Jiaming Zhong – Artificial intelligence – Best Researcher Award

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Jiaming Zhong - Artificial intelligence - Best Researcher Award

Wuyi university - China

AUTHOR PROFILE

SCOPUS

📚 SCIENTIFIC RESEARCH ACHIEVEMENTS

Jiaming Zhong has made significant contributions to the fields of video classification and tactile sensing. His groundbreaking papers include "Exploring Cross-video Matching for Few-shot Video Classification via Dual-Hierarchy Graph Neural Network Learning," published in Image and Vision Computing, and "Text-guided Graph Temporal Modeling for Few-Shot Video Classification," featured in Engineering Applications of Artificial Intelligence. These studies, published in top-tier journals, highlight Zhong's innovative approaches in utilizing graph neural networks and multimodal models for advanced video analysis and classification.

🛠️ PATENTS AND TECHNOLOGICAL INNOVATIONS

Zhong holds several patents that showcase his expertise in developing practical solutions for various technological challenges. His patents include methods for video anomaly classification, chip defect detection, and mobile robot obstacle avoidance. These patents reflect his commitment to translating theoretical research into tangible technological advancements that address real-world problems.

🔬 PROJECT EXPERIENCE: PEEL RECOGNITION

In a project focused on the precise identification of Chenpi years using a multimodal model, Zhong's work involved designing lightweight modules and fine-tuning models to achieve high recognition accuracy. His use of the CLIP multimodal model for feature extraction led to a remarkable 99% accuracy in recognizing Chenpi years with limited sample data. This project, detailed on GitHub, demonstrates his proficiency in applying advanced machine learning techniques to practical problems.

🎥 PROJECT EXPERIENCE: FEW-SHOT VIDEO CLASSIFICATION

Zhong's research in video behavior classification involved addressing challenges related to data scarcity and model capabilities. Collaborating with Macau University of Science and Technology and Wuyi University, he developed a dual-hierarchy graph neural network that significantly improved classification performance through cross-video frame matching. This innovative approach was published in Image and Vision Computing and showcased Zhong's ability to enhance model performance through sophisticated temporal modeling.

🔍 PROJECT EXPERIENCE: MULTIMODAL REPRESENTATION LEARNING

In a project focused on multimodal video behavior analysis, Zhong led efforts to develop a novel framework for self-supervised learning using multimodal data. This project, supported by a 500,000 RMB research grant, involved developing a text-guided feature optimization module and a query text token learning mechanism. His research aimed to leverage multimodal knowledge to improve the classification performance of few-shot video behaviors, with results published in top journals.

📈 IMPACTFUL RESEARCH AND PUBLICATIONS

Zhong's work has significantly impacted the fields of video classification and sensor technology. His papers in renowned journals and his patents contribute to advancing the understanding and application of these technologies. His research not only addresses current challenges but also paves the way for future innovations in these areas.

🏆 ACKNOWLEDGEMENTS AND RECOGNITION

Zhong's contributions to scientific research and technology have earned him recognition within the academic and professional communities. His innovative work in video classification and sensor technology continues to influence the field and inspire further research and development.

NOTABLE PUBLICATION

Ultra-sensitive and stable All-Fiber iontronic tactile sensors under high pressure for human movement monitoring and rehabilitation assessment
Authors: K. Ma, D. Su, B. Qin, Y. Xin, X. He
Year: 2024
Journal: Chemical Engineering Journal

Real-time citrus variety detection in orchards based on complex scenarios of improved YOLOv7
Authors: F. Deng, J. Chen, L. Fu, J. Li, N. Li
Year: 2024
Journal: Frontiers in Plant Science

Exploring cross-video matching for few-shot video classification via dual-hierarchy graph neural network learning
Authors: F. Deng, J. Zhong, N. Li, D. Wang, T.L. Lam
Year: 2023
Journal: Image and Vision Computing

Senbagavalli – Artificial Intelligence – Best Researcher Award

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Senbagavalli - Artificial Intelligence - Best Researcher Award

Alliance University - India

AUTHOR PROFILE

SCOPUS

EXPERT IN OPINION MINING AND FEATURE SELECTION

Senbagavalli's groundbreaking research in opinion mining of health data for cardiovascular disease diagnosis using an unsupervised feature selection algorithm spans five years. Her Ph.D. work is a testament to her dedication to leveraging data for medical advancements.

FACIAL RECOGNITION INNOVATOR

With a master's degree in engineering, Senbagavalli developed a face recognition system using Laplacian faces, showcasing her expertise in computer vision and pattern recognition. This project exemplified her ability to apply complex algorithms to practical applications within six months.

