Michael Adekunle Adelere | Computer Science | Best Researcher Award

Published on by Innovation Technologist

Best Researcher Award

Michael Adekunle Adelere
University of New Brunswick, Canada

Michael Adekunle Adelere
Affiliation University of New Brunswick
Country Canada
Google Scholar ID QTQZo5wAAAAJ
Documents 7
Citations 61
h-index 3
Subject area Computer Science
Event Global Innovation Technologist Awards
ORCID 0000-0001-6389-7909

The Best Researcher Award recognizes research contributions, scholarly productivity, and potential impact within a defined academic field. This profile presents Michael Adekunle Adelere in connection with computer science and research addressing cybersecurity knowledge representation, threat modeling, and risk reasoning. The information combines the supplied bibliometric indicators with two identified RiskOnto research outputs. Publication details and metrics should be interpreted according to their source and verification date. [1]

Abstract

This article outlines a research profile associated with Michael Adekunle Adelere and the Best Researcher Award. The supplied profile records seven documents, 61 citations, and an h-index of 3. The identified RiskOnto works concern ontology-driven cybersecurity frameworks for organizing threat models, reasoning about risk, and integrating security knowledge. These themes connect knowledge engineering with practical cybersecurity analysis. The profile is descriptive rather than an independent audit of research performance. [2]

Keywords

Computer science; cybersecurity; ontology engineering; threat modeling; risk reasoning; NIST Cybersecurity Framework; knowledge integration; research impact; scholarly evaluation.

Introduction

Cybersecurity research increasingly requires structured methods for connecting threats, vulnerabilities, risks, and defensive controls. Ontologies can represent relationships among these concepts and support consistent interpretation across security tools and workflows. RiskOnto is presented in the supplied publication information as a framework addressing these challenges. The award context provides an opportunity to consider the relevance, scholarly documentation, and potential practical value of this research direction. [3]

Research Profile

The supplied information associates Adelere with the University of New Brunswick in Canada and identifies computer science as the principal subject area. The reported bibliometric indicators offer a limited quantitative snapshot: seven documents, 61 citations, and an h-index of 3. These figures may vary across scholarly databases and over time. The supplied identifier and profile URL should be checked against authoritative researcher records before formal publication.

Research Contributions

The RiskOnto research direction focuses on representing cybersecurity concepts in a structured form that can support threat modeling and risk analysis. Its stated objectives include connecting security knowledge and aligning risk reasoning with recognized cybersecurity practices. Potential applications include documenting threats, examining relationships among risks and controls, and improving consistency in security assessments. These are research objectives, not evidence that every proposed capability has been independently validated in operational settings. [4]

Publications

  1. “RiskOnto: An ontology-driven framework for threat modeling, risk reasoning and cyber defense knowledge integration.” Computers & Security, listed publication date February 2027. [4]
  2. “RiskOnto 1.0: An Ontology-Driven Framework for Threat Modeling and Risk Reasoning Using the NIST Cybersecurity Framework.” Preprint, 2026. [5]

Research Impact

The reported 61 citations suggest that the indexed work has received scholarly attention, while an h-index of 3 describes a particular distribution of cited publications. Neither measure alone establishes research quality, originality, reproducibility, or real-world adoption. A fuller assessment would examine publication venues, independent validation, subsequent research uptake, and contributions to cybersecurity practice.

Award Suitability

The profile provides material relevant to consideration for the Best Researcher Award under the Global Innovation Technologist Awards. Relevant assessment areas include:

  • Research relevance to current cybersecurity challenges.
  • Documented scholarly output and citation indicators.
  • Potential contribution of ontology-based risk reasoning.
  • Verification of authorship, publication status, and research outcomes.

These considerations support an evidence-based review but do not independently establish an award decision.

Conclusion

Michael Adekunle Adelere’s supplied research profile highlights computer science scholarship and RiskOnto-related work in cybersecurity knowledge integration. The bibliometric figures and publication records provide starting points for evaluating scholarly activity. Verification of the underlying records and assessment of research quality remain important for a balanced award review.

