into media’s impact on cultural narratives. Her academic journey reflects an interdisciplinary approach, integrating communication theories with computational methodologies.

💼 Experience

Yanchun has conducted extensive data analysis at the State Key Laboratory of Media Convergence and Communication, focusing on public opinion trends. At the National Broadcast Media Language Resources Monitoring and Research Center, she developed a systematic media monitoring ledger. She has collaborated on international research, applying social network analysis and topic modeling to urban communication and media ethics. Her studies on deepfake resurrection, AI-generated narratives, and crisis communication have contributed to scholarly discourse in media ethics. Additionally, she has served as a research assistant on digital geopolitics projects, addressing trust issues in global media.

🏆 Awards and Honors

Yanchun has received the prestigious IAMCR Urban Communication Award (2024) for her groundbreaking research. She has been recognized with first-class scholarships, an Outstanding Graduate award, and the highest-level alumni scholarship at CUC. She also holds a National Computer Level II certificate and a bilingual tour guide certification. Her research has been nominated for the Best Researcher Award at the International Academic Awards. These accolades underscore her contributions to media studies, computational communication, and digital ethics.

🔬 Research Focus

Yanchun’s research explores urban memory in digital media, risk communication, and ethical implications of AI-generated content. She examines visual representation in short-form media and its role in shaping public perceptions. Her work on deepfake resurrection delves into digital immortality and narrative ethics. Additionally, she investigates media trust, particularly in global crisis communication, using computational methods like DTM topic modeling and social network analysis. Her studies contribute to understanding media convergence, digital ethics, and the socio-political impact of emerging communication technologies.

Publication Top Note

Urban visual representation and ethical narrative risks

Conclusion

Dr. Yanchun Chen demonstrates exceptional research contributions, global academic recognition, and innovative methodologies in digital communication and public opinion studies. Their publications in top-tier journals, prestigious awards, and interdisciplinary research focus make them a highly suitable candidate for the Best Researcher Award.

Rania Hamdani | Computer science | Best Researcher Award

Published on by Global Academic Awards

Mrs. Rania Hamdani | Computer science | Best Researcher Award

Mrs. Rania Hamdani, University of Luxembourg, Luxembourg

Rania Hamdani is a research scientist specializing in operational research, data management, and cloud architecture for Industry 5.0. Based in Luxembourg, she is currently affiliated with the University of Luxembourg, where she explores advanced methodologies for integrating and managing heterogeneous data sources. She holds an engineering degree in Software Engineering and has extensive experience in software development, AI, and DevOps. Rania has worked on multiple industry and academic projects, publishing three research papers in Ontology-Driven Knowledge Management and Cloud-Edge AI. With a strong background in programming, cloud computing, and AI-driven solutions, she has contributed to platforms ranging from job recommendation systems to adaptive human-computer interaction systems. Her expertise includes Python, SpringBoot, Kubernetes, and Azure DevOps. She is also an active member of IEEE and other technical organizations, promoting innovation and knowledge-sharing in AI and cloud technologies. 🌍💻🔬

Publication Profile

Orcid

🎓 Education

Rania Hamdani holds an Engineering Degree in Software Engineering from the National Higher School of Engineers of Tunis (2021–2024), where she specialized in advanced design, service-oriented architecture, object-oriented programming, database management, and operational research. Prior to this, she completed a two-year preparatory cycle at the Preparatory Institute for Engineering Studies of Tunis (2019–2021), undertaking intensive coursework in mathematics, physics, and technology to prepare for engineering studies. She also earned a Mathematics-specialized Baccalaureate from Pioneer High School Bourguiba Tunis (2015–2019), graduating with honors. Throughout her academic journey, she gained expertise in artificial intelligence, machine learning, cloud computing, and DevOps methodologies. Her education provided a solid foundation in programming languages, data processing techniques, and full-stack development. Additionally, she holds multiple Microsoft certifications in Azure fundamentals, AI, data security, and compliance, reinforcing her expertise in cloud-based solutions and AI-driven applications. 📚🎓💡

