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

Xuechen Liang | Computer Science | Research Excellence Award

Published on by Innovation Technologist

Mr. Xuechen Liang | Computer Science | Research Excellence Award

Master | East China Jiaotong University | China

Mr. Xuechen Liang is a computer science researcher focused on large language models, multi-agent systems, and intelligent analysis in imperfect information settings. His research addresses commentary generation, strategic reasoning, and agent collaboration, with notable contributions to top-tier venues such as IJCAI, EACL, AAAI, and IEEE Access. His work spans personalized role-playing frameworks, self-evolving and memory-augmented agents, psycholinguistically inspired token reduction, and collaborative tuning methods to enhance model efficiency and performance. His scholarly output includes 7 research documents, receiving 8 citations, and reflects an h-index of 2, demonstrating growing academic impact in advanced AI and language model research.

Citation Metrics (Scopus)

10

8

6

4

2

0

Citations
8

Documents
7

h-index
2

Citations

Documents

h-index

Featured Publications

Self-Evolving Agents with Reflective Memory for Complex Decision Tasks

– arXiv Preprint, 2024

Andrei Kojukhov | Cybersecurity | Research Excellence Award

Published on by Innovation Technologist

Dr. Andrei Kojukhov | Cybersecurity | Research Excellence Award

Senior Lecturer | Holon Institute of Technology | Israel

Dr. Andrei Kojukhov is a computer science researcher specializing in artificial intelligence, AI-driven education, advanced networking, and intelligent system architectures. His scholarly work integrates generative AI, personalized and ubiquitous learning, error correction in memory systems, and cybersecurity for next-generation digital infrastructures. He has published peer-reviewed research that bridges theoretical innovation and applied technology, particularly at the intersection of AI and education. His research impact is reflected in 114 total citations (85 since 2020), an h-index of 6 (5 since 2020), and an i10-index of 5, demonstrating sustained academic influence and relevance in contemporary AI, networking, and educational technology research domains.

Citation Metrics (Google Scholar)

120

100

80

60

40

20

0

Citations 114

h-index 6

i10-index 5
                   🟦 Citations        🟩 h-index        🟥 i10-index


View Google Scholar Profile

Featured Publications

Dasthavejula Roja | Cyber Threat | Best Researcher Award

Published on by Innovation Technologist

Mrs. Dasthavejula Roja | Cyber Threat | Best Researcher Award

Assistant Professor at Chalapathi Institute of Technology, India

Mrs. Dasthavejula Roja is an Assistant Professor in the Department of Computer Science and Engineering (Data Science) at Chalapathi Institute of Technology, Mothadaka, Andhra Pradesh, India. With over 5 years of academic experience, she is known for her commitment to excellence in teaching and mentoring, as well as her active participation in research and professional development. She is currently pursuing her Ph.D. in Computer Science and Engineering at The Glocal University, Saharanpur, Delhi.

Publication Profile 

Google Scholar

Educational Background 🎓

  • Ph.D. (Pursuing) in Computer Science and Engineering
    The Glocal University (Deemed to be University), Saharanpur, Delhi

  • M.Tech in Computer Science and Engineering
    Chebrolu Engineering College, JNTUK, Kakinada – First Class with Distinction (July 2018)

  • B.Tech in Computer Science and Engineering
    PNC & Vijay Institute of Engineering and Technology, JNTUH, Hyderabad – First Class (May 2015)

Professional Experience 💼

Mrs. Dasthavejula Roja is currently serving as an Assistant Professor in the Department of Computer Science and Engineering (Data Science) at Chalapathi Institute of Technology, Mothadaka, since March 2021. In this role, she has been actively involved in teaching core subjects such as C Programming, Machine Learning, Cloud Computing, Blockchain Technology, Operating Systems, Software Project Management, and more. She has also contributed significantly to institutional development by coordinating and mentoring students during hackathons, and playing a key role as an NBA (National Board of Accreditation) member responsible for documentation and criteria fulfillment.

