Mahmood ul Hassan | Computer Science | Editorial Board Member

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Assist. Prof. Dr. Mahmood ul Hassan | Computer Science | Editorial Board Member

Assistant Professor | Najran University | Saudi Arabia

Dr. Mahmood ul Hassan is a distinguished researcher and academic affiliated with the National Industrial Training Institute, TÜV Rheinland Arabia in the Kingdom of Saudi Arabia, with a verified scholarly association at Jazan University (ju.edu.sa). His research expertise spans Wireless Sensor Networks (WSN), Vehicular Ad Hoc Networks (VANET), mobile cloud computing, AI-driven smart systems, and ICT applications in education and healthcare. Over his career, he has built a strong interdisciplinary footprint across computer engineering, artificial intelligence, cybersecurity, and applied networking.Dr. Hassan has authored 157 scholarly documents with over 909 citations, an h-index of 17, and an i10-index of 31, reflecting both the breadth and impact of his contributions. His notable publications include influential works on smart agriculture using AI, tumor classification in MRI using wavelets and SVM, ANN-based secure routing protocols for VANETs, image segmentation models, glioma classification using deep CNNs, and lightweight security frameworks for WSNs. Several of his papers in Energies, Sensors, IEEE Access, Computers, Materials & Continua, and Wireless Communications and Mobile Computing have been widely cited and integrated into ongoing global research.His collaborations with multidisciplinary teams across Saudi Arabia, Pakistan, and international institutions highlight his commitment to advancing digital transformation in critical sectors. Dr. Hassan’s work on intelligent connectivity restoration, blockchain-based secure information routing, microservice optimization, fog computing, and IoT-enabled education systems demonstrates a consistent alignment with emerging technological challenges. Beyond core engineering, he has also contributed research in health informatics, public-sector project planning, archaeology, and medical studies, showcasing his broad academic versatility.Dr. Mahmood ul Hassan’s research has substantive societal impact, particularly in enhancing network reliability, secure communication, healthcare diagnostics, smart agriculture, and technology-driven education. His sustained scholarly productivity and cross-disciplinary influence continue to position him as a leading academic voice in next-generation networked systems and intelligent computing solutions.

Profiles : Googlescholar | Scopus | ORCID

Featured Publications

1. Smart agriculture cloud using AI-based techniquesJunaid, M., Shaikh, A., Hassan, M. U., Alghamdi, A., Rajab, K., Al Reshan, M. S., & … (2021). Smart agriculture cloud using AI-based techniques. Energies, 14(16), 5129. Cited By: 58

2. Classification of tumors in human brain MRI using wavelet and support vector machineAhmad, M., Hassan, M., Shafi, I., & Osman, A. (2012). Classification of tumors in human brain MRI using wavelet and support vector machine. IOSR Journal of Computer Engineering, 8(2), 25–31. Cited By: 53

3. ANN-based intelligent secure routing protocol in vehicular ad hoc networks (VANETs) using enhanced AODVHassan, M. U., Al-Awady, A. A., Ali, A., Sifatullah, Akram, M., Iqbal, M. M., Khan, J., & … (2024). ANN-based intelligent secure routing protocol in vehicular ad hoc networks (VANETs) using enhanced AODV. Sensors, 24(3). Cited By: 44

4. A weighted spatially constrained finite mixture model for image segmentationAhmed, M. M., Shehri, S. A., Arshed, J. U., Hassan, M. U., & Hussain, M. (2021). A weighted spatially constrained finite mixture model for image segmentation. Computers, Materials & Continua, 67(1), 171–185. Cited By: 42

5. A CNN-model to classify low-grade and high-grade glioma from MRI imagesHafeez, H. A., Elmagzoub, M. A., Abdullah, N. A. B., Al Reshan, M. S., Gilanie, G., & … (2023). A CNN-model to classify low-grade and high-grade glioma from MRI images. IEEE Access, 11, 46283–46296. Cited By: 37

Dr. Mahmood ul Hassan’s research advances secure, intelligent, and resilient networked systems that enhance healthcare diagnostics, smart agriculture, and sustainable digital infrastructure. His work bridges AI, wireless communication, and cloud technologies, delivering innovative solutions with direct societal and economic impact.

Deqian Fu | Data Science and Analytics | Best Researcher Award

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Prof. Dr. Deqian Fu | Data Science and Analytics | Best Researcher Award

Professor | Linyi University | China

Prof. Dr. Deqian Fu is a prominent researcher at Linyi University, China, with a strong focus on logistics, data exchange, and trust management in supply chain and intermodal transport systems. His research explores innovative methods for secure and efficient data sharing in the logistics industry, integrating advanced technologies such as blockchain, edge computing, and ontology-based frameworks. Fu has made notable contributions in developing trusted data access control mechanisms and non-intrusive data exchange models that enhance collaboration and operational efficiency across complex logistics networks. He has authored 39 publications, which have collectively garnered 127 citations, reflecting the growing impact of his work in the fields of applied sciences and industrial informatics. His research outputs demonstrate a commitment to advancing the intersection of information technology and logistics, emphasizing both theoretical development and practical applications. With an h-index of 7, Fu’s scholarly contributions have been recognized for their relevance and innovation, particularly in promoting secure and intelligent data-sharing frameworks within the logistics sector. Selected works include “Trusted Data Access Control Based on Logistics Business Collaboration Semantics” in Applied Sciences (2024), alongside conference papers such as “Data Exchange and Sharing Framework for Intermodal Transport Based on Blockchain and Edge Computing” and “Trusted Non-intrusive Data Exchange based on Ontology in Logistics Industry,” underscoring his focus on reliable, technology-driven logistics solutions.