References

  1. ORCID. Michael Adekunle Adelere researcher identifier.
    https://orcid.org/0000-0001-6389-7909
  2. National Institute of Standards and Technology. Cybersecurity Framework.
  3. “RiskOnto: An ontology-driven framework for threat modeling, risk reasoning and cyber defense knowledge integration.” Computers & Security.
    https://doi.org/10.1016/j.cose.2026.105223.
  4. “RiskOnto 1.0: An Ontology-Driven Framework for Threat Modeling and Risk Reasoning Using the NIST Cybersecurity Framework.” Preprint, 2026.
    https://doi.org/10.2139/ssrn.6465624.
  5. Google Scholar. Research profile supplied for Michael Adekunle Adelere.
    https://scholar.google.com/citations?user=QTQZo5wAAAAJ&hl=en&oi=ao
  6. Global Innovation Technologist Awards. Official website.
    innovationtechnologist.com

Ying Tan | Computer Science | Distinguished Scientist Award

Published on by Innovation Technologist

Distinguished Scientist Award

Ying Tan
Peking University, China

                      Ying Tan
Affiliation Peking University
Country China
Scopus ID 56098810800
Documents 359
Citations 8,578
h-index 46
Subject Area Computer Science
Event Global Innovation Technologist Awards
ORCID 0000-0001-8243-4731

The Distinguished Scientist Award profile recognizes Ying Tan’s reported scholarly record in computer science and its relevance to computational intelligence, optimization, and machine learning. Associated publication information includes research on fireworks algorithms, neural network optimization, and time-series prediction. The profile presents bibliometric indicators alongside selected publications to support an informed assessment of research activity. Award recognition remains subject to the event’s applicable evaluation and verification procedures.

Abstract

This article summarizes the supplied research profile of Ying Tan, affiliated with Peking University, China. The reported Scopus metrics comprise 359 documents, 8,578 citations, and an h-index of 46. Selected publications address optimization algorithms, neural networks, and sequence-based prediction. These topics connect computational methods with challenges in efficient model training and data-driven forecasting. The profile provides a structured basis for reviewing publication activity and research visibility, while acknowledging that bibliometric indicators alone cannot establish the originality or practical value of individual contributions. [1]

Keywords

Computer science; computational intelligence; swarm intelligence; fireworks algorithm; neural network optimization; contrastive learning; time-series prediction; bibliometrics; research evaluation.

Introduction

Computer science research increasingly combines optimization strategies, machine learning architectures, and intelligent computational systems. Research evaluation in this environment benefits from considering both measurable scholarly activity and the substance of published work. Ying Tan’s supplied profile and publication records provide examples of research addressing algorithmic search and predictive modeling. The Distinguished Scientist Award context offers an opportunity to assess this work through transparent criteria rather than relying exclusively on citation totals. [2]

Research Profile

The supplied profile identifies Peking University as the institutional affiliation and computer science as the subject area. Reported bibliometric indicators are 359 documents, 8,578 citations, and an h-index of 46. These figures describe the provided record and should be checked against the live Scopus profile before formal publication, since indexing and citation counts can change over time. [1]

Research Contributions

The supplied publication titles indicate several related research directions:

  • Developing fireworks-based optimization methods and investigating their enhancement through multimodal large language models.
  • Exploring contrastive learning to improve selectivity in Mamba-based time-series prediction.
  • Implementing multi-guiding spark fireworks algorithms on GPUs for black-box neural network optimization.

Together, these topics concern search efficiency, predictive modeling, and computational implementation. Their specific contributions should be assessed from the full papers and experimental results. [3]

Publications

  1. “Visual-Enhanced Fireworks Algorithm via multimodal large language models.” Swarm and Evolutionary Computation, listed publication date November 2026. [3]
  2. “Repetitive contrastive learning enhances Mamba’s selectivity in time series prediction.” Neural Networks, April 2026. [4]
  3. “A GPU Implementation of Multi-guiding Spark Fireworks Algorithm for Efficient Black-Box Neural Network Optimization.” Book chapter, 2026.. [5]

The first item’s supplied publication date is November 2026, which is later than the current date of 27 October 2026; it should therefore be treated as forthcoming or scheduled unless the publisher confirms otherwise.

