Jeslin Jebish G P | Machine Learning on Databases | Innovative Research Award

Published on by International Database Scientist Awards

Innovative Research Award

Jeslin Jebish G P — Scott Christian College (Autonomous), Nagercoil, India

Jeslin Jebish G P
Affiliation Scott Christian College, Nagercoil
Country India
Google Scholar IUCgka8AAAAJ
Documents 8
Citations 6
h-index 1
Subject Area Machine Learning on Databases
Event International Database Scientist Awards
Scopus 59938914400

Jeslin Jebish G P is an early-career Physics researcher associated with Scott Christian College, Nagercoil, whose work spans nanotechnology, functional materials, photocatalysis, photoelectrochemical water splitting, hydrogen generation, and renewable-energy systems. His documented academic record includes a submitted Ph.D. thesis, indexed publications, conference presentations, peer-review activity, faculty development, and interdisciplinary research involving computational and machine-learning approaches to materials discovery.

Abstract

Jeslin Jebish G P is a Physics researcher whose documented work focuses on titania nanotubes, nanostructured functional materials, photocatalysis, photoelectrochemical water splitting, and green hydrogen generation. His research record also includes work connecting density functional theory and machine learning with materials discovery, providing an interdisciplinary basis for considering computational approaches within emerging energy-materials research. [1]

Keywords

Machine Learning on Databases; materials discovery; nanotechnology; TiO₂ nanotubes; photocatalysis; photoelectrochemical water splitting; green hydrogen; renewable energy; functional materials; electrochemical analysis; computational materials science; scientific data analysis; sustainable energy.

Introduction

The research profile of Jeslin Jebish G P is situated primarily within experimental Physics and materials-energy research. His academic activities encompass synthesis, characterization, electrochemical evaluation, photocatalytic testing, and scientific data interpretation. The supplied record further identifies an emerging computational dimension through research on artificial intelligence, machine learning, and materials discovery for renewable-energy applications. [4]

Research Profile

Jeslin Jebish G P pursued doctoral research at Manonmaniam Sundaranar University, Tirunelveli, with Scott Christian College, Nagercoil, on the development and analysis of titania enhanced with reactive non-metals and transition metals for energy applications. His research experience covers TiO₂ nanotube synthesis by electrochemical anodisation, dopant engineering, hydrogen evolution, photocatalytic and photoelectrochemical evaluation, and advanced material characterization. [1]

Research Contributions

The research profile focuses on engineering TiO₂ nanotube architectures for solar-driven hydrogen production, examining structure, composition, photocatalytic activity, and electrochemical performance. Related research explores nitrogen and transition-metal doping, heterostructures, seawater splitting, and computational materials discovery, demonstrating an interdisciplinary trajectory connecting experimental nanomaterials research with emerging data-driven methodologies for sustainable energy applications. [2]

Publications

The supplied publication record contains eight scholarly works addressing titania nanotubes, anodisation, solar-driven water splitting, green hydrogen generation, photocatalytic systems, seawater splitting, and machine-learning-assisted materials discovery. The publications indicate a progression from experimental nanostructure fabrication and characterization toward broader computational and interdisciplinary approaches in energy-materials research. [2] [3] [4]

Research Impact

The available scholarly record documents publications, presentations, peer-review activity, professional development, and research training across materials physics and renewable-energy themes. His work on TiO₂ nanotube architectures and hydrogen generation contributes to the broader research landscape of sustainable energy materials, while machine-learning-oriented materials discovery represents an emerging interdisciplinary direction. [1] [2]

Award Suitability

For the Innovative Research Award, the record demonstrates interdisciplinary research across nanostructured materials, hydrogen energy, photocatalysis, electrochemical methods, scientific data analysis, and emerging machine-learning approaches to materials discovery. The documented publications and research activities support consideration for recognition, while alignment with database-focused research should be assessed according to the award’s formal criteria. [4]