Prior to her current position, she worked as an Assistant Professor at PNC & Vijay Institute of Engineering and Technology (PNCVIET) from August 2018 to February 2021. During her tenure there, she contributed to undergraduate teaching, curriculum development, and student mentoring, establishing a strong foundation in technical education and pedagogy. With a cumulative teaching experience of over 5 years, she has demonstrated consistent dedication to academic excellence, innovation in teaching methodologies, and active participation in co-curricular initiatives.

Research Interests 🔬

  • Cybersecurity and Intrusion Detection

  • Internet of Things (IoT)

  • Cloud and Distributed Computing

  • Blockchain Technology

  • Deep Learning and AI

  • Smart Agriculture and Yield Prediction Models

  • Learning Analytics

Awards and Honors🏆✨

  • Best Faculty Award – Chalapathi Institute of Technology (2023–2024)

  • Multiple Certificates of Appreciation from NPTEL (Evangelist, Mentor, Discipline, Believer Star)

  • Educator Mentor Award by AICTE Internship (EDUSKILLS)

  • Celonis Educator – EDUSKILLS

  • Ratified as Assistant Professor by JNTUK, Kakinada (2022)

Memberships

  • IAENG: 294814

  • IAAC: FRTJQC-CE000874

  • ISOC (Internet Society): 2249916

  • IFERP: PM51493806

Conclusion🌟

Mrs. Dasthavejula Roja is a dedicated and forward-thinking academician with a passion for research, innovation, and continuous learning. Her involvement in high-impact research, extensive NPTEL certifications, and accolades from national organizations reflect her dynamic engagement in both teaching and professional development. She aims to contribute significantly to the fields of data science, cybersecurity, and smart agriculture through academic research and collaborative initiatives.

Publications 📚

📚 Credit Investigation and Comprehensive Risk Management System based on Big Data Analytics in Commercial Banking
👥 M. Venkateswara Rao, S.S. Vellela, V. Reddy, N. Vullam, K.B. Sk, D. Roja
📅 2023 | 🏛️ 9th International Conference on Advanced Computing and Communication
🔢 Cited by: 125

📚 Multi-agent Personalized Recommendation System in E-commerce Based on User
👥 N. Vullam, S.S. Vellela, V. Reddy, M.V. Rao, K.B. Sk, D. Roja
📅 2023 | 🏛️ 2nd International Conference on Applied Artificial Intelligence and Computing
🔢 Cited by: 111

📚 Prediction and Classification of Alzheimer’s Disease Using Machine Learning Techniques in 3D MR Images
👥 K.N. Rao, B.R. Gandhi, M.V. Rao, S. Javvadi, S.S. Vellela, S.K. Basha
📅 2023 | 🏛️ International Conference on Sustainable Computing and Smart Systems
🔢 Cited by: 96

📚 Coronary Heart Disease Prediction and Classification Using Hybrid Machine Learning Algorithms
👥 K.B. Sk, D. Roja, S.S. Priya, L. Dalavi, S.S. Vellela, V. Reddy
📅 2023 | 🏛️ International Conference on Innovative Data Communication Technologies
🔢 Cited by: 79

📚 Multi-Class Skin Diseases Classification with Color and Texture Features Using Convolution Neural Network
👥 S.S. Vellela, D. Roja, C. Sowjanya, K.B. Sk, L. Dalavai, K.K. Kumar
📅 2023 | 🏛️ 6th International Conference on Contemporary Computing and Informatics
🔢 Cited by: 70

📚 Design and Implementation of an Integrated IoT Blockchain Framework for Drone Communication
👥 S.S. Priya, S.S. Vellela, V. Reddy, S. Javvadi, K.B. Sk, D. Roja
📅 2023 | 🏛️ 3rd International Conference on Intelligent Technologies (CONIT)
🔢 Cited by: 66

📚 A Cloud-based Smart IoT Platform for Personalized Healthcare Data Gathering and Monitoring System
👥 S.S. Vellela, V.L. Reddy, D. Roja, G.R. Rao, K.B. Sk, K.K. Kumar
📅 2023 | 🏛️ 3rd Asian Conference on Innovation in Technology (ASIANCON)
🔢 Cited by: 63

