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.

Mohammed M Alenazi | Computer Science and Artificial Intelligence | Best Researcher Award

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Dr. Mohammed M Alenazi | Computer Science and Artificial Intelligence | Best Researcher Award

Assistance Professor | University of Tabuk | Saudi Arabia

Dr. Mohammed M. Alenazi is an Assistant Professor of Computer Engineering at the University of Tabuk, Saudi Arabia, whose research focuses on the intersection of energy-efficient communication networks, machine learning, and distributed systems. His work advances intelligent computing architectures that optimize performance, reduce energy consumption, and enable sustainability in next-generation networks. Dr. Alenazi has contributed to several impactful studies, including energy-efficient neural network embedding in IoT over passive optical networks, distributed machine learning in cloud–fog environments, and AI-driven frameworks for 6G-IoT-based remote cardiac monitoring. His research extends to federated learning for low-latency IoT communications, hybrid cloud edge architectures for real-time cryptocurrency forecasting with blockchain integration, and machine learning-optimized energy management for resilient residential microgrids with electric vehicle integration. His scholarly output, cited over 50 times with an h-index of 4 and i10-index of 3, reflects growing recognition in the domains of sustainable networking and intelligent systems. Dr. Alenazi’s work combines AI, IoT, and cloud–fog computing to create adaptive, energy-aware solutions for smart environments, healthcare, and industrial systems. Through his innovative contributions, he continues to enhance the efficiency, reliability, and intelligence of modern communication infrastructures, positioning his research at the forefront of AI-powered green networking and distributed intelligence for the evolving digital ecosystem.

Profiles : ORCID | Scopus | Google Scholar | ResearchGate

Featured Publications

1. Alenazi, M. M., Yosuf, B. A., El-Gorashi, T., & Elmirghani, J. M. H. (2020). Energy efficient neural network embedding in IoT over passive optical networks. Cited By : 13

2.Yosuf, B. A., Mohamed, S. H., Alenazi, M. M., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2021). Energy-efficient AI over a virtualized cloud fog network. Cited By : 12

3.Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2021). Energy-efficient distributed machine learning in cloud fog networks. Cited By : 10

4.Banga, A. S., Alenazi, M. M., Innab, N., Alohali, M., Alhomayani, F. M., Algarni, M. H., et al. (2024). Remote cardiac system monitoring using 6G-IoT communication and deep learning. Cited By : 6

5.Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2022). Energy efficient placement of ML-based services in IoT networks. Cited By : 4

Ruzayn Quaddoura | Computer Science | Best Researcher Award

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Assoc. Prof.Dr.Ruzayn Quaddoura | Computer Science | Best Researcher Award

Zarqa University, Jordanian

Dr. Ruzayn Quaddoura 🇯🇴 is a renowned academic and researcher in the field of computer science, specializing in combinatorial optimization and algorithmic graph theory. he has over two decades of teaching and research experience . Currently serving as Assistant Professor at Zarqa University in Jordan , he has also held teaching positions in Saudi Arabia 🇸🇦, France 🇫🇷, and Syria 🇸🇾. Dr. Quaddoura has made significant contributions to the study of NP-hard problems, scheduling algorithms, and digraph structures . He has authored numerous peer-reviewed publications in prestigious journals and conferences worldwide . Multilingual in Arabic, English, and French , he actively engages in academic committees, technical boards, and student mentorship. His dedication to quality education and advanced algorithm research positions him as a leading voice in theoretical computer science .

🔹Professional Profile

ORCID

🔹 Education & Experience

Dr. Quaddoura’s academic foundation was laid at Damascus University 🇸🇾, where he earned his Bachelor’s and Postgraduate Diploma in Mathematics . He then pursued a Diploma in French Language 🇫🇷 and later completed an MSc (DEA) in Operations Research – Combinatorial Optimization at INPG, Grenoble . He culminated his academic training with a PhD in Algorithmic Graph Theory from the University of Picardie Jules Verne, France .His professional journey began as a lecturer in Syria and France, before moving into Assistant Professor roles at Princess Sumaya University, Zarqa University, and King Abdulaziz University . Since 2011, he has been a key faculty member at Zarqa University’s Faculty of Information Technology. His teaching areas include algorithms, data structures, discrete mathematics, and compilers . His global academic experience and strong theoretical background reflect a career devoted to advancing computer science education and research .

