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

Rounak Raman | Information Technology | Young Scientist Award

Published on by Young Scientists Awards

Mr. Rounak Raman | Information Technology | Young Scientist Award

Netaji Subhas University of Technology | India

Mr. Rounak Raman research journey reflects a diverse and impactful engagement across multidisciplinary domains including Artificial Intelligence, Wireless Sensor Networks, IoT Security, and Neuroinformatics. At the forefront of innovation, multiple projects demonstrate a strong alignment with real-world challenges and technical excellence. The development of the EEG Data Analysis System and multimodal neurofeedback loop underlines expertise in biomedical signal processing and applied AI for cognitive enhancement, contributing to measurable improvements in attentiveness and engagement. In IoT and network optimization, the implementation of the Energy Aware Hybrid Clustering Protocol (EAHCP) showcased advancements in energy-efficient communication, achieving notable performance gains in scalability and mobility management. Research on secure IoT frameworks, such as the Hierarchical Key Rotation and Isolation Protocol (HKRISRP), reinforced resilience in wireless networks through lightweight cryptography and dynamic key rotation, enhancing both security and energy performance. Additional contributions in opportunistic networks introduced context-aware, trust-based, and collision-avoidant models that optimized data aggregation and reliability through intelligent decision mechanisms and trajectory optimization. The pursuit of Generative AI culminated in the development of SyntheX, a document analysis system integrating semantic search and transcription models to streamline enterprise knowledge management. Collectively, these works exemplify a strong commitment to advancing applied research in AI, cybersecurity, and intelligent network systems

Featured Publication

Raman, R., Yadav, A., Kukreja, D., & Sharma, D. K. (2025). CONTEXT-NET: A context-aware nexus-based aggregation protocol for opportunistic networks. Internet of Things, 25, 101809. https://doi.org/10.1016/j.iot.2025.101809

Vaibhav Tummalapalli | Machine learning | Excellence in Innovation Award

Published on by Young Scientists Awards

Mr. Vaibhav Tummalapalli l Machine learning
| Excellence in Innovation Award

Epsilon Data Management, LLC | United States

Mr. Vaibhav Tummalapalli’s research focuses on the advancement of applied machine learning methodologies, predictive modeling, and data-driven optimization across large-scale industrial domains, particularly automotive and telecommunications. His work emphasizes the integration of artificial intelligence in lifecycle analytics, customer engagement, and personalization strategies to enhance business intelligence and operational efficiency. His studies explore innovative modeling frameworks such as EV Conquest modeling, VIN-level mileage prediction, and vehicle recommendation systems, which apply behavioral, telematics, and demographic data to drive precision marketing and service optimization. Additionally, his contributions to outlier detection, cohort-based stratified sampling, and KNN imputation distance metrics extend theoretical and applied understanding in data preprocessing and imbalanced learning. His research also addresses model monitoring and drift management using SAS Viya and PySpark-based architectures, ensuring robust model performance in production environments. Through the development of scalable ML pipelines, channel propensity models, and retention-focused predictive systems, his work demonstrates the transformative potential of AI in driving measurable business outcomes, customer retention, and ethical personalization. His scholarly and technical pursuits collectively aim to advance the design of intelligent, explainable, and sustainable machine learning systems for real-world, high-impact applications

Featured Publications

Tummalapalli, V. (2025). Understanding distance metrics in KNN imputation: Theoretical insights and applications. Journal of Mathematical & Computer Applications, 4(4), 1–4. https://doi.org/10.47363/JMCA

Tummalapalli, V. (2025). Machine learning pipeline for automotive propensity models. International Journal of Core Engineering & Management, 8(3), [Issue-03].

Tummalapalli, V. (2025). Outlier detection & treatment for machine learning models. International Journal of Innovative Research and Creative Technology, 11(3).

Tummalapalli, V. (2025). Stratified sampling in cohort-based data for machine learning model development. International Scientific Journal of Engineering and Management, 4.