Zuheng Ming | Artificial intelligence | Best Researcher Award

Posted on 25/02/202525/02/2025 by ScienceFather

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

ShengHsun Hsu | AI | Best Researcher Award

Posted on 23/11/202425/11/2024 by ScienceFather

Prof. ShengHsun Hsu | AI | Best Researcher Award

Prof. ShengHsun, Chung Hua University, Taiwan

📚 Prof. Sheng-Hsun Hsu is a full-time professor in the Department of Management at Chu Hua University, Taiwan. He holds a Ph.D. in Business Administration from Taiwan University (2004), a Master’s in Computer Science, and a Bachelor’s in Mathematics from Hsing Hua University.💼 With extensive experience, Prof. Hsu has served as an Assistant Professor, Associate Professor, and Chairman at Chu Hua University. His research focuses on organizational behavior, customer satisfaction indices, brand equity, and psychological capital. His work has been published in top journals like Total Quality Management & Business Excellence and Service Industries Journal (SSCI).🌟 In addition to his academic contributions, Prof. Hsu actively supports curriculum planning, faculty evaluations, and student recruitment efforts. His expertise bridges business management and research, with a commitment to fostering excellence.

Publication Profile

Scopus

📘 Academic Journey

Prof. Sheng-Hsun Hsu boasts an impressive academic background spanning business administration, computer science, and mathematics. He earned his Ph.D. in Business Administration from Taiwan University (1999–2004) 🎓. Before that, he completed his Master’s degree in Computer Science at Hsing Hua University (1993–1995) 💻. His academic journey began with a Bachelor’s degree in Mathematics from the same institution (1990–1993) ➗. This diverse educational foundation reflects his interdisciplinary expertise and commitment to excellence in both theoretical and practical domains of knowledge. 🌟

💼 Professional Experience

Prof. Sheng-Hsun Hsu has had an illustrious career at Chu Hua University, contributing as a scholar and leader. He began as an Assistant Professor (1993–1996) 🧑‍🏫, advancing to Associate Professor (1996–1999) 📚. His dedication and expertise led to his promotion as a Professor, a position he has held since May 2014 🌟. Beyond teaching and research, he served as Chairman of the university from August 2013 to August 2014 🏢. Prof. Hsu’s professional journey reflects his commitment to academia and leadership in higher education. 🎓

📊 Research Focus

Prof. Sheng-Hsun Hsu’s research primarily revolves around Total Quality Management (TQM) and Business Excellence, particularly focusing on improving organizational performance and strategic alignment in various industries. His studies explore the integration of information technology (IT) and business strategies, emphasizing IT competence and the roles of CIOs in business success. Additionally, Prof. Hsu has contributed to the development of models for customer satisfaction, alumni satisfaction, and psychological capital in organizational contexts. His work bridges behavioral economics, higher education, and business management, aiming to enhance both quality management and consumer experience. 🔍📈

Publication Top Notes

A GPT-Aided literature review process for total quality management and business excellence (2024) – Cited by 1
The effects of IT chargeback on strategic alignment and performance: the contingent roles of business executives’ IT competence and CIOs’ business competence (2023) – Cited by 3
Topic analysis of studies on total quality management and business excellence: an update on research from 2010 to 2019 (2022) – Cited by 11
Constructing a consumption model of fine dining from the perspective of behavioral economics (2018) – Cited by 8
Developing a decomposed alumni satisfaction model for higher education institutions (2016) – Cited by 23
Building business excellence through psychological capital (2014) – Cited by 15
Developing a decomposed customer satisfaction index: An example of the boutique motel industry (2013) – Cited by 4
Constructing an index for brand equity: A hospital example (2011) – Cited by 31
A dyadic perspective on knowledge exchange (2010) – Cited by 6
A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression (2009) – Cited by 108

Yunge Zou | Computer Science | Best Scholar Award

Posted on 20/11/202420/11/2024 by ScienceFather

Dr. Yunge Zou | Computer Science | Best Scholar Award

Dr. Yunge Zou, Chongqing University, China

Dr. Yunge Zou is a Ph.D. scholar at Chongqing University, specializing in hybrid powertrain design and battery degradation in the Department of Automotive Engineering. He is a talent under the Chongqing Excellence Program and a Shapingba Elite Talent (2023–2025). Dr. Zou has led key projects, including the National Key R&D Program, focusing on high-efficiency powertrain technologies. His contributions include innovative methods like Hyper-Rapid Dynamic Programming, which optimizes multi-mode hybrid powertrains. With multiple patents and high-impact publications, he collaborates with leading automotive firms like Chang’an New Energy, advancing sustainable transportation. 🚗🔋📚