PIONEER IN UNICODE FILE SYSTEMS

During her undergraduate studies, Senbagavalli created a file system using the Unicode character set, a project completed in just six months. Her work in this area highlights her proficiency in software development and system design.

CREATOR OF GRAPHIC GAMING SYSTEMS

In her mini-project as an undergraduate, she developed a gaming system using graphics within three months. This early project laid the foundation for her interest in interactive and visual computing systems.

SEASONED ACADEMIC AND PROFESSOR

With 18 years and 7 months of teaching experience, Senbagavalli has held positions at prestigious institutions, including Alliance University and Kuppam Engineering College. Her extensive experience has made her a respected figure in the academic community.

VERSATILE SUBJECT EXPERT

Senbagavalli has taught a wide range of subjects to undergraduate, postgraduate, and Ph.D. students, including Data Modeling and Optimization, Object-Oriented Programming, and Software Engineering. Her comprehensive knowledge spans multiple domains of computer science.

ACTIVE RESEARCHER AND REVIEWER

An active member of various academic councils and editorial boards, Senbagavalli reviews for renowned publishers like Bentham Science and Elsevier. Her involvement in curriculum development, project evaluation, and seminar organization reflects her commitment to academic excellence and continuous learning.

NOTABLE PUBLICATION

Identification of Biomarker for Autism Spectrum Disorder Using EEG: A Review.
Authors: K. Lalli, M. Senbagavalli
Year: 2023
Conference: Proceedings - 2023 International Conference on Advanced Computing and Communication Technologies, ICACCTech 2023, pp. 45–50

Facemask Detection System Using CNN Model.
Authors: M. Senbagavalli, S. Debnath, R. Rajagopal, K. Ghildial
Year: 2023
Conference: International Conference on Recent Advances in Science and Engineering Technology, ICRASET 2023

An Evaluation of Machine Learning Techniques for Detecting Banking Frauds.
Authors: R. Rajagopal, M. Senbagavalli, S. Debnath, K. Darshan, K.S. Varun Tejas
Year: 2023
Conference: International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023 - Proceedings, pp. 359–365

Deep Learning Model for Flood Estimate and Relief Management System Using Hybrid Algorithm.
Authors: M. Senbagavalli, V. Sathiyamoorthi, S.K. Manju Bargavi, S. Shekarappa G., T. Jesudas
Year: 2023
Book: Artificial Intelligence and Machine Learning in Smart City Planning, pp. 29–44

An Effective Model for Predicting Agricultural Crop Yield on Remote Sensing Hyper-Spectral Images Using Adaptive Logistic Regression Classifier.
Authors: V. Sathiyamoorthi, P. Harshavardhanan, H. Azath, A.M. Viswa Bharathy, B.S. Chokkalingam
Year: 2022
Journal: Concurrency and Computation: Practice and Experience, 34(25), e7242

BASEM A. ALKHALEEL – Machine learning – Best Paper Award

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BASEM A. ALKHALEEL - Machine learning - Best Paper Award

King Saud University - Saudi Arabia

PROFESSIONAL SUMMARY

Basem A. Alkhaleel is a dedicated Assistant Professor in Industrial Engineering with a distinguished record of academic excellence and practical expertise in process reengineering and business continuity. Combining his extensive background in teaching, research, project management, and consulting, he brings a unique perspective to both academia and industry. With skills in leading cross-functional teams, analyzing complex systems, and implementing innovative solutions, he excels in driving process optimization, operational efficiency, and organizational resilience.

EDUCATION

Basem A. Alkhaleel holds a Ph.D. in Industrial Engineering from the University of Arkansas (2021), an MSc in Industrial and Systems Engineering from Texas A&M University (2015), and a BSc in Industrial Engineering from King Saud University (2012).

ACADEMIC AND RESEARCH EXPERIENCE

As an Assistant Professor at King Saud University since September 2021, Basem A. Alkhaleel has been involved in directing the counseling unit for undergraduate engineering students, participating in various departmental committees, and supervising numerous undergraduate student graduation projects. His research focuses on machine learning applications in critical infrastructure resilience, reliability engineering, and business continuity.

PROFESSIONAL EXPERIENCE

In addition to his academic roles, Basem A. Alkhaleel is a Management Consultant at ES Consulting in Riyadh, Saudi Arabia, where he develops and implements process improvement strategies and business continuity plans. He has also served as a Project Manager at the Arkansas Department of Transportation, leading the development and implementation of a decision support system for multi-modal transportation operations.

