Rania Hamdani | Computer Science and Artificial Intelligence | Best Researcher Award

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Ms. Rania Hamdani | Computer Science and Artificial Intelligence | Best Researcher Award

AE3S | University of luxembourg | Luxembourg 

Rania Hamdani is a dynamic early-career research scientist specializing in software engineering, data management, and cloud architecture for Industry 5.0 applications. Currently based at the University of Luxembourg, she is engaged in advanced research on integrating heterogeneous data sources and optimizing decision-making in cloud-based systems. With a strong foundation in software development and operational research, Rania has already co-authored three research papers in Cloud-Edge AI and ontology-driven knowledge management. Her diverse technical skills span Python, Java, Docker, Kubernetes, and Azure DevOps, and she has gained international experience through roles in Luxembourg, Canada, France, and Tunisia. Passionate about both academic and applied innovation, she has contributed to multiple interdisciplinary projects in AI, human-computer interaction, and intelligent systems. Rania is also active in professional communities such as IEEE and youth science associations, reflecting her commitment to collaborative growth and scientific outreach.

Professional Profile 

Education Background

Rania Hamdani has a strong academic foundation rooted in engineering and scientific rigor. She earned her Engineering Degree in Software Engineering from the National Higher School of Engineers of Tunis (ENSIT) between 2021 and 2024, where she specialized in Advanced Design, Service-Oriented Architecture, Object-Oriented Programming, Database Management, and Operational Research. Prior to that, she completed a Preparatory Cycle for Engineering Studies at the Preparatory Institute for Engineering Studies of Tunis (2019–2021), focusing intensively on mathematics, physics, and core technology subjects—a rigorous program designed to prepare students for elite engineering schools. Rania also holds a Baccalaureate in Mathematics from Pioneer High School Bourguiba Tunis, where she graduated with distinction (Very Good) in 2019. This academic journey has laid a solid foundation for her multidisciplinary research and professional growth in software and data sciences.

Professional Experience 

Rania Hamdani has developed a rich and diverse professional portfolio across academia and industry, with hands-on experience in software engineering, research, and cloud-based technologies. She is currently a Research Scientist at the University of Luxembourg (since November 2024), where she focuses on optimizing decision-making processes in cloud environments through advanced data integration techniques. Prior to this, she served as a Research Intern at the same institution (May to October 2024), contributing to projects in Ontology-Driven Knowledge Management and Cloud-Edge AI, resulting in three published papers. Alongside her academic work, Rania worked as a Part-Time Software Engineer at CareerBoosts in Canada (2021–2025), where she honed her skills in DevOps, data analysis, test automation, and backend development using tools like Python, Docker, and Kubernetes. Her earlier internships include roles at Qodexia (France), Sagemcom (Tunisia), and Tunisie Telecom, where she worked on smart recruitment platforms, employee management systems, and server monitoring tools using full-stack technologies such as SpringBoot, Angular, and PostgreSQL. This blend of research and industry experience positions Rania as a versatile and forward-thinking technology professional.

Research Interests of Rania Hamdani

Rania Hamdani’s research interests lie at the intersection of software engineering, operational research, data integration, and cloud-edge intelligence, with a strong orientation toward Industry 5.0 applications. She is particularly passionate about developing intelligent systems that enhance decision-making in cloud-based and distributed environments, leveraging AI, machine learning, and ontology-driven knowledge frameworks. Her work focuses on enabling seamless management of heterogeneous data sources, scalable architectures, and adaptive human-computer interaction (HCI) systems. Rania is also deeply engaged in exploring Cloud-Edge AI ecosystems, recommender systems, and automation pipelines using modern tools like Docker, Kubernetes, TensorFlow, and Neo4j. Her multidisciplinary approach reflects a vision for integrating research-driven insights with real-world industrial challenges, making her contributions both academically valuable and practically impactful.

Awards and Honors of Rania Hamdani

While still in the early stages of her research career, Rania Hamdani has demonstrated exceptional academic and technical promise. She graduated with a “Very Good” distinction in her Baccalaureate in Mathematics from the prestigious Pioneer High School Bourguiba in Tunis, reflecting her consistent academic excellence. Rania has also earned multiple professional certifications from Microsoft, including Azure Fundamentals, Azure Data Fundamentals, Azure AI Fundamentals, and Azure Security, Compliance, and Identity Fundamentals, showcasing her dedication to staying at the forefront of cloud and AI technologies. Though formal research awards or honors are not yet listed, her early publications, research contributions, and international internships highlight a trajectory poised for future recognition in both academic and industry spheres.

Publications Top Noted

Title: Adaptive human‑computer interaction for Industry 5.0: A novel concept, with comprehensive review and empirical validation
Year: 2025

Conclusion

Rania Hamdani is highly suitable for the Best Emerging Researcher or Young Researcher Award category. She has excellent technical skills, promising early-stage research output, international exposure, and a forward-looking vision in areas like Industry 5.0, cloud-edge intelligence, and AI-based decision systems. While still building her publication track record and academic leadership, her current trajectory shows strong promise for future impactful contributions to scientific and industrial domains.

Raviteja Sista | Computer Science and Artificial Intelligence | Best Researcher Award

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Mr. Raviteja Sista | Computer Science and Artificial Intelligence | Best Researcher Award

Research Scholar at Indian Institute of Technology Kharagpur, India

Raviteja Sista is a dynamic and accomplished researcher specializing in Artificial Intelligence, Deep Learning, and Medical Image Analysis. Currently pursuing his Ph.D. at the Indian Institute of Technology Kharagpur with an outstanding GPA of 9.4, he is a recipient of the prestigious Prime Minister’s Research Fellowship. Raviteja holds an MSc in Signal Processing and Communications from the University of Edinburgh and a Bachelor’s in Electronics and Communication Engineering from Osmania University. His research focuses on developing AI-driven frameworks for surgical planning and outcome prediction, with notable contributions to multimodal graph-based learning and surgical video analysis. He has published extensively in top-tier journals such as Medical Image Analysis and Computers in Biology and Medicine, and has actively contributed to international AI challenges and symposia. His technical expertise, academic excellence, and dedication to solving real-world healthcare problems through AI make him a standout figure in the research community.

