Mehdi Saadallah | Computer Science | Best Researcher Award

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Mr. Mehdi Saadallah | Computer Science
| Best Researcher Award

Vrije Universiteit Amsterdam | Netherlands

Mr. Mehdi Saadallah research focuses on advancing the integration of artificial intelligence (AI) and automation in cybersecurity operations, emphasizing the intersection between technology, human behavior, and organizational structures. It investigates how AI-driven tools influence professional identity, decision-making, and collaboration within Security Operations Centers (SOCs), where analysts and algorithms coexist in dynamic threat environments. By applying frameworks such as Paradox Theory, Organizational Routine Theory, and Identity Work Theory, the work uncovers the tensions, adaptations, and emergent practices that arise when automation transforms traditional cybersecurity routines. Empirical insights are drawn from multinational enterprises across diverse sectors, revealing how organizations balance efficiency, control, and trust in AI-augmented defense systems. The research also develops conceptual and operational models for AI-assisted vulnerability management and SOC modernization, providing a blueprint for improving detection, response, and resilience in complex digital ecosystems. Beyond theory, it delivers applied innovations that enhance cybersecurity governance, human–AI trust calibration, and automation ethics. Through interdisciplinary methods combining qualitative inquiry, computational analysis, and organizational modeling, the work contributes to redefining cybersecurity as a socio-technical discipline—bridging academic rigor and industrial application to guide the future of intelligent, adaptive, and human-centered cyber defense frameworks.

Featured Publications

Saadallah, M. (2025). Harmonizing paradoxical tensions in SOCs: A strategic model for integrating AI, automation, and human expertise in cyber defense and incident response. In Proceedings of the 58th Hawaii International Conference on System Sciences (HICSS-58). https://doi.org/10.24251/HICSS.2025.723

Saadallah, M., Shahim, A., & Khapova, S. (2025). Reconciling tensions in Security Operations Centers: A Paradox Theory approach. Big Data and Cognitive Computing, 9(11), 278. https://doi.org/10.3390/bdcc9110278

Saadallah, M., Shahim, A., & Khapova, S. (2025). Optimizing AI and human expertise integration in cybersecurity: Enhancing operational efficiency and collaborative decision-making. PriMera Scientific Engineering, 6(1), 177. https://doi.org/10.56831/psen-06-177

Saadallah, M., Shahim, A., & Khapova, S. (2024). Multi-method approach to human expertise, automation, and artificial intelligence for vulnerability management. In Advances in Intelligent Systems and Computing (pp. xxx–xxx). Springer. https://doi.org/10.1007/978-3-031-65175-5_29

 Saadallah, M., Shahim, A., & Khapova, S. (2024). Synergizing human expertise, automation, and artificial intelligence for vulnerability management. PriMera Scientific Engineering, 5(10), 160. https://doi.org/10.56831/psen-05-160

Vikas Verma | Computer Science | Young Scientist Award

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Mr. Vikas Verma | Computer Science
| Young Scientist Award

The ICFAI University, Jaipur | India

Dr. Vikas Verma’s research contributions focus extensively on Software Defined Networking (SDN), Machine Learning, and Network Optimization, emphasizing energy efficiency, intelligent routing, and data-driven automation. His doctoral research, “Flow Classification and Energy Efficient Routing in Software Defined Networks Using Machine Learning Techniques,” explores the integration of adaptive algorithms for sustainable network management. His projects, including “Routing Optimization for Software-Defined Networking Using Machine Learning Techniques and Multi-Domain Controller” and “Industry-Academia Collaboration of SME with Academics,” demonstrate practical applications of AI in networking and innovation ecosystems. Dr. Verma’s publications in high-impact journals and conferences, such as the Philippine Journal of Science, Suranaree Journal of Science and Technology, IEEE Xplore, and Springer CCIS, address key advancements in SDN, IoT-based smart farming, and quantum communication security. His work “Energy-Efficient Techniques in SDN: Software, Hardware, and Hybrid Approaches” and “Comparative Analysis of Quantum Key Distribution Protocols” highlight optimization in computing systems and secure data transmission. Additionally, he holds two UK design patents—one for an AI-driven finance management device and another for a medical diagnostic system using saliva-based biomarkers. His current research extends to privacy preservation, intelligent traffic classification, and predictive analytics, establishing his expertise in sustainable and secure intelligent network systems.

Featured Publications

Verma, V., & Jain, M. (2024). Energy-efficient techniques in SDN: Software, hardware, and hybrid approaches. Philippine Journal of Science, 153(1).

Agarwal, N., & Verma, V. (2023). Comparative analysis of quantum key distribution protocols: Security, efficiency, and practicality. In Proceedings of the International Conference on Artificial Intelligence of Things (pp. 151–163).