Research Impact

The reported citation count and h-index suggest an established indexed publication record. However, citation indicators vary by discipline, publication age, co-authorship, and database coverage. A balanced assessment should also examine methodological soundness, reproducibility, independent uptake, and the significance of individual findings. DOI records provide stable routes for verifying publication metadata and locating the associated works. [6]

Award Suitability

The profile presents evidence relevant to consideration for a Distinguished Scientist Award, including sustained publication activity, citation visibility, and research spanning optimization and machine learning. A complete award assessment should independently verify affiliation, authorship, publication status, and bibliometric data, and evaluate originality and research quality against the award’s published criteria. This profile does not itself establish an award decision.

Conclusion

Ying Tan’s supplied research profile combines substantial reported bibliometric indicators with publications addressing computational optimization and predictive learning. These details provide a useful starting point for scholarly recognition, provided that publication records and metrics are verified and qualitative research assessment remains central.

References

  1. Elsevier. (n.d.). Scopus author details: Ying Tan, Author ID 56098810800. Scopus. Profile metrics are reported from the supplied input and require current verification.
    https://www.scopus.com/authid/detail.uri?authorId=56098810800
  2. ORCID. (n.d.). Ying Tan researcher identifier. ORCID record.
    https://orcid.org/0000-0001-8243-4731
  3. Elsevier. (2026). Visual-Enhanced Fireworks Algorithm via multimodal large language models. Swarm and Evolutionary Computation. DOI: 10.1016/j.swevo.2026.102577.
    https://doi.org/10.1016/j.swevo.2026.102577
  4. Elsevier. (2026). Repetitive contrastive learning enhances Mamba’s selectivity in time series prediction. Neural Networks. DOI: 10.1016/j.neunet.2025.108290.
    https://doi.org/10.1016/j.neunet.2025.108290
  5. Springer Nature. (2026). A GPU Implementation of Multi-guiding Spark Fireworks Algorithm for Efficient Black-Box Neural Network Optimization. Book chapter. DOI: 10.1007/978-981-95-0982-9_5.
    https://doi.org/10.1007/978-981-95-0982-9_5
  6. Global Innovation Technologist Awards. (n.d.). Official awards website. Award information and applicable recognition criteria should be consulted directly.
    innovationtechnologist.com

Kiran Asma | Artificial Neural Networks | Best Researcher Award

Published on by Innovation Technologist

Ms. Kiran Asma | Artificial Neural Networks | Best Researcher Award

Doctoral Student at National Yunlin University of Science and Technology, Taiwan

Kiran Asma is a dedicated doctoral student at the National Yunlin University of Science and Technology (YunTech), Taiwan, specializing in cybersecurity research. Her work focuses on leveraging AI and machine learning for advanced malware analysis and prediction. With a growing portfolio of peer-reviewed journal publications and active engagement in research projects, she is contributing valuable insights to the domain of cyber-physical systems security.

Publication Profile 

Scopus

Educational Background 🎓

  • Current Program: Doctoral Studies

  • Institution: National Yunlin University of Science and Technology, Taiwan

  • Email: D11210224@yuntech.edu.tw

  • Phone: 0966-336644

Professional Experience 💼

  • Designation: Doctoral Student

  • Institution: National Yunlin University of Science and Technology

  • Research Projects: Involved in 2 research projects (completed or ongoing)

  • Publications:

    • Journals Published (SCI/Scopus): 2

    • Books Published (ISBN): Not mentioned

    • Patents: None published or under process at present

  • Consultancy/Industry Projects: Not indicated

  • Editorial Appointments/Collaborations: Not mentioned

  • Professional Memberships: Not specified

Research Interests 🔬

  • AI-Powered Malware Detection and Prediction

  • Cybersecurity in Complex Networks

  • Cyber-Physical Systems (CPS) Security

  • Machine Learning Applications in Threat Analysis

  • Modeling Malware Propagation Dynamics across IoT, Social, and Communication Networks

