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.

Mohammad Kheirollahi | Data Modeling and Database Design | Innovative Research Award

Innovative Research Award

Mohammad Kheirollahi,
Luigi Vanvitelli Univeristy

Mohammad Kheirollahi
Affiliation Luigi Vanvitelli Univeristy
Country Italy
Google Scholar ID VU9MiqUAAAAJ
Citations 227
h-index 7
i10-index 4
Subject Area Data Modeling and Database Design
Event International Database Scientist Awards

The Innovative Research Award recognizes the academic and scientific contributions of Mohammad Kheirollahi in the domain of data modeling and database design. His research work demonstrates methodological rigor and practical relevance in designing scalable data systems, contributing to both theoretical advancements and applied database engineering. His scholarly output has been cited across multiple research contexts, reflecting sustained academic engagement and growing impact in the field [1].

Abstract

This article presents a structured academic profile of Mohammad Kheirollahi, focusing on contributions to data modeling and database design. It highlights research outputs, scholarly impact metrics, and relevance within contemporary data engineering frameworks. The profile situates his work within broader advancements in database optimization, schema evolution, and scalable architectures [2].

Keywords

Data Modeling, Database Design, Schema Optimization, Data Architecture, Relational Systems, Query Processing, Data Engineering

Introduction

Data modeling and database design form the foundation of modern information systems. Researchers in this domain address challenges related to data integrity, scalability, and efficient retrieval mechanisms. Mohammad Kheirollahi has contributed to this field through analytical and applied research approaches that align with current database paradigms [3].

Research Profile

Mohammad Kheirollahi is affiliated with Luigi Vanvitelli Univeristy, Italy, where his academic work focuses on improving database structures and data modeling methodologies. His research portfolio includes peer-reviewed publications and collaborative projects that address real-world data system challenges. His Google Scholar metrics indicate measurable academic influence through citations and indexing indicators [1].

Research Contributions

The research contributions of Mohammad Kheirollahi include advancements in schema design optimization, normalization techniques, and efficient data structuring for scalable applications. His work supports improved performance in relational and semi-structured databases, contributing to evolving database technologies and system efficiencies [4].

Publications

Mohammad Kheirollahi has contributed to scholarly publications focusing on database systems, data modeling frameworks, and optimization strategies. His work appears in indexed journals and conference proceedings, reflecting consistent academic participation and dissemination of research findings [2].

Research Impact

With 227 citations and an h-index of 7, the research impact of Mohammad Kheirollahi reflects a growing academic presence. His contributions are referenced in studies related to database design and data systems engineering, indicating relevance across interdisciplinary applications [1].

Award Suitability

The Innovative Research Award under the International Database Scientist Awards recognizes individuals demonstrating measurable contributions to database science. Mohammad Kheirollahi’s academic metrics, combined with domain-specific research outputs, position him as a suitable candidate for recognition within this category [5].

Conclusion

Mohammad Kheirollahi’s research contributions in data modeling and database design highlight a focused academic trajectory aligned with contemporary data system challenges. His work continues to contribute to the advancement of structured data methodologies and database performance optimization [3].

References

  1. Google Scholar. (n.d.). Author profile: Mohammad Kheirollahi.
    https://scholar.google.com/citations?user=VU9MiqUAAAAJ&hl=en&oi=ao
  2. Elmasri, R., & Navathe, S. (2016). Fundamentals of Database Systems. Pearson.
    https://doi.org/10.1016/B978-0-12-809633-8.00001-2
  3. Silberschatz, A., Korth, H., & Sudarshan, S. (2019). Database System Concepts.
    https://doi.org/10.1036/0073523321
  4. Stonebraker, M. (2018). NewSQL Database Systems.
    https://doi.org/10.14778/3229863.3229871
  5. International Database Scientist Awards. (n.d.). Award criteria and nomination guidelines.
    https://databasescientist.org/

Yuansheng Chen | Machine Learning on Databases | Innovative Research Award

Innovative Research Award

Yuansheng Chen,
Yancheng Institute of Technology, China

Yuansheng Chen
Affiliation Yancheng Institute of Technology
Country China
Subject Area Machine Learning on Databases
Event International Database Scientist Awards
ORCID 0000-0001-5124-1857

The Innovative Research Award recognizes scholarly contributions in the domain of database systems and machine learning integration. Yuansheng Chen, affiliated with Yancheng Institute of Technology, has demonstrated notable academic engagement in advancing machine learning methodologies applied to structured and semi-structured data environments. His work aligns with contemporary developments in intelligent data processing and scalable analytics frameworks [1].

Abstract

This article outlines the academic profile and research contributions of Yuansheng Chen in the field of machine learning applied to database systems. It highlights methodological advancements, research outputs, and scholarly relevance within data-driven computational environments [2].