📚 An Enhancing Network Security: A Stacked Ensemble Intrusion Detection System for Effective Threat Mitigation
👥 N. Vullam, D. Roja, N.M. Rao, S.S. Vellela, L.R. Vuyyuru, K.K. Kumar
📅 2023 | 🏛️ 3rd International Conference on Innovative Mechanisms for Industry Applications
🔢 Cited by: 57

📚 Cloud-hosted Concept-Hierarchy Flex-based Infringement Checking System
👥 S.S. Vellela, K. Basha Sk, K. Yakubreddy
📅 2023 | 🏛️ International Advanced Research Journal in Science, Engineering and Technology
🔢 Cited by: 56

📚 Prediction and Analysis Using a Hybrid Model for Stock Market
👥 N. Vullam, K. Yakubreddy, S.S. Vellela, K.B. Sk, V. Reddy, S.S. Priya
📅 2023 | 🏛️ 3rd International Conference on Intelligent Technologies (CONIT)
🔢 Cited by: 53

Stefano Galantucci | Cybersecurity | Best Researcher Award

Published on by Innovation Technologist

Dr. Stefano Galantucci | Cybersecurity | Best Researcher Award

Adjunct Professor (Lecturer) at University of Bari Aldo Moro, Italy

Dr. Stefano Galantucci is a cybersecurity researcher and academic affiliated with the University of Bari Aldo Moro, Italy. Born on October 28, 1996, he holds a Ph.D. in Computer Science and specializes in network security, cryptography, malware detection, and firewall optimization. With extensive experience in teaching, research, and publication, Dr. Galantucci is an active contributor to both academic and applied cybersecurity communities.

Publication Profile 

Scopus

Orcid

Educational Background 🎓

  • 2024 – Ph.D. in Computer Science,
    University of Bari Aldo Moro
    – XXXVIth cycle, focus on cybersecurity and computer science research.

  • 2020 – Master’s Degree (MSc) in Cybersecurity,
    University of Bari “Aldo Moro”
    – Graduated cum laude with honors and special mention by the commission.

  • 2018 – Bachelor’s Degree (BSc) in Computer Science,
    University of Bari “Aldo Moro”
    – Graduated cum laude.

Professional Experience 💼

  • 2024–Present – Research Fellow,
    University of Bari Aldo Moro
    – Working on the FAIR project.

  • 2023–Present – Examination Board Member,
    University of Bari Aldo Moro
    – Courses: Biometric Systems, Application Security, Computer Architecture & OS.

  • 2023 – Adjunct Professor (CyberChallenge Course),
    University of Bari Aldo Moro
    – Taught Cryptography and Hardware Security (24 hours, 2 ECTS).

  • 2022–Present – Adjunct Professor of Cryptography,
    MSc in Cybersecurity, University of Bari Aldo Moro
    – 6 ECTS (62 hours).

  • 2021–Present – Member, ISLab Research Group,
    University of Bari Aldo Moro
    – Cybersecurity contact person.

Research Interests 🔬

  • Cryptography and hardware security

  • Malware detection and classification

  • Network intrusion detection (NIDS)

  • Firewall policy optimization

  • Anomaly detection and AI-driven threat detection

  • DevSecOps and secure software development practices

  • Blockchain analysis

  • Adversarial neural networks in security (e.g., GANs)

Awards and Honors🏆✨

  • Special mention by the MSc degree commission (2020)

  • Full marks and honors in both MSc and BSc degrees

Conclusion🌟

Dr. Stefano Galantucci is a dynamic and accomplished researcher in the field of cybersecurity, with a strong academic background, diverse teaching experience, and a rich portfolio of high-impact publications. His work bridges theoretical foundations and real-world applications, particularly in cryptography, malware detection, and network defense systems. A respected member of the research community, Dr. Galantucci actively contributes to scientific progress through both his scholarly output and his involvement in peer review for top-tier journals. With his growing influence and commitment to innovation, he stands out as a promising leader in the evolving landscape of cybersecurity research.

