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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 ...
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        Design of a polarization reconfigurable and frequency tunable patch antenna system on a magnetic substrate 

        Rabbani, Hassan (2024)
        Modern radio frequency (RF) and microwave components are continuously evolving to meet the demands of new wireless technologies. One such demand is the ability of these components to be agile and smart. Thus, the rationale ...
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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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        Leveraging the use of Liquid Metal Channels to Reconfigure Antennas’ Impedance and Radiation Performance 

        Kishore, Siddharth (2025)
        The advent of liquid metals in the domain of RF system has opened new avenues for the researchers in smart antenna designs. Reconfigurability of antenna’s characteristics has been a keen topic of interest for the past ...
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        A novel current source converter-based ultra-high-power offshore wind energy conversion system 

        Yang, Kaiwen (2025)
        In medium voltage (MV) ultra-high-power (over 10 MW) offshore wind energy conversion systems (WECS), current source converter (CSC)-based series-connected configurations are a good candidate. However, existing CSC-based ...
      • Traveling wave-based fault location in power grids using neural networks 

        Southgate, Jeffery (2025)
        downtime and improving public safety. One practical FL approach involves utilizing traveling waves (TWs) to locate the fault along a transmission line. TW-based FL methods are highly regarded for their speed and resilience ...

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