Jiaming Zhong – Artificial intelligence – Best Researcher Award

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

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

Everton – Artificial Intelligence – Best Researcher Award

Everton - Artificial Intelligence - Best Researcher Award

Universidade Federal da Grande Dourados - Brazil

AUTHOR PROFILE

SCOPUS

ACADEMIC AFFILIATION

Everton is associated with Universidade Católica Dom Bosco, where he contributes to cutting-edge research in computer vision and its applications in agriculture and urban studies.

PRECISION AGRICULTURE RESEARCH

His research in precision agriculture includes the integration of UAV technology and machine learning to optimize farming practices. By improving weed and pest detection methods, his work supports sustainable agriculture and food security.

COMPUTER VISION IN AGRICULTURE

Everton's expertise in computer vision extends to various agricultural applications, from crop monitoring to automated harvesting systems. His innovative solutions help in increasing agricultural productivity and efficiency.

REAL-TIME WEED DETECTION IN SOYBEAN USING UAV IMAGES

Everton Castelão Tetila specializes in the real-time detection of weeds in soybean fields through the innovative use of UAV (Unmanned Aerial Vehicle) images. His work significantly contributes to precision agriculture, enabling farmers to identify and manage weeds more efficiently.

YOLO PERFORMANCE ANALYSIS FOR SOYBEAN PEST DETECTION

Everton has conducted extensive performance analysis of the YOLO (You Only Look Once) algorithm for the real-time detection of soybean pests. His research enhances pest management practices, ensuring timely interventions and reducing crop damage.

URBAN AREA CLASSIFICATION AND MONITORING USING COMPUTER VISION

He applies advanced computer vision techniques for the classification and monitoring of urbanized areas. This work aids in urban planning and development, providing detailed and accurate assessments of urban growth and infrastructure.

EDUCATIONAL CONTRIBUTIONS

He is dedicated to advancing education in his field, sharing his knowledge and findings through publications and presentations. His contributions help train the next generation of researchers and professionals in computer vision and its agricultural applications.

NOTABLE PUBLICATION

YOLO performance analysis for real-time detection of soybean pests
Authors: E.C. Tetila, F.A.G. da Silveira, A.B. da Costa, H. Pistori, J.G.A. Barbedo
Year: 2024
Journal: Smart Agricultural Technology, 7, 100405

Title: Classification and monitoring of urbanized areas using computer vision techniques | Classificação e monitoramento de áreas urbanizadas usando técnicas de visão computacional
Authors: E.C. Tetila, P.M. de Moraes, M. Constantino, M.M.D.M. Greco, H. Pistori
Year: 2023
Journal: Desenvolvimento e Meio Ambiente, 61, pp. 32–42

Title: An approach for applying natural language processing to image classification problems
Authors: G. Astolfi, D.A. Sant'Ana, J.V.D.A. Porto, E.T. Matsubara, H. Pistori
Year: 2022
Journal: Neurocomputing, 513, pp. 372–382

Title: Performance Analysis of YOLOv3 for Real-Time Detection of Pests in Soybeans
Authors: F.A.G. Silveira, E.C. Tetila, G. Astolfi, A.B. Costa, W.P. Amorim
Year: 2021
Conference: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 13074 LNAI, pp. 265–279

Title: Associative classification model for forecasting stock market trends
Authors: E.C. Tetila, B.B. MacHado, J.F. Rorigues, M. Constantino, H. Pistori
Year: 2021
Journal: International Journal of Business Intelligence and Data Mining, 19(1), pp. 97–112

Dalhatu Muhammed – Computer Science – Best Researcher Award

Dalhatu Muhammed - Computer Science - Best Researcher Award

Institut Supérieur D’electronique De Paris - France

AUTHOR PROFILE

Google Scholar

EARLY ACADEMIC PURSUITS

Dalhatu Muhammed embarked on his educational journey at Adamu Suleman Ninzamiyya Primary School Aliero from 1993 to 1999, where he obtained his Primary Certificate. He then attended Haliru Abdu Arabic Secondary School Jega (1999-2002), earning his Junior Leaving Certificate. Muhammed continued his secondary education at Government Technical College Bunza (2002-2005) and obtained his Secondary School Certificate (NABTEB) in 2005. Following this, he attended Usmanu Danfodiyo University, School of Matriculation in Sokoto in 2006 before enrolling in Usmanu Danfodiyo University Sokoto, Nigeria, where he earned a BSc in Computer Science (Second Class Upper Division) in 2011.