Verma, V., Ramakant, Mathur, H., & Agarwal, N. (2022). IoT assisted smart farming using data science techniques. In 2022 IEEE World Conference on Applied Intelligence and Computing (AIC). IEEE.

Verma, V. (2017). Automatic mood classification of Indian popular music. International Journal for Research in Applied Science and Engineering.

Verma, V., & Jain, M. (2023). Optimization of routing using traffic classification in software defined networking. Suranaree Journal of Science and Technology, 30(1), 010198(1–8).*

Majid khan | Computational Chemistry | Best Researcher Award

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Dr. Majid khan | Computational Chemistry | Best Researcher Award

Assistant Professor, Fuzhou University, Pakistan

Majid Khan, Ph.D., is an accomplished biochemist from Pakistan with extensive experience in drug development. He specializes in both in-vitro and in-vivo drug testing phases, demonstrating expertise in delivering high-quality results. Khan is proficient in refining testing protocols and optimizing development processes, ensuring rigorous and accurate outcomes. His leadership, communication, and teamwork skills have earned him a reputation for mentoring and guiding new team members in a collaborative environment. Khan’s dedication to scientific research and his ability to simplify complex ideas for non-technical audiences have set him apart as a respected figure in his field. 🌟🧬

Publication Profile

Google Scholar

Education:

Majid Khan holds a Ph.D. in Biochemistry from the H.E.J. Research Institute of Chemistry, University of Karachi (2018-2021), where he investigated inhibitors of ureases. He completed his M.Phil. in Biochemistry (2014-2017) at the same institute, focusing on the effects of urease inhibitors on urolithiasis-causing bacteria. His academic journey began with a BS (Hons) in Biochemistry from Hazara University (2008-2012) and was preceded by pre-medical studies at Govt Post-Graduate College, Charssada. 🎓📚

Experience:

Dr. Khan’s expertise spans across drug development, specifically focusing on the mechanism of action of various enzyme inhibitors. His work in biochemistry has not only involved cutting-edge research but also practical applications in addressing major health issues, such as urolithiasis, diabetes, and cancer. Khan’s contributions to both academic and industrial sectors showcase his proficiency in lab-based and computational research. His role in scientific communities, guiding young researchers, and his collaborations with other professionals have been key to his successful career. 🔬💼

Awards and Honors:

Dr. Khan’s outstanding contributions to biochemistry and drug development have been recognized through various accolades and citations. His research publications have garnered significant attention, with a high h-index of 11 and over 340 citations. Khan’s work continues to contribute meaningfully to the field, and his expertise is acknowledged globally. 🏅🎖

Research Focus:

Dr. Khan’s research primarily involves the discovery and mechanistic study of enzyme inhibitors, with a focus on urease inhibitors and their therapeutic potential. His ongoing work explores compounds that show promise in treating conditions such as diabetes, cancer, and peptic ulcers. His studies also emphasize the in-silico and biochemical screening of natural and synthetic compounds to combat these diseases, highlighting his commitment to developing novel drug therapies. 🔬💊

Conclusion:

Dr. Majid Khan’s extensive academic background, combined with his diverse experience in drug development and biochemical research, places him at the forefront of biochemistry and pharmacology. His work, which bridges the gap between basic and applied research, continues to advance the understanding of disease mechanisms and drug efficacy. With a passion for teaching and mentoring, he plays a significant role in shaping the next generation of scientists. 👨‍🔬🌱

Publications:

Majid Khan, Sobia Ahsan Halim, Najeeb Ur Rehman, et al. “Cytotoxic natural products from Aloe vera resin inhibit carbonic anhydrases-II and -IX” (Accepted in International Journal of Biological Macromolecules, IF: 8.2)

Majid Khan, Atta Ullah, Sobia Ahsan Halim, et al. “Exploring Tryptamine Derivatives as Potential Agents for Diabetes and Cancer Treatment” (Accepted in International Journal of Biological Macromolecules, IF: 8.2)

Majid Khan, Arsalan Nizamani, et al. “Utilizing the drug repurposing strategy on current drugs” (Journal of Biomolecular Structure and Dynamics, 2024, IF: 5.235)

Majid Khan, Sobia Ahsan Halim, et al. “Isoxazole analogues of dibenzazepine as possible leads against ulcers and skin disease” (Saudi Pharmaceutical Journal, 2023, IF: 4.562)

Majid Khan, Sobia Ahsan Halim, et al. “Substrate-like novel inhibitors of prolyl specific oligopeptidase for neurodegenerative disorders” (Journal of Biomolecular Structure and Dynamics, 2023, IF: 5.235)

Majid Khan, Satya Kumar Avula, et al. “Biochemical and in silico inhibition of bovine and human carbonic anhydrase-II by 1H-1,2,3-triazole analogs” (Frontiers in Chemistry, 2022, IF: 4.873)