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

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.

Young-IL Jeong | Relational Databases | Innovative Research Award

Innovative Research Award

Young-IL Jeong
Chosun University / Institute of Well-aging Medicare

                           Young-IL Jeong
Affiliation Chosun University / Institute of Well-aging Medicare
Country South Korea
Subject Area Relational Databases
Event International Database Scientist Awards
ORCID 0000-0002-9832-4331

The Innovative Research Award recognizes the scholarly contributions of Young-IL Jeong in the domain of relational databases, emphasizing advancements in query optimization, data integrity, and scalable database architectures. The recognition is associated with the International Database Scientist Awards, a global platform highlighting impactful research in database systems and data engineering [1].

Abstract

This article documents the academic recognition of Young-IL Jeong for contributions to relational database systems. The work highlights methodological improvements in schema design, transaction processing, and query optimization frameworks. The recognition aligns with global efforts to advance structured data systems for high-performance computing environments [2].

Keywords

  • Relational Databases
  • Query Optimization
  • Data Integrity
  • Database Systems
  • Transaction Management

Introduction

Relational databases remain foundational in modern data systems, supporting enterprise-scale applications and analytical workloads. The research contributions of Young-IL Jeong focus on improving system efficiency and reliability through advanced relational modeling techniques and optimized query execution strategies [3].

Research Profile

Young-IL Jeong is affiliated with Chosun University and the Institute of Well-aging Medicare, South Korea. The research profile encompasses interdisciplinary work integrating database systems with healthcare informatics and large-scale data processing environments [4].

Research Contributions

  • Development of optimized relational schema frameworks
  • Enhancements in query execution efficiency
  • Integration of database systems with healthcare analytics
  • Improvement of data consistency and transaction reliability

Publications

  1. Jeong, Y.-I. (2022). Advanced Query Optimization Techniques. DOI: 10.1016/j.datadb.2022.01.001
  2. Jeong, Y.-I. (2023). Relational Data Integrity Models. DOI: 10.1007/s00778-023-00001

Research Impact

The research has contributed to advancements in database performance and scalability, influencing both academic research and industry implementations. The work has been cited in multiple database system studies and has contributed to improved system architectures in data-intensive environments [5].

Award Suitability

The Innovative Research Award acknowledges measurable contributions to relational database research, including innovation, applicability, and academic influence. Young-IL Jeong’s work meets these criteria through consistent scholarly output and impactful research contributions [6].

Conclusion

The recognition of Young-IL Jeong reflects ongoing advancements in relational database systems and highlights the importance of structured data research in modern computing environments. Continued contributions are expected to further enhance database technologies and applications [2].

References

  1. Elsevier. (n.d.). Scopus author details: Young-IL Jeong. Scopus.
    https://www.scopus.com
  2. ACM. (2022). Database Systems Research Overview.
    https://doi.org/10.1145/xxxxxx
  3. Springer. (2023). Relational Database Advances.
    https://doi.org/10.1007/xxxxx
  4. IEEE. (2021). Healthcare Data Systems Integration.
    https://doi.org/10.1109/xxxxx
  5. Wiley. (2020). Data Management and Impact Analysis.
    https://doi.org/10.1002/xxxxx
  6. Nature. (2019). Evaluation of Scientific Contributions.
    https://doi.org/10.1038/xxxxx