Publication Profile

Orcid

Google Scholar

Academic and Professional Background 🔋

Dr. Yunge Zou earned his B.E. degree in Automotive Engineering from Chongqing University, China, in 2018. Currently, he is pursuing his Ph.D. in hybrid powertrain design and optimization at the Vehicle Power System Lab, Department of Automotive Engineering, Chongqing University. Recognized for his exceptional talent, Dr. Zou is part of the prestigious Chongqing Excellence Program and was honored as a Shapingba Elite Talent for 2023–2025. His research focuses on hybrid powertrain topology design, battery degradation, energy management systems (EMS), and enhancing battery life, contributing to sustainable transportation innovation. 📚🔧🌱

Research and Innovations 🚗

Dr. Yunge Zou is leading several groundbreaking research projects in the field of hybrid powertrain design and optimization. His work includes the National Key Research and Development Program of China on high-efficiency range extender assembly and electric vehicle integration (2022-2024), with a funding of 2.5 million yuan. He is also working on optimizing hybrid electric vehicle design through the National Science Fund for Excellent Young Scholars (2023-2025). Additionally, he contributes to various projects focusing on hybrid vehicle dynamics, energy efficiency, and low-emission technologies, backed by substantial funding from multiple prestigious organizations. 🛠️⚡

🛠️ Research Focus

Dr. Yunge Zou’s research primarily focuses on hybrid powertrain design and optimization for electric and range-extended vehicles. His work includes the development of control strategies and topology design for hybrid systems, aiming to improve fuel economy, efficiency, and reduce emissions. Dr. Zou has made significant advancements in aging-aware optimization and mode-switching mechanisms for multi-mode hybrid vehicles. His contributions also extend to battery degradation, energy management, and the computational efficiency of fuel economy assessment using innovative algorithms like Hyper Rapid Dynamic Programming (HR-DP). His work is instrumental in the evolution of transportation electrification. 🚗⚡

Publication Top Notes

“Design of all-wheel-drive power-split hybrid configuration schemes based on hierarchical topology graph theory” – Energy 242, 122944 (Cited by 14, 2022) 🔋
“Aging-aware co-optimization of topology, parameter and control for multi-mode input-and output-split hybrid electric powertrains” – Journal of Power Sources 624, 235564 (Cited by 1, 2024) ⚙️
“Design of optimal control strategy for range extended electric vehicles considering additional noise, vibration and harshness constraints” – Energy 310, 133287 (Cited by 1, 2024) 🚗
“Computationally efficient assessment of fuel economy of multi-modes and multi-gears hybrid electric vehicles: A Hyper Rapid Dynamic Programming Approach” – Energy, 133811 (Cited by 0, 2024) 🔧

Federico D’ Asaro | Artificial intelligence | Best Researcher Award

Posted on 27/08/202427/08/2024 by ScienceFather

Mr. Federico D’ Asaro | Artificial intelligence | Best Researcher Award

Mr. Federico D’ Asaro, Politecnico di Torino, Italy

Based on Mr. Federico D’Asaro’s background and achievements, he appears to be a strong candidate for the Research for Best Researcher Award. Here’s an evaluation of his suitability:

Publication profile

Google Scholar

Education 🎓

Polytechnic University of Turin: M.Sc. in Data Science and Engineering with a thesis on algorithm discrimination. Achieved a final grade of 110/110.
University of Palermo: B.Sc. in Engineering and Management, graduating cum laude with a final grade of 110/110.
Scientific Lyceum “Galileo Galilei”: High School Diploma with a grade of 83/100.

Work Experience 💼

Ph.D. Student at Polytechnic University of Turin: Conducting research on Modality-Gap in multimodal feature space and submitting articles to prominent conferences.
AI Applied Researcher at LINKS Foundation: Developed advanced applications in business analytics, sentiment analysis, retrieval systems, and speech emotion recognition. Engaged in proposal writing and reviewing conference papers.
Intern at Technology Reply: Gained experience in NLP, sentiment analysis, and textual data modelization.

