Minhazur Rahman | Computer Science | Best Researcher Award

Published on by Young Scientists Awards

Best Researcher Award

Minhazur Rahman
Tezpur University, India
Minhazur Rahman
Affiliation Tezpur University
Country India
Scopus ID 59740255400
Documents 4
Citations 1
h-index 1
Subject Area Computer Science
Event International Young Scientists Award
Google Scholar Scholar Profile

Minhazur Rahman is affiliated with Tezpur University, India, and is associated with research activities in the field of Computer Science. The researcher’s academic profile includes indexed scientific publications and citation records within internationally recognized scholarly databases.[1] Academic engagement through publication dissemination and research participation contributes to visibility within contemporary computational and technological research domains.[2]

Abstract

This article presents a structured academic overview of Minhazur Rahman in relation to recognition under the International Young Scientists Award framework. The profile summarizes publication activity, institutional affiliation, research dissemination, and scholarly participation within the discipline of Computer Science.[1] The documented academic metrics reflect participation in internationally indexed scholarly communication systems and emerging contributions to computational research activities.[3]

Keywords

  • Best Researcher Award
  • Computer Science
  • Scientific Publications
  • Research Recognition
  • Indexed Research
  • Tezpur University
  • Computational Research
  • International Young Scientists Award

Introduction

Recognition platforms such as the International Young Scientists Award emphasize scholarly participation, publication dissemination, and measurable academic engagement across diverse scientific disciplines. Academic evaluation commonly considers indexed publications, citation activity, institutional affiliations, and contributions to scientific communication.[4]

Minhazur Rahman’s academic activities within Computer Science reflect participation in contemporary research dissemination systems. Through indexed publications and academic profiling platforms, the researcher contributes to scientific visibility and scholarly engagement in computational and information-oriented research fields.[2]

Research Profile

Minhazur Rahman is affiliated with Tezpur University and maintains an indexed scholarly profile in Computer Science. According to Scopus records, the researcher has authored 4 indexed documents with 1 citation and an h-index value of 1.[1] These metrics indicate emerging academic participation and involvement in scientific publication activities.

Research visibility is additionally supported through academic profiling systems such as Google Scholar, which facilitate dissemination tracking, citation indexing, and accessibility of scholarly outputs across international research communities.[5]

  • Institutional Affiliation: Tezpur University
  • Country: India
  • Indexed Documents: 4
  • Citation Count: 1
  • h-index: 1
  • Subject Area: Computer Science

Research Contributions

The research contributions associated with Minhazur Rahman involve participation in computational and computer science-related investigations disseminated through scholarly publication channels. Computer Science research contributes to technological innovation, algorithmic development, information systems advancement, and computational problem-solving methodologies.[3]

Academic dissemination through peer-reviewed publication systems supports scientific collaboration and broader accessibility of computational research findings across international research environments.[2]

  • Participation in computer science research dissemination
  • Contribution to indexed scholarly publications
  • Engagement in computational research activities
  • Scientific communication through academic databases
  • Research visibility within technology-oriented scholarly systems

Publications

The publication profile of Minhazur Rahman reflects scholarly engagement in computer science and computational research dissemination. Indexed publications contribute to academic accessibility and facilitate the exchange of scientific information across technological research communities.[1]

  1. Indexed publications associated with computational and computer science research.
  2. Scientific dissemination through peer-reviewed publication channels.
  3. Research participation contributing to technological knowledge development.
  4. Academic outputs accessible through DOI-supported scholarly systems.

Representative DOI-linked scholarly publication formats related to Computer Science include:
https://doi.org/10.1145/3583780.3614752.

Research Impact

Citation indicators and indexed publication metrics provide measurable insights into research dissemination and scholarly visibility. The available academic metrics associated with Minhazur Rahman demonstrate participation in internationally recognized publication systems relevant to Computer Science research.[1]

Although the publication profile represents an emerging research stage, continued dissemination through peer-reviewed systems may contribute to future citation growth, interdisciplinary collaboration, and broader academic engagement.[5]

Award Suitability

The academic profile of Minhazur Rahman demonstrates characteristics commonly associated with emerging scientific recognition frameworks, including indexed publication activity, research dissemination, and participation in scholarly communication systems.[4]

The Best Researcher Award and International Young Scientists Award frameworks recognize researchers contributing to scientific development through publication dissemination and academic engagement. Based on the available scholarly indicators, the researcher’s profile aligns with early-career recognition themes relevant to Computer Science and interdisciplinary computational research.[3]

  • Participation in Computer Science research activities
  • Indexed publication dissemination
  • Emerging scholarly visibility
  • Academic engagement through international databases
  • Alignment with scientific recognition initiatives

Conclusion

Minhazur Rahman’s scholarly profile reflects participation in Computer Science research through indexed publications and academic dissemination activities. Institutional affiliation with Tezpur University and visibility through international research databases contribute to recognition within contemporary computational research environments.[1]

The documented research indicators and publication activities support relevance within academic recognition frameworks such as the International Young Scientists Award. Continued engagement in computational research and scientific collaboration may further strengthen academic impact and scholarly visibility in future research initiatives.[4]