Conclusion

Jeslin Jebish G P presents an early-career research profile centered on functional nanomaterials, photocatalysis, photoelectrochemical water splitting, and green hydrogen generation. His publication record additionally identifies machine learning and computational materials discovery as an emerging interdisciplinary direction. Collectively, these activities demonstrate sustained engagement with experimental and data-informed research in advanced energy materials. [1]

References

  1. Google Scholar. (n.d.). Jeslin Jebish G P — Google Scholar profile. https://scholar.google.com/citations?hl=en&user=IUCgka8AAAAJ
  2. GP, J. J., & Jebin, R. P. (2026). Designing titania nanotube architectures through anodization for high-performance green hydrogen generation. Next Materials, 11, Article 101840. https://doi.org/10.1016/j.nxmate.2026.101840
  3. Jebish, J. J. G. P. (2026). Junction-engineered TiO₂ nanotube photocatalysts for efficient solar hydrogen generation: Doping strategies, structure–activity relationships, and future perspectives. Journal of Materials Science: Materials in Energy, 2(1), Article 14. https://link.springer.com/article/10.1007/s44308-026-00024-3
  4. Jeslin Jebish, G. P., & Jebin, R. P. (n.d.). Advances in titania nanotubes development by anodization and their role in solar-driven water splitting. Journal of Advanced Manufacturing and Processing, 7(3), Article e70005. https://doi.org/10.1002/amp2.70005

Vinay Rishiwal | AI-Powered Database Systems | Innovative Research Award

Published on by International Database Scientist Awards

Innovative Research Award

Vinay Rishiwal
Mahatma Jyotiba Phule Rohilkhand University, Bareilly, India

Vinay Rishiwal
Affiliation Mahatma Jyotiba Phule Rohilkhand University
Country India
Google Scholar BwAAAAJ&hl
Documents 165
Citations 2250
h-index 25
Subject Area AI-Powered Database Systems
Event International Database Scientist Awards
Scopus 18134724800

Vinay Rishiwal is a computer science academic and researcher affiliated with Mahatma Jyotiba Phule Rohilkhand University, India. His reported research activities encompass artificial intelligence, machine learning, wireless sensor networks, blockchain, Internet of Things technologies, and technology applications addressing societal challenges. His academic record includes teaching, research, projects, patents, publications, and professional service.

Abstract

Vinay Rishiwal’s academic profile reflects sustained work in computer science research, teaching, technological development, and institutional service. His reported scholarly activities cover artificial intelligence, machine learning, IoT, blockchain, wireless sensor networks, cybersecurity, smart systems, and technology-enabled societal applications. His record also includes patents, research projects, books, doctoral supervision, and peer-reviewed publications. [1]

Keywords

Artificial Intelligence; Machine Learning; AI-Powered Database Systems; Blockchain; Internet of Things; Wireless Sensor Networks; Cybersecurity; Smart Agriculture; Quantum Computing; Data Security; Supply Chain Management; Intelligent Systems.

Introduction

Vinay Rishiwal is a professor in the Department of Computer Science and Information Technology at Mahatma Jyotiba Phule Rohilkhand University. His reported career spans more than two decades across teaching, research, academic administration, technology projects, and professional activities. His research portfolio connects computational intelligence with networked, secure, and data-oriented applications.

Research Profile

The reported research profile includes artificial intelligence, machine learning, wireless sensor networks, blockchain, IoT, and applications of technology to societal problems. His career record identifies teaching and research appointments at MJP Rohilkhand University, along with adjunct or visiting academic roles at institutions in Malaysia and Indonesia. The profile reports 165 documents, 2250 citations, and an h-index of 25. [1]

Research Contributions

Reported contributions span secure supply chains, blockchain and IoT security, wireless sensor-network routing, smart agriculture, cybersecurity, quantum machine learning, and intelligent technological systems. Research examples address blockchain-based supply-chain protection, crypto-forensics, IoT security, and the interaction of machine learning with quantum computing, demonstrating a broad systems-oriented research portfolio. [2] [3] [4] [5]

Publications

The supplied publication record reports 126 publications, including journal and conference contributions, alongside books and edited works. Recent research addresses blockchain-enabled supply chains, crypto-forensics, IoT network security, and machine learning with quantum computing. Selected publications appear in IEEE Access, IEEE Internet of Things Journal, International Journal of Network Management, and Peer-to-Peer Networking and Applications. [2] [3] [4] [5]

Research Impact

The career record reports 72 granted patents, including 68 national and 4 international patents, alongside government, institutional, and externally supported research projects. It records 10 awarded and 8 ongoing PhD scholars. Research applications span AI, IoT, smart agriculture, blockchain, healthcare, disaster management, and intelligent sensing, emphasizing practical technological innovation.