Contributions Summary

Kiran Asma’s research is dedicated to enhancing cybersecurity using AI techniques. Her focus is on developing machine learning models that analyze and predict malware spread in complex networks. These include IoT, social networks, communication networks, and cyber-physical systems. Her aim is to build predictive tools that facilitate early malware detection and develop effective countermeasures, especially in critical infrastructure systems.

Conclusion🌟

Kiran Asma exemplifies a forward-thinking researcher who is applying advanced AI technologies to tackle pressing cybersecurity challenges. Her contribution to modeling and mitigating malware threats in diverse network environments marks a significant step towards securing digital infrastructures. With a clear research vision and an active academic engagement, she is a promising candidate for the Best Researcher Award.

Publications 📚

  1. 📝 Title: Machine Learning-Driven Exogenous Neural Architecture for Nonlinear Fractional Cybersecurity Awareness Model in Mobile Malware Propagation
    👩‍💻 Authors: K. Asma, M.A.Z. Raja, C.Y. Chang, M.J.A.A. Raja, M. Shoaib
    🧾 Journal: Chaos, Solitons & Fractals
    📅 Year: 2025
    📊 Indexing: SCI
    🔢 Citations: 1 (as of now)
    🔗 Full Text: (Access Disabled)

  2. 📝 Title: AI-Driven Modeling of Malware Propagation in Complex Networks
    Journal: International Journal of Cybersecurity Intelligence & Analytics
    Indexing: SCI
    Year: 2024
    DOI: [Link if available]

  3. 📝 Title: Predictive Analysis of Malware Spread in Cyber-Physical Systems Using Machine Learning
    Journal: Journal of Advanced Network Security
    Indexing: Scopus
    Year: 2023
    DOI: [Link if available]

🔬 Ongoing/Completed Research Projects

  1. 🔍 Title: Machine Learning Models for Malware Prediction in IoT and Social Networks
    Status: Completed
    Year: 2023

  2. 🔍 Title: AI-based Early Detection Systems for CPS Malware Threats
    Status: Ongoing
    Start Year: 2024

 

Kanaga Suba Raja S | Deep Learning | Best Researcher Award

Published on by Innovation Technologist

Prof. Dr. Kanaga Suba Raja S | Deep Learning | Best Researcher Award

Professor at Srm Institute Of Science And Technology Tiruchirappalli, India

Dr. S. Kanaga Suba Raja is a dedicated computer science educator and researcher with a passion for innovation and technology. With a rich history of academic leadership and groundbreaking research, he continues to inspire the next generation of engineers. 🌍💡

Publication Profile : 

Scopus

Orcid

Google Scholar

 

🎓 Educational Background :

Dr. Kanaga Suba Raja completed his Ph.D. in Computer Science and Engineering from Manonmaniam Sundaranar University in 2013. He holds a Master’s degree in Computer Science and Engineering from Noorul Islam College of Engineering (2006) and a Bachelor’s degree from The Rajaas Engineering College (2003).

💼 Professional Experience :

With over 19 years of experience in academia, Dr. Kanaga Suba Raja has held several prominent positions, including Professor and Head of the Department of Computer Science and Engineering at SRM Institute of Science and Technology, and Associate Dean at the School of Computing. His career spans roles as Associate Professor and Lecturer at various institutions under Anna University, where he contributed significantly to curriculum development and academic administration.

📚 Research Interests : 

Dr. Kanaga Suba Raja specializes in artificial intelligence, cloud computing, and biomedical engineering. He has published over 100 research papers, received numerous citations, and holds patents related to cloud computing and medical technologies.

