Om Sahu | Computer Science & Artificial Intelligence | Best Researcher Award

Published on by Computer Scientists Awards

Best Researcher Award

Om Sahu
University Of Petroleum & Energy Studies (UPES) Dehradoon, India

Om Sahu
Affiliation University Of Petroleum & Energy Studies (UPES) Dehradoon
Country India
Scopus ID 57215754785
Documents 10
Citations 47
h-index 4
Subject Area Computer Science & Artificial Intelligence
Event Computer Scientists Awards
ORCID 0000-0003-0515-399X

Om Sahu is a researcher affiliated with the University Of Petroleum & Energy Studies (UPES) Dehradoon, India, whose documented research activity is associated with computer science and artificial intelligence. The available scholarly record identifies 10 documents, 47 citations, and an h-index of 4. These indicators provide a bibliometric snapshot of the researcher’s indexed publication activity and citation impact.

Abstract

Om Sahu’s research profile reflects activity in computer science and artificial intelligence, with published work applying deep learning to practical detection problems. His documented publications include research on multi-disease detection and smart-factory product defect detection. The available bibliometric information records 10 documents, 47 citations, and an h-index of 4. [1]

Keywords

Artificial intelligence; deep learning; computer science; machine learning; medical image analysis; disease detection; smart manufacturing; product defect detection; computer vision; intelligent systems.

Introduction

Deep learning is increasingly used for classification, recognition, and automated detection across scientific and industrial applications. Within this broader area, Sahu’s documented work addresses both healthcare-oriented detection and manufacturing-oriented quality inspection. His publications indicate an applied research orientation in which computational methods are used to address domain-specific recognition and decision-support problems. [2]

Research Profile

The researcher’s indexed profile is associated with the University Of Petroleum & Energy Studies (UPES) Dehradoon and the subject area of computer artificial intelligence. The reported Scopus identifier is 57215754785, while the ORCID identifier is 0000-0003-0515-399X. The bibliometric record supplied for this recognition page comprises 10 documents, 47 citations, and an h-index of 4. [1]

Research Contributions

Sahu’s recent publications demonstrate the application of deep learning to two distinct detection contexts. The first examines multi-disease detection, while the second focuses on identifying product defects in smart-factory environments. Together, these studies illustrate the use of computational intelligence and pattern-recognition techniques across healthcare and industrial settings. [2] [3]

Publications

  • Multi-Disease Detection Using Deep Learning. O.P. Sahu, Om Prakash, A. Singh, Arya, D. Prakash, Deo. ISED 2025 – 13th International Conference on Intelligent Systems and Embedded Design, Proceedings, 2025. IEEE Xplore. [2]
  • Smart Factory Product Defect Detection Using Deep Learning. Prakash, D.; Singh, A.; Bais, Y.D.S.; Majumdar, V.; Patel, E.; Sahu, O.P. Lecture Notes in Mechanical Engineering, conference paper, 2025. [3]

Research Impact

The reported 47 citations and h-index of 4 indicate measurable scholarly attention to the researcher’s indexed output. [1] The subject applications also demonstrate relevance beyond a single domain, connecting artificial intelligence methods with medical detection and smart manufacturing. The available evidence supports describing the profile as an emerging applied research contribution rather than making broader claims about field-wide influence.

Award Suitability

Based on the supplied publication and bibliometric information, Om Sahu presents a documented research profile relevant to the Best Researcher Award category at the Computer Scientists Awards. The combination of indexed publications, citations, and research activity in artificial intelligence provides objective material for consideration. Final award decisions should remain subject to the applicable evaluation criteria and review by the responsible award committee.

Conclusion

Om Sahu’s academic profile reflects research activity in computer artificial intelligence, particularly the application of deep learning to disease detection and smart-factory quality inspection. With 10 indexed documents, 47 citations, and an h-index of 4 in the supplied record, the profile provides a measurable basis for academic recognition. [1] His documented publications further indicate an applied orientation connecting artificial intelligence with practical research challenges.

