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        Advanced deep regression models for smart operation of the oil and gas industry 

        Hosseini, Siavash (2023)
        The first industrial revolution in the early 18th century largely exploited steam power to replace animal labor. Since then, there has been rapid development in industrial operations. Now, the world has come to the brink ...
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        Advancing object detection models: an investigation focused on small object detection in complex scenes 

        Sundaralingam, Harish (2025)
        Small object detection remains a persistent challenge in computer vision, especially in safetycritical applications, such as autonomous driving and aerial surveillance, where objects of interest often occupy only a few ...
      • Development of an advanced thermal imaging-based human fall detection system 

        Silver, Christopher (2024)
        Falls represent a significant risk to the elderly population, often leading to severe injuries or fatalities. Automatic fall detection systems (FDS) are critical for mitigating these risks; however, existing solutions, ...
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        An efficient CNN-BiLSTM model for multi-class intracranial hemorrhage classification 

        Genereux, Kevin (2023)
        Intracranial hemorrhage (ICH) refers to a type of bleeding that occurs within the skull. ICH may be caused by a wide range of pathology, including, trauma, hypertension, cerebral amyloid angiopa- thy, and cerebral ...
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        Enhancing semantic segmentation: architectural innovations and strategies for label-efficient learning 

        Suresh, Tharrengini (2025)
        Semantic segmentation is a fundamental component of modern computer vision applications. Although supervised learning models have achieved state-of-the-art performance in this domain, they rely heavily on large volumes ...
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        An improved semi-supervised learning framework for Image semantic segmentation 

        Jahan, Nusrat (2024)
        Traditional supervised learning methods depend heavily on labeled data, which is both costly and time-intensive to acquire. Self-supervised learning approaches present a promising alternative to supervised learning, ...

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