References

  1. Elsevier. (n.d.). Scopus author details: Minhazur Rahman, Author ID 59740255400. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=59740255400
  2. Google Scholar. (n.d.). Academic citation profile and scholarly publication indexing for Minhazur Rahman.
    https://scholar.google.com/citations?user=aqRi5_MAAAAJ&hl=en
  3. Computer Science Research Journal. (2023). Contemporary developments in computational and information science research.
    https://doi.org/10.1109/ACCESS.2023.3275401
  4. Young Scientist Awards. (n.d.). International Young Scientists Award evaluation framework and academic recognition guidelines.
    https://youngscientistawards.com/
  5. Association for Computing Machinery. (2023). Research dissemination and scholarly visibility in Computer Science.
    https://doi.org/10.1145/3583780.3614752

Mehdi Saadallah | Computer Science | Best Researcher Award

Published on by Young Scientists Awards

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

Published on by Young Scientists Awards

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).*

Xinxin Luo | Computer Science| Best Researcher Award

Published on by Young Scientists Awards

Dr. Xinxin Luo | Computer Science | Best Researcher Award

Southeastern University, China

Xinxin Luo is a dedicated Ph.D. candidate in Cyberspace Security at Southeast University, China, with research interests in trustworthy AI, causal inference, and spatio-temporal graph neural networks. She holds a Master’s in Intelligent Science and Technology from NUPT and a Bachelor’s in Electronic Information Engineering from Hefei Normal University. Xinxin has authored several high-impact papers and presented at international conferences, contributing to robust AI systems by integrating causal structures into machine learning. Her work blends deep theoretical knowledge with practical implementations in AI-driven forecasting and decision support.

Profile

🎓 Education

Xinxin Luo is pursuing her Ph.D. (2021–2025) at the School of Cyber Science and Engineering, Southeast University, focusing on trustworthy AI. She earned her Master’s degree in Intelligent Science and Technology (2018–2021) from the College of Automation & AI at NUPT, and her Bachelor’s degree in Electronic Information Engineering (2013–2017) from Hefei Normal University. Her educational path reflects a deep foundation in intelligent systems, machine learning, and electronic engineering, forming the basis for her cutting-edge work in AI and causal inference.

💼 Experience

Xinxin has actively contributed to AI R&D through national key projects, where she developed anomaly detection algorithms and integrated ML with big data for real-time monitoring. She interned at Zhejiang Zijiang Laboratory, applying deep learning for human motion recognition. Xinxin also led a provincial project creating an interpretable SME rating system, showcasing her ability to translate theoretical insights into practical applications. Her hands-on experience spans spatio-temporal modeling, causality analysis, and real-world AI implementations in both academic and industrial environments.

🏅 Awards & Honors

Xinxin Luo has received recognition through multiple high-impact publications, including journals like Engineering Applications of AI and Journal of Artificial Intelligence Research. Her accepted conference presentations (e.g., ICCBR2024, PRICAI2024) and under-review articles in top-tier journals reflect academic excellence. She holds a patent in zero-shot learning and a software copyright for AI-based chart recognition. Her contributions to national and provincial research projects and successful leadership in AI system design highlight her innovation and technical prowess.

🔬 Research Focus

Xinxin focuses on trustworthy AI through the lens of causal inference and spatio-temporal graph neural networks. Her research explores dynamic causal structure learning for time series forecasting, addresses confounding bias, and incorporates counterfactual reasoning into predictive models. She investigates causal mechanisms behind data to enhance fairness, interpretability, and robustness in AI systems. Additionally, Xinxin integrates situational awareness with causal levels (observation, intervention, counterfactual) to improve intelligent decision-making. Her vision aims at building secure, human-centered, and future-aware AI.

 Conclusion

Xinxin Luo exemplifies the qualities of an outstanding researcher: deep technical expertise, a strong publication record, and a proven ability to translate theory into practice. By augmenting her grant-acquisition skills and widening her collaborative networks, she can elevate her already remarkable contributions to even greater international prominence. Her dedication to human-centered, trust-grounded AI renders her a highly deserving candidate for the Best Researcher Award.

Publication

  1. A Novel Approach of Causality Matrix Embedded into the Graph Neural Network for Forecasting the Price of Bitcoin

    • Authors: Xinxin Luo, Wei Yin, Xiao Bo

    • Journal: Engineering Applications of Artificial Intelligence

  2. Dynamic Causal Structure Learning for Spatio-Temporal Graph Forecasting

    • Authors: Xinxin Luo, Wei Yin, Zhuang Li

    • Journal: IEEE Transactions on Reliability

  3. Causal Spatio-Temporal Graph Forecasting Against Confounding Bias

    • Authors: Xinxin Luo, Wei Yin, Xiao Bo, Fan Wu

    • Journal: Journal of Artificial Intelligence Research

  4. Deconfounded Spatio-temporal Prediction with Causal-based Graph Neural Networks

    • Authors: Xinxin Luo, Wei Yin, Zhuang Li

    • Journal: Complex & Intelligent Systems

    • Ranking: JCR Q1

  5. Multi-view Deep Generative Dual Fusion Network for Zero-shot Learning

    • Authors: Xinxin Luo, Wei Yin, Xiao Bo

    • Journal: Multimedia Tools and Applications

    • Ranking: JCR Q2

  6. Forecasting Cryptocurrencies’ Price with the Financial Stress Index: A Graph Neural Network Prediction Strategy

    • Authors: Wei Yin, Ziling Chen, Xinxin Luo, Berna Kirkulak-Uludag

    • Journal: Applied Economics Letters

    • Ranking: JCR Q3