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.

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

 

Rainer Knauf | Evolutionary Algorithms | Lifetime achievement Award

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Prof Dr Rainer Knauf |  Evolutionary Algorithms |  Lifetime achievement Award

Fachgebietsleiter für KI at  Technische Universität Ilmenau, Germany

Rainer Knauf is an apl. Prof. Dr.-Ing. habil., currently serving as the Chair of Artificial Intelligence at the Faculty of Computer Science and Automation, Technical University Ilmenau, Germany. He earned his Diploma Engineer (Dipl.-Ing.) in Electrical and Computer Engineering in 1987, followed by a Doctor of Engineering (Dr.-Ing.) in Computer Engineering in 1990, and a Doctor of Engineering habilitatus (Dr.-Ing. habil.) in Computer Science in 2000, all from Technical University Ilmenau. His research focuses on knowledge acquisition, validation, and refinement of intelligent systems, inductive inference, and machine learning.

 

profile

🎓 Education:

  • Dipl.-Ing. in Electrical and Computer Engineering
    Technical University Ilmenau, Germany
    📅 February 5, 1987
  • Dr.-Ing. in Computer Engineering
    Technical University Ilmenau, Germany
    📅 September 25, 1990
    Dissertation: “Applying Logic Programming to Design Knowledge Based Systems for Diagnostic Problems”
  • Dr.-Ing. habil. in Computer Science
    Technical University Ilmenau, Germany
    📅 November 15, 2000
    Habilitation: “Validating Rule Based Systems: A Complete Methodology”

💼 Professional Experience:

  • Full Professor (apl. Prof.)
    Chair of Artificial Intelligence, Technical University Ilmenau
    📅 March 2010 – Present
  • Associate Professor (Privatdozent)
    Chair of Artificial Intelligence, Technical University Ilmenau
    📅 April 2004 – February 2010
  • Assistant Professor (Privatdozent)
    Technical University Ilmenau
    📅 December 2000 – March 2004
  • Scientific Assistant
    Technical University Ilmenau
    📅 September 1991 – November 2000
  • Scientific Associate
    Ilmenau Institute of Technology
    📅 March 1987 – August 1991

🏅 Awards & Recognitions

  • Fellowship Awards from the Japan Society for the Promotion of Science 📜 (2008, 2011, 2015)
  • Graduate Faculty Scholar at the University of Central Florida 🎓 (2010)

Research Focus: Evolutionary Algorithms 🧬💡

Research Interests:

  • Optimization and Search Algorithms: Rainer Knauf’s work in evolutionary algorithms involves developing and improving algorithms for optimization and search problems. These algorithms are inspired by the principles of natural selection and genetics.
  • Artificial Intelligence Applications: He applies evolutionary algorithms to various AI challenges, including machine learning, robotics, and automated reasoning.
  • Knowledge Acquisition and Refinement: His research integrates evolutionary algorithms with knowledge-based systems to enhance the processes of knowledge acquisition, validation, and refinement.
  • Data Mining: Knauf explores the use of evolutionary algorithms in data mining, particularly in extracting meaningful patterns and insights from large datasets.
  • Inductive Inference: His work also includes using evolutionary algorithms for inductive inference, aiming to generalize from specific data to broader rules or patterns.

Citation:

Cited by:

  • All: 1082 citations
  • Since 2019: 253 citations

h-index:

  • Overall: 16
  • Since 2019: 7

i10-index:

  • Overall: 33
  • Since 2019: 4

Publication Top Notes:

  • “Didactic design through storyboarding: Standard concepts for standard tools”
    • Authors: KP Jantke, R Knauf
    • Publication: Proceedings of the 4th International Symposium on Information and Communication Technologies
    • Citations: 122 (2005)
    • Summary: This paper explores the use of storyboarding as a method for didactic design, emphasizing standard concepts to standardize tools for educational purposes.
  • “A framework for validation of rule-based systems”
    • Authors: R Knauf, AJ Gonzalez, T Abel
    • Publication: IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
    • Citations: 80 (2002)
    • Summary: This paper presents a comprehensive framework for validating rule-based systems, addressing the need for systematic validation processes in artificial intelligence.
  • “Validation of human behavior representation”
    • Authors: SY Harmon, VB Barr, AJ Gonzalez, DC Hoffmann, R Knauf
    • Publication: University Library
    • Citations: 45 (2006)
    • Summary: The authors discuss methodologies for validating models of human behavior representation, crucial for developing reliable AI systems that simulate human actions.
  • “Modeling didactic knowledge by storyboarding”
    • Authors: R Knauf, Y Sakurai, S Tsuruta, KP Jantke
    • Publication: Journal of Educational Computing Research
    • Citations: 39 (2010)
    • Summary: This research focuses on the use of storyboarding to model didactic knowledge, enhancing the design and delivery of educational content through structured visual methods.
  • “Toward reducing human involvement in validation of knowledge-based systems”
    • Authors: R Knauf, S Tsuruta, AJ Gonzalez
    • Publication: IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans
    • Citations: 25 (2006)
    • Summary: This paper proposes methods to minimize human intervention in the validation process of knowledge-based systems, aiming for more autonomous and efficient validation techniques.
  • “Tweet credibility analysis evaluation by improving sentiment dictionary”
    • Authors: T Kawabe, Y Namihira, K Suzuki, M Nara, Y Sakurai, S Tsuruta, R Knauf
    • Publication: 2015 IEEE Congress on Evolutionary Computation (CEC)
    • Citations: 24 (2015)
    • Summary: This work evaluates the credibility of tweets by enhancing sentiment dictionaries, leveraging evolutionary computation techniques to improve the accuracy of sentiment analysis.
  • “A simple optimization method based on backtrack and GA for delivery schedule”
    • Authors: Y Sakurai, K Takada, N Tsukamoto, T Onoyama, R Knauf, S Tsuruta
    • Publication: 2011 IEEE Congress of Evolutionary Computation (CEC)
    • Citations: 22 (2011)
    • Summary: The authors present an optimization method combining backtracking and genetic algorithms (GA) to improve delivery scheduling, demonstrating the application of evolutionary algorithms in logistics.
  • “Generation of a minimal set of test cases that is functionally equivalent to an exhaustive set, for use in knowledge-based system validation”
    • Authors: T Abel, R Knauf, AJ Gonzalez
    • Publication: Proceedings of the 9th FLAIRS Conference
    • Citations: 22 (1996)
    • Summary: This paper discusses a method for generating a minimal set of test cases that maintains functional equivalence to an exhaustive set, enhancing the efficiency of knowledge-based system validation.
  • “Modeling academic education processes by dynamic storyboarding”
    • Authors: Y Sakurai, S Dohi, S Tsuruta, R Knauf
    • Publication: Journal of Educational Technology & Society
    • Citations: 21 (2009)
    • Summary: The study models academic education processes through dynamic storyboarding, offering a structured approach to designing and implementing educational curricula.
  • “Validating Rule-Based Systems: A Complete Methodology”
    • Author: R Knauf
    • Publication: Shaker
    • Citations: 21 (2000)
    • Summary: This book provides a comprehensive methodology for the validation of rule-based systems, detailing systematic approaches to ensure the reliability and accuracy of these systems.