Despite promising results, several challenges remain. First, many deep-learning studies rely on laboratory datasets that do not fully represent industrial variability (load changes, sensor placement, environmental noise). Second, there is limited work on computationally efficient architectures suited for edge deployment in resource-constrained monitoring devices. Third, the impact of preprocessing choices (denoising, windowing, transform parameters) on model robustness is not well quantified in the literature.
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In conclusion, Sinha Namrata's research contributions in IEEE Access are a testament to her expertise and dedication to excellence. Her publications in this esteemed journal reflect her passion for innovation and her commitment to advancing knowledge in her field. As a researcher, Namrata serves as an inspiration to others, demonstrating the importance of rigorous research and collaboration in driving technological advancements.