References

  1. Elsevier. (n.d.). Scopus author details: Om Sahu, Author ID 57215754785. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57215754785
  2. Sahu, O.P.; Singh, A.; Prakash, D. et al. (2025). Multi-Disease Detection Using Deep Learning. ISED 2025 – 13th International Conference on Intelligent Systems and Embedded Design, Proceedings.
    https://ieeexplore.ieee.org/document/11405003
  3. Prakash, D.; Singh, A.; Bais, Y.D.S.; Majumdar, V.; Patel, E.; Sahu, O.P. (2025). Smart Factory Product Defect Detection Using Deep Learning. Lecture Notes in Mechanical Engineering. DOI: 10.1007/978-981-95-0063-5_34.
    https://doi.org/10.1007/978-981-95-0063-5_34
  4. ORCID. (n.d.). Om Sahu, ORCID iD 0000-0003-0515-399X.
    https://orcid.org/0000-0003-0515-399X
  5. IEEE. (2025). IEEE Xplore Digital Library: Multi-Disease Detection Using Deep Learning.
    https://ieeexplore.ieee.org/document/11405003
  6. Springer Nature. (2025). Lecture Notes in Mechanical Engineering: Smart Factory Product Defect Detection Using Deep Learning.
    https://link.springer.com/chapter/10.1007/978-981-95-0063-5_34

Lin Wang | Cybersecurity and Cryptography | Innovative Research Award

Published on by Computer Scientists Awards

Innovative Research Award

Lin Wang
China University of Petroleum (East China), China

Lin Wang
Affiliation China University of Petroleum (East China)
Country China
Scopus ID 57215074985
Documents 35
Citations 1,443
h-index 19
Subject Area Cybersecurity and Cryptography
Event Computer Scientists Awards
ORCID 0000-0001-5644-8865

Lin Wang is a researcher affiliated with China University of Petroleum (East China), China. His reported scholarly record includes 35 documents, 1,443 citations, and an h-index of 19. The research publications supplied for this profile address catalytic materials, single-atom catalysts, photocatalytic carbon dioxide conversion, oxygen evolution, and membrane-based ion transport. These topics demonstrate a research portfolio situated within advanced materials and chemical-energy technologies. The following article presents a structured academic overview based on the supplied bibliographic information and does not constitute an independent verification of authorship, metrics, or award eligibility.

Abstract

This academic profile examines the reported research activities of Lin Wang in the context of the Innovative Research Award. The supplied publications describe investigations into covalent triazine frameworks, ruthenium-based single-site catalysts, manganese-doped oxide nanoarrays, two-dimensional metal–organic framework membranes, and iridium single-atom catalysts. Collectively, these studies concern the design of functional materials for energy conversion and selective transport. The profile summarizes the available research themes, publication record, and reported bibliometric indicators while distinguishing documented information from award assessment.

Keywords

Innovative Research Award; Lin Wang; China University of Petroleum; photocatalytic CO2 reduction; single-atom catalysts; ruthenium catalysis; oxygen evolution; metal–organic frameworks; membrane separation; advanced materials.

Introduction

Contemporary materials research increasingly combines nanoscale structural engineering with catalytic and separation technologies. Research into carbon dioxide conversion, water oxidation, and selective ion transport seeks to improve efficiency, stability, and material functionality. The publications supplied for Lin Wang identify several studies within this broad scientific landscape. Their reported approaches include coordination-environment engineering, template-directed synthesis, and the construction of hybrid membrane architectures. [2]

Research Profile

The supplied profile records 35 documents, 1,443 citations, and an h-index of 19, associated with Scopus author identifier 57215074985. These figures are presented as reported values and may change as bibliographic databases are updated. The listed subject area is Cybersecurity and Cryptography, whereas the supplied publication titles primarily concern catalysis and advanced materials. Consequently, subject classification and publication authorship should be independently checked before formal institutional or award use.