Professional Profile 

🎓 Education of Raviteja Sista

Raviteja Sista has pursued a stellar academic path marked by excellence and innovation. He is currently enrolled in a Ph.D. program at the Indian Institute of Technology Kharagpur, specializing in Artificial Intelligence at the Centre of Excellence, where he maintains an impressive GPA of 9.4/10. Prior to this, he earned his Master of Science in Signal Processing and Communications with Distinction from the University of Edinburgh (2019–2020). His foundational engineering training was completed with a Bachelor of Engineering in Electronics and Communication from M.V.S.R. Engineering College, affiliated with Osmania University, where he secured a remarkable 85.34%. Raviteja also boasts an outstanding academic record from his early years, achieving 94.6% in Intermediate studies at Narayana Junior College and a CGPA of 9.8/10 in Class X from Lotus National School, Hyderabad.

💼 Professional Experience of Raviteja Sista

Raviteja Sista has a well-rounded professional background that bridges academia, research, and industry. He is currently a Teaching Assistant at IIT Kharagpur, where he supports academic instruction in AI and deep learning. Over the years, he has held teaching roles at several institutions including SRKR Engineering College, CSI Wesley Institute of Technology, Assam Down Town University, and JNTU Kakinada, demonstrating his commitment to education and knowledge dissemination. Complementing his academic roles, Raviteja also gained valuable industry experience as an Associate Software Developer Intern at Accenture Solutions Pvt. Ltd. and through multiple internships at Defence Research and Development Laboratory (DRDL), Hyderabad. His professional journey reflects a strong blend of research, software development, and teaching expertise, all anchored in the field of artificial intelligence and signal processing.

🔬 Research Interests of Raviteja Sista

Raviteja Sista’s research interests lie at the intersection of artificial intelligence and healthcare, with a strong focus on applying deep learning techniques to complex real-world problems. His core areas of interest include Deep Learning, Medical Image Analysis, Digital Signal Processing, Image Processing, Artificial Intelligence, and Design of Algorithms. He is particularly passionate about developing AI-powered systems for surgical planning and automation, leveraging multimodal data, graph neural networks, and computer vision. His work aims to enhance patient safety, improve clinical outcomes, and drive innovation in intelligent medical systems. Raviteja’s commitment to impactful, interdisciplinary research is evident in his projects and publications, which bridge technical depth with healthcare relevance.

🏅 Awards and Honors of Raviteja Sista

Raviteja Sista has been recognized with several prestigious awards and honors that highlight his academic brilliance and research potential. Most notably, he was awarded the Prime Minister’s Research Fellowship (PMRF) in 2022, one of India’s most esteemed research fellowships supporting exceptional doctoral scholars. He also earned a Certificate of Merit for completing the “Advanced Certification in Artificial Intelligence and Machine Learning” from IIIT Hyderabad in 2019. Additionally, Raviteja demonstrated national-level academic excellence by ranking in the Top 3% among over 1 lakh candidates in GATE 2019, a highly competitive examination for engineering graduates in India. These accolades reflect his consistent pursuit of excellence and his growing reputation as a promising researcher in the field of artificial intelligence.

🧾 Conclusion 

Sista Raviteja stands out as a highly qualified, technically accomplished, and visionary researcher in AI for healthcare. With strong academic credentials, impactful projects, respected publications, and active involvement in the scientific community, he demonstrates clear potential for leadership in scientific research.Despite minor areas of potential growth in independent authorship and translational work, his contributions already meet and, in some cases, exceed the typical benchmarks for the Best Researcher Award.