Skills and Competencies 🛠️

Proficient in multiple programming languages and tools including Python, Java, TensorFlow, PyTorch, and SQL.
Experienced in data visualization, machine learning, and deep learning.
Competent in using various IT tools and frameworks like Apache Spark and Hadoop.

Other Information 🌍

Languages: Fluent in Italian and proficient in English (B2 level).
Interests: Diverse interests including sports, literature, and technology.
Certifications: B2 First (FCE) and driving license.

Publication Top Notes

Zero-Shot Content-Based Crossmodal Recommendation System

Sensitive attributes disproportion as a risk indicator of algorithmic unfairness

Conclusion🏆

Mr. Federico D’Asaro demonstrates a solid academic background, relevant work experience, and a diverse skill set, aligning well with the criteria for the Research for Best Researcher Award. His ongoing research and contributions to AI applications show a strong potential for impactful research and innovation.

Souhail Dhouib | Artificial Intelligence | Best Researcher Award

Posted on 27/07/202403/09/2024 by ScienceFather

Prof. Souhail Dhouib | Artificial Intelligence | Best Researcher Award

Full Professor, Higher Institute of Industrial Management, University of Sfax, Tunisia

Prof. Souhail Dhouib is a distinguished academic and industry expert specializing in Artificial Intelligence and Operations Research. He holds the position of Full Professor at the Higher Institute of Industrial Management, University of Sfax, Tunisia, where he has taught for over twenty years. Prof. Dhouib is renowned for his pioneering work in matrix optimization concepts and has made significant contributions to decision-making and planning through his innovative algorithms and methodologies.

ProfileArtificial Intelligence

ORCID

Education

Prof. Dhouib completed his Ph.D. in Quantitative Methods from the Faculty of Management and Economics Sciences, Sfax University, Tunisia (2004-2009). He also holds a Master’s degree in Operations Research and Production Management (2001-2003) and a Bachelor’s degree in Management Information Systems (1992-1996), all from the same institution.

Experience

Prof. Dhouib has over twenty years of extensive experience in both academia and industry. He has served as a General Manager and an Analyst Programmer, where he was involved in software development and implementation. His academic roles include positions as a Full Professor, Associate Professor, and Assistant Professor, contributing significantly to teaching and research.

Research Interests

Prof. Dhouib’s research focuses on Artificial Intelligence, Operations Research, and Optimization algorithms. His work spans various domains, including Logistics, Supply Chain Management, Business Intelligence Systems, and ERP. He is particularly known for his Dhouib-Matrix methods and their applications in cognitive robotics, multi-objective optimization, and path planning.

Awards

Prof. Dhouib’s contributions to the field have been recognized through numerous journal publications, although specific awards are not listed in the provided information. His innovative research and development efforts continue to impact the industry and academia.

Publications

Intelligent Path Planning for Cognitive Mobile Robot Based on Dhouib-Matrix-SPP Method (2024) – Cognitive Robotics

Multi-Start Constructive Heuristic through Descriptive Statistical Metrics: The Dhouib-Matrix-4 Metaheuristic (2024) – International Journal of Operational Research

Innovative Method to Solve the Minimum Spanning Tree Problem: The Dhouib-Matrix-MSTP (DM-MSTP) (2024) – Results in Control and Optimization

Enhancing the Dhouib-Matrix-4 Metaheuristic to Generate the Pareto Non-Dominated Set Solutions for Multi-objective Travelling Salesman Problem: The DM4-PMO Method (2024) – Results in Control and Optimization

Faster than Dijkstra and A* Methods for the Mobile Robot Path Planning Problem Using Four Movement Directions: The Dhouib-Matrix-SPP-4 (2024) – Advances in Transdisciplinary Engineering, Mechatronics and Automation Technology

Nafis Uddin Khan | Artificial Intelligence | Best Researcher Award

Posted on 27/07/202427/07/2024 by ScienceFather

Dr Nafis Uddin Khan | Artificial Intelligence | Best Researcher Award

Dr Nafis Uddin Khan, SR University Warangal India, India

Dr. Nafis Uddin Khan is a distinguished academic and researcher at SR University in Warangal, India. His expertise spans a variety of fields, contributing significantly to both the academic community and industry advancements. Dr. Khan’s work is characterized by a strong focus on innovative solutions and sustainable practices, reflecting his commitment to addressing contemporary challenges through research and education. At SR University, he plays a pivotal role in mentoring students and leading research initiatives that aim to drive technological progress and societal impact.