Research Contributions

The supplied studies indicate several recurring research directions:

  • Single-site ruthenium coordination within covalent triazine frameworks for photocatalytic carbon dioxide reduction. [1]
  • Manganese-doped ruthenium–titanium oxide nanoarrays designed for acidic oxygen evolution. [2]
  • Two-dimensional metal–organic framework and graphene oxide hybrid membranes for selective ion transport. [3]
  • Coordination engineering of iridium single-atom catalysts for carbon dioxide hydrogenation to formic acid. [4]

Publications

The following publication titles and author lists are reproduced from the supplied information. Lin Wang appears among the listed authors of each work; the exact contribution and publication metadata require verification from the original records.

  1. Lu Wang, Lin Wang, Saifei Yuan, Liping Song, Hao Ren, Yuankang Xu, Manman He, Yuheng Zhang, Hang Wang, Yichao Huang, Tong Wei, Jiangwei Zhang, Yuichiro Himeda, and Zhuangjun Fan. Covalently-bonded single-site Ru-N2 knitted into covalent triazine frameworks for boosting photocatalytic CO2 reduction.
  2. Xinyuan Qin, Ruili Gao, Yuan Li, Junyu Zhang, Lin Wang, Yan Zhou, Lianming Zhao, Chuande Wu, and Yichao Huang. In-situ construction of Mn-doped RuTiOx nanoarray via template-directed replacement reaction for highly efficient and durable acidic oxygen evolution.
  3. Yu Cheng, Hang Wang, Longyu Wang, Zhong Wang, Zhibang Liu, Ziyi Zhang, Qiang Ma, Chao Zhang, Xiaoxue Zhang, Dongmin Bian, Lin Wang, and Chuan-De Wu. Interlayer-engineered 2D MOF-GO hybrid membranes with sub-nanometer channels for selective ion transport.
  4. Yuankang Xu, Lin Wang, Yuanying Liu, Linghao Liu, Yanzhuo Zhao, Tao Shu, Fanqiang Meng, Hang Wang, Tong Wei, Yichao Huang, and Zhuangjun Fan. Coordination engineering directs d-band center optimization for efficient CO2 hydrogenation to formic acid on Ir–Nx single-atom catalysts.

Research Impact

The reported citation count and h-index suggest measurable scholarly visibility. The supplied publication themes are relevant to energy conversion, catalytic reaction engineering, and membrane-based separations. However, the practical, industrial, or societal impact of these studies cannot be established solely from titles and bibliometric indicators. Such assessment would require analysis of experimental results, independent citations, technological adoption, and verified research contributions.

Award Suitability

The supplied research themes may be relevant to an Innovative Research Award because they concern the development and optimization of advanced functional materials. Evidence potentially relevant to an award assessment includes originality, methodological rigor, reproducibility, documented outcomes, and the significance of the research problem. Final suitability should be determined by the Computer Scientists Awards committee using verified publications, eligibility requirements, and an independent evaluation of the nominee’s contribution.

Conclusion

Lin Wang’s supplied profile combines reported bibliometric indicators with publications addressing catalytic materials, carbon dioxide conversion, oxygen evolution, and selective ion transport. The listed works provide a basis for documenting research activity in advanced materials and energy-related technologies. Verification of author identity, database metrics, institutional affiliation, and individual contributions remains necessary before the profile is used as a definitive academic or award record.