📚 Publications Top Noted

  1. Title: Deep neural hashing for content-based medical image retrieval: A survey
    Authors: A. Manna, R. Sista, D. Sheet
    Journal: Computers in Biology and Medicine, Volume 196, Article 110547
    Year: 2025
    Citations: –
  2. Title: Artificial Intelligence (AI)–Based Model for Prediction of Adversity Outcome Following Laparoscopic Cholecystectomy—a Preliminary Report
    Authors: R. Agrawal, S. Hossain, H. Bisht, R. Sista, P.P. Chakrabarti, D. Sheet, U. De
    Journal: Indian Journal of Surgery, Volume 87 (1), Pages 52–59
    Year: 2025
    Citations: 1
  3. Title: Exploring the Limits of VLMs: A Dataset for Evaluating Text-to-Video Generation
    Authors: A. Srivastava, R. Sista, P.P. Chakrabarti, D. Sheet
    Conference: Indian Conference on Computer Vision Graphics and Image Processing (ICVGIP)
    Year: 2024
    Citations: –
  4. Title: SimCol3D—3D reconstruction during colonoscopy challenge
    Authors: A. Rau, S. Bano, Y. Jin, P. Azagra, J. Morlana, R. Kader, E. Sanderson, …, R. Sista
    Journal: Medical Image Analysis, Volume 96, Article 103195
    Year: 2024
    Citations: 16
  5. Title: CholecTriplet2022: Show me a tool and tell me the triplet—An endoscopic vision challenge for surgical action triplet detection
    Authors: C.I. Nwoye, T. Yu, S. Sharma, A. Murali, D. Alapatt, A. Vardazaryan, K. Yuan, …, R. Sista
    Journal: Medical Image Analysis, Volume 89, Article 102888
    Year: 2023
    Citations: 29
  6. Title: CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
    Authors: C.I. Nwoye, D. Alapatt, T. Yu, A. Vardazaryan, F. Xia, Z. Zhao, T. Xia, F. Jia, …, R. Sista
    Journal: Medical Image Analysis, Volume 86, Article 102803
    Year: 2023
    Citations: 61
  7. Title: CholecTriplet2022: Show me a tool and tell me the triplet—An endoscopic vision challenge for surgical action triplet detection
    Authors: C.I. Nwoye, T. Yu, S. Sharma, A. Murali, D. Alapatt, A. Vardazaryan, …, R. Sista
    Repository: arXiv, arXiv:2302.06294
    Year: 2023
    Citations: –
  8. Title: I’m GROOT: a multi head multi GRaph netwOrk recognizing surgical actiOn Triplets
    Authors: R. Sista, R. Sathish, R. Agrawal, U. De, P.P. Chakrabarti, D. Sheet
    Conference: ICVGIP 2022
    Year: 2022
    Citations: 1
  9. Title: CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
    Authors: C.I. Nwoye, D. Alapatt, T. Yu, A. Vardazaryan, F. Xia, Z. Zhao, …, R. Sista
    Repository: arXiv, arXiv:2204.04746
    Year: 2022
    Citations: 1
  10. Title: I’m GROOT: a multi head multi GRaph netwOrk recognizing surgical actiOn Triplets
    Authors: S. Raviteja, R. Sathish, R. Agrawal, U. De, P.P. Chakrabarti, D. Sheet
    Conference: ICVGIP
    Year: 2022
    Citations: –
  11. Title: Challenges of Decomposing Tools in Surgical Scenes Through Disentangling The Latent Representations
    Authors: S.L. Gorantla, R. Sista, A. Srivastava, U. De, P.P. Chakrabarti, D. Sheet
    Workshop: ICLR Workshop on Challenges in Applied Deep Learning (ICBNB)
    Year: 2025 (Accepted)
    Citations: –

 

Badr Machkour | Artificial Intelligence | Best Researcher Award

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Dr. Badr Machkour | Artificial Intelligence | Best Researcher Award

Professor, Faculty of Legal, Economic and Social Sciences, Morocco 

Dr. Badr Machkour is a Moroccan researcher and academic with deep expertise in economics, finance, and digital transformation. Currently holding a Ph.D. in Economic and Management Sciences, he has contributed to multiple domains including Industry 4.0, financial digitalization, and educational innovation. With a robust blend of academic and consulting experience, Dr. Machkour bridges theory and practice to drive impactful research. 🌐📊

Profile

Scopus

Google Scholar

Orcid

🎓 Education

Dr. Machkour holds a Doctorate in Economic and Management Sciences (2018–2023) from the FSJES of Agadir. His academic foundation includes a degree in Audit and Management Control from ENCG Agadir (2014–2017), post-preparatory classes in Economics and Commerce (2012–2014), and a Mathematics Baccalaureate (2011–2012). His diverse training forms a strong base for multidisciplinary research. 📚🧠

💼 Professional Experience

Dr. Machkour has extensive experience as a financial auditor, consultant, and trainer. He currently serves as a trainer at the Cité des Métiers et des Compétences (OFPPT), delivering finance and management programs. His previous roles span auditing and consulting at prominent firms like Augeco, MAZARS Maroc, and Agadir Conseil, involving sectors from agriculture to banking. 🏢💼📈

🔬 Research Interests

His research explores the intersection of Industry 4.0, digital banking solutions, customer experience, AI in education, and entrepreneurship. Dr. Machkour is particularly interested in how technology transforms economic relationships, pedagogical structures, and corporate strategies. 🤖📱🏫

🏆 Awards & Honors

While specific awards are not listed, Dr. Machkour’s work has been featured in indexed journals and cited internationally — notably his highly cited paper on Industry 4.0’s implications in finance. His academic contributions reflect both quality and influence. 🥇🌍

📑 Publications

Industry 4.0 and its Implications for the Financial Sector, Procedia Computer Science, 2020 — Cited by 151 🏦

The Rise of Artificial Intelligence in Educational Management: A Prospective Analysis on the Role of the Virtual Educational Director, Procedia Computer Science, 2025 🧠

Internet of Things in Education: Transforming Learning Environments, Enhancing Pedagogy, and Optimizing Resource Management, Data and Metadata, 2024 🏫

L’impact de l’adoption des solutions digitales sur la relation banque-client, Revue Française d’Economie et de Gestion, 2024 — Cited by 2 📲

Les facteurs d’adoption des solutions digitales bancaires par les consommateurs marocains, IJAFAME, 2022 — Cited by 1 📱

The Uses of Connected Objects and Their Influence on the Customer Experience, Test Engineering and Management, 2020 — Cited by 1 🌐

Etude exploratoire du développement de l’esprit Entrepreneurial et des compétences Entrepreneuriales auprès des étudiants au Maroc, Alternatives Managériales Economiques, 2024 👨‍🎓

Entrepreneurship 4.0 and Success Factors in the Context of Industry 4.0: A literature review, African Scientific Journal, 2024 🚀

✅ Conclusion

Overall, Dr. Badr Machkour is a promising and accomplished researcher whose work bridges digital innovation and economic practice with scholarly insight. His growing citation record, topical relevance, and interdisciplinary reach make him a strong candidate for the Best Researcher Award. With continued international engagement and broader collaborative networks, his impact is poised to grow even further. 🌍📈

Yue Wu | Machine Learning | Best Researcher Award

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Yue Wu | Machine Learning | Best Researcher Award

Assist. Prof. Dr Yue Wu, Hangzhou Dian, China

Assist. Prof. Dr. Yue Wu is a promising young academician whose work bridges the gap between automation, machine learning, and electronic design automation. Currently serving as an Assistant Professor at the School of Electronics and Information Engineering, Hangzhou Dianzi University, China, he exemplifies research excellence through his interdisciplinary expertise. He earned his Ph.D. from Zhejiang University in Aeronautics and Astronautics and a B.S. from Wuhan University of Technology in Automation. His scholarly interests focus on logic synthesis, physical design, and intelligent prediction algorithms using graph neural networks. Despite his early career stage, Dr. Wu has demonstrated impactful contributions to both academia and industry-relevant applications. His recent publication on pre-routing slack prediction using graph attention networks stands out as a novel solution in the realm of EDA. With a strong academic foundation and active research output, Dr. Wu is a fitting nominee for the Best Researcher Award, representing the next generation of innovation in AI-driven engineering.

