Nael Radwan | Computer Science | Innovative Research Award

Published on by Forensic Scientist Awards

Innovative Research Award

Nael Radwan
University of Idaho, United States

Nael Radwan
Affiliation University of Idaho
Country United States
Google Scholar ID 3SBNe6gAAAAJ
Documents 10
Citations 24
h-index 3
Subject Area Computer Science
Event International Forensic Scientist Awards
ORCID 0009-0007-3006-5509

Nael Radwan is recognised through the Innovative Research Award for research addressing the performance, reliability, and security of message-oriented communication systems in the Internet of Things (IoT). His listed 2026 publications examine MQTT broker behaviour under high-load conditions and the correct enforcement of authentication and access-control policies. These topics are relevant to dependable connected systems, where communication efficiency must be considered alongside security and operational stability. The research profile and publication details provide the basis for this academic recognition. [1] [2]

Abstract

This research profile focuses on experimental investigations into MQTT, a lightweight messaging protocol widely associated with IoT communication. The two supplied 2026 journal articles address complementary challenges: the effect of wildcard subscriptions on broker performance, stability, and security, and the correctness of authentication enforcement and the consequences of access-control list (ACL) misconfiguration. Together, these topics concern how messaging infrastructure behaves under demanding workloads and whether security policies are applied as intended. The publications appeared in Electronics and Applied Sciences, respectively. [1] [2]

Keywords

Internet of Things; MQTT; message brokers; wildcard subscriptions; authentication; access-control lists; cybersecurity; performance evaluation; system stability; secure communication.

Introduction

IoT environments depend on communication services that can distribute messages reliably while controlling access to sensitive information. MQTT supports publish–subscribe communication, but practical deployments require careful configuration and evaluation. Subscription patterns can influence message distribution and broker workload, while authentication and ACL rules determine which clients can connect and access particular topics. Experimental research into these mechanisms can help identify implementation weaknesses and inform more dependable deployment practices. [1] [2]

Research Profile

Radwan’s documented research interests, as represented by the supplied publications, encompass experimental evaluation of IoT messaging infrastructure, security enforcement, and broker behaviour under authenticated, high-load conditions. This work connects software performance assessment with practical cybersecurity concerns. Rather than treating efficiency and security as isolated objectives, the publication topics place them within the same operational environment, where configuration decisions may affect service behaviour and access restrictions. The supplied bibliographic information establishes the publication focus but does not independently verify every experimental result or establish additional institutional roles.

Research Contributions

The two studies address distinct but related evaluation priorities:

  • Performance and stability: Examining wildcard subscriptions under authenticated high-load IoT conditions provides a framework for investigating broker behaviour when message distribution demands increase. [1]
  • Security enforcement: Evaluating authentication correctness and ACL misconfiguration addresses whether access restrictions operate consistently with intended policies. [2]
  • Practical relevance: These research directions can inform testing, configuration reviews, and risk assessment for systems relying on MQTT-based communication.

Publications

  1. “Experimental Evaluation of Wildcard Subscription Impact on MQTT Broker Performance, Stability, and Security Under Authenticated High-Load IoT Conditions.” Electronics, August 2026. [1]
  2. “Experimental Quantification of Authentication Enforcement Correctness and ACL Misconfiguration Impact in Standards-Compliant MQTT Deployments.” Applied Sciences, April 2026. [2]

Research Impact

The supplied researcher metrics list 10 documents, 24 citations, and an h-index of 3. These figures offer a quantitative snapshot of the research record as provided; citation totals and author metrics can vary between databases and over time. The two 2026 publications address issues with practical relevance to IoT reliability and security. Their potential value lies in supporting evidence-based testing and safer configuration, although broader impact should be assessed using independently verified findings, subsequent citations, and adoption in operational settings.

Award Suitability

The research themes align with the Innovative Research Award’s emphasis on systematic investigation of technical problems. The publications focus on measurable system behaviour and security-policy enforcement rather than relying solely on conceptual discussion. Their complementary scope supports consideration of performance, stability, and access control as connected requirements for IoT infrastructure. This assessment is based on the supplied publication records and researcher metrics; it should not be interpreted as an independent audit of experimental methodology or a formal adjudication of award results.