References

  1. Wang, L., Wang, L., et al. Covalently-bonded single-site Ru-N2 knitted into covalent triazine frameworks for boosting photocatalytic CO2 reduction. ScienceDirect.
    https://www.sciencedirect.com/science/article/abs/pii/S0926337322010384
  2. Qin, X., Gao, R., et al. In-situ construction of Mn-doped RuTiOx nanoarray via template-directed replacement reaction for highly efficient and durable acidic oxygen evolution. ScienceDirect.
    https://www.sciencedirect.com/science/article/abs/pii/S0021979726013111
  3. Cheng, Y., Wang, H., et al. Interlayer-engineered 2D MOF-GO hybrid membranes with sub-nanometer channels for selective ion transport. ScienceDirect.
    https://www.sciencedirect.com/science/article/abs/pii/S1005030226002100
  4. Xu, Y., Wang, L., et al. Coordination engineering directs d-band center optimization for efficient CO2 hydrogenation to formic acid on Ir–Nx single-atom catalysts. ScienceDirect.
    https://www.sciencedirect.com/science/article/pii/S2772834X26000576
  5. Elsevier. (n.d.). Scopus author details: Lin Wang, Author ID 57215074985. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57215074985
  6. ORCID. (n.d.). ORCID record: Lin Wang, ORCID iD 0000-0001-5644-8865. ORCID.
    https://orcid.org/0000-0001-5644-8865

Murat Dener | Cybersecurity | Best Researcher Award

Published on by Computer Scientists Awards

Best Researcher Award

Murat Dener
Gazi University,Türkiye

Murat Dener
Affiliation Gazi University
Country Turkey
Scopus ID 57212000309
Documents 38
Citations 880
h-index 12
Subject Area Cybersecurity
Event Computer Scientists Awards
ORCID 0000-0001-5746-6141

The Best Researcher Award recognition highlights the scholarly achievements and scientific contributions of Murat Dener, a researcher affiliated with Gazi University in Türkiye. His work primarily focuses on cybersecurity, industrial internet security, wireless sensor networks, blockchain-enabled authentication, intrusion detection systems, explainable artificial intelligence, and secure cyber-physical infrastructures. Through peer-reviewed publications and interdisciplinary research activities, Dener has contributed to advancing secure digital ecosystems and emerging cybersecurity methodologies.[1]

Abstract

Murat Dener has established a research portfolio centered on cybersecurity and intelligent digital systems. His scholarly output includes studies on industrial control system protection, wireless sensor network security, blockchain-based authentication frameworks, and artificial intelligence-assisted cyber defense mechanisms. The combination of practical cybersecurity applications and emerging computational methods has positioned his research within contemporary discussions on secure and resilient digital infrastructures.[2]

Keywords

Cybersecurity, Industrial IoT, SCADA Security, Wireless Sensor Networks, Blockchain Authentication, Explainable Artificial Intelligence, Deep Learning, Intrusion Detection Systems, Smart Grids, Information Security.

Introduction

Cybersecurity continues to be one of the most significant challenges in modern digital transformation. Researchers working in this area contribute to protecting critical infrastructure, enterprise systems, and connected devices. Murat Dener’s academic activities reflect this global need through investigations into advanced security frameworks, resilient communication networks, and machine learning applications for threat detection.[3]

Research Profile

Based at Gazi University, Ankara, Türkiye, Dener has contributed to research spanning cybersecurity engineering, Internet of Things security, blockchain systems, and secure communication technologies. His scholarly profile includes multiple peer-reviewed publications, international visibility, and measurable citation impact across cybersecurity-related disciplines.[1]

Research Contributions

  • Development of AI-driven intrusion detection systems for IoT environments.
  • Research on blockchain-based authentication mechanisms for wireless sensor networks.
  • Security analysis of smart grids and critical infrastructure networks.
  • Studies on SCADA resilience and industrial cybersecurity frameworks.
  • Application of explainable artificial intelligence in document security classification.

Publications

  • SACHN-DeBERTa-v3-Large: Automated Document Security Classification with XAI and LLM Comparison (2026).
  • Symmetrical Resilience: Detection of Cyberattacks for SCADA Systems Used in IIoT in Big Data Environments (2025).
  • Security with Wireless Sensor Networks in Smart Grids: A Review (2024).
  • IoT-Based Intrusion Detection System Using New Hybrid Deep Learning Algorithm (2024).
  • BBAP-WSN: A New Blockchain-Based Authentication Protocol for Wireless Sensor Networks (2023).