Publication Profile

Orcid

Education

Dr. Yue Wu has a solid educational foundation in engineering and automation. He earned his Bachelor of Science (B.S.) in Automation from the Wuhan University of Technology in 2018. There, he developed a robust understanding of control systems, signal processing, and computational modeling. Pursuing his academic passion, he undertook doctoral studies at the School of Aeronautics and Astronautics, Zhejiang University, one of China’s premier research institutions. He completed his Ph.D. in 2023, focusing on interdisciplinary topics combining aeronautical engineering, data science, and intelligent systems. His doctoral work incorporated advanced machine learning techniques and their applications in hardware-aware environments, preparing him to lead novel research at the intersection of automation and electronics. This strong academic background equips him with the theoretical depth and practical experience essential for future-forward research in intelligent systems and electronic design automation.

Experience

Dr. Yue Wu is currently serving as an Assistant Professor at the School of Electronics and Information Engineering, Hangzhou Dianzi University, since 2023. Despite being in the early phase of his academic career, he has demonstrated exceptional scholarly promise through teaching, mentorship, and high-impact research. His role involves designing and delivering advanced courses on machine learning, logic circuits, and digital system design while actively supervising undergraduate and graduate research projects. He collaborates with interdisciplinary teams, focusing on the integration of machine learning techniques into physical design and logic synthesis processes, bridging hardware and AI innovations. Prior to this, he was involved in multiple research projects at Zhejiang University during his Ph.D., contributing to algorithm development and experimental validation of graph-based learning techniques. Dr. Wu’s combined expertise in automation, EDA tools, and machine learning positions him as a rising leader in academic research and technological advancement.

Awards and Honors

As a rising scholar, Dr. Yue Wu has been recognized for his academic achievements and research contributions. While specific institutional or national awards are yet to be recorded in the public domain, his selection as a faculty member at Hangzhou Dianzi University, known for its emphasis on electronic and information technology research, is a testament to his academic caliber. His recent first-author publication in the peer-reviewed journal “Automation” (2025) highlights his research excellence and innovation in the application of graph attention networks to pre-routing slack prediction, a complex problem in VLSI design. Additionally, his collaborative projects during his Ph.D. at Zhejiang University received internal recognition and contributed to multiple research grants. Dr. Wu’s research profile is steadily growing, and he is well on the path toward future accolades at the national and international levels as he continues to publish and lead in cutting-edge interdisciplinary domains.

Research Focus

Dr. Yue Wu’s research focuses on the intersection of machine learning and electronic design automation (EDA). His primary interest lies in developing intelligent systems that enhance the physical design and logic synthesis processes used in integrated circuit (IC) design. By leveraging advanced models like graph neural networks (GNNs) and attention-based architectures, Dr. Wu aims to address critical challenges such as slack prediction, timing analysis, and routing optimization. His expertise also extends to hardware-aware machine learning, wherein algorithmic efficiency is optimized for real-world applications in chip manufacturing. His recent work—“Pre-Routing Slack Prediction Based on Graph Attention Network”—demonstrates his ability to combine theoretical AI models with practical EDA problems. By pushing the boundaries of design automation through AI integration, Dr. Wu contributes to faster, smarter, and more power-efficient chip design—critical for the next generation of computing devices. His vision is to make intelligent design automation a core component of future electronics engineering.

Publication Top Notes

Pre-Routing Slack Prediction Based on Graph Attention Network

 

Shuai Cao | Computer Science and Artificial Intelligence | Best Researcher Award

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Shuai Cao | Computer Science and Artificial Intelligence | Best Researcher Award

Dr Shuai Cao, School of Automation, Wuhan University of Technology, China

Dr. Shuai Cao is a dynamic researcher in the field of Computational Intelligence, currently pursuing graduate studies at Kunming University of Science and Technology and engaging in joint research at the Guangdong Academy of Sciences. With a focus on enhancing meta-heuristic algorithms, Dr. Cao has contributed significantly to engineering optimization, especially in AGV path planning and offset printing machine design. He is the mind behind the innovative Piranha Foraging Optimization Algorithm (PFOA) and co-author of several impactful SCI/EI publications. His expertise in algorithm improvement, machine learning, and pattern recognition is reflected through funded projects and hands-on roles in top research institutions like the South China Intelligent Robot Innovation Institute. With a remarkable blend of theoretical insight and practical application, Dr. Cao is a promising candidate for the Best Researcher Award, embodying academic rigor and real-world impact.

Publication Profile 

Orcid

Education

Dr. Shuai Cao’s academic journey began at Baotou Rare Earth High-tech No. 1 High School (2014–2017), where he laid a strong foundation in the sciences. He pursued his undergraduate degree in Mechanical and Electronic Engineering at Chongqing University of Humanities, Science and Technology (2017–2021), gaining critical insights into systems design and robotics. Since 2021, he has been a postgraduate student in Electronic Information at Kunming University of Science and Technology, further sharpening his expertise in computational theory and algorithmic systems. Complementing his studies, Dr. Cao has been engaged in a joint training program at the Intelligent Manufacturing Institute of the Guangdong Academy of Sciences since 2022. His coursework includes meta-heuristic algorithms, machine learning, digital signal processing, and pattern recognition, all of which feed directly into his research in Computational Intelligence and engineering optimization. His interdisciplinary background empowers him to tackle complex problems with innovative solutions.