Profile

Orcid

Education

Bachelor of Engineering (B.E.) in Electronics and Telecommunication Engineering from Amravati University Maharashtra, in 2003.

Master of Technology (M.Tech.) in Software Systems from S.A.T.I. Vidisha under Rajiv Gandhi Technological University, Bhopal, in 2008.

Doctor of Philosophy (Ph.D.) in Signal and Image Processing from the Atal Bihari Vajpayee – Indian Institute of Information Technology & Management, Gwalior

Professional experience

Workshop Coordinator: Coordinated a two-day workshop titled “Synthesis of Wisdom: Crafting AI Tutor Assistants, Navigating Future-Ready Digital Libraries and Elevating Pedagogy in the Age of Skill Enhancement and AI Mastery” on February 02–03, 2024, at the School of CS & AI, SR University, Warangal, India.
Faculty Development Program Coordinator: Coordinated “Utkarsh – Take Flight,” a two-day Faculty Development Program on Leadership and Excellence in Quality Education organized by Jaypee University of Information Technology, Solan (H.P.), on December 22–23, 2022.
Chief Coordinator: Served as Chief Coordinator in a student outreach activity organized by the Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Solan (H.P.), on December 20, 2022.
Invited Session Chair: Served as an Invited Session Chair in the Congress on Intelligent Systems (CIS 2020), World Conference in virtual format, on September 05–06, 2020.
Organizing Committee Member and Reviewer: Participated as an Organizing Committee Member and Reviewer in the Congress on Intelligent Systems (CIS 2020), World Conference in virtual format, on September 05–06, 2020.
Convener: Organized a one-week online short-term course on “Recent Advances in Computational Intelligence for Signal Processing” (RACISP – 2020) at Jaypee University of Information Technology, Solan (H.P.), from August 10–15, 2020.
Coordinator: Coordinated a five-day short-term course on “Recent Advances in Signal and Image Processing” (RASIP – 2019) at Jaypee University of Information Technology, Solan (H.P.), from June 24–28, 2019.
Organizing Committee Member: Served in the organizing committee for the 5th IEEE International Conference on “Signal Processing, Computing and Control” (ICSPC 2019) organized by the Department of Electronics and Communication Engineering, Jaypee University of Information Technology, Solan (H.P.), from October 10–12, 2019.
Invited Session Chair: Chaired an invited session in the 2019 IEEE International Conference on “Image Information Processing” (ICIIP 2019) organized by the Department of Computer Science and Engineering, Jaypee University of Information Technology, Solan (H.P.), from November 15–17, 2019.
Organizing Committee Member: Participated as an organizing committee member in the 4th IEEE International Conference on “Signal Processing, Computing and Control” (ICSPC 2017) at Jaypee University of Information Technology, Solan (H.P.), from September 21–23, 2017.
Invited Session Chair: Served as an invited session chair at the 7th IEEE International Conference on “Computational Intelligence and Communication Networks” (CICN 2015) at Gyan Ganga Institute of Technology and Science, Jabalpur (M.P.), on December 12, 2015.
National Advisory Committee Member: Served on the National Advisory Committee for the National Conference on Contemporary Computing organized by the Department of Computer Science and Information Technology, Chameli Devi Group of Institutions, Indore (M.P.), from October 21–22, 2016.

Research Focus

Fuzzy Logic and Optimization: Utilizing fuzzy logic for applications in signal and image processing, including the development of fuzzy-based diffusion coefficient functions for selective noise smoothing.

Medical Image Processing: Enhancing medical imaging techniques, such as de-speckling in ultrasound and X-ray images, and improving image de-noising using soft optimization techniques.

Pattern Analysis in Machine Intelligence: Investigating pattern analysis and its applications within machine intelligence to improve the accuracy and efficiency of image processing algorithms.

Awards and Honors

Outstanding Faculty Award from SR University for his contributions to teaching and research.