Research Impact

The documented citation count and h-index demonstrate sustained engagement with Dener’s research outputs within the cybersecurity community. His work contributes to the advancement of secure industrial systems, connected infrastructure, and intelligent threat detection technologies. These contributions support both theoretical developments and practical implementations relevant to contemporary information security challenges.[4]

Award Suitability

Murat Dener demonstrates characteristics commonly associated with recipients of research excellence distinctions, including peer-reviewed publication activity, measurable citation impact, interdisciplinary collaboration, and contributions to cybersecurity innovation. His research portfolio aligns closely with the objectives of the Computer Scientists Awards, particularly in recognizing advances that enhance digital security and resilience.[5]

Conclusion

The academic record of Murat Dener reflects continued engagement in cybersecurity research and technological innovation. Through publications addressing IoT security, blockchain authentication, SCADA resilience, and artificial intelligence applications, he has contributed to important areas of modern computer science. His scholarly achievements provide a strong basis for recognition within international research award programs.

References

  1. Elsevier. (n.d.). Scopus author details: Murat Dener, Author ID 57212000309. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57212000309
  2. Dener, M. (2026). SACHN-DeBERTa-v3-Large: Automated Document Security Classification with XAI and LLM Comparison.
    https://doi.org/10.3390/app16115661
  3. Dener, M. (2025). Detection of Cyberattacks for SCADA Systems Used in IIoT.
    https://doi.org/10.3390/sym17040480
  4. Dener, M. (2024). Security with Wireless Sensor Networks in Smart Grids: A Review.
    https://doi.org/10.3390/sym16101295
  5. Dener, M. (2024). IoT-Based Intrusion Detection System Using New Hybrid Deep Learning Algorithm.
    https://doi.org/10.3390/electronics13061053
  6. Dener, M. (2023). BBAP-WSN: A New Blockchain-Based Authentication Protocol for Wireless Sensor Networks.
    https://doi.org/10.3390/app13031526

Mr. Siddhant Srinivas | Cyber Security | Best Researcher Award

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Mr. Siddhant Srinivas | Cyber Security | Best Researcher Award

California State University | United States

Siddhant Srinivas is an emerging researcher in Artificial Intelligence and Cybersecurity, currently contributing to the advancement of AI-augmented Security Operations Centers (SOC) through the integration of Large Language Models (LLMs) and AI agents. His research primarily focuses on developing intelligent frameworks that enhance the efficiency, scalability, and trustworthiness of SOC workflows. As the first author of a peer-reviewed publication in MDPI, Siddhant has presented a comprehensive taxonomy of AI-driven applications across SOC processes, highlighting their potential in transforming traditional alert triage, threat detection, and incident response systems. His work introduces a capability-maturity model that outlines the evolution from manual to autonomous SOC operations while addressing the challenges of explainability, safety, and reliability in AI deployments. Siddhant’s contributions emphasize bridging the gap between theoretical AI models and their practical implementation in cybersecurity domains. He has been recognized for his scholarly excellence through published research and active involvement in Dr. Alzahrani’s AI Research Lab. His published works are cited in indexed databases such as Scopus and Google Scholar, reflecting a growing academic footprint and influence in the emerging intersection of AI and security research. His citation records and h-index metrics from both Scopus and Google Scholar demonstrate his contributions to advancing secure, transparent, and automated AI systems.

Profile

ORCID

Featured Publication

Srinivas, S., Kirk, B., Zendejas, J., Bari, A., Dajani, K., & Alzahrani, N. (2025). AI-Augmented SOC: A survey of LLMs and agents for security automation. MDPI Informatics, 5(4), 95.

Shan Dacheng | Network Security | Best Researcher Award

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Dr. Shan Dacheng | Network Security | Best Researcher Award

Engineer | Tianjin University | China

Dacheng Shan is a dedicated researcher in the field of computer science, currently pursuing a Ph.D. at Tianjin University. His academic journey is centered around network verification and information security, with a special focus on spatial mapping techniques. Shan has contributed to the scientific community through his research publications and collaborative projects with fellow scholars. Although early in his academic career, his work demonstrates a strong commitment to advancing enterprise network analysis. His contributions aim to enhance the accuracy and efficiency of network security methodologies, which are crucial in today’s increasingly connected digital landscape.

