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.

Mohammed Almulla | Artificial Intelligence | Best Researcher Award

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Prof. Mohammed Almulla | Artificial Intelligence | Best Researcher Award

Prof. Mohammed Almulla, Kuwait University, Kuwait

Prof. Mohammed Ali Almulla, a distinguished Kuwaiti computer scientist, serves as a Professor at Kuwait University. With a Ph.D. in Computer Science from McGill University, he has contributed extensively to academia, research, and administrative leadership. His expertise spans artificial intelligence, automated theorem proving, and IT consultancy. Prof. Almulla has held prominent roles, including Chairman of the Computer Science Department and Acting Vice President for Academic Support Services. Beyond academia, he has influenced national IT policies as an advisor. A dedicated educator and researcher, he actively supports academic development and technological innovation.

Publication Profile

Scopus

Google Scholar

🎓 Education

Prof. Mohammed Almulla earned his B.Sc., M.Sc., and Ph.D. in Computer Science from McGill University, Canada. His Ph.D. thesis, “Analysis of the Use of Semantic Trees in Automated Theorem Proving,” reflects his deep interest in artificial intelligence. His rigorous academic training equipped him with comprehensive expertise in programming, networking, and advanced computer science concepts. With a solid foundation in both theoretical and applied research, Prof. Almulla has contributed to academic growth and scientific discovery. His multilingual proficiency in Arabic and English further enhances his research collaborations and educational impact.

💼 Experience

Prof. Mohammed Almulla has an illustrious career in academia and administration. Since 1995, he has progressed from Assistant Professor to Professor at Kuwait University. He served as Chairman of the Computer Science Department, securing ABET accreditation. His leadership extended to acting roles as Vice President for Academic Support Services and Planning. Prof. Almulla also contributed as an IT Consultant for Kuwait’s Council of Ministers and led the AI Policy Implementation Committee. With decades of service in education, administration, and national IT development, his expertise remains highly influential in Kuwait’s technological landscape.

🏆 Awards and Honors

Prof. Mohammed Almulla has received numerous accolades for his academic and administrative contributions. Notably, he served as a member and coordinator of the Evaluation Committee for the H.H. Sheik Salem Al-Ali AlSabah Award for Informatics, earning recognition for his dedication to technological advancements. As a valued IT consultant and university leader, his work has significantly shaped Kuwait’s digital landscape. His participation in major university and national projects has further solidified his reputation as a pioneer in computer science and informatics.

🔎 Research Focus

Prof. Mohammed Almulla’s research interests include artificial intelligence, automated theorem proving, and decision support systems. His work explores the applications of semantic trees in AI-driven problem-solving. With a passion for advancing intelligent systems, he investigates areas like AI policy implementation and large-scale data analysis. His contributions as a reviewer for over 30 prestigious journals emphasize his influence in the field. Additionally, Prof. Almulla is committed to mentoring students and advancing AI technologies to address real-world challenges.

 

Publication Top Notes

  • 📝 The Effectiveness of the Project-Based Learning (PBL) Approach as a Way to Engage Students in Learning – 947 citations (2020)

  • 🌿 Integrated Social Cognitive Theory with Learning Input Factors: The Effects of Problem-Solving Skills and Critical Thinking Skills on Learning Performance Sustainability – 104 citations (2023)

  • 🎓 Constructivism Learning Theory: A Paradigm for Students’ Critical Thinking, Creativity, and Problem Solving to Affect Academic Performance in Higher Education – 94 citations (2023)

  • 📖 Investigating Teachers’ Perceptions of Their Own Practices to Improve Students’ Critical Thinking in Secondary Schools in Saudi Arabia – 56 citations (2018)

  • 🧠 Using Conceptual Mapping for Learning to Affect Students’ Motivation and Academic Achievement – 47 citations (2021)

  • 🏫 An Investigation of Teachers’ Perceptions of the Effects of Class Size on Teaching – 46 citations (2015)

  • 📘 The Efficacy of Employing Problem-Based Learning (PBL) Approach as a Method of Facilitating Students’ Achievement – 44 citations (2019)

  • 💻 Technology Acceptance Model (TAM) and E-Learning System Use for Education Sustainability – 38 citations (2021)

  • 🤖 Investigating Influencing Factors of Learning Satisfaction in AI ChatGPT for Research: University Students Perspective – 24 citations (2024)

  • 🧑‍🏫 Using Digital Technologies for Testing Online Teaching Skills and Competencies During the COVID-19 Pandemic – 24 citations (2022)

  • 🧑‍🤝‍🧑 An Investigation of Cooperative Learning in a Saudi High School: A Case Study on Teachers’ and Students’ Perceptions and Classroom Practices – 24 citations (2017)

