Muhammad Furqan Zia | AI for Communication Systems | Research Excellence Award

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Mr. Muhammad Furqan Zia | AI for Communication Systems | Research Excellence Award

Researcher | University of Quebec at Trois Rivieres | Canada

Mr. Muhammad Furqan Zia is a doctoral researcher in Electrical Engineering at the University of Quebec at Trois-Rivières, Canada, with expertise in intelligent wireless communication systems, explainable artificial intelligence, and physical layer security. He is currently supported by a fully funded NSERC project, focusing on explainable AI–aided semantic communication and domain generalization for next-generation networks. His research portfolio spans 6G, IoT security, non-orthogonal multiple access (NOMA), and next-generation Wi-Fi, developed through strong collaborations with academia and industry, including Koç University and VESTEL Electronics. Dr. Zia has authored multiple peer-reviewed journal and conference publications and holds an international patent, with over 56 citations on Google Scholar. He has actively contributed to IEEE 802.11 standardization activities and serves the research community as a reviewer, guest editor, and technical trainer. His work aims to enhance the security, transparency, and reliability of future communication systems, delivering meaningful societal impact in smart cities and connected technologies

Citation Metrics (Google Scholar)

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Top 5 Featured Publications


An Advanced Non-Orthogonal Multiple Access Security Technique for Future Wireless Communication Networks

– RS Open Journal on Innovative Communication Technologies, 2020 (Citations: 24)

An Advanced NOMA Security Technique for Future Wireless Communication

– IEEE International Conference, 2020 (Citations: 7)

Integrated Security for Smart Homes in IoT-Enabled Cities: Trust, Privacy, and Resilience – A Survey

– International Journal of Multidisciplinary Conference Proceedings, 2025

Junbeom Hur | Computer Science | Best Researcher Award

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Prof. Junbeom Hur | Computer Science | Best Researcher Award

Professor | Korea University | South Korea

Prof. Junbeom Hur’s research career is defined by impactful contributions to the fields of computer and cyber security, applied cryptography, and network defense systems. His extensive experience as a project leader and researcher encompasses advanced investigations into identity and attribute-based encryption, secure multi-party computation, and privacy-enhancing technologies such as differential privacy and anonymity protocols. He has directed several major research initiatives, including the Brain Korea 21 (BK21) FOUR project in computer science and engineering, which focuses on developing next-generation security frameworks for cloud computing, AI-driven systems, and data protection infrastructures. His work on virtualization security and side-channel attack defenses has strengthened cloud computing environments against sophisticated vulnerabilities, while his exploration of cloud data access control has contributed to secure, scalable information management solutions. In network security, Dr. Hur’s projects have tackled challenges in group key management, secure multicast, RDMA-based attack mitigation, and TLS vulnerability detection, ensuring resilient and trustworthy network communication. His more recent research ventures expand into AI and blockchain security, particularly focusing on protecting machine-learning-as-a-service platforms, enhancing neural network resilience against adversarial threats, and improving anonymity and deanonymization mechanisms in blockchain ecosystems and dark web analysis. Through collaborative and interdisciplinary projects, Dr. Hur continues to advance the understanding of how cryptographic and computational techniques can safeguard emerging technologies. His sustained leadership roles, including as Head of the Center for Information System Security and Vice Dean at Korea University’s College of Informatics, underscore his ability to integrate academic insight with practical innovation, fostering secure digital transformation in academia and industry alike. His future research trajectory is poised to further strengthen global cybersecurity frame

Publications:

Yoon, H., Yu, M., Hahn, C., Koo, D., & Hur, J. (2024). Exploiting hidden information leakages in backward privacy for dynamic searchable symmetric encryption. Applied Sciences, 14(6), 2287.

Yoon, H., Moon, S., Kim, Y., Hahn, C., Lee, W., & Hur, J. (2020). SPEKS: Forward private SGX-based public key encryption with keyword search. Applied Sciences, 10(21), 7842.

Koo, D., Shin, Y., Yun, J., & Hur, J. (2018). Improving security and reliability in Merkle tree-based online data authentication with leakage resilience. Applied Sciences, 8(12), 2532.

Hur, J. (2017). Privacy-preserving aggregation and authentication of multi-source smart meters in a smart grid system. Applied Sciences, 7(10), 1007.

Dr. Mukesh Kumar Sharma – Fuzzy Optimization – Excellence in Research

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Dr. Mukesh Kumar Sharma - Fuzzy Optimization - Excellence in Research

Chaudhary Charan Singh University - India

AUTHOR PROFILE

ORCID
GOOGLE SCHOLAR

SUMMARY

Dr. Mukesh Kumar Sharma is an accomplished academic and researcher specializing in Operations Research and advanced fuzzy reliability systems. With extensive expertise in mathematical modeling, artificial intelligence, blockchain, and healthcare systems, Dr. Sharma integrates theoretical rigor with practical application. His scholarly output spans multiple interdisciplinary areas including IoMT, optimization, and decision-making. His leadership in academia and scholarly societies reflects both administrative acumen and deep subject knowledge.