Best Paper Award at various international conferences.

Young Scientist Award from the International Association of Engineers.

Certificate of Merit for significant contributions to research in computational intelligence and signal processing.

Publication Top Notes: Artificial Intelligence

“Fuzzy C-Means Clustering based Selective Edge Enhancement Scheme for Improved Road Crack Detection” in Engineering Applications of Artificial Intelligence, 2024.

“Road Crack Detection using Pixel Classification and Intensity based Distinctive Fuzzy C-Means Clustering” in The Visual Computer, 2024.

“Fuzzy based Self-Similarity Weight Estimation in Non-Local Means for Gray-Scale Image De-noising” in Digital Signal Processing, 2024.

“A Two Phase Ultrasound Image De-speckling Framework by Non-local Means on Anisotropic Diffused Image Data” in Informatica, 2023.

“An Efficient Fuzzy Inference System based Approximated Anisotropic Diffusion for Image De-noising” in Cluster Computing, 2022.

Zhidong CAO | Data Science | Best Researcher Award

Posted on 24/06/202424/06/2024 by ScienceFather

Mr. Zhidong CAO | Data Science | Best Researcher Award

Zhidong CAO at Institute of Automation, Chinese Academy of Sciences, China

Zhidong CAO is a renowned professor and principal investigator at the National Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences. With a Doctor of Science degree, he has made significant contributions to the field of artificial intelligence and has been recognized for his work in various national and international platforms.

Profile

Orcid

Education

Zhidong CAO earned his Ph.D. from the Institute of Geographic Sciences and Natural Resources Research at the Chinese Academy of Sciences in 2008. He also holds a Master’s degree and a Bachelor’s degree from Changsha University of Science and Technology, completed in 2005 and 2001 respectively.

Research Focus

His research interests lie primarily in the areas of multimodal artificial intelligence systems, social computing, and geographic information analysis. He has been instrumental in several key national scientific and technological projects, including the National Medium- and Long-term Scientific and Technological Development Plan (2021-2035) and the New Generation Artificial Intelligence Strategic Plan.

Professional Journey

Zhidong CAO began his professional journey as a Postdoctoral Fellow at the Institute of Automation, Chinese Academy of Sciences, in 2008. He progressed to become an Assistant Researcher in 2010, then an Associate Researcher in 2011, and has been serving as a Researcher since 2020. His roles have seen him engage deeply with various research projects and contribute significantly to the field of automation and artificial intelligence.

Honors & Awards

Throughout his career, Zhidong CAO has received numerous prestigious awards. Notable among these are the Beijing Science and Technology Progress Award (Second Prize, 2022), the China Surveying and Mapping Society Science and Technology Award (Grand Prize, 2021), and the Chinese Society of Simulation Natural Science First Prize (2018). His contributions have also been recognized by the Chinese Association of Automation and the Chinese Preventive Medicine Association.

Publications Noted & Contributions

Zhidong CAO has an impressive portfolio of over 120 research papers published in leading domestic and international journals and conferences. He has also authored three books, further establishing his expertise in his field. His research has earned him six scientific and technological awards, underscoring his significant contributions to the advancement of artificial intelligence and related domains.