Publication Profile

Scopus

Education Background

Dacheng Shan is obtaining his Doctorate in Computer Science from Tianjin University, a prestigious institution known for its strong emphasis on technological research and innovation. His academic path began with a solid undergraduate foundation, which provided him the technical expertise to explore complex areas such as network systems and cyber defense. His graduate studies are marked by rigorous coursework and intensive research, particularly in network verification and spatial mapping applications in information security. The academic environment at Tianjin University has equipped him with the critical thinking and analytical skills necessary for meaningful contributions to the computer science discipline.

Professional Experience

As a doctoral candidate, Dacheng Shan is primarily engaged in academic research, focusing on enterprise-level network security. His experience includes collaborative research work, authorship of peer-reviewed publications, and contributions to ongoing academic discussions in network exposure surface analysis. He has worked alongside senior researchers and co-authors on interdisciplinary projects that bridge the gap between network engineering and cybersecurity. Though still in the early stages of his professional career, his efforts have been instrumental in formulating theoretical models that improve the scalability and precision of network analysis tools used in enterprise settings.

Awards and Honors

At this point in his academic and professional journey, there are no recorded awards or honors listed under Dacheng Shan’s profile. However, his active involvement in scholarly research and publication in reputable journals like Electronics (Switzerland) demonstrates his potential for future recognition. His dedication to scientific rigor and innovative thinking suggests that accolades and honors may follow as he continues to contribute to the evolving landscape of computer science and information security research. His current trajectory positions him well for future academic and professional achievements in the domain of network verification.

Research Focus

Dacheng Shan’s primary research interests lie in network verification and information security, with a unique focus on spatial mapping. His work seeks to improve how enterprise networks are modeled and analyzed, aiming to reduce vulnerabilities and enhance system resilience. He has explored the development of efficient methodologies for network exposure surface analysis, contributing valuable insights to the field. His interdisciplinary approach combines elements of cybersecurity, data analysis, and spatial computation, making his research highly relevant in the context of growing threats to digital infrastructure. Shan’s work addresses practical problems with theoretical precision.

Publication

Towards Efficient and Accurate Network Exposure Surface Analysis for Enterprise Networks
Published Year: 2025

Conclusion

Dacheng Shan is an emerging academic in the field of computer science, whose focused research on network verification and information security has already begun to make an impact. As a Ph.D. candidate at Tianjin University, he has authored research that addresses complex problems in enterprise network systems. Although early in his academic journey, his trajectory indicates promise, particularly in advancing secure network design methodologies. With a strong academic foundation, collaborative experience, and targeted research, Shan is poised to become a significant contributor to the domains of cybersecurity and computer science research in the years ahead.

 

 

Dr. Maher Alrahhal | Security | Best Researcher Award

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Dr. Maher Alrahhal | Security | Best Researcher Award

Postdoctoral, University of Sharjah, United Arab Emirates

Dr. Maher Abdul Moein Alrahhal is a Postdoctoral Research Associate at the Research Institute of Science and Engineering, University of Sharjah, UAE, and a Postdoctoral Fellow at Amity University Dubai, UAE. He holds a Ph.D. in Computer Science and Engineering from Jawaharlal Nehru Technological University (JNTU), Hyderabad, India, specializing in Artificial Intelligence, Big Data, and Data Analysis. With a solid background in computer science and engineering, Dr. Alrahhal has made significant contributions to the fields of machine learning, image retrieval, and data mining 🌐💡.

