Breakthrough Research Award
Harun Mindivan
Bilecik Şeyh Edebali University, Turkey
| Harun Mindivan | |
|---|---|
| Affiliation | Bilecik Şeyh Edebali University |
| Country | Turkey |
| Scopus ID | 6506588546 |
| Documents | 59 |
| Citations | 657 |
| h-index | 13 |
| Subject Area | Machine Learning Algorithms |
| Event | Metallurgical Engineering Awards |
| ORCID | 0000-0003-3948-253X |
Harun Mindivan is a researcher affiliated with Bilecik Şeyh Edebali University in Turkey whose documented scholarly profile includes research associated with machine learning algorithms. The supplied bibliometric record identifies 59 documents, 657 citations, and an h-index of 13 under Scopus Author ID 6506588546. These indicators provide a bibliographic description of the researcher’s indexed publication and citation record and should be interpreted in relation to field, publication year, database coverage, and citation practices. [1]
Abstract
Harun Mindivan is affiliated with Bilecik Şeyh Edebali University and is identified in the supplied academic record with a research focus on machine learning algorithms. His indexed profile reports 59 documents, 657 citations, and an h-index of 13. The research area is situated within the broader field of computational methods, where machine learning algorithms are used to identify patterns, develop predictive models, classify observations, and support data-driven scientific and engineering analysis. Machine learning has become an established research methodology across numerous disciplines, with algorithmic approaches ranging from ensemble learning to deep neural networks and attention-based architectures. [2] [3] This article presents a structured academic recognition profile based on the supplied bibliometric information and publicly provided researcher identifiers.
Keywords
Harun Mindivan; machine learning algorithms; machine learning; computational methods; predictive modeling; data analysis; artificial intelligence; algorithmic research; scholarly impact; Bilecik Şeyh Edebali University.
Introduction
Machine learning algorithms constitute an important area of contemporary computational research. Their development and application involve statistical learning, optimization, pattern recognition, predictive modeling, and computational representation of complex datasets. Classical ensemble methods such as random forests demonstrated the utility of combining multiple decision trees to improve classification and regression performance, while subsequent advances in deep learning expanded the application of multilayer neural architectures to complex data representations. [2] [3]
Research Profile
Harun Mindivan is affiliated with Bilecik Şeyh Edebali University, Turkey. The supplied researcher information identifies Machine Learning Algorithms as the principal subject area. His Scopus Author ID is 6506588546, providing a persistent identifier for the corresponding indexed author profile. The supplied record reports 59 documents, 657 citations, and an h-index of 13. [1] [3]
Research Contributions
The supplied subject classification places Mindivan’s academic profile within machine learning algorithms, a field concerned with constructing computational procedures capable of learning relationships or patterns from data. Research in this area may address algorithm development, model comparison, classification, regression, optimization, feature engineering, data-driven prediction, and computational decision support.[2] [5]
Publications
The supplied Scopus profile reports 59 documents associated with Scopus Author ID 6506588546. A complete publication bibliography is best obtained directly from the indexed author profile because publication metadata, citation counts, document types, and author associations can change as databases are updated. [1] For machine learning research more broadly, influential methodological literature includes work on random forests and deep learning.[4]
Research Impact
The supplied bibliometric record reports 657 citations and an h-index of 13 for the researcher. These measures indicate citation activity associated with the indexed publication record, but their interpretation depends on the disciplinary environment, publication chronology, database coverage, collaboration patterns, and citation practices. [3] [4]
Award Suitability
The Breakthrough Research Award profile recognizes research activity associated with machine learning algorithms and computational research. The supplied academic record identifies Harun Mindivan as a researcher at Bilecik Şeyh Edebali University with 59 Scopus-indexed documents, 657 citations, and an h-index of 13. [1] The recognition is associated with Metallurgical Engineering Awards, an academic recognition platform that provides award-related information and nomination resources.
Conclusion
Harun Mindivan’s supplied academic profile places his research within the field of machine learning algorithms and associates him with Bilecik Şeyh Edebali University in Turkey. The provided Scopus record reports 59 documents, 657 citations, and an h-index of 13. These indicators describe a measurable indexed research record, while the scholarly significance of individual contributions should be assessed through the publications and their methodological and scientific context. [1]
External Links
References
- Elsevier. (n.d.). Scopus author details: Harun Mindivan, Author ID 6506588546. Scopus.
https://www.scopus.com/authid/detail.uri?authorId=6506588546 - H Mindivan, ES Kayali, H Cimenoglu. (2008). Tribological behavior of squeeze cast aluminum matrix composites.
https://www.sciencedirect.com/science/article/pii/S0043164807007739 - H Mindivan, A Efe, AH Kosatepe, ES Kayali. (2014). Fabrication and characterization of carbon nanotube reinforced magnesium matrix composites.
https://www.sciencedirect.com/science/article/pii/S0169433214009015 - H Mindivan, H Çimenoǧlu, ES Kayali. (2003). Microstructures and wear properties of brass synchroniser rings.
https://www.sciencedirect.com/science/article/pii/S0043164803000231 - H Mindivan, M Baydogan, ES Kayali, H Cimenoglu. (2005). Wear behaviour of 7039 aluminum alloy.
https://www.sciencedirect.com/science/article/pii/S104458030400289X