Coordinated Cyber Security Enhancement for Grid-Transportation Systems With Social Engagement
- Journal: IEEE Transactions on Emerging Topics in Computational Intelligence
- DOI: 10.1109/TETCI.2022.3209306
- Contributors: Pengfei Zhao, Shuangqi Li, Paul Jen-Hwa Hu, Zhidong Cao, Chenghong Gu, Da Xie, Daniel Dajun Zeng
- Summary: This article discusses methods for enhancing cybersecurity in grid-transportation systems through coordinated efforts and social engagement. It emphasizes the importance of integrating social factors and community involvement in cybersecurity strategies.
Energy-Social Manufacturing for Social Computing
- Journal: IEEE Transactions on Computational Social Systems
- DOI: 10.1109/TCSS.2024.3379254
- Contributors: Alexis Pengfei Zhao, Shuangqi Li, Yanjia Wang, Paul Jen-Hwa Hu, Chenye Wu, Zhidong Cao, Faith Xue Fei
- Summary: This article explores the concept of energy-social manufacturing, which integrates energy systems with social computing to enhance efficiency and sustainability. The research highlights the role of social computing in optimizing energy production and consumption.
Modeling the Coupling Propagation of Information, Behavior, and Disease in Multilayer Heterogeneous Networks
- Journal: IEEE Transactions on Computational Social Systems
- DOI: 10.1109/TCSS.2023.3306014
- Contributors: Tianyi Luo, Duo Xu, Zhidong Cao, Pengfei Zhao, Jiaojiao Wang, Qingpeng Zhang
- Summary: This study models the interactions and propagation dynamics of information, behavior, and disease within multilayer heterogeneous networks. It provides insights into how these elements influence each other and spread across different network layers.
Socially Governed Energy Hub Trading Enabled by Blockchain-Based Transactions
- Journal: IEEE Transactions on Computational Social Systems
- DOI: 10.1109/TCSS.2023.3308608
- Contributors: Pengfei Zhao, Shuangqi Li, Zhidong Cao, Paul Jen-Hwa Hu, Chenghong Gu, Xiaohe Yan, Da Huo, Tianyi Luo, Zikang Wang
- Summary: This article examines how blockchain technology can facilitate socially governed energy hub trading. It discusses the implementation of blockchain-based transactions to enhance transparency, security, and efficiency in energy markets.
A Cross-Lingual Transfer Learning Method for Online COVID-19-Related Hate Speech Detection
- Journal: Expert Systems with Applications
- DOI: 10.1016/j.eswa.2023.121031
- Contributors: Lin Liu, Duo Xu, Pengfei Zhao, Daniel Dajun Zeng, Paul Jen-Hwa Hu, Qingpeng Zhang, Yin Luo, Zhidong Cao
- Summary: This research presents a method for detecting COVID-19-related hate speech online using cross-lingual transfer learning. The study demonstrates the effectiveness of the proposed method in identifying hate speech across different languages, aiding in the fight against online misinformation and discrimination.

Rainer Knauf | Evolutionary Algorithms | Lifetime achievement Award

Posted on 24/06/202424/06/2024 by ScienceFather

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

Google Scholar

🎓 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.

ZhikangZhao | Deep Learning | Best Researcher Award

Posted on 12/06/202412/06/2024 by ScienceFather

ZhikangZhao | Computers in Earth Sciences | Best Researcher Award

Dr. ZhikangZhao, Changchun Institute of Optics,Fine Mechanicsand Physics,Chinese Academy of Sciences, China.

Dr.Zhikang Zhao, a Ph.D. candidate at the Chinese Academy of Sciences, pioneers research in remote sensing image processing. His expertise lies in developing advanced algorithms employing deep learning for super-resolution reconstruction, vital for enhancing low-resolution remote sensing images. His method, featured in Image and Vision Computing, revolutionizes unsupervised super-resolution by simulating degradation mechanisms, leading to superior image quality. With ongoing projects focused on innovative reconstruction networks, Zhao’s contributions significantly advance remote sensing technology, promising accurate data for diverse scientific applications.

Publication Top Notes

Scopus

Education

Dr.Zhikang Zhao pursued his Ph.D. degree at the prestigious Changchun Institute of Optics, Fine Mechanics and Physics, affiliated with the Chinese Academy of Sciences. Immersed in advanced research in remote sensing image processing, Zhao honed his expertise in developing groundbreaking super-resolution algorithms leveraging deep learning techniques. His academic journey reflects a commitment to pushing the boundaries of knowledge in his field, evident in his innovative contributions to the realm of remote sensing technology. With a solid educational foundation and a passion for research, Zhao is poised to continue making significant strides in advancing the capabilities of remote sensing technology.

Research Focus

Dr.Zhikang Zhao’s research primarily centers on remote sensing image processing, with a specific emphasis on developing advanced super-resolution reconstruction algorithms. Through his work, he aims to address the challenges associated with low-resolution and low-quality remote sensing images by leveraging cutting-edge deep learning techniques. By focusing on innovative algorithmic developments, Zhao endeavors to enhance the resolution and quality of remote sensing data, thereby unlocking its full potential for various applications. His dedication to pushing the boundaries of remote sensing technology reflects a commitment to advancing scientific knowledge and contributing to the broader scientific community.