💼 Experience

Rania Hamdani is a research scientist at the University of Luxembourg, where she focuses on integrating and managing heterogeneous data sources for cloud-based decision-making. Previously, she was a research intern at the same institution, contributing to Ontology-Driven Knowledge Management and Cloud-Edge AI, with three published papers. She also worked as a part-time software engineer at CareerBoosts in Quebec (2021–2025), specializing in Python, Azure DevOps, Docker, and test automation. She gained industry experience through internships at Qodexia (Paris), Sagemcom (Tunisia), and Tunisie Telecom, working on smart recruitment platforms, employee management systems, and server monitoring solutions using SpringBoot, Angular, and PostgreSQL. Her technical expertise spans full-stack development, DevOps, AI-driven applications, and cloud computing. She has contributed to major projects, including an adaptive human-computer interaction system, a job recommendation system, and a problem-solving platform, demonstrating her versatility in research and software engineering. 🚀🖥️🔍

🏆 Awards & Honors

Rania Hamdani has been recognized for her outstanding contributions to AI-driven cloud computing and operational research. She received excellence awards during her engineering studies at the National Higher School of Engineers of Tunis and was among the top-performing students in her Mathematics-specialized Baccalaureate. Her research papers in Ontology-Driven Knowledge Management and Cloud-Edge AI have been acknowledged in academic circles, contributing to the advancement of Industry 5.0 technologies. She has also earned multiple Microsoft certifications in cloud and AI fundamentals, reinforcing her technical expertise. As an active member of IEEE and the Youth and Science Association, she has been involved in technology outreach and innovation-driven initiatives. Her leadership in ENSIT Junior Enterprise as a project manager further showcases her ability to lead and contribute to tech communities. These recognitions highlight her dedication to research, software development, and cloud-based AI applications. 🏅📜🌟

🔬 Research Focus

Rania Hamdani’s research focuses on operational research, data management, cloud-edge AI, and Industry 5.0 applications. She specializes in ontology-driven knowledge management, exploring methodologies for integrating heterogeneous data sources to optimize cloud-based decision-making processes. Her work includes artificial intelligence, machine learning, reinforcement learning, and human-computer interaction systems. She has contributed to projects involving job recommendation systems, adaptive human-computer interaction platforms, and cloud-based problem-solving platforms. Rania is particularly interested in scalable cloud architectures, leveraging technologies like FastAPI, Kubernetes, Docker, and Azure DevOps to build efficient AI-powered solutions. Her research also integrates graph databases, Apache Airflow, and big data analytics for enhanced data processing. By combining AI and cloud computing, she aims to develop innovative, data-driven solutions for automation, decision support, and optimization in various industrial applications. Her expertise bridges the gap between theoretical research and real-world software engineering. ☁️🤖📊

 

Publication Top Notes

Adaptive human-computer interaction for industry 5.0: A novel concept, with comprehensive review and empirical validation

 

Abba Bashir | Machine Learning | Best Researcher Award

Published on by Global Academic Awards

Mr. Abba Bashir | Machine Learning | Best Researcher Award

Mr. Abba Bashir, Federal University Dutsin-ma, Nigeria

Abba Bashir is a civil engineer and academic dedicated to sustainable infrastructure and structural optimization. He is a lecturer at the Federal University Dutsin-ma (FUDMA), Katsina, Nigeria, specializing in structural engineering and artificial intelligence applications in construction. With over 100 citations and an h-index of 6, his research focuses on recyclability, fiber-reinforced concrete, and computational mechanics. He has authored a book on bamboo fiber-reinforced concrete and actively contributes to accreditation and curriculum development. As the AI Research Leader at FUDMA’s Faculty of Engineering, he integrates machine learning into structural design for sustainable and resilient infrastructures.