Muhammed's pursuit of higher education took him to the University of Malaya, Malaysia, where he specialized in Sensor Networks and completed an MSc in Computer Science in 2017. Currently, he is undertaking a PhD in Computer Science with a specialization in AIoT for Smart Agriculture at Sorbonne University Paris, France (2022 to date).

PROFESSIONAL ENDEAVORS

Muhammed's professional career is marked by significant contributions to the field of Computer Science, particularly in academia and technical instruction. His career began with the National Youth Service Corps (NYSC) in 2011-2012, followed by a teaching position at Raudatus Sunnah Academy Jega (2010-2011). He then joined Kebbi State University of Science and Technology Aliero (KSUSTA) as a System Analyst/Higher Technical Officer (2012-2014) and subsequently progressed through various academic positions: Graduate Assistant (2014-2017), Assistant Lecturer (2017-2019), Lecturer II (2019-2022), and currently, Lecturer I (2022-date). Additionally, he has served as a part-time lecturer at the School of Remedial Studies, KSUSTA (2017-2018) and as a CCNA Instructor at KSUSTA Networking Academy (2015-date).

CONTRIBUTIONS AND RESEARCH FOCUS

Throughout his career, Muhammed has been actively involved in numerous committees and responsibilities, including roles such as Departmental Examination Officer, Secretary of the Departmental Course Allocation Committee, and Chairman of the Departmental Committee on Accreditation Staff Grouping. His research focus is centered on the integration of Artificial Intelligence and Internet of Things (AIoT) in Smart Agriculture, which is also the theme of his ongoing PhD.

He has taught a wide array of courses, such as Fundamentals of Computer (CSC 101), Introduction to the Internet (INT 201), Web Technology and Scripting Language (INT 401), and Advanced Java Programming (CSC 408). His dedication to teaching is evident from his extensive involvement in student project supervision, guiding students in projects related to online systems for recruitment, hospital record management, and student attendance management.

IMPACT AND INFLUENCE

Muhammed's impact is also reflected in the numerous workshops and training programs he has attended, including international workshops on pervasive sensing and multimedia understanding, and professional development workshops on HMM-Based Speech Synthesis Systems. His contributions to academic conferences, such as the International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), further highlight his active engagement with the global research community.

ACADEMIC CITATIONS

Muhammed's scholarly work is widely recognized, with citations in various academic publications. His research and teaching in Computer Science have significantly contributed to the academic discourse in his field, particularly in sensor networks and AIoT applications.

LEGACY AND FUTURE CONTRIBUTIONS

Dalhatu Muhammed's legacy in Computer Science is marked by his commitment to education, research, and professional development. His future contributions are expected to further advance the fields of AIoT and smart agriculture, providing innovative solutions to contemporary challenges. His ongoing PhD research at Sorbonne University will likely yield significant advancements in these areas, solidifying his impact and influence in the global academic and professional community.

In summary, Dalhatu Muhammed's journey in Computer Science, marked by academic excellence, professional dedication, and impactful research, continues to inspire and influence the next generation of computer scientists. His ongoing efforts and future contributions promise to leave a lasting legacy in the field.

NOTABLE PUBLICATION

Artificial Intelligence of Things (AIoT) for smart agriculture: A review of architectures, technologies and solutions 2024

Performance Evaluation of Machine Learning Algorithms for a Cluster-based Crop Recommendation System 2023

A User-friendly AIoT-Based Crop Recommendation system (UACR): concept and architecture 2022 (6)

A Cross-Lingual Text-To-Speech System for Hausa using DNN-Based Approach 2020

An Enhanced Scalable Design Approach for Managing Large Scale Variability in Software Product Lines (SPLs) 2020