Publications 📚

  • 🔐 CARIOCA: Prioritizing the Use of IoC by Threats Assessment Shared on the MISP Platform
    📘 International Journal of Information Security, 2025-04
    [DOI: 10.1007/s10207-025-01006-2]
    👥 Piero Delvecchio, Stefano Galantucci, Andrea Iannacone, Giuseppe Pirlo

  • 🧮 FOBICS: Assessing Project Security Level through a Metrics Framework that Evaluates DevSecOps Performance
    📘 Information and Software Technology, 2025-02
    [DOI: 10.1016/j.infsof.2024.107605]
    👥 Alessandro Caniglia, Vincenzo Dentamaro, Stefano Galantucci, Donato Impedovo

  • 🛡️ PROGESI: A PROxy Grammar to Enhance Web Application Firewall for SQL Injection Prevention
    📘 IEEE Access, 2024
    [DOI: 10.1109/ACCESS.2024.3438092]
    👥 Antonio Coscia, Vincenzo Dentamaro, Stefano Galantucci, Antonio Maci, Giuseppe Pirlo

  • 🌐 Automatic Decision Tree-Based NIDPS Ruleset Generation for DoS/DDoS Attacks
    📘 Journal of Information Security and Applications, 2024-05
    [DOI: 10.1016/j.jisa.2024.103736]
    👥 Antonio Coscia, Vincenzo Dentamaro, Stefano Galantucci, Antonio Maci, Giuseppe Pirlo

  • 🧬 YAMME: A YAra-byte-signatures Metamorphic Mutation Engine
    📘 IEEE Transactions on Information Forensics and Security, 2023
    [DOI: 10.1109/TIFS.2023.3294059]
    👥 Antonio Coscia, Vincenzo Dentamaro, Stefano Galantucci, Antonio Maci, Giuseppe Pirlo

  • 🧠 Comparing Deep Learning and Shallow Learning Techniques for API Calls Malware Prediction
    📘 Applied Sciences, 2022-02
    [DOI: 10.3390/app12031645]
    👥 Angelo Cannarile, Vincenzo Dentamaro, Stefano Galantucci, Andrea Iannacone, Donato Impedovo, Giuseppe Pirlo

  • 💰 BACH: A Tool for Analyzing Blockchain Transactions Using Address Clustering Heuristics
    📘 Information, 2024-09-26
    [DOI: 10.3390/info15100589]
    👥 Michele Caringella, Francesco Violante, Francesco De Lucci, Stefano Galantucci, Matteo Costantini

  • 🔑 One Time User Key: A User-Based Secret Sharing XOR-ed Model for Multiple User Cryptography
    📘 IEEE Access, 2021
    [DOI: 10.1109/ACCESS.2021.3124637]
    👥 Stefano Galantucci, Donato Impedovo, Giuseppe Pirlo

  • 📉 Combining Unsupervised Approaches for Near Real-Time Network Traffic Anomaly Detection
    📘 Applied Sciences, 2022-02
    [DOI: 10.3390/app12031759]
    👥 Francesco Carrera, Vincenzo Dentamaro, Stefano Galantucci, Andrea Iannacone, Donato Impedovo, Giuseppe Pirlo

  • 📈 Ensemble Consensus: An Unsupervised Algorithm for Anomaly Detection in Network Security Data
    📄 CEUR Workshop Proceedings, 2021
    👥 Vincenzo Dentamaro, V.N. Convertini, Stefano Galantucci, P. Giglio, T. Palmisano, Giuseppe Pirlo

  • 🧭 A Case Study of Navigation System Assistance with Safety Purposes in the Context of Covid-19
    📘 Lecture Notes in Computer Science, 2021
    [DOI: 10.1007/978-3-030-85607-6_35]
    👥 Galantucci, S.; Giglio, P.; Dentamaro, V.; Pirlo, G.

