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Peer-reviewed research in artificial intelligence, computer vision, and automation systems
My research interests focus on the intersection of artificial intelligence and industrial automation, with particular emphasis on computer vision for autonomous systems and graph neural networks for intelligent decision-making. I strive to bridge theoretical research with practical industrial applications.
This paper presents an efficient approach for detecting and recognizing traffic lights in autonomous vehicle systems using Convolutional Neural Networks based on the YOLOv3 architecture. The proposed method demonstrates high accuracy and real-time performance under various environmental conditions including different lighting scenarios, weather conditions, and traffic densities. The research contributes to the advancement of safer autonomous driving technology by providing a robust traffic light detection system that can operate reliably in real-world conditions.
This comprehensive survey paper examines the latest advancements in integrating Graph Neural Networks (GNNs) into recommender systems. The research analyzes various GNN architectures, their applications in collaborative filtering and content-based recommendation, and provides a comparative evaluation of performance metrics across different datasets. The paper identifies current challenges, research gaps, and proposes future directions for GNN-based recommender systems, contributing to the growing body of knowledge in this rapidly evolving field.
This comprehensive review examines the integration of UAVs and artificial intelligence in modern precision agriculture. The paper synthesizes emerging technologies, applications, and methodologies for autonomous agricultural systems, including crop monitoring, yield prediction, and resource optimization using AI‑driven analysis and drone‑based data collection.
This paper presents an integrated system combining unmanned ground vehicles (UGVs) with drone‑assisted 3D mapping and deep learning for automated rural road inspection and restoration. The research proposes innovative approaches to infrastructure maintenance using multi‑sensor perception, AI‑based defect identification, and coordinated autonomous systems for improved efficiency and safety in road restoration operations.
Ongoing PhD research on innovative regenerative braking systems for vertical transport and industrial applications. This work develops the VAGRB concept with comprehensive modeling, detailed simulation studies, and planned experimental validation to improve energy recovery, safety, and control performance in vertical motion systems.
Exploring advanced **AI techniques** including deep learning, neural network architectures, and their applications in solving complex real-world problems across various domains.
Developing **vision-based systems** for object detection, recognition, and scene understanding with focus on autonomous vehicles and industrial automation applications.
Investigating **GNN architectures** and their integration in recommender systems, social network analysis, and knowledge graph applications.
Combining embedded systems expertise with **AI capabilities** to develop intelligent edge computing solutions for IoT and industrial automation.
Research in **renewable energy optimization**, smart grid integration, and intelligent energy management systems for sustainable power solutions.
Advancing automation technologies through intelligent **control systems**, predictive maintenance, and Industry 4.0 implementation strategies.
Active participation in academic conferences, workshops, and seminars presenting research findings and engaging with the research community to advance knowledge in automation and AI.
Open to research collaborations, joint publications, and academic partnerships