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    Performance evaluation of enhanced deep learning classifiers for person identification and gender classification by Vasu Krishna Suravarapu, Hemprasad Yashwant Patil

    Published 2025-08-01
    “…Similarly, for gender classification, an accuracy of 98.4% and 99.68% using SSA-RTNet was obtained for the UBIPr and UFPR datasets. …”
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    Enhancing Lidar-Based 3D Classification Through an Improved Deep Learning Framework With Residual Connections by Shih-Lin Lin, Jun-Yi Wu

    Published 2025-01-01
    “…Although the increased network complexity and added layers lead to longer training times, the robust performance justifies the computational cost. The proposed VoxNetVR5 architecture and its associated hyperparameter optimizations offer a powerful and efficient solution for processing large-scale, high-dimensional LiDAR data, paving the way for safer and more reliable vehicle perception systems.…”
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