  • 🔥 Firewall Optimization: An Innovative Two-Stage Algorithm to Optimize Firewall Rule Ordering
    📘 Computers & Security, 2023-11
    [DOI: 10.1016/j.cose.2023.103423]
    👥 Antonio Coscia, Vincenzo Dentamaro, Stefano Galantucci, Antonio Maci, Giuseppe Pirlo

Yi Sun | Information Security | Best Researcher Award

Published on by Innovation Technologist

Dr. Yi Sun | Information Security | Best Researcher Award

Associate Professor at Beijing University of Posts and Telecommunications, China

Dr. Yi Sun is an Associate Professor at the School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, China. She is a prominent researcher in the field of information security with a focus on cryptography, secure multi-party computation, blockchain, and big data security. With a strong presence in international collaborations, over 150 journal publications, 170+ patent applications, and numerous honors, Dr. Sun has established herself as a global leader in cybersecurity and privacy computing.

Publication Profile 

Scopus

Educational Background 🎓

  • Ph.D. in Computer Science
    Institution: State Key Laboratory of Network and Switching Technology, Beijing University of Posts and Telecommunications
    Graduation Date: June 2015

Professional Experience 💼

  • Associate Professor
    School of Computer Science, Beijing University of Posts and Telecommunications (Since July 2015)

    • Oversees national and international research and industry projects (100+ including National 863, Key R&D, and enterprise-funded).

    • Editorial roles in reputed journals and reviewer for SCI-indexed publications.

    • Active participation in organizing and chairing major international conferences such as IEEE GLOBECOM, ICME, DSC, CAIML, CNCIS, ICAIIC, etc.

Research Interests 🔬

  • Cryptography

  • Network Security

  • Secure Multi-party Computation

  • Blockchain Technology

  • Big Data Security

  • Privacy-Preserving Computation

  • RISC-V Processor Security

Awards and Honors🏆✨

  • Top 10 Pioneers of Privacy Computing (2022)

  • Excellent Scientific Research Teacher, Ministry of Education’s Class B Laboratory

  • Excellent Instructor, 7th China International “Internet+” Innovation and Entrepreneurship Competition (2021)

  • Outstanding Instructor, C4 Network Technology Challenge (2023)

  • Excellent Paper, 2023 “Academic Achievements in Frontier IoT in the Capital”

  • 4 ESI Highly Cited Papers

  • Multiple honors for academic and research excellence

Conclusion🌟

Dr. Yi Sun’s career is marked by groundbreaking research, high-impact publications, and international collaboration. Her work in information security has led to real-world applications in secure computing and microarchitecture protection. Through her extensive publication record, editorial contributions, and successful leadership in over 100 research and industrial projects, she has played a critical role in shaping the future of cybersecurity technologies. Dr. Sun’s achievements and accolades make her a highly deserving candidate for the Best Researcher Award.

Publications 📚

📄 Article:
Title: ADPF: Anti-inference differentially private protocol for federated learning
👨‍🔬 Authors: Zhao, Zirun; Lin, Zhaowen; Sun, Yi
📚 Journal: Computer Networks
📅 Year: 2025
🔢 Citations: 0

📄 Article:
Title: A combined side-channel and transient execution attack scheme on RISC-V processors
👨‍🔬 Authors: Dong, Renhai; Cui, Baojiang; Sun, Yi; Yang, Jun
📚 Journal: Computers and Security
📅 Year: 2025
🔢 Citations: 0

📄 Article:
Title: Improved twin support vector machine algorithm and applications in classification problems
👨‍🔬 Authors: Sun, Yi; Wang, Zhouyang
📚 Journal: China Communications
📅 Year: 2024
🔢 Citations: 0

📄 Article:
Title: AdaDpFed: A Differentially Private Federated Learning Algorithm with Adaptive Noise on Non-IID Data
👨‍🔬 Authors: Zhao, Zirun; Sun, Yi; Bashir, Ali Kashif; Lin, Zhaowen
📚 Journal: IEEE Transactions on Consumer Electronics
📅 Year: 2024
🔢 Citations: 7