Publication Profile

Google Scholar

🌍

🎓Education Background

Dr. Alrahhal’s educational journey is marked by excellence, with a Ph.D. in Computer Science and Engineering from JNTU, Hyderabad, India (March 2024). He completed his Master of Technology in Computer Science and Engineering with First Division from the National Institute of Technology, Warangal, India (July 2018). He holds a Bachelor’s degree in Computer Engineering from the University of Aleppo, Syria, graduating with honors and securing the first rank in his department 🏆📚.

👨‍🏫Professional Experience

Dr. Alrahhal has a robust academic career with over five years of teaching experience at prominent institutions in Syria and India. He has served as a Teaching Assistant at the University of Aleppo, and later as a Lecturer and Assistant Supervisor at JNTU, Hyderabad. Dr. Alrahhal also led the Big Data Lab at JNTU and played a key role in mentoring seven master’s students. His postdoctoral roles involve research and teaching at the University of Sharjah and Amity University Dubai, UAE 💻📖.

🛰️

🏅Awards and Honors

Dr. Alrahhal has received several prestigious awards, including the Best Paper Award at IEMTRONICS 2025 for his work on “Hybrid CNN for Efficient Content-Based Image Retrieval Cognitive Systems” 🥇. In recognition of his outstanding achievements, he was honored with the Alan Turing Award at the International Royal Golden Award ceremony (2023). Other notable accolades include the University Excellence Distinction for first-ranking in 2014 and multiple Al-Basel Certificates for Excellence 🏅🎖️.

🔍 Research Focus

Dr. Alrahhal’s research focuses on Artificial Intelligence, Machine Learning, Big Data, Data Mining, and Image Retrieval. His work explores the integration of deep learning techniques with image and video processing, multimedia systems, and the application of Hadoop for scalable data analysis. His contributions aim to advance content-based image retrieval systems and the development of intelligent systems for real-world applications 📊🤖.

💡🌐Conclusion

Dr. Maher Abdul Moein Alrahhal is a dynamic researcher and academic, committed to advancing the fields of Artificial Intelligence and Data Science. With numerous published works in high-impact journals and ongoing research initiatives, he continues to shape the future of intelligent systems and multimedia applications 🌟📈.

🔧

📚Publications

Disruptive Attacks on Artificial Neural Networks: A Systematic Review of Attack Techniques, Detection Methods, and Protection Strategies, Intelligent Systems with Applications, in press.

MapReduce model for efficient image retrieval: a Hadoop-based framework, International Journal of Information Technology (Springer, Scopus Q1).

Enhancing Image Retrieval Systems: A Comprehensive Review of Machine Learning Integration In CBIR, International Journal of Intelligent Systems and Applications in Engineering, 12(4), 4195–4214.

Integrating Machine Learning Algorithms for Robust Content-Based Image Retrieval, International Journal of Information Technology, DOI: 10.1007/s41870-024-02169-2 (Springer, Scopus Q1).

Automatic diagnosis of epileptic seizures using entropy-based features and Multimodal Deep Learning Approaches, Medical Engineering and Physics, DOI: 10.1016/j.medengphy.2024.104206, (Elsevier, Scopus Q1).

Enhancing image retrieval accuracy through multi-resolution HSV-LNP feature fusion and modified K-NN relevance feedback, International Journal of Information Technology, DOI: 10.1007/s41870-024-02000-y, (Springer, Scopus Q1).

Zongbao Jiang | Cybersecurity | Best Researcher Award

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Mr. Zongbao Jiang | Cybersecurity | Best Researcher Award

Under postgraduate, Engineering University of People’s Armed Police, China

📘 Zongbao Jiang is an emerging researcher specializing in computer technology at the Engineering University of People’s Armed Police. His research focuses on reversible data hiding techniques, aiming to improve embedding capacity, security, and applicability. Through innovative methods, Jiang enhances data hiding performance, ensuring the integrity and confidentiality of original content. Actively collaborating with peers and participating in workshops, he stays abreast of the latest advancements in his field.

Profile

Scopus

 

🎓 Education:

Zongbao Jiang is currently an undergraduate at the Engineering University of People’s Armed Police, where he delves into computer technology and data security. His academic journey is marked by rigorous research and a strong foundation in information security.