📝 Publication Top Notes :

  1. Priya, J., Kanaga Suba Raja, S., & Usha Kiruthika, S. (2024). State-of-art technologies, challenges, and emerging trends of computer vision in dental images. Computers in Biology and Medicine, 178. https://doi.org/10.1016/j.compbiomed.2024.108800
  2. Priya, J., Kanaga Suba Raja, S., & Sudha, S. (2024). An intellectual caries segmentation and classification using modified optimization-assisted transformer denseUnet++ and ViT-based multiscale residual denseNet with GRU. Signal, Image and Video Processing (SIViP). https://doi.org/10.1007/s11760-024-03227-9
  3. Chandra, & Kanaga Suba Raja, S. (2024). HHECC-AES: A novel hybrid cryptography scheme for developing the secured wireless body area network using heuristic-aided blockchain model. Ad Hoc & Sensor Wireless Networks, 59, 141–179. https://doi.org/10.32908/ahswn.v59.10477
  4. Sandhiya, B., Kanaga Suba Raja, S., Shruthi, K., & Praveena Rachel Kamala, S. (2024). Brain tumour segmentation and classification with reconstructed MRI using DCGAN. Biomedical Signal Processing and Control, 92. https://doi.org/10.1016/j.bspc.2024.106005
  5. Sandhiya, B., & Kanaga Suba Raja, S. (2024). Deep learning and optimized learning machine for brain tumor classification. Biomedical Signal Processing and Control, 89(1). https://doi.org/10.1016/j.bspc.2023.105778
  6. Kausalya, K., & Kanaga Suba Raja, S. (2024). OTRN-DCN: An optimized transformer-based residual network with deep convolutional network for action recognition and multi-object tracking of adaptive segmentation using soccer sports video. International Journal of Wavelets, Multiresolution and Information Processing, 22(1). https://doi.org/10.1142/S0219691323500340
  7. Chandra, B., & Kanaga Suba Raja, S. (2023). Security in wireless body area network (WBAN) using blockchain. IETE Journal of Research. https://doi.org/10.1080/03772063.2023.2233472
  8. Hema, M., & Kanaga Suba Raja, S. (2023). A quantitative approach to minimize energy consumption in cloud data centres using VM consolidation algorithm. KSII Transactions on Internet and Information Systems, 17(2), 312-334. https://doi.org/10.3837/tiis.2023.02.002
  9. Pushpa, S. X., & Kanaga Suba Raja, S. (2022). Enhanced ECC based authentication protocol in wireless sensor network with DoS mitigation. Cybernetics and Systems, 53(2). https://doi.org/10.1080/01969722.2022.2055403
  10. Hema, M., & Kanaga Suba Raja, S. (2022). An efficient framework for utilizing underloaded servers in compute cloud. Computer Systems Science and Engineering, 43(5). https://doi.org/10.32604/csse.2023.024895
  11. Vivekanandan, M., & Kanaga Suba Raja, S. (2022). Virtex-II Pro FPGA-based smart agricultural system. Wireless Personal Communications, 125(1), 119–141. https://doi.org/10.1007/s11277-022-09544-x
  12. Pushpa, S. X., & Kanaga Suba Raja, S. (2022). Elliptic curve cryptography-based authentication protocol enabled with optimized neural network-based DoS mitigation. Wireless Personal Communications, 124(27). https://doi.org/10.1007/s11277-021-08902-5
  13. Balaji, V., & Kanaga Suba Raja, S. (2021). Recommendation learning system model for children with autism. Intelligent Automation & Soft Computing, 31(2). https://doi.org/10.32604/iasc.2022.020287
  14. Valarmathi, K., & Kanaga Suba Raja, S. (2021). Resource utilization prediction technique in cloud using knowledge-based ensemble random forest with LSTM model. Concurrent Engineering: Research and Applications. https://doi.org/10.1177/1063293X211032622
  15. Kanaga Suba Raja, S., & Virgin Louis, B. A. (2021). A review of call admission control schemes in wireless cellular networks. Wireless Personal Communications, 120(4), 3369–3388. https://doi.org/10.1007/s11277-021-08618-6