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    Post-Disaster Building Damage Segmentation Using Convolutional Neural Networks by Revaldi Rahmatmulya, Agung Teguh Wibowo Almais, Mokhamad Amin Hariyadi

    Published 2025-07-01
    “…Effective and efficient actions are needed to assist in the recovery following natural disasters, one of which is aiding in the identification of building damage levels post-disaster. To address this issue, this research proposes a system capable of performing segmentation to determine the level of building damage post-natural disaster using convolutional neural network methods. …”
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    Lifetime prediction of epoxy coating using convolutional neural networks and post processing image recognition methods by Fandi Meng, Yufan Chen, Jianning Chi, Huan Wang, Fuhui Wang, Li Liu

    Published 2024-11-01
    “…Initially, a targeted image recognition approach containing convolutional neural network (CNN) and post-processing was constructed for the crack area detection. …”
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    Predicting Return-to-Sport Timeline: Classifying Anterior Cruciate Ligament Health Levels Post-Reconstruction Surgery Using Convolutional Neural Networks by Zeinab Jafari, Ali Sharifnezhad, Mohammad Razi, Mohammad Haghpanahi, Arash Maghsoudi

    Published 2025-01-01
    “…This study introduces a deep convolutional neural network (DCNN) designed to classify ACL health levels in injured athletes, aiding in RTS estimation. …”
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    Computer-aided cholelithiasis diagnosis using explainable convolutional neural network by Dheeraj Kumar, Mayuri A. Mehta, Ketan Kotecha, Ambarish Kulkarni

    Published 2025-02-01
    “…Although several computer-aided cholelithiasis diagnosis approaches have been introduced in the literature, their use is limited because Convolutional Neural Network (CNN) models are black box in nature. …”
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    Convolutional Neural Network Compression via Dynamic Parameter Rank Pruning by Manish Sharma, Jamison Heard, Eli Saber, Panagiotis Markopoulos

    Published 2025-01-01
    “…While Convolutional Neural Networks (CNNs) excel at learning complex latent-space representations, their over-parameterization can lead to overfitting and reduced performance, particularly with limited data. …”
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    Multichannel convolutional transformer for detecting mental disorders using electroancephalogrpahy records by Mamadou Dia, Ghazaleh Khodabandelou, Syed Muhammad Anwar, Alice Othmani

    Published 2025-05-01
    “…Our proposed model, the multichannel convolutional transformer, integrates the strengths of both convolutional neural networks and transformers. …”
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