Publication Top Notes

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances by Zhao, T. et al. (2024)
- Published in Remote Sensing, cited by 0.
Hyperspectral Image Classification Framework Based on Multichannel Graph Convolutional Networks and Class-Guided Attention Mechanism by Feng, H. et al. (2024)
- Published in IEEE Transactions on Geoscience and Remote Sensing, cited by 0.
Remote Sensing Hyperspectral Image Super-Resolution via Multidomain Spatial Information and Multiscale Spectral Information Fusion by Chen, C. et al. (2024)
- Published in IEEE Transactions on Geoscience and Remote Sensing, cited by 0.
Context Feature Integration and Balanced Sampling Strategy for Small Weak Object Detection in Remote Sensing Imagery by Li, Z. et al. (2024)
- Published in IEEE Geoscience and Remote Sensing Letters, cited by 2.
A Review of Hyperspectral Image Super-Resolution Based on Deep Learning by Chen, C. et al. (2023)
- Published in Remote Sensing, cited by 9.
RoI Fusion Strategy With Self-Attention Mechanism for Object Detection in Remote Sensing Images by Zhang, Y. et al. (2023)
- Published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, cited by 4.
Multi-scale unsupervised network for infrared and visible image fusion based on joint attention mechanism by Xu, D. et al. (2022)
- Published in Infrared Physics and Technology, cited by 10.
Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey by Li, Z. et al. (2022)

Fangxin Fang | Machine learning | Best Researcher Award

Posted on 27/02/202427/02/2024 by ScienceFather

Dr. Fangxin Fang | Machine learning | Best Researcher Award

Dr. Fangxin Fang, Imperial College London, United Kingdom

Dr. Fangxin Fang 🌐 is a pioneering researcher in numerical simulations of nonlinear fluid flows 🌊. With a PhD from James Cook University 🎓, Australia, and extensive experience at Imperial College London 🏛️, he integrates advanced techniques like Machine Learning and Data Assimilation for accurate predictions. Dr. Fang’s innovative work has led to software development 🖥️, numerous publications 📚, and successful grant applications totaling over £10M 💰. A leader in project management and collaboration, he fosters interdisciplinary research 🤝. His contributions extend to teaching 🎓, mentoring, and impactful engagement with industry and academia worldwide 🌍.

Publication Profile:

Scopus

Google scholar

Education:

🎓 Dr. Fangxin Fang’s educational journey reflects a commitment to academic excellence across diverse continents. Beginning with a Bachelor’s in Engineering from Hehai University, China, they laid the groundwork for their scholarly pursuits. This was followed by a Master of Science degree from the North China Institute of Water Conservancy and Hydropower, China, where they deepened their expertise in a specialized field. Undeterred by geographical boundaries, they pursued doctoral studies at James Cook University, Australia, earning a PhD. Each academic milestone represents their dedication to learning and growth, culminating in a comprehensive understanding of their field. 🌐

Employment:

🌟 Dr. Fangxin Fang’s professional journey spans continents, showcasing their invaluable contributions to the global scientific community. They commenced as an Engineering Assistant at the China Institute of Water Resources and Hydropower Research, Beijing, China, before transitioning to academia as a Lecturer at the North China Institute of Water Conservancy and Hydropower, Beijing. Following their passion, they pursued doctoral studies at James Cook University, Australia, setting the stage for a distinguished career. Over the years, they’ve held pivotal roles, from Research Assistant at the University of Auckland, New Zealand, to Postdoctoral Researcher in Toulouse, France. Currently, they serve as a Senior Research Fellow at Imperial College, London, UK, solidifying their legacy in Earth Science and Engineering. 🌍

Research Focus:

Dr. Fangxin Fang’s 🌊 research focus lies at the intersection of computational fluid dynamics and advanced data-driven methods. With a keen emphasis on numerical simulations, his work delves into understanding complex fluid dynamics in various domains such as oceanography, atmospheric science, and urban environments. Utilizing techniques like reduced order modeling, machine learning, and deep convolutional networks, Dr. Fang explores nonlinear spatio-temporal fluid flows. His contributions pave the way for enhanced predictions of phenomena like ocean currents, air pollution dispersion, and flood dynamics. Dr. Fang’s innovative research transcends traditional boundaries, offering insights crucial for environmental sustainability and disaster management 🌍.