📄 Article:
Title: BTIDEC: A Novel Detection Scheme for CPU Security of Consumer Electronics
👨‍🔬 Authors: Dong, Renhai; Cui, Baojiang; Sun, Yi; Yang, Jun
📚 Journal: IEEE Transactions on Consumer Electronics
📅 Year: 2024
🔢 Citations: 0

📄 Article:
Title: Ins Finder: A Practical CPU Undocumented Instruction Detection Framework
👨‍🔬 Authors: Dong, Renhai; Cui, Baojiang; Sun, Yi; Yang, Jun
📚 Journal: Journal of Circuits, Systems and Computers
📅 Year: 2024
🔢 Citations: 0

📄 Article:
Title: KeyLight: Intelligent Traffic Signal Control Method Based on Improved Graph Neural Network
👨‍🔬 Authors: Sun, Yi; Lin, Kaixiang; Bashir, Ali Kashif
📚 Journal: IEEE Transactions on Consumer Electronics
📅 Year: 2024
🔢 Citations: 6

Touhid Bhuiyan | Cybersecurity | Outstanding Scientist Award

Published on by Innovation Technologist

Prof. Dr. Touhid Bhuiyan | Cybersecurity | Outstanding Scientist Award

Professor at Washington University of Science and Technology, United States

Dr. T. Bhuiyan is a distinguished academic and consultant in Cyber Security, with extensive experience in teaching, research, and industry. His contributions span several continents and include a wealth of publications and keynotes, focusing on the transformative role of technology in education and security. 🌍🔐📖

Publication Profile : 

Orcid

 

🎓 Educational Background :

Dr. T. Bhuiyan is a seasoned professional in Cyber Security, Software Engineering, and Databases, with over 27 years of experience in teaching and research across the USA, Australia, and Bangladesh. He holds a PhD in Computer Science from Queensland University of Technology, Australia, along with an MSc from The American University in London and a BSc (Hons) in Computing & Information Systems from the University of London. His education is complemented by numerous certifications, including Certified Information System Auditor (CISA) and Certified Ethical Hacker (CEH).

💼 Professional Experience :

In his professional journey, Dr. Bhuiyan has served as a Professor of Cyber Security at Washington University of Science and Technology, and previously held leadership roles at Daffodil International University and Polytechnic Institute Australia. His consulting experience includes a significant role as a National Consultant for Cyber Security with the UNDP and the Government of Bangladesh, where he led initiatives to enhance cyber security measures for government portals. Dr. Bhuiyan has delivered keynote speeches at international conferences and has authored multiple influential publications, including books on intelligent recommendation systems and cyber security. His research interests lie at the intersection of information security, trust management, and e-Learning, exploring how technology can enhance educational practices and health informatics.

📚 Research Interests : 

🔍 Information Security
🌐 Social Networks
🔒 Trust Management
💻 Database Management
🏥 e-Health
📚 e-Learning

📝 Publication Top Notes :