  • 🏗 Investigating Important Elements That Affect Students’ Readiness for and Practical Use of Teaching Methods in Higher Education – 15 citations (2022)

  • 📊 Developing a Validated Instrument to Measure Students’ Active Learning and Actual Use of Information and Communication Technologies for Learning in Saudi Arabia’s Higher Education – 8 citations (2022)

  • 🏅 An Investigation of Saudi Teachers’ Perceptions Towards Training in Cooperative Learning – 8 citations (2016)

  • 🌐 The Changing Educational Landscape for Sustainable Online Experiences: Implications of ChatGPT in Arab Students’ Learning Experience – 5 citations (2024)

  • 📲 Investigating Students’ Intention to Use M-Learning: The Mediating Role of Mobile Usefulness and Intention to Use – 5 citations (2024)

  • 🖥 Using Digital Technologies for Testing Online Teaching Skills and Competencies During the COVID-19 Pandemic – 4 citations (2022)

  • 👫 Students’ Perceptions of the Academic and Social Benefits of Working with Cooperative Learning – 3 citations (2016)

 

 

Zuheng Ming | Artificial intelligence | Best Researcher Award

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Dr. Zuheng Ming | Artificial intelligence | Best Researcher Award

Associate professor at Sorbonne Paris North University, France

🧑‍🏫 Dr. Zuheng Ming is an Assistant Professor at L2TI, Sorbonne Paris North University, France. He earned his PhD in 2013 from Grenoble Alpes University 🇫🇷, specializing in speech parameter mapping. His expertise spans multimodal learning, computer vision, and deep learning 🤖. Dr. Ming has 30+ publications 📝 in top-tier journals (JCR Q1/Q2) and conferences (ICIP, ICPR, ICDAR). He has supervised doctoral and master’s theses and collaborated internationally with CVC, RIKEN AIP, and Oulu University 🌍. He has led funded research projects on face anti-spoofing and document analysis 📄. Additionally, he serves as a guest editor and reviewer for prestigious journals. ✨

Publication Profile

Google Scholar

🏅 Professional Experience

Dr. Zuheng Ming is an accomplished researcher and educator in computer vision and deep learning 🤖. Since September 2022, he has been serving as an Assistant Professor at L2TI, Sorbonne Paris North University, France 🇫🇷. Prior to this, he was a Lecture-Researcher at L3i, La Rochelle University (2021-2022) 📚. From 2016 to 2021, he worked as a Postdoctoral Fellow and Assistant Lecturer at L3i, La Rochelle University. Earlier, from 2014 to 2015, he pursued a postdoctoral fellowship at Bordeaux University 🏛️, contributing significantly to cutting-edge research in multimodal learning and artificial intelligence. ✨

🎓 Educational Background

Dr. Zuheng Ming holds a PhD in Computer Science from Grenoble Alpes University, France (2013) 🇫🇷, where he specialized in spectral parameters mapping for cued speech using multi-linear and GMM approaches 🔬. He earned his Master’s degree in Pattern Recognition and Artificial Intelligence from Beijing Institute of Technology (2008) 🎭🤖. His academic journey began with a Bachelor’s degree in Electronic and Automatic Systems Engineering from Hunan University, China (2003) ⚡. His strong educational foundation has driven his research contributions in computer vision, deep learning, and multimodal learning 📚✨.

🔬 Research Activities

Dr. Zuheng Ming has been actively involved in research supervision, mentoring 1 PhD thesis, 2 Master’s theses, and 6 internships 🎓📖. He has established six international collaborations with prestigious institutions, including CVC (Spain) 🇪🇸, RIKEN AIP (Japan) 🇯🇵, Oulu University (Finland) 🇫🇮, Northwestern Polytechnical University (China) 🇨🇳, and Xidian University (China) 🇨🇳. His global academic engagement also includes an academic visit to Kyoto University, Japan, in 2015 🌍🏫. Through his extensive research network, Dr. Ming continues to make significant contributions to computer vision, deep learning, and multimodal learning 📊🤖.

🎓 Teaching Experience

Dr. Zuheng Ming has extensive teaching experience in cutting-edge technologies related to artificial intelligence and computer vision 🧠📸. He has taught courses on Deep Learning, Advanced Image Processing, and Intelligent Systems in Computer Vision 🤖🖼️, equipping students with the latest advancements in AI. Additionally, he has imparted knowledge in Database Management and Object-Oriented Programming 💾💻, fostering strong software development skills. His expertise in both theoretical foundations and practical applications makes him a valuable mentor in the field of AI and computer vision, guiding students toward innovative research and industry-ready solutions 🚀📚.