💼 Experience:

Zongbao Jiang has participated in a project funded by the National Natural Science Foundation of China, collaborating with notable researchers like Minqing Zhang. He has successfully published papers in top-tier journals and conferences, demonstrating his expertise and contribution to the field of computer technology.

🔬 Research Interests:

Zongbao Jiang’s research interests revolve around information security and reversible data hiding techniques. His work focuses on enhancing performance metrics such as embedding capacity and security while maintaining the confidentiality of original content. Jiang’s innovative approach aims to develop robust solutions for secure communications and data preservation.

🏆 Awards:

Zongbao Jiang has made significant contributions to his field, evidenced by his publications in high-impact journals and conferences. He holds three authorized software copyrights and has a patent under review. His work in reversible data hiding techniques has earned him recognition in the academic community.

Publications

Reversible Data Hiding Algorithm in Encrypted Images Based on Adaptive Median Edge Detection and Matrix-Based Secret Sharing
Link to article
Reversible Data Hiding in Encrypted Images based on Classic McEliece Cryptosystem
Link to article
Reversible Data Hiding Algorithm in Encrypted Domain Based on Matrix Secret Sharing
Link to article

Jihyeon Ryu | Computer Science | Best Researcher Award

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Assist Prof Dr. Jihyeon Ryu | Computer Science | Best Researcher Award

Professor, Kwangwoon University, South Korea

👩‍🏫 Jihyeon Ryu is an Assistant Professor at the School of Computer and Information Engineering, Kwangwoon University. She specializes in split learning, convolutional neural networks, and user authentication, contributing significantly to the field of computer science with her extensive research and numerous publications.

Profile

Google Scholar

 

Education

Ph.D. in Software (Integrated Master’s and Doctorate Course) from Sungkyunkwan University (Mar. 2018 – Feb. 2023), advised by Prof. Dongho Won and Prof. Hyoungshick Kim. B.S. in Mathematics and Computer Science and Engineering from Sungkyunkwan University (Mar. 2013 – Feb. 2018). Early Graduation from Sejong Science High School (Mar. 2011 – Feb. 2013).

 

Research Interests:

Split Learning. Convolutional Neural Networks. User Authentication

Awards

Family Company Workshop Lecture, Hoseo University (Feb. 2022). Invited Lecturer, Mental Women’s High School (Nov. 2021). Excellence Prize, Software Department Excellence Research Awards (Feb. 2021). Graduate Merit Scholarship, Sungkyunkwan University (Mar. 2018 – Feb. 2021). SimSan Scholarship, Multiple instances (Mar. 2018 – Sep. 2020). Excellence Award, 국가 암호기술 전문인력 양성과정 (Nov. 2019). Grand Prize, Software Department Excellence Research Awards (Feb. 2019). Samsung Science Scholarship (Mar. 2013 – Feb. 2018)

Publications

Lightweight Hash-Based Authentication Protocol for Smart Grids. Sensors, 2024. Sangjin Kook, Keunok Kim, Jihyeon Ryu, Youngsook Lee, Dongho Won.

Enhanced Lightweight Medical Sensor Networks Authentication Scheme Based on Blockchain. IEEE ACCESS, 2024. Taewoong Kang, Naryun Woo, Jihyeon Ryu.

Secure and Anonymous Authentication Scheme for Mobile Edge Computing Environments. IEEE Internet of Things Journal, 2024. Hakjun Lee, Jihyeon Ryu, Dongho Won.

Distributed and Federated Authentication Schemes Based on Updatable Smart Contracts. Electronics, 2023. Keunok Kim, Jihyeon Ryu, Hakjun Lee, Youngsook Lee, Dongho Won.

An Improved Lightweight User Authentication Scheme for the Internet of Medical Things. Sensors, 2023. Keunok Kim, Jihyeon Ryu, Youngsook Lee, Dongho Won.