🔹 Professional Development 

Throughout his career, Dr. Quaddoura has actively contributed to academic growth, institutional leadership, and scholarly collaboration . He has served on various academic committees at Zarqa University, including the Scientific Committee, Study Plan Committee, and Course Equivalence Committee. As Chairman of Exams and Committee Leader, he has shaped curriculum and assessment strategies with excellence.He played similar roles at King Abdulaziz University, contributing to master’s program oversight and curriculum development in computing and information technology . His refereeing activities include serving on the technical committees of prominent journals and conferences like IAJIT and ACIT . Additionally, he managed the Colleges of Computing and Information Society office in 2015, demonstrating organizational and strategic leadership.His professional footprint showcases not only academic rigor but also collaborative leadership, quality assurance, and international engagement in the computing education community .

🔹 Research Focus Category 

Dr. Ruzayn Quaddoura’s primary research lies in Theoretical Computer Science, focusing on Combinatorial Optimization, Algorithmic Graph Theory, and Complexity Theory . His work explores the deep structure of graphs, creating efficient solutions to NP-hard problems using novel algorithmic techniques . From linear-time scheduling algorithms for specific graph families to optimization in series-parallel digraphs and bipartite graphs, his research bridges abstract theory and real-world computational problems .He has also extended his expertise to applied fields such as the Internet of Things (IoT), encryption, and machine learning in wildfire detection . His publications in top-tier journals like IAJIT, Algorithms, and Symmetry highlight his contributions to both pure and applied research .Dr. Quaddoura’s innovative approaches to graph decomposition, structural analysis, and algorithmic efficiency contribute significantly to solving modern computing challenges through mathematical elegance and logical precision .

🔹 Awards and Honors 

 Dr. Quaddoura’s academic excellence has been recognized through several prestigious awards and honors. He earned a First Rank Honor Certificate from Damascus University in 1992 for academic distinction in Mathematics . He received a scholarship from INPG (France) for his MSc in Combinatorial Optimization and another scholarship from Picardie Jules Verne University to pursue his PhD in Theoretical Computer Science , In 2014, he was honored with a Recognition Paper Award from the World of Computer Science and Information Technology Journal for his innovative algorithm on induced matchings in bipartite graphs .These accolades reflect his commitment to research excellence, international academic collaboration, and impactful contributions to the field of computer science 🌍🔬.His scholarly achievements not only affirm his status as a leading researcher but also inspire a generation of students and scientists dedicated to algorithmic innovation and problem-solving .

🔹Publication of Top Notes

1. The Clique-Width of Minimal Series-Parallel Digraphs

Authors: Frank Gurski, Ruzayn Quaddoura
Year: 2025
Citation: Algorithms, 2025-05-28. DOI: 10.3390/a18060323

2.Early Wildfire Smoke Detection Using Different YOLO Models

Authors: Yazan Al-Smadi, Mohammad Alauthman, Ahmad Al-Qerem, Amjad Aldweesh, Ruzayn Quaddoura, Faisal Aburub, Khalid Mansour, Tareq Alhmiedat
Year: 2023
Citation: Machines, 2023. DOI: 10.3390/machines11020246

3. Internet of Things Protection and Encryption: A Survey

Authors: Ghassan Samara, Ruzayn Quaddoura, M. I. Al-Shalout, K. AL-Qawasmi, G. A. Besani
Year: 2022
Citation: arXiv, 2022. DOI: 10.48550/arxiv.2204.04189

4.Scheduling UET-UCT DAGs of Depth Two on Two Processors

Authors: Ruzayn Quaddoura, Ghassan Samara
Year: 2022
Citation: arXiv, 2022. DOI: 10.48550/arxiv.2203.15726

5. Scheduling UET-UCT DAGs of Depth Two on Two Processors

Authors: Ruzayn Quaddoura, Ghassan Samara
Year: 2021
Citation: 22nd International Arab Conference on Information Technology (ACIT), 2021. DOI: 10.1109/ACIT53391.2021.9677100

6.On 2-Colorability Problem for Hypergraphs with P₈-Free Incidence Graphs

Authors: Ruzayn Quaddoura
Year: 2020
Citation: International Arab Journal of Information Technology, 2020. DOI: 10.34028/iajit/17/2/14

🧾 Conclusion

Dr. Ruzayn Quaddoura is highly suitable for the Best Researcher Award. His research exhibits a rare balance of theoretical depth and practical relevance, particularly in the areas of graph theory, AI for environmental monitoring, and cybersecurity. His ongoing contributions to both academia and applied science solidify his standing as a leading and impactful researcher deserving of recognition.