Hybrid of DSR-GAN and CNN for Alzheimer disease detection based on MRI images by Sarah Oraby, Ahmed Emran, Basel El-Saghir, Saeed Mohsen

“…Also, the performance of the DSR-GAN is assessed using three different metrics: structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and multi-scale structural similarity index measure (MS-SSIM). …”
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An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection by Sarah Tipper, Hany F. Atlam, Harjinder Singh Lallie

“…This paper also evaluates model performance across different datasets, identifies key factors influencing detection accuracy, and explores how CNN-LSTM models can be optimised. …”
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Automating skin cancer screening: a deep learning by Nada M. Rashad, Noha MM. Abdelnapi, Ahmed F. Seddik, M. A. Sayedelahl

“…This model is able to recognize seven different kinds of skin lesions. On the ISIC dataset, an analysis has been done. …”
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Deep Learning for Automated Kellgren–Lawrence Grading in Knee Osteoarthritis Severity Assessment by Daniel Nasef, Demarcus Nasef, Viola Sawiris, Peter Girgis, Milan Toma

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Modeling Parking Occupancy Using Algorithm of 3D Visibility Network by Matej Babic, Branko Ster, Ladislav Hluchy, Giuliana Baiamonte, Michele Cali

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Offline Arabic handwritten word recognition: A transfer learning approach by Mohamed Awni, Mahmoud I. Khalil, Hazem M. Abbas

“…We carried out four different sets of experiments using two popular offline Arabic handwritten word datasets: the AlexU-W and the IFN/ENIT (v2.0p1e) to figure out the most effective way of applying transfer learning. …”
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Application of Deep Learning to Identify Flutter Flight Testing Signals Parameters and Analysis of Real F-18 Flutter Flight Test Data by Sami Abou-Kebeh, Roberto Gil-Pita, Manuel Rosa-Zurera

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Enhancing signal-to-noise ratio in real-time LED-based photoacoustic imaging: A comparative study of CNN-based deep learning architectures by Avijit Paul, Srivalleesha Mallidi

“…Our findings reveal that while U-Net architectures generally exhibit comparable performance, the Dense U-Net model shows promise in denoising different noise distributions in the PA image. Notably, hierarchical depth variations did not significantly impact performance, emphasizing the efficacy of the standard U-Net architecture for practical applications. …”
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A Cross-Machine Intelligent Fault Diagnosis Method with Small and Imbalanced Data Based on the ResFCN Deep Transfer Learning Model by Juanru Zhao, Mei Yuan, Yiwen Cui, Jin Cui

“…Existing transfer learning-based IFD methods typically use data from different operating conditions of the same equipment as the source and target domains for the transfer learning process. …”
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Machine learning of automatic hierarchical multi-label classification method for identifying metal failure mechanisms by Ruitong Han, Chang-Bo Liu, Wanting Sun, Shuai Yu, Haoran Zheng, Lin Deng

“…The method combines the advantages of convolutional neural networks (CNN) and Vision Transformers (ViT) to effectively realize hierarchical feature extraction and classification of SEM images of fracture morphologies, enabling accurate identification of metal failure mechanisms at different scales. …”
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MSALNet: a multi-scale adaptive learning network for high-resolution remote sensing scene classification by Chao Yang, Chengbo Wei, Yiming Zhao, Liming Wang, Peigang Xu, Kunlun Qi, Yuanzheng Shao, Huayi Wu

“…It then learns optimal scale parameters from these features to generate scale-transformed representations tailored to different scene contexts. To ensure seamless integration, the original and scale-transformed features are dynamically aligned and fused across multiple network layers. …”
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MediScan: A Framework of U-Health and Prognostic AI Assessment on Medical Imaging by Sibtain Syed, Rehan Ahmed, Arshad Iqbal, Naveed Ahmad, Mohammed Ali Alshara

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A Novel Framework for Quantum-Enhanced Federated Learning with Edge Computing for Advanced Pain Assessment Using ECG Signals via Continuous Wavelet Transform Images by Madankumar Balasubramani, Monisha Srinivasan, Wei-Horng Jean, Shou-Zen Fan, Jiann-Shing Shieh

“…These transformations capture both temporal and frequency characteristics of pain-induced cardiac variations, providing a comprehensive representation of autonomic nervous system responses to different pain intensities. Our framework processes these CWT images through a sophisticated quantum–classical hybrid architecture, where edge devices perform initial preprocessing and feature extraction while maintaining data privacy. …”
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Multi-Scale Differentiated Network with Spatial–Spectral Co-Operative Attention for Hyperspectral Image Denoising by Xueli Chang, Xiaodong Wang, Xiaoyu Huang, Meng Yan, Luxiao Cheng

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Dynamic Q value wavelet transform for IF estimation and seizure detection via QT plane-CNN framework by Amaya Rose Abraham, Anurag Nishad, Mihir Jaipuria, Abhay Upadhyay, Sudeep Baudha

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A real time monitoring system for accurate plant leaves disease detection using deep learning by Kazi Naimur Rahman, Sajal Chandra Banik, Raihan Islam, Arafath Al Fahim

“…Subsequently, the dataset was partitioned for individual plant disease detection, applying nine different CNN models (custom CNN, VGG16, VGG19, InceptionV3, MobileNet, DenseNet121, Xception, and two hybrid models) to each plant type. …”
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State-of-the-Art Deep Learning Algorithms for Internet of Things-Based Detection of Crop Pests and Diseases: A Comprehensive Review by Jean Pierre Nyakuri, Celestin Nkundineza, Omar Gatera, Kizito Nkurikiyeyezu

“…This research presents a comprehensive review of the state-of-the-art DL architectures integrated with IoT-based systems applied to plant pest and disease detection (PPDD) by investigating different potential approaches that have been employed using DL and IoT up to the year 2024 to address challenges in agriculture. …”
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Multi-Scale Spatial Perception Attention Network for Few-Shot Hyperspectral Image Classification by Yang Li, Jian Luo, Haoyu Long, Qianqian Jin

“…Next, the multi-scale spatial attention (MSSA) module is proposed to capture spatial information at different convolution kernel scales and cascade them to form a more comprehensive representation structure. …”
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MultiV_Nm: a prediction method for 2′-O-methylation sites based on multi-view features by Lei Bai, Fei Liu, Yile Wang, Junle Su, Lian Liu

“…By integrating the powerful local feature extraction ability of convolutional neural networks, the ability of graph attention networks to capture global structural information, and the efficient interaction advantage of cross-attention mechanisms for different features, it deeply explores and integrates multi-view features, and finally realizes the prediction of Nm modification sites. …”
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Data driven assessment of built environment impacts on urban health across United States cities by Siavash Ghorbany, Ming Hu, Siyuan Yao, Matthew Sisk, Chaoli Wang, Kai Zhang, Quynh Camthi Nguyen

“…Yet, a comprehensive analysis across multiple U.S. cities covering different geographical conditions has been missing. …”
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