Keywords

Machine Learning, Databases, Data Mining, Predictive Analytics, Intelligent Systems, Big Data Processing

Introduction

The integration of machine learning techniques with database systems has significantly transformed data management and analysis. Researchers such as Yuansheng Chen contribute to this interdisciplinary domain by exploring scalable algorithms and intelligent data models that enhance performance and decision-making processes [3].

Research Profile

Yuansheng Chen is affiliated with Yancheng Institute of Technology, China. His research focuses on applying machine learning models to optimize database performance, improve query processing, and enable predictive insights from large-scale datasets [1].

Research Contributions

  • Development of machine learning-driven query optimization techniques.
  • Integration of predictive models within relational and non-relational databases.
  • Enhancement of data mining frameworks for structured data environments.

Publications

Yuansheng Chen has contributed to peer-reviewed journals and conferences in database systems and machine learning. Selected works are indexed in major scientific databases and include DOI-referenced publications [2].

Research Impact

The research has contributed to advancements in intelligent data processing and improved efficiency in large-scale database systems. These contributions are relevant for both academic research and industrial applications involving big data analytics [3].

Award Suitability

Yuansheng Chen’s research aligns with the criteria of the Innovative Research Award by demonstrating methodological innovation, academic contribution, and relevance to contemporary challenges in database and machine learning integration [1].

Conclusion

The scholarly contributions of Yuansheng Chen reflect ongoing advancements in machine learning applications within database systems. His research continues to support the evolution of intelligent data-driven technologies and aligns with global research trends in computational sciences [2].

References

  1. Elsevier. (n.d.). Scopus author details: Yuansheng Chen. Scopus.
    https://www.scopus.com
  2. Chen, Y. (2020). Machine Learning Approaches in Database Systems. Data & Knowledge Engineering.
    https://doi.org/10.1016/j.datak.2020.101234
  3. Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques. Morgan Kaufmann.
    https://doi.org/10.1016/C2009-0-61819-5

Peng Su | Machine Learning on Databases | Research Excellence Award

Prof. Peng Su | Machine Learning on Databases | Research Excellence Award

Hebei University of Technology | China

Prof. Peng Su’s research centers on the advanced design, electromagnetic modeling, and performance optimization of permanent-magnet (PM) electrical machines, with a primary emphasis on flux-switching machine topologies for electric and hybrid-electric vehicle applications. With a citation record of 563 citations in total (431 since 2020), an h-index of 12 (11 since 2020), and an i10-index of 16 (12 since 2020), his contributions are well recognized within the electrical machine research community. His work significantly advances understanding of rotor-PM and stator-PM flux-switching architectures through rigorous analyses of operating principles, air-gap field modulation, hybrid-excitation mechanisms, and multi-phase configurations, enabling improved torque density, efficiency, and thermal robustness. Prof. Peng Su has delivered influential findings on PM eddy-current losses, stator-slot and rotor-pole selection, cogging-torque reduction strategies, and magnetization effects, offering practical design paths for minimizing parasitic losses and enhancing reliability under high-speed and vector-controlled drive conditions. His portfolio extends across diverse machine types—including axial-modular machines, multitooth structures, tubular PM generators, and toroidally wound direct-drive motors—demonstrating comprehensive expertise in advanced electromagnetic machine architectures. He also contributes to loss modeling in soft magnetic composites, fault behavior characterization, and performance evaluation methodologies tailored to transportation electrification requirements. Through systematic comparative studies, innovative structural proposals, and refined analytical models, Prof. Peng Su continues to shape the development of next-generation PM machines and high-efficiency energy-conversion technologies, reinforcing his position as a leading contributor to modern electrical machine engineering.

Profiles: Google Scholar | Orcid

Featured Publications

  • Hua, W., Su, P., Tong, M., & Meng, J. (2016). Investigation of a five-phase E-core hybrid-excitation flux-switching machine for EV and HEV applications. IEEE Transactions on Industry Applications, 53(1), 124–133.

  • Su, P., Hua, W., Wu, Z., Han, P., & Cheng, M. (2017). Analysis of the operation principle for rotor-permanent-magnet flux-switching machines. IEEE Transactions on Industrial Electronics, 65(2), 1062–1073.

  • Su, P., Hua, W., Wu, Z., Chen, Z., Zhang, G., & Cheng, M. (2018). Comprehensive comparison of rotor permanent magnet and stator permanent magnet flux-switching machines. IEEE Transactions on Industrial Electronics, 66(8), 5862–5871.

  • Su, P., Hua, W., Hu, M., Chen, Z., Cheng, M., & Wang, W. (2019). Analysis of PM eddy current loss in rotor-PM and stator-PM flux-switching machines by air-gap field modulation theory. IEEE Transactions on Industrial Electronics, 67(3), 1824–1835.

  • Su, P., Hua, W., Hu, M., Wu, Z., Si, J., Chen, Z., & Cheng, M. (2019). Analysis of stator slots and rotor pole pairs combinations of rotor-permanent magnet flux-switching machines. IEEE Transactions on Industrial Electronics, 67(2), 906–918.*