  1. Hossain, M.A., Rahman, M.Z., Bhuiyan, T., Moni, M.A. (2024). Identification of Biomarkers and Molecular Pathways Implicated in Smoking and COVID-19 Associated Lung Cancer Using Bioinformatics and Machine Learning Approaches. International Journal of Environmental Research and Public Health, 21(11), 1392.
  2. Hanip, A., Sarower, A.H., Bhuiyan, T. (2024). The Transformative Role of Generative AI in Education: Challenges and Opportunities for Enhancing Student Learning and Assessment Through Mass Integration. International Journal of Advanced Research in Engineering and Technology, 15(5), 161-175.
  3. Mahmud, A., Sarower, A.H., Sohel, A., Assaduzzaman, M., Bhuiyan, T. (2024). Adoption of ChatGPT by university students for academic purposes: Partial least square, artificial neural network, deep neural network and classification algorithms approach. Array, 21, 100339.
  4. Zannah, T.B., Abdulla-Hil-Kafi, M., Sheakh, M.A., Hasan, M.Z., Shuva, T.F., Bhuiyan, T., Rahman, M.T., Khan, R.T., Kaiser, M.S., Whaiduzzaman, M. (2024). Bayesian Optimized Machine Learning Model for Automated Eye Disease Classification from Fundus Images. Computation, 12(190).
  5. Bishshash, P., Nirob, M.A.S., Shikder, M.H., Sarower, M.A.H., Bhuiyan, T., Noori, S.R.H. (2024). A Comprehensive Cotton Leaf Disease Dataset for Enhanced Detection and Classification. Data in Brief, 57, 110913.
  6. Tamal, M.A., Islam, M.K., Bhuiyan, T., Sattar, A., Prince, N.U. (2024). Unveiling suspicious phishing attacks: enhancing detection with an optimal feature vectorization algorithm and supervised machine learning. Frontiers in Computer Science, 6, 1428013.
  7. Tamal, M.A., Islam, M.K., Bhuiyan, T., Sattar, A. (2024). Dataset of suspicious phishing URL detection. Frontiers in Computer Science, 6, 1308634.
  8. Sholi, R.T., Sarker, …, Bhuiyan, T., Shakil, S.M.K.A., Ahmed, M.F. (2024). Application of Computer Vision and Mobile Systems in Education: A Systematic Review. International Journal of Interactive Mobile Technologies, 18(1), 168-187.
  9. Ahad, M.T., Li, Y., Song, B., Bhuiyan, T. (2023). Comparison of CNN-based deep learning architectures for rice diseases classification. Artificial Intelligence in Agriculture, 9(1), 22-35.
  10. Basar, M.A., Hosen, M.F., Paul, B.K., …, Bhuiyan, T. (2023). Identification of drug and protein-protein interaction network among stress and depression: A bioinformatics approach. Informatics in Medicine Unlocked, 37, 101174.
  11. Paul, S.G., Saha, A., Arefin, M.S., Bhuiyan, T., …, Moni, M.A. (2023). A Comprehensive Review of Green Computing: Past, Present, and Future Research. IEEE Access, 11, 87445-87494.
  12. Sarker, S., Arefin, M.S., Kowsher, M., Bhuiyan, T., Dhar, P.K., Kwon, O.J. (2022). A Comprehensive Review on Big Data for Industries: Challenges and Opportunities. IEEE Access, 11, 744-769.
  13. Asaduzzaman, S., Rehana, H., Bhuiyan, T., Eid, M.M.A., Rashed, A.N.Z. (2022). Extremely high birefringent slotted core umbrella-shaped photonic crystal fiber in terahertz regime. Applied Physics B, 128(148).
  14. Bhuiyan, T. (2022). Transitioning from education to work during the 4th Industrial Revolution. AIB Review, 7, Adelaide, Australia.
  15. Ali, M.N.B., Saudi, M.M., Bhuiyan, T., Ahmad, A.B., Islam, M.N. (2021). NIPSA Intrusion Classification. Journal of Engineering Science and Technology, 16(4), 3534-3547.

 

 

 

 

Negalign Wake Hundera | Cybersecurity | Best Researcher Award

Published on by Innovation Technologist

Assist. Prof. Dr. Negalign Wake Hundera | Cybersecurity | Best Researcher Award

Postdoctoral Researcher of Zhejiang Normal University, China

Dr. Negalign Wake Hundera is a seasoned researcher and educator with a Ph.D. in Software Engineering and extensive experience in network security and IoT technologies. His career reflects a strong commitment to advancing research and teaching in technology-driven fields. With a proven track record in publishing, guiding students, and leading technical teams, he is now seeking opportunities to further his research and contribute to innovative projects in academia or industry. 🌍🔐💻📚

Publication Profile : 

Scopus

🎓 Educational Background :

Negalign Wake Hundera completed his Ph.D. in Software Engineering at the University of Electronic Science and Technology of China (UESTC), Chengdu, in June 2021, under the guidance of Prof. Hu Xiong. Prior to this, he earned an M.Sc. in Computer Science and Technology from UESTC in 2016 and a Bachelor’s degree in Information Technology from Jimma University, Ethiopia, in 2009.