PROJECT MANAGEMENT AND CONSULTING

Basem A. Alkhaleel has managed several high-profile projects, including process documentation and modeling for the Ministry of Municipal and Rural Affairs and Housing and the Advanced Electronics Company. His work involves process modeling, documentation, digitalization, and KPI alignment to improve operational and strategic efficiencies.

RESEARCH AND INNOVATION

During his Ph.D. research at the University of Arkansas, Basem A. Alkhaleel developed resilience-based restoration models for disrupted critical infrastructures and combined risk mitigation with resilience restoration and simulation modeling. His MSc research at Texas A&M University focused on data-driven approaches to improve decision-making processes in engineering projects.

TEACHING AND MENTORING

With a strong commitment to education, Basem A. Alkhaleel has lectured on various industrial engineering subjects, including manufacturing processes and reliability engineering, at King Saud University. His teaching philosophy emphasizes innovative strategies to enhance student performance and academic success.

STRATEGIC PLANNING AND DEVELOPMENT

Earlier in his career, Basem A. Alkhaleel worked as a Strategic Planner at Ma’aden Company, developing long-term strategic plans and applying business development tools to identify areas of improvement. His strategic insights and communication skills have been instrumental in achieving organizational objectives and driving continuous improvement initiatives.

NOTABLE PUBLICATIONS

Risk and resilience-based optimal post-disruption restoration for critical infrastructures under uncertainty 2022 (44)

Hybrid simulation to support interdependence modeling of a multimodal transportation network 2021 (19)

Machine learning applications in the resilience of interdependent critical infrastructure systems—A systematic literature review 2023 (8)

Model and solution method for mean-risk cost-based post-disruption restoration of interdependent critical infrastructure networks 2022 (10)

Samir Khatir – AI for fast prediction – Best Researcher Award

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Samir Khatir - AI for fast prediction - Best Researcher Award

Ho Chi Minh City Open university - Belgium

AUTHOR PROFILE

Google Scholar
Scopus

EARLY ACADEMIC PURSUITS

Dr. Samir Khatir embarked on his academic journey by earning a PhD in Mechanical Engineering from Boumerdes University, Algeria, in collaboration with Centre Val de Loire, France, focusing on damage detection using optimization techniques. He later pursued a second PhD in Civil Engineering at Ghent University, Belgium, specializing in artificial intelligence for fast crack identification in steel plate structures. His academic pursuits reflect a strong foundation in engineering and a commitment to advancing knowledge in his field.

PROFESSIONAL ENDEAVORS

Dr. Samir Khatir has held various prestigious positions, including Technical Manager at Btecch in Brussels, Belgium, and part-time distinguished researcher at CEATS Centre, Ho Chi Minh City Open University, Vietnam. Additionally, he serves as an editor-in-chief and member of the editorial board for several scientific journals, contributing to the dissemination of knowledge in his field. His extensive experience spans research, academia, and industry, showcasing his versatility and expertise.

CONTRIBUTIONS AND RESEARCH FOCUS

Dr. Samir Khatir's research focuses on a wide range of topics, including characterization of metals and composite materials, design optimization, damage identification, static and dynamic tests, machine learning, and tribological analysis in metal contact. He has made significant contributions to projects addressing modal updating and structural health monitoring in metal bridges, fast crack identification using machine learning in steel plates, and impact identification in composite materials. His research underscores his dedication to advancing engineering solutions through innovative methodologies.

IMPACT AND INFLUENCE

Dr. Samir Khatir's work has had a profound impact on the field of engineering, particularly in the areas of structural health monitoring, damage identification, and optimization techniques. His collaborations with prestigious institutions and his role as a visiting researcher and research member highlight his influence and recognition within the global research community. His contributions have contributed to advancements in the understanding and application of artificial intelligence for predictive analysis in engineering structures.

ACADEMIC CITES

Dr. Samir Khatir's research has been widely cited and recognized in the academic community, with numerous publications and collaborations with renowned institutions. His work has been instrumental in shaping the discourse and driving innovation in engineering research, particularly in the application of machine learning techniques for fast prediction and damage identification in structural materials.

LEGACY AND FUTURE CONTRIBUTIONS

As Dr. Samir Khatir continues to excel in his career, his legacy in the field of engineering is poised to grow. His future contributions are expected to further enhance our understanding of structural behavior and advance predictive modeling techniques using artificial intelligence. Through his dedication to research and innovation, he will continue to shape the future of engineering and inspire the next generation of researchers and practitioners.

NOTABLE PUBLICATION

An efficient approach for damage identification based on improved machine learning using PSO-SVM  2022 (82)

YUKI Algorithm and POD-RBF for Elastostatic and dynamic crack identification 2021 (80)