Experience

Dr. Shuai Cao has held impactful roles in prestigious research institutions. From May 2022 to July 2023, he worked at the Intelligent Manufacturing Institute of the Guangdong Academy of Sciences, where he conducted advanced research on AGV handling robots. This included applying improved intelligent algorithms for path planning and optimization scheduling—work closely aligned with his master’s thesis. Since July 2023, he has been with the South China Intelligent Robot Innovation Institute, applying swarm intelligence methods to optimize the structure of high-speed multi-color offset printing machines. Dr. Cao’s work integrates theoretical research with industrial application, setting a benchmark for practical relevance. His involvement in key science and innovation projects also reflects his growing leadership in the field. From optimization algorithms to real-world robotic systems, Dr. Cao’s hands-on approach is shaping the future of intelligent manufacturing.

Awards and Honors

Dr. Shuai Cao has earned distinguished recognition in both academic and research circles for his innovative contributions to engineering optimization. As a lead researcher on multiple government-funded projects—including “Research and Application of Intelligent Scheduling of Mobile Collaborative Robot Clusters for Intelligent Manufacturing” (Project Code: 2130218003022) and the “Foshan Science and Technology Innovation Team Project” (Project Code: FS0AA-KJ919-4402-0060)—he has demonstrated expertise in bridging theory with practical industrial solutions. His pioneering research has been published in high-impact SCI and EI journals and conferences, such as IEEE ACCESS and the International Conference on Robotics and Automation Engineering (ICRAE). A highlight of his work is the development of the Piranha Foraging Optimization Algorithm (PFOA), which has garnered considerable attention in the optimization community for its novelty and effectiveness. Dr. Cao’s sustained dedication to cutting-edge innovation, along with his leadership in collaborative, cross-disciplinary projects, makes him a compelling nominee for the Best Researcher Award.

Research Focus

Dr. Shuai Cao’s research is centered on Computational Intelligence, specifically the improvement and engineering application of swarm intelligence algorithms. His work addresses key challenges in traditional optimization methods, such as premature convergence, low population diversity, and slow optimization speeds. He has successfully designed algorithms that overcome these limitations, notably the Piranha Foraging Optimization Algorithm (PFOA). His research extends to practical applications like automated guided vehicle (AGV) path planning, scheduling in smart factories, and mechanical structure optimization for high-speed printing systems. Through interdisciplinary methods, he combines machine learning, pattern recognition, and digital signal processing to bring theoretical advancements into real-world manufacturing challenges. With a clear aim of enhancing intelligent manufacturing systems, his research contributes to both academic knowledge and industrial innovation. His growing body of work reflects originality, technical rigor, and a strong alignment with modern engineering demands.

Publication Top Notes

Research on the Design and Application of Multi-Port Energy Routers

 

Duantengchuan Li | Computer Science and Artificial Intelligence | Best Researcher Award

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Duantengchuan Li | Computer Science and Artificial Intelligence | Best Researcher Award

Assoc. Prof. Dr Duantengchuan Li, School of Information Management, Wuhan University, Wuhan, China, Cahina

Assoc. Prof. Dr. Duantengchuan Li is a distinguished researcher at the School of Information Management, Wuhan University, China 🎓. His expertise spans Recommender Systems, Knowledge Graphs, Reinforcement Learning, Autonomous Driving, Large Language Models, and Computer Vision 🤖📊. With 40+ publications in top-tier journals and conferences such as IEEE TKDE, ACM TWEB, and AAAI 📜, Dr. Li has earned over 800 citations on Google Scholar 🌍. He has served as a Guest Editor for Electronics and a reviewer for premier journals, including IEEE TNNLS, IEEE TII, and Information Sciences 📝. Dr. Li’s impactful research contributions in AI and machine learning make him a leading expert in the field 🚀. His achievements include multiple national and provincial scholarships and a Bronze Medal in the “Internet+” Competition 🏅. His commitment to advancing AI-driven solutions for real-world applications makes him a strong candidate for the Best Researcher Award 🌟.

Publication Profile

Google Scholar

Education

Dr. Duantengchuan Li holds a Ph.D. in Computer Science from Wuhan University, China 🎓, where he specialized in AI-driven Recommender Systems and Knowledge Graphs 🤖📊. Prior to his Ph.D., he earned a Master’s degree from the Faculty of Artificial Intelligence in Education, Central China Normal University 🏫. His academic journey began with a Bachelor’s degree in Computer Science, where he honed his skills in machine learning, deep learning, and computational intelligence 💻. Throughout his education, he actively engaged in cutting-edge research and contributed to high-impact publications 📜. His strong academic foundation has paved the way for groundbreaking work in large-scale AI applications and intelligent systems 🚀. With an outstanding academic record and multiple scholarships, Dr. Li has established himself as a leading AI researcher, dedicated to advancing computational intelligence, knowledge-based systems, and deep learning architectures 🏆.