🔍 Research Focus

Dr. Zuheng Ming’s research primarily focuses on computer vision, deep learning, and document security 🧠📸🔏. His contributions span facial recognition, anti-spoofing techniques, and face liveness detection 🤖😃, enhancing biometric security. He has also worked extensively on document image classification and authentication 📄🔍, improving identity verification systems. His expertise in multi-modal learning, pattern recognition, and deep feature fusion enables advancements in AI-driven document forensics and secure authentication 🚀🔐. Collaborating internationally, he applies machine learning and self-attention networks to solve real-world challenges in face recognition, fraud detection, and intelligent systems 🌍🔬.

Publication Top Notes

📸 A survey on anti-spoofing methods for facial recognition with RGB cameras of generic consumer devices – Z Ming, M Visani, MM Luqman, JC Burie | Journal of Imaging | 88 citations | 2020

📄 Visual and textual deep feature fusion for document image classification – S Bakkali, Z Ming, M Coustaty, M Rusiñol | IEEE/CVF Conference on Computer Vision | 63 citations | 2020

🔍 Simple triplet loss based on intra/inter-class metric learning for face verification – Z Ming, J Chazalon, MM Luqman, M Visani, JC Burie | IEEE/CVF International Conference on Computer Vision | 57 citations | 2017

😊 Facial action units intensity estimation by fusion of features with multi-kernel SVM – Z Ming, A Bugeau, JL Rouas, T Shochi | IEEE International Conference on Automatic Face and Gesture Recognition | 54 citations | 2015

🆔 MIDV-2020: A comprehensive benchmark dataset for identity document analysis – BK Bulatovich, EE Vladimirovna, TD Vyacheslavovich, SN Sergeevna, … | Computer Optics | 51 citations | 2022

🙂 Dynamic Multi-Task Learning for Face Recognition with Facial Expression – Z Ming, J Xia, MM Luqman, JC Burie, K Zhao | IEEE/CVF International Conference on Computer Vision Workshop | 40 citations | 2019

📜 VLCDoC: Vision-language contrastive pre-training model for cross-modal document classification – S Bakkali, Z Ming, M Coustaty, M Rusiñol, OR Terrades | Pattern Recognition | 33 citations | 2023

🔐 FaceLiveNet: End-to-end networks combining face verification with interactive facial expression-based liveness detection – Z Ming, J Chazalon, MM Luqman, M Visani, JC Burie | International Conference on Pattern Recognition | 30 citations | 2018

📑 Cross-modal deep networks for document image classification – S Bakkali, Z Ming, M Coustaty, M Rusiñol | IEEE International Conference on Image Processing | 23 citations | 2020

📃 Document liveness challenge dataset (DLC-2021) – DV Polevoy, IV Sigareva, DM Ershova, VV Arlazarov, DP Nikolaev, Z Ming, … | Journal of Imaging | 21 citations | 2022

📹 ViTransPAD: Video Transformer using convolution and self-attention for Face Presentation Attack Detection – Z Ming, Z Yu, M Al-Ghadi, M Visani, M Muzzamil Luqman, JC Burie | IEEE International Conference on Image Processing | 21 citations | 2022

🌲 Multiple sources data fusion via deep forest – J Xia, Z Ming, A Iwasaki | IGARSS IEEE International Geoscience and Remote Sensing Symposium | 15 citations | 2018

🆔 Face detection in camera captured images of identity documents under challenging conditions – S Bakkali, MM Luqman, Z Ming, JC Burie | International Conference on Document Analysis and Recognition Workshops | 11 citations | 2019

📑 EAML: Ensemble self-attention-based mutual learning network for document image classification – S Bakkali, Z Ming, M Coustaty, M Rusiñol | International Journal on Document Analysis and Recognition | 10 citations | 2021

🧠 Synthetic evidential study as augmented collective thought process – Preliminary report – T Nishida, M Abe, T Ookaki, D Lala, S Thovuttikul, H Song, Y Mohammad, … | ACIIDS Asian Conference | 10 citations | 2015

🆔 Identity documents authentication based on forgery detection of guilloche pattern – M Al-Ghadi, Z Ming, P Gomez-Krämer, JC Burie | arXiv preprint | 8 citations | 2022

 

Hong Wang | Artificial Intelligence | Best Researcher Award

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Prof. Hong Wang | Artificial Intelligence | Best Researcher Award

