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    An Attention-Based ResNet Architecture for Acute Hemorrhage Detection and Classification: Toward a Health 4.0 Digital Twin Study by Aftab Hussain (1385985)

    Published 2022
    “…The existing pre-trained convolutional neural network (CNN) models are state-of-the-art for ICH classification. However, they employ poor feature extraction techniques which hinder overall model performance. …”
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    On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network by Nasru Minallah (9427338)

    Published 2020
    “…<p>This research work aims to develop a deep learning-based crop classification framework for remotely sensed time series data. …”
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    Comparing deep learning models for low-light natural scene image enhancement and their impact on object detection and classification: Overview, empirical evaluation, and challenges by Al Sobbahi, Rayan

    Published 2022
    “…Fourth, we present a comparative analysis for object detection and classification performance achieved by 4 different object detection models applied on LLIs and their enhanced counterparts. …”
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    An Arabic social media based framework for incidents and events monitoring in smart cities by Alkhatib, Manar

    Published 2019
    Subjects: “…Smart cities Sustainable cities Incident management Social media Arabic language Text classification…”
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    Fault detection and classification in hybrid energy-based multi-area grid-connected microgrid clusters using discrete wavelet transform with deep neural networks by S. N. V. Bramareswara Rao (21768302)

    Published 2024
    “…<p dir="ltr">Microgrid control and operation depend on fault detection and classification because it allows quick fault separation and recovery. …”
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    EEG-Based Multi-Level Mental State Classification Using Partial Directed Coherence and Graph Convolutional Networks: Impact of Binaural Beats on Stress Mitigation by Badr, Yara

    Published 2025
    “…PDC connectivity showed a shift to the temporal region during mitigation, indicating a return to a more balanced state. GCN classification achieved accuracies of 76.43 ± 9.01% and 76.32 ± 7.79% for each group, and 76.37 ± 8.40% for a common baseline. …”
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