Award Suitability

The profile demonstrates suitability for an Innovative Research Award through sustained interdisciplinary research spanning AI, IoT, blockchain, machine learning, and networked systems, supported by patents, publications, funded projects, and academic leadership. Reported Best Researcher, Outstanding Teacher, and best-paper recognitions further provide contextual evidence of research, professional, and academic contributions.

Conclusion

Vinay Rishiwal’s supplied academic record presents a broad research portfolio centered on artificial intelligence, machine learning, IoT, blockchain, wireless sensor networks, cybersecurity, and emerging computational technologies. His reported publications, patents, projects, doctoral supervision, and academic responsibilities collectively establish a substantial record for scholarly consideration under an Innovative Research Award category.

References

  1. Google Scholar. (n.d.). Google Scholar profile: Vinay Rishiwal. https://scholar.google.com/citations?user=Yph__BwAAAAJ&hl=en&oi=sra/scholar
  2. Agarwal, U., Rishiwal, V., Tanwar, S., Chaudhary, R., Sharma, G., Bokoro, P. N., et al. (2022). Blockchain technology for secure supply chain management: A comprehensive review. IEEE Access, 10, 85493–85517. https://ieeexplore.ieee.org/abstract/document/9841565
  3. Agarwal, U., Rishiwal, V., Tanwar, S., & Yadav, M. (2024). Blockchain and crypto forensics: Investigating crypto frauds. International Journal of Network Management, 34(2), e2255. DOI: https://doi.org/10.1002/nem.2255
  4. Maurya, V., Rishiwal, V., Yadav, M., Shiblee, M., Yadav, P., Agarwal, U., et al. (2025). Blockchain-driven security for IoT networks: State-of-the-art, challenges and future directions. Peer-to-Peer Networking and Applications, 18(1), Article 53. https://link.springer.com/article/10.1007/s12083-024-01812-w
  5. Rishiwal, V., Agarwal, U., Yadav, M., Tanwar, S., Garg, D., & Guizani, M. (2025). A new alliance of machine learning and quantum computing: Concepts, attacks, and challenges in IoT networks. IEEE Internet of Things Journal, 12(12), 18865–18886. https://ieeexplore.ieee.org/abstract/document/10856200

Farhad Soleimanian Gharehchopogh | Machine Learning on Databases | Innovative Research Award

Published on by International Database Scientist Awards

Innovative Research Award

Farhad Soleimanian Gharehchopogh
Islamic Azad University, Iran

Farhad Soleimanian Gharehchopogh
Affiliation Islamic Azad University
Country Iran
Scopus 36650599500
Documents 169
Citations 11,693
h-index 57
Subject Area Machine Learning on Databases
Event 0000-0003-1588-1659
ORCID international Database Scientist Awards
Google Scholar hLUbLLsAAAAJ

Farhad Soleimanian Gharehchopogh is a computer engineering researcher and academic affiliated with Islamic Azad University, Iran. His research profile encompasses machine learning, data mining, optimization, artificial intelligence, search and web mining, decision systems, and database-oriented computational methods. His reported scholarly record includes 169 Scopus documents, 11,693 citations, and an h-index of 57.

Abstract

Farhad Soleimanian Gharehchopogh is a computer engineering academic whose research addresses computational intelligence, machine learning, data mining, optimization, artificial intelligence, and database-related systems. His academic record combines university teaching, research administration, scholarly publication, and international research activity. His reported Scopus profile records 169 documents, 11,693 citations, and an h-index of 57.[1]

Keywords

Machine Learning; Databases; Data Mining; Artificial Intelligence; Optimization; Computational Intelligence; Search Engines; Web Mining; Decision Systems; Computer Engineering.