Conclusion

Nael Radwan’s listed publications present a focused research direction in MQTT performance evaluation and deployment security. By addressing wildcard subscriptions, authentication enforcement, and ACL configuration, the work engages with practical challenges in dependable IoT communication. The publication record and supplied metrics provide a basis for academic recognition, subject to verification against the original articles and authoritative researcher profiles.

References

  1. MDPI. (2026). Experimental Evaluation of Wildcard Subscription Impact on MQTT Broker Performance, Stability, and Security Under Authenticated High-Load IoT Conditions. Electronics, 15(15), 3393.
    https://doi.org/10.3390/electronics15153393
  2. MDPI. (2026). Experimental Quantification of Authentication Enforcement Correctness and ACL Misconfiguration Impact in Standards-Compliant MQTT Deployments. Applied Sciences, 16(7), 3583.
    https://doi.org/10.3390/app16073583
  3. ORCID. (n.d.). Nael Radwan researcher identifier.
    https://orcid.org/0009-0007-3006-5509
  4. Google Scholar. (n.d.). Research profile supplied for Nael Radwan.
    https://scholar.google.com/citations?user=3SBNe6gAAAAJ&hl=en
  5. International Forensic Scientist Awards. (n.d.). Official awards website.
    forensicscientist.org

Shaikhah Rashed Alabdouli | Computer Science and Artificial Intelligence | Best Researcher Award

Published on by Forensic Scientist Awards

Best Researcher Award

Shaikhah Rashed Alabdouli
Abu Dhabi University · United Arab Emirates

       Shaikhah Rashed Alabdouli
Affiliation Abu Dhabi University
Country United Arab Emirates
Scopus ID 58650664300
Documents 1
Citations 6
h-index 1
Subject Area Computer Science and Artificial Intelligence
Event International Forensic Scientist Awards
ORCID 0009-0002-8809-3899

Shaikhah Rashed Alabdouli is a researcher affiliated with Abu Dhabi University in the United Arab Emirates. Her documented scholarly record includes research associated with artificial intelligence, electronic records management, and information systems in the energy sector. Her publication, “Innovative Adoption of AI-empowered Electronic Records and Information Management System in the Energy Sector,” provides the basis for examining her research interests and potential relevance to technology-enabled organizational information management. Her profile is considered in the context of the Best Researcher Award presented through the International Forensic Scientist Awards, with emphasis on documented research activity and its relevance to technological innovation. [1] [2]

Abstract

This research profile examines Shaikhah Rashed Alabdouli’s documented scholarly activity, focusing on the intersection of artificial intelligence and electronic information management. Her listed article addresses the adoption of an AI-enabled electronic records and information management system in the energy sector. This topic is relevant to organizations seeking improved information accessibility, records organization, and technology-supported administrative processes. Available bibliographic indicators record one document, six citations, and an h-index of one. These metrics provide a limited quantitative snapshot rather than a comprehensive measure of research quality or long-term influence. [1] [2]

Keywords

Artificial intelligence; electronic records management; information systems; energy sector; digital transformation; research impact; information governance.

Introduction

Artificial intelligence is increasingly considered in the development of digital information systems, including platforms used to manage organizational records. In energy-sector environments, electronic information management can support the organization, retrieval, and handling of operational documentation. The effective adoption of such systems depends on implementation choices, data quality, information security, governance, and user requirements. Alabdouli’s listed publication connects these broader technological concerns with electronic records and information management. The article’s bibliographic record provides a starting point for understanding the subject of her documented research. [2]

Research Profile

Alabdouli’s documented research profile is associated with computer science, artificial intelligence, and information management applications. Her listed publication focuses on the adoption of an AI-empowered records management system in an industry where organizations handle substantial volumes of operational information. The topic sits within a broader field concerned with applying computational technologies to practical organizational processes. Her institutional affiliation is Abu Dhabi University. Further evaluation of research specialization, methods, and sustained scholarly development would require additional verified publications and research documentation. [1] [2]