Profiles : ORCID | Scopus 

Featured Publications

1. Wang, W., Li, Q., Jiang, Z., Fu, D., & Camacho, D. (2025). An efficient framework for general long-horizon time series forecasting with Mamba and diffusion probabilistic models. Engineering Applications of Artificial Intelligence.

2.Liu, Z., Shi, Z., Wang, W., Kong, R., Fu, D., & Qiu, J. (2025). Research on data ownership and controllable sharing schemes in the process of logistics data flow.

3.Wang, L., Zhang, X., Xu, L., Fu, D., & Qiu, J. (2024). Data exchange and sharing framework for intermodal transport based on blockchain and edge computing. In Communications in Computer and Information Science. Springer.

4.Zhang, X., Jing, C., Chen, Y.-C., Wang, L., Xu, L., & Fu, D. (2024). Trusted data access control based on logistics business collaboration semantics.

5.Zhang, X., Wang, L., Xu, L., & Fu, D. (2023). A distributed logistics data security sharing model based on semantics and CP-ABE. In Proceedings of the ACM International Conference (pp. 1–8).

Dayu Jia | Big Data Management | Best Researcher Award

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Mr. Dayu Jia | Big Data Management | Best Researcher Award

Associate Professor | Liaoning University | China

Dr. Jia Dayu is an Associate Professor and Master’s Supervisor at the School of Information Science, Liaoning University. He completed his Ph.D. in Computer Science at Northeastern University under the guidance of Prof. Wang Guoren and further gained international experience as a joint Ph.D. student at the National University of Singapore under Prof. Ooi Beng Chin. He also worked as a postdoctoral fellow at the School of Information Science and Engineering, Northeastern University. Dr. Jia has been actively involved in national, provincial, and ministerial research projects and has collaborated on international research initiatives. His research focuses on big data management, blockchain data analysis, and artificial intelligence, with expertise in scalable storage, secure data retrieval, and privacy-preserving techniques. He has published 21 high-quality papers in reputed journals and conferences, including Q1 journals such as Advanced Materials and Light: Science & Applications, and has served as the first or corresponding author on 12 publications in prestigious venues like JCST, WWW, and Software Journal. Dr. Jia has also been granted 13 national invention patents, demonstrating his innovative contributions, and has hosted or participated in six funded research projects. His skills include blockchain architecture design, data analytics, AI-driven optimization, and secure distributed systems. His work has earned recognition with 176 citations by 14 documents and an h-index of 5, reflecting the impact and relevance of his research in the academic community.

Profile : Scopus 

Featured Publication

1. Jia, D., Hu, Y., Huang, M., Zhang, J., He, G., Xu, S., Liu, S., & Wang, X. (2025). Security risks and solutions of concurrent PBFT. Expert Systems with Applications, 294, 128737.

Jun Peng | Big Data Analysis | Best Researcher Award

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Prof. Jun Peng | Big Data Analysis | Best Researcher Award

Professor | Ningbo University | China

Prof. Jun Peng is a distinguished scholar in the field of educational technology with expertise in big data in education, artificial intelligence in learning, blended learning, and curriculum design. He earned his PhD in Education from the University of Hong Kong and has built a strong academic career through teaching, research, and international collaborations. Currently serving as a professor and doctoral supervisor at Ningbo University, he has also contributed to the University of Hong Kong and the City University of Macau in research and teaching capacities. His professional experience includes leading several funded projects across China and Macau, with a focus on AI integration in education and innovative digital learning models. His research interests span online learning environments, project-based education, and sustainable approaches to technology-enhanced learning, reflected in numerous publications in leading SSCI, SCI, and Scopus-indexed journals such as Computers & Education, Education and Information Technologies, and Sustainability. Recognized with multiple commendations for research excellence, he has also received awards for educational innovation and course design. Prof. Peng is active in academic service as an editorial board member, peer reviewer for reputed journals, and keynote speaker at international conferences. His research skills include quantitative and qualitative analysis, big data applications, machine learning for education, and curriculum development. With a proven record of impactful research, leadership, and mentoring, he continues to advance the field of educational technology while contributing to the global academic community.