Experience

Dr. Duantengchuan Li is currently an Associate Researcher at the School of Information Management, Wuhan University, China 🏫. He has extensive experience in artificial intelligence, knowledge graphs, recommender systems, and deep learning 🤖. Dr. Li has been actively involved in academic publishing, serving as a Guest Editor for Electronics and as a reviewer for prestigious journals like IEEE TKDE, ACM TKDD, and IEEE TNNLS 📝. His research has been featured in top CCF A & B-ranked journals and conferences, including AAAI, ICWS, CAiSE, and IEEE Transactions 📊. Before joining Wuhan University, he completed his Ph.D. in Computer Science, contributing to AI-driven recommendation models 💡. His expertise extends to autonomous driving, reinforcement learning, and computer vision, and he continues to mentor young researchers in AI applications 🚀. His contributions in intelligent computing and AI research have made him a leading figure in his field 🌍.

Awards & Honors

Dr. Duantengchuan Li has received numerous accolades for his contributions to AI and computer science 🏆. In 2023, he led a team to win the Bronze Award in the prestigious “Internet+” Competition 🏅. His academic excellence was recognized with the National Scholarship (2019) 🎓 and the Provincial Outstanding Graduate Award (2017) 🏅. Additionally, he was honored with the Provincial Government Scholarship (2015) for his outstanding performance in research and academics 📜. Dr. Li also holds a Network Engineer Qualification Certification (2016), further demonstrating his technical expertise 💻. His contributions in AI research, particularly in deep learning, recommender systems, and autonomous driving, have earned him a spot among China’s top researchers 🚀. With 40+ high-impact publications and 800+ citations, Dr. Li’s work continues to shape the future of artificial intelligence and machine learning 🌟.

Research Focus

Dr. Duantengchuan Li’s research primarily focuses on Recommender Systems, Knowledge Graphs, Reinforcement Learning, Large Language Models, Autonomous Driving, and Computer Vision 🤖📊. His work explores efficient AI-driven recommendations, leveraging graph neural networks, deep learning, and sequential modeling to improve information retrieval 📜. He has also contributed to structured output evaluation for Large Language Models (LLMs), optimizing their prompt engineering and reasoning capabilities 💡. In autonomous driving, his research enhances intelligent vehicle navigation using deep reinforcement learning 🚗. Additionally, he has developed advanced cold-start QoS prediction models and multi-relation modeling for personalized recommendations 🔍. His work has been published in IEEE TKDE, ACM TOSEM, AAAI, and Information Sciences, demonstrating his cutting-edge innovations in AI applications 🚀. By integrating machine learning, knowledge graphs, and neural networks, Dr. Li continues to advance intelligent computing solutions for real-world problems 🌍.

Publication Top Notes

MFDNet: Collaborative poses perception and matrix Fisher distribution for head pose estimation

EDMF: Efficient deep matrix factorization with review feature learning for industrial recommender system

Multi-perspective social recommendation method with graph representation learning

CARM: Confidence-aware recommender model via review representation learning and historical rating behavior in the online platforms

Knowledge graph representation learning with simplifying hierarchical feature propagation

Knowledge graph representation learning with simplifying hierarchical feature propagation

Precise head pose estimation on HPD5A database for attention recognition based on convolutional neural network in human-computer interaction

Integrating user short-term intentions and long-term preferences in heterogeneous hypergraph networks for sequential recommendation

 

Bhanu Shrestha | Computer Science and Artificial Intelligence | Best Researcher Award

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Bhanu Shrestha | Computer Science and Artificial Intelligence | Best Researcher Award

Prof. Dr Bhanu Shrestha, Kwangwoon University, South Korea

Prof. Dr. Bhanu Shrestha is a distinguished academic in Electronic Engineering, with a Ph.D. from Kwangwoon University, Seoul, Korea. He has been active in various leadership roles, including Chairman of ICT-AES and Editor-in-Chief of the International Journal of Advanced Engineering. Dr. Shrestha has contributed extensively to research, with notable book publications and multiple awards, including the “Achievement Award” from IIBC Korea and “Best Paper Award” at ISSAC 214. His work spans various international conferences, focusing on advanced engineering, nanotechnology, and biosensor applications. 🌍📚🏅💻🧑‍🔬

Publication Profile

Scopus

Education

Prof. Dr. Bhanu Shrestha has an extensive academic background in Electronic Engineering. He completed his Ph.D. in Electronic Engineering at Kwangwoon University, Seoul, Korea (2004-2008), after earning his M.S. in the same field at the same institution (2002-2004). Dr. Shrestha’s journey in engineering began with a B.S. in Electronic Engineering from Kwangwoon University (1994-1998). His years of dedication to education and research have contributed significantly to advancements in the field of electronics. ⚙️🎓📡

Experience

Prof. Dr. Bhanu Shrestha is a distinguished leader in engineering, serving as Chairman of ICT-AES from 2022 to 2024. With a long tenure as the Editor-in-Chief of the International Journal of Advanced Engineering, he has shaped academic discourse in the field. His active involvement with the Nepal Engineering Council (NEC) and Nepal Engineers’ Association (NEA) further cements his influence in Nepal’s engineering community. Prof. Shrestha’s commitment to advancing engineering practices is evident through his leadership roles and active contributions to both national and international engineering platforms. 🛠️📚🔧🌍

Honor & Awards

Prof. Dr. Bhanu Shrestha has received numerous prestigious awards throughout his career. Notably, he was honored with the “Achievement Award” from IIBC Korea (2015) 🏆 and multiple “Best Paper Awards” from ISSAC 214 and ICACT (2014) 📄. He also earned the “Excellent Paper Award” from the Korea Institute of Information Technology (2012) 🏅 and the “Certificate of Honorary Citizenship” from the Mayor of Seong-buk, Seoul (2012) 🏙️. His accolades extend to Nepal, where he received the presidential “Nepal Vidhyabhusan Padak ‘Ka’” Gold Medal (2009) 🥇, and several honors for his contributions to Taekwondo and Hapkido 🥋.

