Nael Radwan | Computer Science | Innovative Research Award

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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

Dinh-Thai Kim | Computer Science and Artificial Intelligence | Best Researcher Award

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Best Researcher Award

Dinh-Thai Kim
Vietnam National University, Vietnam

Dinh-Thai Kim
Affiliation Vietnam National University
Country Vietnam
Scopus ID 57265279400
Documents 49
Citations 194
h-index 8
Subject Area Computer Science and Artificial Intelligence
Event International Forensic Scientist Awards
ORCID 0000-0002-9060-4769

Dinh-Thai Kim is a researcher affiliated with Vietnam National University whose documented research profile is situated within computer science and artificial intelligence. The available bibliometric record lists 49 documents, 194 citations, and an h-index of 8. [1] Recent publications demonstrate work spanning computer vision, deep learning, federated learning, speech emotion recognition, agricultural imaging, and Vietnamese sign-language classification.

Abstract

Dinh-Thai Kim’s research profile reflects applied artificial intelligence research addressing visual recognition, natural-language and speech-related intelligence, and machine-learning applications. Recent work includes lightweight object detection for cashew kernel assessment, growth-stage detection in lychee orchards, privacy-preserving speech emotion recognition, and graph-based Vietnamese sign-language classification. [2]–[5]

Keywords

Artificial intelligence; computer vision; deep learning; object detection; federated learning; speech emotion recognition; sign-language classification; agricultural AI.

Introduction

The research record associated with Kim illustrates the application of contemporary machine-learning methods to practical recognition problems. The publications supplied for this profile indicate an emphasis on deployable computer-vision systems and specialized intelligent models, including applications involving agricultural products, orchard monitoring, human speech, and gesture-based language recognition.

Research Profile

Kim’s documented research spans several complementary areas of artificial intelligence. The work on YOLOv8s-based lychee detection applies object-detection techniques under natural orchard conditions, while CSTAR-Det addresses lightweight vision-based assessment of cashew kernels. [2] [3] Other research extends machine learning toward Vietnamese speech emotion recognition and sign-language classification. [4] [5]

Research Contributions

  • Development of lightweight vision-based approaches for agricultural quality assessment.
  • Application of object detection to crop growth-stage recognition under natural conditions.
  • Investigation of prototype-based federated learning for privacy-preserving speech emotion recognition.
  • Use of graph convolutional networks and skeletal points for Vietnamese sign-language classification.

Publications

CSTAR-Det: A lightweight vision-based framework for conveyor-based cashew kernel quality assessment. Array, listed publication date December 2026. [2]

YOLO-Lychee: A YOLOv8s-Based Detector for Lychee Growth-Stage Detection under Natural Orchard Conditions. EAI Endorsed Transactions on AI and Robotics, 27 July 2026. [3]

EmoFedProto: Privacy-Preserving Vietnamese Speech Emotion Recognition via Prototype-Based Federated Learning. EAI Endorsed Transactions on AI and Robotics, 23 April 2026. [4]

Deep Learning Based Graph Convolutional Network Using Hand Skeletal Points For Vietnamese Sign Language Classification. Iranian Journal of Electrical and Electronic Engineering, March 2026. [5]

Research Impact

The supplied Scopus metrics record 194 citations across 49 documents, with an h-index of 8. [1] These indicators provide a quantitative view of scholarly visibility, while the publication record shows application-oriented research across multiple AI domains. Citation indicators should be interpreted in relation to publication age, field practices, and database coverage.

Award Suitability

The documented record provides evidence relevant to recognition in a research category focused on computer science and artificial intelligence. The combination of bibliometric activity and recent research involving computer vision, federated learning, and deep-learning classification offers a factual basis for consideration under the Best Researcher Award category. [1]–[5]

Conclusion

Dinh-Thai Kim’s documented research profile combines measurable scholarly activity with recent applications of artificial intelligence to agricultural vision, speech processing, privacy-preserving learning, and sign-language recognition. The available record supports a multidisciplinary description within computer science and artificial intelligence and provides the principal bibliographic and impact indicators presented on this page.