Prof. Hong Wang, Shandong Normal University, China

Prof. Wang earned his Ph.D. in Computer Science from the Chinese Academy of Sciences. His research focuses on Artificial Intelligence, Machine Learning, Healthcare Big Data, and Bioinformatics. 🧠 He has extensive teaching experience, with roles from Lecturer to Doctoral Supervisor. He has received multiple honors, including the Outstanding Graduate Tutor award and Shandong Province Science and Technology Progress prizes. 🏆 Prof. Wang has published widely, including papers on molecular property prediction and drug interactions. His current research includes cutting-edge AI applications in health. 💻

 

Publication Profile

Google Scholar

Education Background 🎓

Prof. Hong Wang completed his PhD in Computer Science from the Chinese Academy of Sciences in Beijing, China, from 1999 to 2002. Prior to that, he earned a Master of Science in Computer Science from Tianjin University in Tianjin, China, between 1988 and 1991. His academic journey began at Tianjin University, where he obtained his Bachelor of Science in Computer Science in 1988. His strong educational foundation has supported his exceptional career in AI, machine learning, and bioinformatics. 📚💻

 

Working Experience 👨‍🏫

Prof. Hong Wang has had a distinguished academic career at Shandong Normal University, starting as a Teaching Assistant from 1991 to 1995. He then served as a Lecturer from 1995 to 2000 and quickly advanced to the position of Associate Professor from 2000 to 2006. Since 2006, he has held the prestigious title of Professor, contributing significantly to the university’s academic growth. In 2009, Prof. Wang also became a Doctoral Supervisor, guiding the next generation of scholars and researchers. His career spans over three decades, focusing on teaching, research, and mentorship. 🎓📚👨‍🔬

 

Honors and Awards 🏅

Prof. Hong Wang has received numerous prestigious honors throughout his career, reflecting his dedication and contributions to academia. In March 2021, he was recognized as a March 8th Red Banner Holder. He was named Outstanding Graduate Tutor in September 2021 for his exceptional mentoring. In March 2019, he received the award for Outstanding Contribution to Achievement. His excellence in teaching was acknowledged with the University-Level Distinguished Teacher award in December 2014, followed by the Individual with Excellence in Teacher Ethics award in September 2014. Additionally, he was honored as a Good Teacher and Friend to College Students in January 2003. 🌟🎓👨‍🏫

 

Research Experience and Achievements 🔬

Prof. Hong Wang has led impactful research projects, including funding from the National Natural Science Foundation of China, with programs spanning from 2021 to 2024 (62072290) and 2017 to 2020 (61672329). He is also part of the Jinan City Science and Technology Bureau project from 2023 to 2024 (202228110). His outstanding contributions have earned him several prestigious awards, such as the Shandong Computer Society Science and Technology Progress Second Prize (First Place) in July 2024. Additionally, he received the Shandong Province Science and Technology Progress First Prize (7th place) in December 2022 and the Shandong Province Higher Education Outstanding Research Achievements Second Prize (First Place) in both 2020 and 2018. 🏆📚

 

Publication Top Notes

  • EDDINet: Enhancing drug-drug interaction prediction via information flow and consensus constrained multi-graph contrastive learning – 2024
  • EMPPNet: Enhancing Molecular Property Prediction via Cross-modal Information Flow and Hierarchical Attention – Cited by 3, 2023
  • GCNs–FSMI: EEG recognition of mental illness based on fine-grained signal features and graph mutual information maximization – Cited by 8, 2023
  • Detecting depression tendency with multimodal features – Cited by 9, 2023
  • A Soft-Attention Guidance Stacked Neural Network for neoadjuvant chemotherapy’s pathological response diagnosis using breast dynamic contrast-enhanced MRI – Cited by 1, 2023
  • Adaptive dual graph contrastive learning based on heterogeneous signed network for predicting adverse drug reactions – Cited by 6, 2023
  • Predicting drug-drug adverse reactions via multi-view graph contrastive representation model – Cited by 11, 2023
  • Explainable knowledge integrated sequence model for detecting fake online reviews – Cited by 9, 2023
  • CasANGCL: Pre-training and fine-tuning model based on cascaded attention network and graph contrastive learning for molecular property prediction – Cited by 19, 2023
  • Dual network contrastive learning for predicting microbe-disease associations – Cited by 2, 2022
  • Knowledge graph construction for computer networking course group in secondary vocational school based on multi-source heterogeneous data – Cited by 2, 2022
  • Test Paper Generation Based on Improved Genetic Simulated Annealing Algorithm – 2022
  • MS-ADR: Predicting drug–drug adverse reactions based on multi-source heterogeneous convolutional signed network – Cited by 6, 2022
  • Medical concept integrated residual short‐long temporal convolutional networks for predicting clinical events – Cited by 1, 2022