EDUCATION

Dr. Sharma possesses a robust educational foundation in mathematical sciences, with a specialized focus on reliability theory and fuzzy systems. His advanced academic training supports a multidisciplinary research agenda, integrating computational intelligence, engineering applications, and operations management. His academic trajectory supports his broad contributions to both theoretical research and real-world problem solving across various technical domains.

PROFESSIONAL EXPERIENCE

Dr. Sharma serves at Chaudhary Charan Singh University, Meerut, where he holds several senior roles including Head of Department and Coordinator of the UG Cell. His responsibilities span academic leadership, examination control, evaluation oversight, and policy implementation. He has contributed actively in multiple university committees, further demonstrating his commitment to institutional development and student-centric governance.

RESEARCH INTEREST

His research interests include Fuzzy Sets, Vague Sets, Transportation Models, Artificial Intelligence, Blockchain Technology, and IoMT. Dr. Sharma is particularly known for his work in Fuzzy Reliability, Mathematical Modeling, and their applications in healthcare and autonomous systems. He focuses on developing new computational models and decision-making frameworks that solve complex real-life problems in dynamic environments.

AWARD AND HONOR

Dr. Sharma holds life memberships in several prestigious organizations, including the Indian Mathematical Society, Operational Research Society of India, and Indian Science Congress Association. He is also associated with international bodies such as the American Biographic Institute and contributes actively to the Soft Computing Research Society. These memberships recognize his scholarly standing and commitment to global research engagement.

RESEARCH SKILL

Dr. Sharma demonstrates expertise in fuzzy logic, multi-criteria decision-making (MCDM), optimization, and soft computing techniques. His skills encompass simulation, algorithmic development, and hybrid systems modeling. He is proficient in applying machine learning and artificial intelligence to fields such as energy, transportation, healthcare diagnostics, and structural reliability, aligning theory with actionable solutions.

PUBLICATIONS TOP NOTED

He has authored high-impact papers in OPSEARCH, Scientific Reports, Journal of Discrete Mathematical Sciences and Cryptography, and Experimental Cell Research. Notable contributions include novel models for fuzzy transportation, digital twin protocols, waste management systems, and COVID-19 analysis through fuzzy logic. His interdisciplinary work reflects innovation in mathematical modeling and real-world implementation.

Title: A sustainable 4-dimensional transportation system under dual hesitant Fermatean fuzzy configuration with safety constraints
Authors: M. K. Sharma, Sadhna Chaudhary
Journal: OPSEARCH (2025-04-24)
DOI: 10.1007/s12597-025-00947-5

Title: Novel Pythagorean fuzzy score function to optimize fuzzy transportation models
Authors: Ritu, Tarun Kumar, Jahnvi, Kapil Kumar, Nitesh Dhiman, M. K. Sharma
Journal: Results in Engineering (2025-03)
DOI: 10.1016/j.rineng.2025.104048

Title: Exploring the role of exosomal lncRNA in cancer immunopathogenesis: Unraveling the immune response and EMT pathways
Authors: Sharif Alhajlah, Saade Abdalkareem Jasim, Farag M.A. Altalbawy, Pooja Bansal, Harpreet Kaur, Jaafaru Sani Mohammed, Mohammed N. Fenjan, Reem Turki Edan, M. K. Sharma, Ahmed Hussein Zwamel
Journal: Experimental Cell Research (2025-02)
DOI: 10.1016/j.yexcr.2024.114401

Title: A circular economy based nonlinear corrugated waste management system using Fermatean bipolar hesitant fuzzy logic
Authors: Sadhna Chaudhary, Apu Kumar Saha, M. K. Sharma
Journal: Scientific Reports (2025-02-27)
DOI: 10.1038/s41598-025-90948-7

Title: Enhancing Hotel Customer Service With AI-Powered Chatbots
Authors: Tarun Kumar Vashishth, Vikas Sharma, Mukesh Kumar Sharma, Rajeev Sharma
Journal: Book Chapter (2025-01-17)
DOI: 10.4018/979-8-3693-7127-5.ch004

CONCLUSION

Dr. Mukesh Kumar Sharma stands as a distinguished figure in mathematical and computational sciences. Through active research, publication, and academic leadership, he has substantially advanced the field of fuzzy systems and their applications. His diverse expertise, global affiliations, and continuous innovation make him a key contributor to contemporary science and technology research.

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

Everton – Artificial Intelligence – Best Researcher Award

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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