Publication Profile

Scopus

Orcid

Google Scholar

🎓 Education

Abba Bashir is currently pursuing a Master of Technology in Structural Engineering at Mewar University, India (2023–2025). He holds a Bachelor of Technology in Civil Engineering from Sharda University, India, graduating in 2017 with an 8.3/10 CGPA. His early education includes a Senior Secondary School Certificate from Nasara Academy, Kano, Nigeria (2007) and a Primary School Leaving Certificate from Maitasa Special Primary School, Kano, Nigeria (2001). His academic journey has equipped him with expertise in structural analysis, computational mechanics, and sustainable construction materials. His continuous pursuit of knowledge fuels his research in optimizing civil engineering designs through artificial intelligence and machine learning.

💼 Experience

Abba Bashir has been a lecturer at Federal University Dutsin-ma (FUDMA) since 2020, teaching courses such as Structural Analysis, Concrete Design, and Construction Materials. He has supervised undergraduate research projects and actively contributes to curriculum development and accreditation at the university. As a practicing civil engineer since 2017, he has designed and constructed residential, commercial, and institutional structures, integrating AI-driven optimization techniques. He is a member of FUDMA’s Concrete and Steel Research Group and serves as the AI Research Leader. His expertise spans finite element modeling, numerical analysis, and sustainable building materials. He is proficient in ABAQUS, ANSYS, AutoCAD, MATLAB, and Python for structural simulations.

🏆 Awards & Honors

Abba Bashir has been recognized for his contributions to structural engineering and AI-driven construction methodologies. He has received accolades for his research on bamboo fiber-reinforced concrete and his role in advancing sustainable materials. His academic leadership in AI applications within civil engineering has earned him university recognition. His book on bamboo fiber-reinforced concrete is a significant contribution to sustainable construction literature. As a mentor and research leader, he plays a crucial role in developing new undergraduate programs and fostering innovation in civil engineering education. His expertise in computational mechanics and recyclability research continues to influence the field.

🔬 Research Focus

Abba Bashir’s research integrates artificial intelligence, machine learning, and optimization algorithms into structural engineering. His work focuses on fiber-reinforced concrete, recyclability, and sustainability in construction materials. He has extensive experience in finite element modeling using ABAQUS and ANSYS, with a strong emphasis on computational mechanics. His studies explore mechanical properties and durability of cementitious materials with micro/nano reinforcements. He also investigates the optimization of structural designs to reduce environmental impact and enhance resilience. His multidisciplinary research combines AI, numerical modeling, and advanced construction materials to create sustainable and cost-effective infrastructure solutions.

 

Publication Top Notes

1️⃣ Implementation of soft-computing models for prediction of flexural strength of pervious concrete hybridized with rice husk ash and calcium carbide waste | Cited by: 50 | 📅 2022

2️⃣ An overview of streamflow prediction using random forest algorithm | Cited by: 19 | 📅 2022 🌊🤖

3️⃣ Analysis of Bamboo fibre reinforced beam | Cited by: 17 | 📅 2018 🎍🏗️

4️⃣ Antioxidant, hypolipidemic and angiotensin converting enzyme inhibitory effects of flavonoid-rich fraction of Hyphaene thebaica (Doum Palm) fruits on fat-fed obese Wistar rats | Cited by: 16 | 📅 2019 🏥🧪

5️⃣ Assessment of Water Quality Changes at Two Locations of Yamuna River Using the National Sanitation Foundation of Water Quality (NSFWQI) | Cited by: 15 | 📅 2015 🚰📊

6️⃣ High strength concrete compressive strength prediction using an evolutionary computational intelligence algorithm | Cited by: 14 | 📅 2023 🏗️🤖

7️⃣ Performance analysis and control of wastewater treatment plant using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multi-Linear Regression (MLR) techniques | Cited by: 8 | 📅 2022 🌊🧠

8️⃣ Comparison of Properties of Coarse Aggregate Obtained from Recycled Concrete with that of Conventional Coarse Aggregates | Cited by: 5 | 📅 2018 ♻️🏗️

9️⃣ Machine Learning: A Way to Smart Environment | Cited by: 1 | 📅 2021 🤖🌱

🔟 A new strategy using intelligent hybrid learning for prediction of water binder ratio of concrete with rice husk ash as a supplementary cementitious material | 📅 2025 🏗️📊