Research Contributions

The listed study addresses a practical technology-adoption question: how AI-enabled electronic records and information management systems may be introduced within the energy sector. This research area is relevant to digital transformation because organizations need reliable processes for storing, locating, and managing information. Potential areas of relevance include document classification, search, information accessibility, and workflow support; these are general applications of the technology, not independently verified findings of the article. The publication’s methods and conclusions should be consulted directly before attributing specific outcomes or measurable improvements to the research. [2]

Publications

The supplied bibliographic information identifies the following journal article:

  • Innovative Adoption of AI-empowered Electronic Records and Information Management System in the Energy Sector. International Journal of Innovation Studies, October 2026. [2]

The publication date and bibliographic details are presented as supplied and should be confirmed against the publisher’s record when used for formal evaluation.

Research Impact

The supplied Scopus profile figures report one indexed document, six citations, and an h-index of one. These indicators suggest that the listed work has received citation attention, but they do not independently establish methodological quality, practical adoption, or societal impact. Citation counts can change over time and may vary between bibliographic databases. A fuller assessment would examine the article’s research design, contribution to existing knowledge, relevance to industry practice, and subsequent engagement by researchers and practitioners. [1]

Award Suitability

The research topic is relevant to recognition that considers technological innovation and applied research. Its focus on AI-enabled information management in the energy sector offers a basis for discussing the practical importance of digital systems. In assessing suitability for the Best Researcher Award under the International Forensic Scientist Awards, relevant considerations include the originality of the work, methodological rigor, documented contribution, publication verification, and research influence. The information available supports a concise overview of the research topic but is insufficient, on its own, to establish comparative standing against other nominees.

Conclusion

Shaikhah Rashed Alabdouli’s documented publication concerns the adoption of artificial intelligence in electronic records and information management within the energy sector. The subject connects computer science with organizational information practices and digital transformation. Her available bibliographic indicators offer an initial view of scholarly activity, while a balanced assessment of research excellence requires examination of the original article and additional evidence of contribution and impact. This profile provides a structured account of the supplied information without assuming unverified research outcomes or award decisions.

References

  1. Elsevier. (n.d.). Scopus author details: Shaikhah Rashed Alabdouli, Author ID 58650664300. Scopus. Author metrics as supplied for this profile.
    https://www.scopus.com/authid/detail.uri?authorId=58650664300
  2. Alabdouli, S. R. (2026). Innovative Adoption of AI-empowered Electronic Records and Information Management System in the Energy Sector. International Journal of Innovation Studies. DOI: 10.1016/j.ijis.2026.100230. Bibliographic information supplied for this article; consult the publisher record to verify full authorship and publication details.
    https://doi.org/10.1016/j.ijis.2026.100230
  3. ORCID. (n.d.). Shaikhah Rashed Alabdouli: ORCID researcher identifier. ORCID.
    https://orcid.org/0009-0002-8809-3899
  4. Abu Dhabi University. (n.d.). Official institutional website. Institutional affiliation reference.
  5. International Forensic Scientist Awards. (n.d.). Official awards website. Award event and program information.
    forensicscientist.org

Md Sadman Haque | Computer Science | Best Researcher Award

Published on by Forensic Scientist Awards

Best Researcher Award

Md Sadman Haque
United International University, Bangladesh

Md Sadman Haque
Affiliation United International University
Country Bangladesh
Google Scholar ID 3fMbSl8AAAAJ
Documents 5
Citations 11
h-index 2
Subject Area Computer Science
Event International Forensic Scientist Awards
ORCID 0009-0001-0471-8197

Md Sadman Haque is a computer science researcher affiliated with United International University, Bangladesh. His documented research output includes work spanning interpretable machine learning, computer vision datasets, laboratory equipment recognition, and human–computer interaction involving large language models. His publication record reflects an applied orientation toward machine learning methods and datasets designed for practical research and educational contexts. Current bibliometric information records five documents, 11 citations, and an h-index of 2. [1]

Abstract

Md Sadman Haque’s research profile is characterized by applied computer science studies using machine learning and data-driven approaches. Recent publications address interpretable prediction of financial market stress, large-scale image datasets for physics laboratory equipment, chemistry laboratory apparatus recognition, and student perspectives on artificial intelligence-assisted academic search. These works collectively indicate engagement with machine learning, computer vision, dataset development, and human-centered artificial intelligence. [2] [3]

Keywords

Computer Science; Machine Learning; Explainable AI; Deep Learning; Computer Vision; Image Datasets; Artificial Intelligence; Human–Computer Interaction; Financial Analytics.

