Yazar "Alfaras, Mohammed Shukur" seçeneğine göre listele

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    A review of advancements in driver emotion detection: deep learning approaches and dataset analysis
    (Institute of Electrical and Electronics Engineers Inc., 2024) Alfaras, Mohammed Shukur; Karan, Oǧuz
    This review paper delves into the rapidly evolving field of driver emotion detection, with a specific focus on the contributions of deep learning methodologies and the diverse datasets that facilitate this research. Facial emotion detection and recognition is a highly dynamic and challenging field in Machine Learning (ML) and Artificial Intelligence (AI). It has gained attraction for several decades, but it is extremely challenging due to the intrinsic complexities associated with understanding and interpreting human emotions. Understanding and responding to a driver's emotions is increasingly important when autonomous or assisted driving becomes common. To ensure safety, comfort, and optimal interaction between the driver and the car's systems, predicting the emotional state of the driver is essential, also of great significance in practical applications. We give a comprehensive survey of the state-of-the-art driver emotion recognition works that can effectively make use of the recent deep-learning approaches to identify complex emotional cues. Moreover, we explain a variety of datasets that play a vital role in flourishing this field along with the analysis of their effect, like AffectNet, CK+, and EMOTIC. Via this survey we try to investigate the challenges authors are faced with involving this field, e.g., concerns about data privacy, real-time processing demands, and the need for interdisciplinary collaboration. More importantly, the potential of these technologies to improve the driving experience and road safety has been highlighted. We hope this survey can benefit researchers and practitioners to have more insights and provide directions for advancing drivers' emotions.
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    OPTIMIZING ROAD SAFETY: THE ROLE OF GEOGRAPHIC INFORMATION SYSTEMS (GIS) IN TRAFFIC ACCIDENT ANALYSIS AND PREDICTION
    (Faculty of Engineering, University of Kragujevac, 2025) Alfaras, Mohammed Shukur; Karan, Oğuz; Kurnaz, Sefer
    This study investigates the application of Geographic Information Systems (GIS) in traffic accident analysis and prediction. By integrating GIS with deep learning techniques, the research highlights how spatial data management and analysis can enhance road safety. Key objectives include identifying accident hotspots, optimizing traffic control systems, and improving emergency response. The methodology involves a comprehensive review of existing literature, emphasizing GIS's role in data integration, spatial analysis, and predictive modeling. Findings demonstrate that GIS significantly contributes to understanding traffic patterns, predicting accidents, and formulating targeted safety interventions. Challenges such as data complexity, real-time processing, and model interpretability are addressed, offering future directions for leveraging GIS in road safety management. The study concludes that GIS, combined with advanced analytics, presents a powerful tool for reducing traffic accidents and enhancing overall traffic safety.