💼 Professional Experience :

Negalign’s professional journey spans roles in academia and industry. He has served as a Postdoctoral Research Fellow at Zhejiang Normal University, Jinhua, China, from August 2022 to August 2024, contributing to high-impact journals and guiding students through their research projects. Before this, he was an Assistant Professor and Lecturer at Wolkite University, Ethiopia, where he not only taught but also led significant projects related to ICT infrastructure and network security. He has also held a leadership position as the Information Communication and Network Infrastructure Team Leader at Wolkite University, overseeing the development and management of the institution’s network infrastructure.

📚 Research Interests : 

Negalign’s research focuses on several cutting-edge areas including network security, public key cryptography, information security, IoT, wireless sensor networks, cloud computing, deep learning, real-time object detection, vehicular networks, and UAV networks. His work aims to apply these technologies in practical fields such as healthcare, intelligent transportation systems, smart agriculture, and cybersecurity.

📝 Publication Top Notes :

  1. Hundera, N. W., Aftab, M. U., Mesfin, D., Xu, H., & Zhu, X. (2024). An efficient heterogeneous online/offline anonymous certificateless signcryption with proxy re-encryption for Internet of Vehicles. Vehicular Communications, 49, 100811. [Open Access]
  2. Hundera, N. W., Shumeng, W., Mesfin, D., Xu, H., & Zhu, X. (2024). An efficient online/offline heterogeneous proxy signcryption for secure communication in UAV networks. Journal of King Saud University – Computer and Information Sciences, 36(5), 102044. [Open Access]
  3. Leka, H. L., Fengli, Z., Kenea, A. T., Tohye, T. G., Tegene, A. T., & Hundera, N. W. (2023). PSO-Based Ensemble Meta-Learning Approach for Cloud Virtual Machine Resource Usage Prediction. Symmetry, 15(3), 613. [Open Access]
  4. Tohye, T. G., Qin, Z., Leka, H. L., & Hundera, N. W. (2023). Glaucoma Detection Using Convolutional Neural Network (CNN). In Proceedings of the 20th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP 2023).
  5. Assefa, A. A., Tian, W., Hundera, N. W., & Aftab, M. U. (2022). Crowd Density Estimation in Spatial and Temporal Distortion Environment Using Parallel Multi-Size Receptive Fields and Stack Ensemble Meta-Learning. Symmetry, 14(10), 2159. [Open Access]
  6. Hundera, N. W., Jin, C., Geressu, D. M., Olanrewaju, O. A., & Xiong, H. (2022). Proxy-based public-key cryptosystem for secure and efficient IoT-based cloud data sharing in the smart city. Multimedia Tools and Applications, 81(21), 29673–29697.
  7. Hundera, N. W., Jin, C., Aftab, M. U., Mesfin, D., & Kumar, S. (2021). Secure outsourced attribute-based signcryption for cloud-based Internet of Vehicles in a smart city. Annales des Telecommunications/Annals of Telecommunications, 76(9-10), 605–616.
  8. Leka, H. L., Fengli, Z., Kenea, A. T., Atandoh, P., & Hundera, N. W. (2021). A Hybrid CNN-LSTM Model for Virtual Machine Workload Forecasting in Cloud Data Center. In Proceedings of the 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP 2021), 474–478.
  9. Hundera, N. W., Mei, Q., Xiong, H., & Geressu, D. M. (2020). A secure and efficient identity-based proxy signcryption in cloud data sharing. KSII Transactions on Internet and Information Systems, 14(1), 455–472.
  10. Oluwasanmi, A., Akeem, S., Jehoaida, J., Baagere, E., Qin, Z., & Hundera, N. W. (2019). Sequential multi-kernel convolutional recurrent network for sentiment classification. In Proceedings of the IEEE International Conference on Software Engineering and Service Sciences (ICSESS), 129–133.