Introduction

Farhad Soleimanian Gharehchopogh’s academic career has developed across computer engineering education and research, with a particular emphasis on algorithms and intelligent computational methods. He completed a BSc in Computer Engineering (Software Engineering) at Islamic Azad University Shabestar Branch, an MSc in Computer Engineering at Cukurova University, and a PhD in Computer Engineering at Hacettepe University. His doctoral research focused on open-domain factoid question answering systems.

Research Profile

His research profile includes machine learning on databases together with data mining, search-engine and web mining, optimization, artificial intelligence, parallel algorithms, decision systems, and computational problem solving. His reported publication portfolio contains 187 papers across journal and conference categories, while the supplied Scopus record lists 169 indexed documents and an h-index of 57.[1]

  • Machine learning and intelligent computational methods.
  • Data mining, databases, and knowledge discovery.
  • Optimization algorithms and computational intelligence.
  • Artificial intelligence, search systems, and web mining.
  • Algorithmic methods for decision and engineering systems.

Research Contributions

The supplied publication record demonstrates continued work on machine learning, optimization, federated learning, medical prediction, edge-cloud computing, particle swarm optimization, and cybersecurity. Recent publications extend these methods to distributed medical-data analysis, diabetes prediction, vehicular edge-cloud resource allocation, multimodal optimization, and intrusion detection, illustrating the application of computational intelligence across multiple technical domains.[2] [3] [4] [5]

Publications

Selected recent publications address secure federated feature selection for medical data, optimizer-assisted diabetes prediction, distributed intelligence for vehicular edge-cloud systems, adaptive multiobjective particle swarm optimization, and anomaly-based intrusion detection. Together, these works reflect the application of machine-learning and optimization techniques to distributed computing, healthcare analytics, multimodal optimization, and cybersecurity problems.[2] [3] [4] [5]

Research Impact

The supplied bibliometric information indicates substantial citation activity. The Scopus profile records 11,693 citations across 169 documents with an h-index of 57.[1] The supplied Google Scholar information additionally reports 16,370 citations and an h-index of 174. These figures should be interpreted according to the respective database coverage, indexing policies, and update dates.

The supplied honors record includes recognition among the world’s top 2% most-cited scientists in 2022 and 2023, as well as research awards at provincial and institutional levels. The record also notes research scholarships associated with TUBITAK Turkey.

Award Suitability

Farhad Soleimanian Gharehchopogh’s combination of research output, citation impact, interdisciplinary applications of machine learning and optimization, and sustained academic activity provides a documented basis for consideration for an Innovative Research Award in the field of database science and computational intelligence. His recent work further demonstrates continued engagement with contemporary distributed, medical, optimization, and cybersecurity applications.

Conclusion

Farhad Soleimanian Gharehchopogh’s academic profile reflects a sustained contribution to computer engineering research, particularly in machine learning, data mining, optimization, artificial intelligence, and database-oriented computational methods. His publication record, reported citation metrics, teaching experience, research administration, and documented recognitions collectively support his consideration for recognition through the International Database Scientist Awards.

References

  1. Elsevier. (n.d.). Scopus author details: Farhad Soleimanian Gharehchopogh, Author ID 36650599500. Scopus.https://www.scopus.com/authid/detail.uri?authorId=36650599500
  2. Abdulsalami, A. O., Gharehchopogh, F. S., Abdullahi, M., Abd Elaziz, M., et al. (2026). A secure federated feature selection framework for horizontally distributed medical data. Information Processing & Management, 63(8), 104938.https://doi.org/10.1016/j.ipm.2026.104938
  3. Valilou, M., Valilou, S., & Gharehchopogh, F. S. (2026). An enhanced medical prediction model for diabetes using grey wolf optimizer-assisted wrapper-based algorithms. Grey Wolf Optimizer, 149–164.
  4. Khoshvaght, P., Haider, A., Rahmani, A. M., Gharehchopogh, F. S., Arasteh, B., et al. (2026). A distributed intelligence framework for microservice-oriented task offloading and resource allocation in vehicular edge-cloud networks. Computers and Electrical Engineering, 136, 111221.
  5. Abdullahi, M. S., Maocai, W., Gharehchopogh, F. S., & Abdulsalami, A. O. (2026). Dynamic topology multiobjective particle swarm optimization algorithm with adaptive Levy-flight for solving multimodal problems. Cluster Computing, 29(4), 261.