References

  1. Elsevier. (n.d.). Scopus author details: Dinh-Thai Kim, Author ID 57265279400. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57265279400
  2. Kim, Dinh-Thai. (2026). CSTAR-Det: A lightweight vision-based framework for conveyor-based cashew kernel quality assessment. Array.
    https://doi.org/10.1016/j.array.2026.101282
  3. Kim, Dinh-Thai. (2026). YOLO-Lychee: A YOLOv8s-Based Detector for Lychee Growth-Stage Detection under Natural Orchard Conditions. EAI Endorsed Transactions on AI and Robotics.
    https://doi.org/10.4108/airo.12140
  4. Kim, Dinh-Thai. (2026). EmoFedProto: Privacy-Preserving Vietnamese Speech Emotion Recognition via Prototype-Based Federated Learning. EAI Endorsed Transactions on AI and Robotics.
    https://doi.org/10.4108/airo.11595
  5. Kim, Dinh-Thai. (2026). Deep Learning Based Graph Convolutional Network Using Hand Skeletal Points For Vietnamese Sign Language Classification. Iranian Journal of Electrical and Electronic Engineering.
    https://doi.org/10.22068/IJEEE.22.1.3717
  6. ORCID. (n.d.). Dinh-Thai Kim, ORCID record.
    https://orcid.org/0000-0002-9060-4769
  7. International Forensic Scientist Awards. (n.d.). Official award website.
    forensicscientist.org

Olzhas Akylbekov | Data Science and Analytics | Research Excellence Award

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Mr. Olzhas Akylbekov | Data Science and Analytics | Research Excellence Award

Satbayev University | Kazakhstan

Mr. Olzhas Akylbekov is a researcher in data science and machine learning with a strong focus on applied artificial intelligence for spatial analysis and intelligent systems. His scholarly work emphasizes deep learning architectures, hybrid neural networks, and spatial data modeling for real-world decision support systems. According to Scopus, he has 2 peer-reviewed publications, with a total of 20 citations and an h-index of 2. His research contributions are published in internationally recognized journals indexed in Web of Science and Scopus, reflecting impact in areas such as urban spatial analytics and AI-driven content analysis.

                            Citation Metrics (Scopus)

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20
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View Scopus Profile  View Google Scholar Profile  View ORCID Profile

Featured Publications


Hybrid CNN-LSTM Network for Cyberbullying Detection on Social Networks using Textual Contents

– International Journal of Advanced Computer Science and Applications(IJACSA), 2023 · Cited by 19

Amrita Ganguly | Computer Science and Artificial Intelligence | Research Excellence Award

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Mrs. Amrita Ganguly | Computer Science and Artificial Intelligence | Research Excellence Award

George Mason University | United States

Mrs. Amrita Ganguly is a researcher working at the intersection of Responsible and Ethical Artificial Intelligence, Human–AI Interaction, and AI applications in education. Her scholarly contributions focus on ethical governance of generative AI, human-centered AI design, and collaborative intelligence frameworks. According to Scopus, she has 7 peer-reviewed publications, 19 citations, and an h-index of 2. Her work appears in high-impact international journals and premier conferences in AI ethics, human–computer interaction, and computing education. Her research emphasizes evidence-based guidelines, design frameworks, and ethical considerations that shape responsible AI deployment in academic and socio-technical contexts.

                            Citation Metrics (Scopus)

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View Scopus Profile  View Google Scholar Profile  View ORCID Profile

Featured Publications


Collaborative job seeking for people with autism: Challenges and design opportunities

– CHI ’24: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems · Cited by 18


ShadowMagic: Designing Human-AI Collaborative Support for Comic Professionals’ Shadowing

– UIST ’24: Proceedings of the 37th Annual ACM Symposium on User Interface Software and Technology · Cited by 5

Yinfeng Yang | Computer Science and Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Yinfeng Yang | Computer Science and Artificial Intelligence | Best Researcher Award

Anhui University of Chinese Medicine | China

Dr. Yinfeng Yang is a distinguished researcher specializing in artificial intelligence, bioinformatics, and traditional Chinese medicine (TCM), with a strong emphasis on AI-enabled drug discovery and biomedical big data analysis. Her work integrates multi-omics analytics, computational modeling, and machine learning to uncover disease biomarkers, elucidate therapeutic mechanisms, and accelerate the discovery of bioactive compounds from natural medicines. Dr. Yinfeng Yang has made significant contributions to AI-assisted drug discovery (AIDD), advancing methodologies in molecular docking, molecular dynamics simulations, high-throughput virtual screening, and quantitative structure–activity relationship modeling. She has authored 44 Scopus-indexed publications, accumulating 5,663 citations across 5,524 documents, supported by a Scopus h-index of 18, underscoring her impact in computational biology and integrative medicine research. Her studies, published in leading journals such as Journal of Advanced Research, Phytomedicine, ACS Omega, Drug Discovery Today, and Current Medicinal Chemistry, span diverse topics including cancer prognosis modeling, multi-scale mechanisms of herbal medicine, ADMET prediction frameworks, and the therapeutic potential of Ginkgo biloba in oncology and neurological disorders. Dr. Yinfeng Yang has led and contributed to numerous scientific research projects focused on TCM modernization, biomedical intelligence, and compound drug discovery. She also plays an active role in scholarly publishing as an editorial board member for journals such as PLOS ONE and Journal of Hebei Medical University, and serves as a recognized reviewer for more than 30 international journals, including Nature Communications, Phytomedicine, ACS Omega, and Journal of Ethnopharmacology. Her research excellence continues to advance innovation in AI-driven precision medicine and the global understanding of natural-product-based therapeutics.