Research Focus

Prof. Dr. Bhanu Shrestha’s research focuses on advanced computational techniques, particularly in the intersection of artificial intelligence (AI) and engineering. He explores areas such as machine learning, metaheuristics, and optimization methods applied to real-world challenges in fields like medical imaging (e.g., SPECT-MPI cardiovascular disease classification), traffic accident prediction, and network security. His work also extends to customer churn prediction in telecom industries and network security improvements. Shrestha’s contributions aim to enhance system efficiency, prediction accuracy, and security across diverse technological and engineering domains. 🧠💻⚙️🩺📡

Editorial and Conference

Prof. Dr. Bhanu Shrestha has made significant contributions to the field of engineering through his active involvement in international conferences like ISGMA 2015 and the International Conference on ICT & Digital Convergence (2018) 🌍📡. His dedication to global collaboration is evident in his participation in these events. Additionally, his editorial roles highlight his commitment to maintaining high-quality research output 📚📝. Prof. Dr. Shrestha continues to play a crucial role in advancing engineering through his global outreach, fostering innovation, and contributing to the growth of academic knowledge in his field. 🌟💡

Publication Top Notes

Multi-Scale Dilated Convolution Network for SPECT-MPI Cardiovascular Disease Classification with Adaptive Denoising and Attenuation

CorrectionSpecial Issue on Data Analysis and Artificial Intelligence for IoT

Correction to: A Proposed Waiting Time Algorithm for a Prediction and Prevention System of Traffic Accidents Using Smart Sensors (Electronics, (2022), 11, 11, (1765), 10.3390/electronics11111765)

Levy Flight-Based Improved Grey Wolf Optimization: A Solution for Various Engineering Problems

Leveraging metaheuristics with artificial intelligence for customer churn prediction in telecom industries

A Study on Improving M2M Network Security through Abnormal Traffic Control

Generative Adversarial Networks with Quantum Optimization Model for Mobile Edge Computing in IoT Big Data

 

Yunqiang Sun | Artificial Intelligence | Best Researcher Award

Published on by Research Hypothesis

Yunqiang Sun | Artificial Intelligence | Best Researcher Award

Prof. Dr Yunqiang Sun, 中北大学, China

Prof. Dr. Yunqiang Sun🌐📡 is a distinguished scholar specializing in automatic modulation recognition (AMR), wireless communications, and intelligent sensor networks. He has contributed groundbreaking research, including the development of the Multimodal Parallel Hybrid Neural Network (MPHNN), which achieves 93.1% recognition accuracy with reduced complexity. His expertise spans spatio-temporal signal processing, attention mechanisms, and hybrid neural networks. Prof. Sun has published extensively, with works featured in prestigious journals like Electronics (Switzerland) and IEEE Access. His research also explores gait recognition algorithms, millimeter-wave cavity filters, and ultrasonic signal transmission. A dedicated innovator, Prof. Sun’s work advances technologies in communication and sensing systems. 📊📖✨

Publication Profile

Scopus

Proposed Solution 🤖✨

The Multimodal Parallel Hybrid Neural Network (MPHNN) is an advanced model designed to address limitations in processing modulated signals. It preprocesses these signals in multimodal formats, enhancing data interpretation. By combining Convolutional Neural Networks (CNN) for spatial feature extraction and Bidirectional Gated Recurrent Units (Bi-GRU) for temporal feature processing, MPHNN efficiently captures both spatial and temporal dependencies. This innovative approach enables more accurate and robust signal processing, making it highly effective in various applications. Prof. Dr. Yunqiang Sun’s work highlights the power of integrating multiple neural network models for improved performance. 🧠🔧📡📊

Attention Mechanisms 🎯🔗

Prof. Dr. Yunqiang Sun’s research leverages advanced deep learning techniques to enhance recognition accuracy. By integrating the Convolutional Block Attention Module (CBAM) and Multi-Head Self-Attention (MHSA), his work in the Multi-Path Hierarchical Neural Network (MPHNN) effectively combines both temporal and spatial features. This fusion allows for improved recognition performance in complex tasks, as the model focuses on the most relevant information across time and space. Prof. Sun’s innovative approach showcases the power of attention mechanisms in modern neural networks. 🤖📊🧠🔍

Results 📊✅

Prof. Dr. Yunqiang Sun, MPHNN, has achieved an impressive 93.1% accuracy across multiple datasets, setting a new benchmark in model performance. His work stands out due to its lower complexity and reduced number of parameters compared to existing models, making it more efficient and scalable. This breakthrough represents a significant advancement in the field, offering a solution that balances high accuracy with computational efficiency. Prof. Sun’s innovative approach holds great promise for a wide range of applications, offering potential improvements in performance and resource utilization. 🔬📊💡📈

Diverse Publication Record

Prof. Dr. Yunqiang Sun is an accomplished researcher with a focus on AMR, gait recognition algorithms, and plasmonic waveguide-coupled systems. He has published extensively in prestigious journals such as IEEE Access, Electronics (Switzerland), and Advanced Composites and Hybrid Materials. Notable works include impactful publications like CTRNet: An Automatic Modulation Recognition Based on Transformer-CNN Neural Network and Research on Modulation Recognition Algorithm Based on Channel and Spatial Self-Attention Mechanism. Prof. Sun’s research continues to push the boundaries of technology, contributing significantly to the fields of signal processing and machine learning. 📚🔬📈💡

Citations and Recognition

Prof. Dr. Yunqiang Sun has contributed significantly to the field, with some recent works gaining traction and fewer citations, while others, like his paper on MEMS sensors in Cluster Computing, showcase a higher citation count, reflecting their enduring influence. His research spans various areas, where his innovative approaches and technical expertise continue to shape discussions and advancements in the field. Despite the varying citation impact, Prof. Sun’s work maintains its relevance and continues to inspire future developments in the areas he studies. 🌟📚🔬🧠📈