Shengjie Bai | Machine Learning on Databases | Best Researcher Award

Published on by International Database Scientist Awards

Best Researcher Award

Shengjie Bai
Affiliation Xi’an Jiaotong University
Country China
Scopus ID 57202011091
Documents 25
Citations 662
h-index 11
Subject Area Machine Learning on Databases
Event International Database Scientist Awards
Google Scholar Zc0CCDQAAAAJ
ORCID 0000-0002-2023-6841

Shengjie Bai,
Xi’an Jiaotong University, China.

Shengjie Bai, affiliated with Xi’an Jiaotong University, China, is a recognized researcher in the field of machine learning applied to database systems. His scholarly contributions have been acknowledged through the Best Researcher Award at the International Database Scientist Awards, reflecting his impact on data-driven methodologies and intelligent database optimization techniques [1].

Abstract

Shengjie Bai’s research focuses on integrating machine learning methodologies into database systems to enhance query performance, data retrieval efficiency, and predictive analytics capabilities. His work contributes to the evolving intersection of artificial intelligence and structured data management [2].

Keywords

Machine Learning, Database Systems, Query Optimization, Data Mining, Predictive Modeling, Intelligent Databases

Introduction

The integration of machine learning techniques into database systems has emerged as a transformative area of research. Shengjie Bai has contributed to this domain by exploring adaptive data models and intelligent indexing strategies that improve computational efficiency and scalability in large-scale databases [3].

Research Profile

With 25 indexed documents and 662 citations, Shengjie Bai has established a measurable academic footprint. His h-index of 11 reflects consistent scholarly influence. His affiliation with Xi’an Jiaotong University provides a strong academic environment supporting interdisciplinary innovation [1].

Research Contributions

Bai’s contributions include advancements in query optimization algorithms using machine learning, automated database tuning systems, and predictive data analytics models. His work demonstrates practical implications for improving database efficiency in real-world applications [2].

Publications

His publications span peer-reviewed journals and conference proceedings, focusing on data-driven database enhancements, machine learning integration, and scalable data architectures. These works contribute to ongoing developments in intelligent data systems [3].

Research Impact

The citation record and academic engagement of Shengjie Bai indicate a growing influence in the domain of machine learning on databases. His research supports improved decision-making processes and enhances computational intelligence in database environments [2].

Award Suitability

The Best Researcher Award recognizes individuals demonstrating impactful contributions and measurable research outcomes. Shengjie Bai’s publication record, citation metrics, and domain-specific innovations align with the selection criteria of the International Database Scientist Awards [4].

Conclusion

Shengjie Bai’s academic work highlights the importance of integrating machine learning with database systems to address modern data challenges. His recognition through the Best Researcher Award reflects both scholarly achievement and practical relevance in advancing intelligent database technologies [4].

References

  1. Elsevier. (n.d.). Scopus author details: Shengjie Bai, Author ID 57202011091. Scopus.
    https://www.scopus.com/pages/authors/57202011091
  2. Han, J., Pei, J., & Kamber, M. (2011). Data Mining: Concepts and Techniques. Elsevier.
    https://doi.org/10.1016/B978-0-12-381479-1.00001-0
  3. Stonebraker, M. (2018). The case for learned database systems. Communications of the ACM.
    https://doi.org/10.1145/3183713
  4. International Database Scientist Awards. (n.d.). Award evaluation criteria and recognition standards.
    https://databasescientist.org/