Profiles: Scopus | Google Scholar | ORCID

Featured Publications

  • Fan, N., Chen, J., Wang, J., Chen, Z. S., & Yang, Y. (2025). Bridging data and drug development: Machine learning approaches for next-generation ADMET prediction. Drug Discovery Today, Article 104487.

  • Han, Z., Liu, Q., Yang, J., Wang, X., Song, W., Wang, J., & Yang, Y. (2025). Exploration of the mechanism of Ginkgo biloba leaves targeted angiogenesis against gastric cancer. ACS Omega, 10(35), 40460–40476.

  • Li, H., Fu, S., Shen, P., Zhang, X., Yang, Y., & Guo, J. (2025). Mitochondrial pathways in rheumatoid arthritis: Therapeutic roles of traditional Chinese medicine and natural products. Phytomedicine, Article 157106.

  • Yang, P., Wang, X., Yang, J., Yan, B., Sheng, H., Li, Y., Yang, Y., & Wang, J. (2025). AI-driven multiscale study on the mechanism of Polygonati Rhizoma in regulating immune function in STAD. ACS Omega, 10(19), 19770–19796.

  • Zhang, H., Xu, Q., Kan, H., Yang, Y., & Cai, Y. (2025). Exploration of the clinicopathological and prognostic significance of BRCA1 in gastric cancer. Discover Oncology, 16(1), 381.

Nafiz Fahad | Computer Science | Best Researcher Award

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Mr. Nafiz Fahad | Computer Science | Best Researcher Award

Multimedia University | Malaysia

Mr. Nafiz Fahad is an emerging AI researcher at Multimedia University, Cyberjaya, Malaysia, recognized for his growing contributions to artificial intelligence in healthcare, computer vision, and natural language processing. His research focuses extensively on explainable AI, clinical decision support systems, and data-driven healthcare intelligence. According to Scopus, he has 19 indexed publications, 122 citations, and an h-index of 6, reflecting the influence and visibility of his scholarly work within the global research community. His scientific output spans chronic disease prediction, dementia analytics, lung disease classification, hypertension ontology development, wound-image segmentation, obesity prediction, and precision public health. These studies incorporate techniques such as deep learning, transfer learning, ensemble learning, hybrid architectures, and explainable machine learning to advance diagnostic accuracy and interpretability in medical AI systems. Beyond health-focused research, Fahad has also contributed high-impact work in fake news detection, generative AI, machine learning security, student performance prediction, agricultural disease detection, vision transformers for physics data, and federated learning enhanced with homomorphic encryption. His ongoing research extends to mental health analytics, EEG decoding models, diabetic retinopathy detection, and agentic AI solutions for healthcare innovation. Fahad’s growing academic recognition includes research awards, best paper achievements, and contributions to high-impact journals and conferences. His multidisciplinary scholarship positions him as a promising young researcher advancing applied AI at the intersection of healthcare, societal well-being, and intelligent systems.

Profiles: Scopus | Google Scholar | LinkedIn

Featured Publications

1. Ahmed, Z., Shanto, S. S., Rime, M. H. K., Morol, M. K., Fahad, N., Hossen, M. J., … (2024). The generative AI landscape in education: Mapping the terrain of opportunities, challenges and student perception. IEEE Access.

2. Mahamud, E., Fahad, N., Assaduzzaman, M., Zain, S. M., Goh, K. O. M., & Morol, M. K. (2024). An explainable artificial intelligence model for multiple lung diseases classification from chest X-ray images using fine-tuned transfer learning. Decision Analytics Journal, 12, 100499.

3. Ahmed, R., Fahad, N., Miah, M. S. U., Hossen, M. J., Morol, M. K., Mahmud, M., … (2024). A novel integrated logistic regression model enhanced with recursive feature elimination and explainable artificial intelligence for dementia prediction. Healthcare Analytics, 6, 100362.

4. Fahad, N., Goh, K. M., Hossen, M. I., Shopnil, K. M. S., Mitu, I. J., Alif, M. A. H., & Tee, C. (2023). Stand up against bad intended news: An approach to detect fake news using machine learning. Emerging Science Journal, 7(4), 1247–1259.

5. Hossain, M. N., Fahad, N., Ahmed, R., Sen, A., Al Huda, M. S., & Hossen, M. I. (2024). Preventing student’s mental health problems with the help of data mining. International Journal of Computing, 23(1), 101–108.