Introduction

The research record associated with Haque demonstrates the application of contemporary computational techniques to problems in finance, laboratory education, visual recognition, and information-seeking behavior. Rather than being limited to a single application domain, the publications show a common methodological emphasis on machine learning and structured data. Such cross-domain applications are relevant to the broader development of practical and interpretable artificial intelligence systems. [4]

Research Profile

A principal feature of the profile is the use of machine learning for prediction and recognition tasks. The 2026 study on firm-level market stress in the Dhaka Stock Exchange emphasizes interpretability, connecting predictive modeling with the need to understand model outputs. [2] Related research develops image datasets for identifying laboratory equipment, supporting computer vision research and potentially automated educational environments. [3]

Research Contributions

  • Development of an interpretable machine learning framework for next-day firm-level market-stress prediction.
  • Contribution to large-scale image dataset development for physics laboratory equipment detection.
  • Creation of a real-world chemistry laboratory image dataset covering 25 apparatus categories.
  • Investigation of university students’ use of LLMs and traditional search engines for academic problem solving.

Publications

“An interpretable machine learning framework for predicting next day firm level market stress in the Dhaka Stock Exchange” was published in Discover Artificial Intelligence on 6 September 2026. [2] Other recent works include the Scientific Data article on physics laboratory equipment detection, the chemistry laboratory image dataset study, and the HCI+NLP conference paper examining LLMs and traditional search. [3] [4] [5]

Research Impact

The available bibliometric record lists five documents, 11 citations, and an h-index of 2. [1] The broader significance of the research can also be considered through its application-oriented scope, particularly the development of reusable datasets and interpretable computational methods. Publications in Scientific Data further position dataset construction as a research contribution in its own right. [3]

Award Suitability

The profile is relevant to the Best Researcher Award within the International Forensic Scientist Awards based on its documented publication activity and demonstrated engagement with contemporary computer science research. The combination of explainable machine learning, computer vision, dataset development, and human-centered AI provides a substantive basis for academic recognition, while the assessment remains grounded in the documented research record and bibliometric indicators.

Conclusion

Md Sadman Haque presents an emerging computer science research profile centered on practical applications of machine learning and artificial intelligence. His recent publications demonstrate methodological breadth across financial prediction, computer vision datasets, laboratory equipment recognition, and academic information-seeking. The available record supports consideration for research recognition based on these documented contributions.

References

  1. Elsevier. (n.d.). Google Scholar author details: Md Sadman Haque, Author ID 3fMbSl8AAAAJ.
    https://scholar.google.com/citations?user=3fMbSl8AAAAJ&hl=en
  2. Haque, M. S., et al. (2026). An interpretable machine learning framework for predicting next day firm level market stress in the Dhaka Stock Exchange. Discover Artificial Intelligence.
    https://doi.org/10.1007/s44163-026-02065-7
  3. Haque, M. S., et al. (2026). Large-Scale Image Dataset for Physics Laboratory Equipment Detection Using Deep Learning Models. Scientific Data.
    https://doi.org/10.1038/s41597-026-07839-3
  4. Haque, M. S., et al. (2025). Real-world chemistry lab image dataset for equipment recognition across 25 apparatus categories. Scientific Data.
    https://doi.org/10.1038/s41597-025-05952-3
  5. Haque, M. S., et al. (2025). Rethinking Search: A Study of University Students’ Perspectives on Using LLMs and Traditional Search Engines in Academic Problem Solving. Proceedings of the Fourth Workshop on Bridging Human-Computer Interaction and Natural Language Processing.
    https://doi.org/10.18653/v1/2025.hcinlp-1.5
  6. ORCID. (n.d.). Md Sadman Haque — ORCID record.
    https://orcid.org/0009-0001-0471-8197