Profile: ORCID

Featured Publications

1.Shu, X., Peng, J., & Wang, G. (2023). Deciding alone or with others: Employment anxiety and social distance predict intuitiveness in career decision making. International Journal of Environmental Research and Public Health, 20(2), 1484.
2. Su, B., & Peng, J. (2023). Sentiment analysis of comment texts on online courses based on hierarchical attention mechanism. Applied Sciences, 13(7), 4204.
3. Zhou, J., Ran, F., Li, G., Peng, J., Li, K., & Wang, Z. (2022). Classroom learning status assessment based on deep learning. Mathematical Problems in Engineering, 2022, 7049458.
4. Peng, J., Yuan, B., Sun, M., Jiang, M., & Wang, M. (2022). Computer-based scaffolding for sustainable project-based learning: Impact on high- and low-achieving students. Sustainability, 14(19), 12907.
5. Li, Y., & Peng, J. (2022). Evaluation of expressive arts therapy on the resilience of university students in COVID-19: A network analysis approach. International Journal of Environmental Research and Public Health, 19(13), 7658.

Assist Prof Dr Jaya Singh Dhas L | Data Science | Best Researcher Award | 1229

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Assist Prof Dr Jaya Singh Dhas L | Data Science | Best Researcher Award

Head of the Department at Scott Christian College (Autonomous),India

Dr. L. Jaya Singh Dhas is the Head of the Department of Computer Science at Scott Christian College (Autonomous), Nagercoil, Tamil Nadu, India. With over two decades of experience in academia, Dr. Dhas is a distinguished researcher and educator, specializing in areas like Artificial Intelligence, Machine Learning, Data Mining, and Cloud Computing. His work combines theoretical research with practical applications, particularly in the fields of clustering techniques, heart disease prediction, and network security. Dr. Dhas has contributed significantly to the academic community through his research publications, conference participation, and various professional development activities.

Profile

Scopus

Education 🎓

  • Ph.D. in Computer Science – Bharathidasan University, Tiruchirappalli (2022), First Class
  • M.Phil. in Computer Science – Alagappa University, Karaikudi (1998), First Class
  • M.C.A. (Master of Computer Applications) – Bharathidasan University, Tiruchirappalli (1996), First Class
  • B.Sc. in Computer Science – Madurai Kamaraj University, Madurai (1991), First Class

Dr. Dhas’ academic qualifications reflect his deep commitment to the field of computer science and his expertise in both foundational and advanced topics within the discipline.

Professional Experience 💼

Dr. Dhas joined Scott Christian College (Autonomous) in 1998, where he has served as the Head of the Department of Computer Science since then. With more than 20 years of teaching and leadership experience, Dr. Dhas has significantly influenced the department’s curriculum and research direction. He is dedicated to fostering academic growth and promoting innovative research among students and faculty.

Research Interests 🔬

Dr. Dhas’ primary research interests lie in Artificial Intelligence, Data Science, Clustering Techniques, Big Data Analytics, and Network Security. He has worked extensively on the following areas:

  • Clustering Techniques: Investigating different clustering algorithms for analyzing temporal relational data.
  • Heart Disease Prediction: Using machine learning techniques for early-stage heart disease prediction.
  • Network Intrusion Detection: Optimizing deep learning approaches for network security.
  • Big Data: Exploring synergetic filtering and neural network techniques for handling large datasets.

Awards & Honors 🏆

Dr. Dhas has received multiple recognitions for his outstanding contributions in research and education, including:

  • Indian Patent (2022) for “Monitoring E-Health Care System Using Artificial Intelligence Techniques”.
  • Member of the Internet Society and International Association of Engineers (IAENG), further reflecting his international recognition in the field.
  • Reviewer for several renowned journals, including International Journal of Information Technology and Decision Making (IJITDM) and Journal of Scientific Research and Reports (JSRR).

Achievements 🌟

  • Successfully published numerous papers in high-impact journals such as Expert Systems With Applications (Elsevier), International Journal of Engineering and Advanced Technology (IJEAT), and Indian Journal of Natural Sciences (IJONS).
  • Served as a reviewer for several prestigious international journals and conferences, contributing to the academic community’s growth.
  • Authored multiple book chapters in edited volumes on topics like data clustering and artificial intelligence, further establishing his expertise.

Upcoming Projects 🚀

  • Dr. Dhas is currently engaged in projects related to AI-driven healthcare systems, particularly focusing on AI in early disease detection.
  • He is also exploring the use of neural networks and big data analytics to tackle contemporary challenges in network security and data privacy.

Publications 📚

  1. “Hybrid Fast Correlation-based Feature Selection with Improved Weighed Particle Swarm Optimization to Predict and Classify Heart Disease at an Early Stage”, Indian Journal of Natural Sciences (IJONS), Vol. 15, Issue 85, August 2024, Pages 76542 – 76550.
  2. “Network Intrusion Detection: An Optimized Deep Learning Approach Using Big Data Analytics”, Expert Systems With Applications, Elsevier, Volume 251, 1 October 2024, 123919.
  3. “Kulczynski Similarity Index Feature Selection based Map Estimated Rocchio Classification for Brain Tumor Disease Diagnosis”, International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), December 2023.
  4. “Identification of Clustering Techniques with Temporal Relational Data Points”, International Journal of Interdisciplinary Global Studies (IJIGS), Volume 14, Issue 04, Oct-Dec’ 2020.
  5. “Efficient Synergetic Filtering in Big Dataset using Neural Network Technique”, International Journal of Recent Technology and Engineering (IJRTE), Volume 8, Issue 5, January 2020, Pages 1349 – 1360.