Research Excellence Award

Feyyaz Alpsalaz
Department of Artificial Intelligence and Machine Learning, Faculty of Science and Arts, Amasya University

Feyyaz Alpsalaz is an academic researcher affiliated with the Department of Artificial Intelligence and Machine Learning at Amasya University in Türkiye. His research integrates advanced computational intelligence with engineering systems, focusing on machine learning applications in energy systems, predictive maintenance, explainable artificial intelligence, and intelligent fault detection. His scholarly work contributes to the development of robust AI-based analytical models that enhance the reliability, monitoring, and predictive capabilities of modern technological infrastructures. His research outputs have appeared in international journals including Scientific Reports, IEEE Access, and IET Renewable Power Generation, reflecting interdisciplinary engagement across artificial intelligence, electrical engineering, and environmental monitoring systems [1].

Abstract

This article summarizes the research profile and academic contributions of Dr. Feyyaz Alpsalaz, a researcher specializing in artificial intelligence and machine learning applications in engineering systems. His work focuses on predictive analytics, hybrid machine learning models, explainable artificial intelligence, and intelligent diagnostics for power systems and environmental monitoring. Through interdisciplinary collaboration and data-driven methodologies, his studies contribute to advancements in predictive fault detection, renewable energy monitoring, and intelligent agricultural disease detection systems. The integration of deep learning, ensemble learning, and signal processing techniques within his work highlights the growing importance of AI-driven solutions in complex engineering infrastructures [1].

Keywords

• Artificial Intelligence
• Machine Learning
• Explainable Artificial Intelligence
• Fault Detection Systems
• Renewable Energy Monitoring
• Predictive Maintenance

Introduction

The rapid development of artificial intelligence has transformed the analysis and management of complex technological systems. Researchers across engineering and computational sciences are increasingly integrating machine learning algorithms to enhance predictive capabilities and optimize system performance. Dr. Feyyaz Alpsalaz contributes to this evolving domain by applying machine learning methodologies to energy infrastructure monitoring, environmental prediction systems, and biomedical data analysis. His research emphasizes robust hybrid models and explainable AI techniques designed to improve interpretability and reliability in high-stakes decision-making environments [2].

Research Profile

Dr. Alpsalaz conducts research at the intersection of artificial intelligence, electrical engineering, and environmental monitoring. His work explores the design of hybrid machine learning frameworks capable of identifying anomalies, forecasting environmental parameters, and diagnosing mechanical faults in complex engineering systems. His research integrates deep neural networks, ensemble learning strategies, signal processing methods, and explainable AI models to improve predictive accuracy and system interpretability. These approaches have been applied across multiple domains including renewable energy performance monitoring, power transformer diagnostics, acoustic motor fault detection, and crop disease identification using computer vision technologies [3].

Research Contributions

• Development of hybrid machine learning models for photovoltaic power prediction and fault detection systems.
• Application of explainable artificial intelligence methods to interpret complex deep learning models in engineering diagnostics.
• Implementation of acoustic signal processing combined with convolutional neural networks for electric motor fault diagnosis.
• Machine learning frameworks for environmental forecasting, particularly air quality prediction using ensemble models.
• Deep learning-based image classification models for agricultural disease detection and plant pathology research.

Publications

1. Hybrid Machine Learning Approach for Enhanced Fault Detection and Power Estimation in Photovoltaic Systems. IET Renewable Power Generation. DOI: https://doi.org/10.1049/rpg2.70153
2. Hybrid Machine Learning Approach for Predicting Power Transformer Failures Using IoT Monitoring and Explainable AI. IEEE Access. DOI: https://doi.org/10.1109/access.2025.3583773
3. Classification of Maize Leaf Diseases with Deep Learning. Chemometrics and Intelligent Laboratory Systems. DOI: https://doi.org/10.1016/j.chemolab.2025.105412
4. Air Quality Forecasting Using Machine Learning. Water, Air, & Soil Pollution. DOI: https://doi.org/10.1007/s11270-025-08122-8
5. Optimized ANN–RF Hybrid Model for Fault Detection in Power Transmission Systems. Scientific Reports. DOI: https://doi.org/10.1038/s41598-025-31008-y
6. Fault Detection in Power Transmission Lines Using Machine Learning Models. Maintenance & Reliability. DOI: https://doi.org/10.17531/ein/203949
7. Acoustic-Based Fault Diagnosis of Electric Motors Using CNNs. Scientific Reports. DOI: https://doi.org/10.1038/s41598-025-33269-z
8. Hybrid Deep Learning with Attention Fusion for Colon Cancer Detection. Scientific Reports. DOI: https://doi.org/10.1038/s41598-025-29447-8
9. Hybrid Deep Learning Model for Maize Leaf Disease Classification. New Zealand Journal of Crop and Horticultural Science.
10. Detection of Arc Faults in Transformer Windings via Transient Signal Analysis. Applied Sciences. DOI: https://doi.org/10.3390/app14209335

Research Impact

The research contributions of Dr. Alpsalaz demonstrate the growing relevance of artificial intelligence in predictive engineering systems and sustainable infrastructure management. His studies integrate machine learning techniques with engineering diagnostics to improve reliability and predictive maintenance capabilities. Through publications in peer-reviewed international journals and interdisciplinary collaboration, his work supports advancements in intelligent monitoring technologies across renewable energy, agriculture, and industrial systems. These contributions illustrate the practical impact of AI-driven analytical methods in modern scientific and engineering research environments [1].

Award Suitability

Dr. Alpsalaz’s scholarly activities demonstrate interdisciplinary innovation within artificial intelligence applications for engineering systems. His work combines computational intelligence, predictive analytics, and explainable AI frameworks to address real-world challenges in energy infrastructure and environmental monitoring. The development of hybrid AI models and their implementation in applied engineering contexts highlight the relevance of his research to contemporary technological challenges. Such contributions align with the evaluation criteria commonly associated with international research recognition programs focused on artificial intelligence innovation and technological impact [3].

Conclusion

The academic profile of Dr. Feyyaz Alpsalaz reflects the integration of artificial intelligence techniques with complex engineering applications. His research emphasizes hybrid machine learning architectures, explainable AI methodologies, and predictive diagnostic systems designed to enhance reliability across multiple technological domains. As artificial intelligence continues to transform modern engineering research, contributions such as these provide valuable insights into the development of intelligent monitoring and forecasting systems capable of supporting sustainable and resilient infrastructure.

External Links

• ORCID Profile
• Scopus Author Profile
• Google Scholar Profile

References

1. Elsevier. (n.d.). Scopus author details: Feyyaz Alpsalaz, Author ID 59221704100. Scopus.
   https://www.scopus.com/authid/detail.uri?authorId=59221704100
2. Google Scholar. (n.d.). Scholar profile of Feyyaz Alpsalaz.
   https://scholar.google.com.tr/citations?user=EP2ybTEAAAAJ&hl=tr&oi=ao
3. ORCID. (n.d.). ORCID record for Feyyaz Alpsalaz.
   https://orcid.org/0000-0002-7695-6426