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

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.