Research Focus

Prof. Dr. Yunqiang Sun’s research focuses on advanced signal processing, modulation recognition, and sensor technologies. He explores machine learning models like transformers and convolutional neural networks (CNNs) for automatic modulation recognition and signal analysis, with applications in communication systems. His work also extends to gait recognition using algorithms based on compressed sensing and MEMS sensors, which contribute to innovations in human-computer interaction and health monitoring. Prof. Sun’s expertise spans across ultrasonic wave transmission in negative refractive materials and advanced filter designs in millimeter-wave systems, with a strong emphasis on the intersection of signal processing and emerging technologies. 📡🤖📊

Publication Top Notes

CTRNet: An Automatic Modulation Recognition Based on Transformer-CNN Neural Network

Quadrule-passband millimeter-wave cavity filter based on non-resonant node

Transmission characteristics of ultrasonic longitudinal wave signals in negative refractive index materials

Numerical calculus solution of gait recognition algorithm based on compressed sensing

Application and research of MEMS sensor in gait recognition algorithm

 

 

TaiLong Lv | Computer Science and Artificial Intelligence | Best Researcher Award

Published on by Research Hypothesis

TaiLong Lv | Computer Science and Artificial Intelligence | Best Researcher Award

Mr Lu Tailong, Xi’an University of Posts and Telecommunications, China

Based on the provided information, Mr. Tailong Lv appears to have a solid academic and research background, but whether he is a suitable candidate for the Best Researcher Award would depend on various factors such as the scope of his contributions, the significance of his research, and his overall impact. Below is an analysis of his qualifications:

Publication profile

Orcid

Educational Background

Mr. Tailong Lv holds a Bachelor’s degree in Automation from Henan University of Urban Construction and is currently pursuing a Master’s degree in Mechanical Engineering at Xi’an University of Posts and Telecommunications. His educational background shows strong technical skills in automation and mechanical engineering, which are highly relevant to his research on human activity recognition.

Research Projects

His primary research involves developing a deep learning-based neural network for human activity recognition. This project is technically sophisticated, as it focuses on optimizing neural networks to improve accuracy in recognizing both simple and complex human actions. This level of complexity shows his ability to handle advanced machine learning and AI concepts, making his research valuable in fields like robotics, healthcare, and automation.

Awards and Scholarships

Mr. Tailong Lv has been recognized with scholarships from Xi’an University of Posts and Telecommunications in 2022 and 2023. These awards demonstrate his academic excellence and indicate that he is a strong performer within his institution.

Publication

His publication, “Multihead-Res-SE Residual Network with Attention for Human Activity Recognition,” is an impressive achievement. This peer-reviewed article, published in Electronics, showcases his contribution to deep learning and neural networks. Collaborative work with other experts also highlights his ability to work in a team and contribute to impactful research.

Skills

His proficiency in Python and deep learning neural networks, as well as his fluency in English, are essential skills for international collaboration and publishing. These competencies make him a versatile researcher capable of tackling modern challenges in AI and automation.

Conclusion

Mr. Tailong Lv has demonstrated academic excellence, technical expertise, and research accomplishments that make him a strong candidate for research-based recognition. However, the Best Researcher Award typically requires groundbreaking contributions or a significant body of work. While he shows promise, his current profile might be better suited for emerging researcher or early-career researcher awards rather than the highest accolades in research.

Publication top notes

Multihead-Res-SE Residual Network with Attention for Human Activity Recognition

 

ABDULKADIR DAUDA | Computer Science and Artificial Intelligence | Best Researcher Award

Published on by Research Hypothesis

ABDULKADIR DAUDA | Computer Science and Artificial Intelligence | Best Researcher Award

ABDULKADIR DAUDA, University of Reims Champagne-Ardenne, France

Based on the information provided, Mr. Abdulkadir Dauda appears to be a strong candidate for the Best Researcher Award. His academic background, professional experience, and research contributions highlight his qualifications and impact in the field of computer science. Below is an evaluation of his suitability for the award:

Publication profile

Orcid

Academic and Professional Qualifications

Mr. Dauda has a robust academic background, including a Master of Science Degree in Computer Science with Distinction and ongoing doctoral studies at Universite De Reims Champagne-Ardenne, France. His academic achievements, particularly his distinction at the Master’s level, underscore his dedication and capability in his field.

Work Experience and Contributions

Mr. Dauda’s professional experience as a Lecturer II in the Department of Computer Science at the Federal University of Lafia (2014-2022) demonstrates his commitment to education and research. He has taken on significant roles, such as Departmental Examination Officer and Programme Coordinator, which show his leadership and involvement in academic administration. His work in system and network administration during his tenure at the Federal Capital Territory Judiciary further highlights his practical expertise in computer science.

Research Contributions

Mr. Dauda has an impressive portfolio of research publications that focus on critical areas such as IoT Security, High-Performance Computing, and Distributed and Parallel Architectures. His publications in reputed journals and conferences, including the 2023 International Conference on Wireless Networks and Mobile Communications (WINCOM), demonstrate his active engagement in advancing knowledge in these fields. His collaborative work with international scholars further reflects the quality and impact of his research.

Research Interests and Impact

Mr. Dauda’s research interests in emerging and high-impact areas like IoT Security and Big Data are particularly relevant in today’s technological landscape. His contributions to these fields, through both his research and practical work, suggest a deep understanding and innovative approach to solving complex problems in computer science.

Conclusion

Mr. Abdulkadir Dauda’s academic excellence, professional experience, and significant research contributions make him a suitable candidate for the Best Researcher Award. His work not only advances the field of computer science but also demonstrates a commitment to teaching, mentoring, and community service, further solidifying his qualification for this honor.

Publication top notes

A Survey on IoT Application Architectures