Explainable clinical diagnosis through unexploited yet optimized fine-tuned ConvNeXt Models for accurate monkeypox disease classification by Muhammad Waqar, Zeshan Aslam Khan, Shanzey Tariq Khawaja, Naveed Ishtiaq Chaudhary, Saadia Khan, Khalid Mehmood Cheema, Muhammad Farhan Khan, Syed Sohail Ahmed, Muhammad Asif Zahoor Raja

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Optimisation Techniques for Compact CNN on Embedded Systems for Gesture Recognition by João Carlos Bittencourt, Walber Conceição de Jesus Rocha

“…However, the computational constraints of embedded devices require compact and efficient neural network models, as well as software frameworks and optimisation techniques tailored to their hardware resources. …”
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Domain adaptation spatial feature perception neural network for cross-subject EEG emotion recognition by Wei Lu, Wei Lu, Xiaobo Zhang, Xiaobo Zhang, Lingnan Xia, Hua Ma, Tien-Ping Tan

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Automated Recognition of Abnormalities in Gastrointestinal Endoscopic Images – Evaluation of an AI Tool for Identifying Polyps and Other Irregularities by Weronika Jarych, Elżbieta Tokarczyk, Patryk Iglewski, Daria Ziemińska, Karina Motolko, Rafał Burczyk, Konrad Duszyński, Michał Kociński, Jan Reinald Wendt

“…In conclusion, the study highlights the promising role of AI in gastrointestinal endoscopy while underscoring the importance of continued research, algorithmic refinement, and the establishment of regulatory frameworks to fully harness its clinical potential. …”
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A Low-Complexity Transformer-CNN Hybrid Model Combining Dynamic Attention for Remote Sensing Image Compression by L. L. Zhang，X. J. Wang，J. H. Liu, Q. Z. Fang

“…To address these issues, we propose a new low-complexity end-to-end image compression framework combining CNN and transformer. This framework includes two critical modules: the Dynamic Attention Model (DAM) and the Hyper-Prior Hybrid Attention Model (HPHAM). …”
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Deep nested U-structure network with frequency attention for building semantic segmentation by Khaled Moghalles, Zaid Al-Huda, Dalal AL-Alimi, Yeong Hyeon Gu, Mugahed A. Al-antari

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Research progress of digital image forensic techniques based on deep learning by Tong QIAO, Hongwei YAO, Binmin PAN, Ming XU, Yanli CHEN

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Graph-Driven Deep Reinforcement Learning for Vehicle Routing Problems with Pickup and Delivery by Dapeng Yan, Qingshu Guan, Bei Ou, Bowen Yan, Hui Cao

“…In this study, we propose a graph-driven deep reinforcement learning (GDRL) approach that employs an encoder–decoder framework to address this shortcoming. The encoder incorporates stacked graph convolution modules (GCMs) to aggregate neighborhood information via updated edge features, producing enriched node representations for subsequent decision-making. …”
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A Two-Stage Method for Diagnosing COVID-19, Leveraging CNN, and Transfer Learning on CT Scan Images by Touba torabipour, Abolfazl Gandomi, Mohammad Ghanimi

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FFT-RDNet: A Time–Frequency-Domain-Based Intrusion Detection Model for IoT Security by Bingjie Xiang, Renguang Zheng, Kunsan Zhang, Chaopeng Li, Jiachun Zheng

“…To address this, we propose FFT-RDNet, a lightweight IDS framework leveraging depthwise separable convolution and frequency-domain feature fusion. …”
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DGL-STFA: Predicting lithium-ion battery health with dynamic graph learning and spatial–temporal fusion attention by Zheng Chen, Quan Qian

“…The framework employs multi-scale convolutional neural networks to capture diverse temporal patterns, a self-attention mechanism to construct dynamic adjacency matrices that adapt over time, and a temporal attention mechanism to identify and prioritize key moments that influence battery degradation. …”
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Seismic Waveform Feature Extraction and Reservoir Prediction Based on CNN and UMAP: A Case Study of the Ordos Basin by Lifu Zheng, Hao Yang, Guichun Luo

“…To address these limitations, this paper proposes a novel framework combining Convolutional Neural Network (CNN) and Uniform Manifold Approximation and Projection (UMAP) for seismic waveform feature extraction and analysis. …”
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The analysis of landscape design and plant selection under deep learning by Lian Li, JiYon Lee

“…Abstract This paper explores the application of deep learning (DL) techniques in landscape design and plant selection, aiming to enhance design efficiency and quality through automated plant leaf image recognition (PLIR). A novel framework based on Convolutional Neural Network (CNN) and Fully Convolutional Network (FCN) is proposed. …”
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Hybrid Deep Learning Architecture with Adaptive Feature Fusion for Multi-Stage Alzheimer’s Disease Classification by Ahmad Muhammad, Qi Jin, Osman Elwasila, Yonis Gulzar

“…Methods: This research proposes a novel deep learning framework for multi-stage Alzheimer’s disease (AD) classification using T1-weighted MRI scans. …”
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Enhanced Blockchain-Based Data Poisoning Defense Mechanism by Song-Kyoo Kim

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Extraction of built-up areas using Sentinel-1 and Sentinel-2 data with automated training data sampling and label noise robust cross-fusion neural networks by Yu Li, Patrick Matgen, Marco Chini

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An Adaptive Methodology for Constructing Domain-Specific Sentiment Lexicons Based on Chinese Social Media Data by Xue Xu, Haidong Liu, Lei Liu

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A Deep Learning-Driven CAD for Breast Cancer Detection via Thermograms: A Compact Multi-Architecture Feature Strategy by Omneya Attallah

“…The suggested framework mitigates the limitations of current CAD systems, which frequently utilize intricate convolutional neural network (CNN) structures and resource-intensive preprocessing, by incorporating streamlined CNN designs, transfer learning strategies, and multi-architecture ensemble methods. …”
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Towards scalable medical image compression using hybrid model analysis by Shunlei Li, Jiajie Lu, Yingbai Hu, Leonardo S. Mattos, Zheng Li

“…Experimental results confirm the efficiency of our framework for storing and retrieving medical images in healthcare applications. …”
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Capacity Constraint Analysis Using Object Detection for Smart Manufacturing by Hafiz Mughees Ahmad, Afshin Rahimi, Khizer Hayat

“…In this study, we propose a novel framework for capacity constraint analysis that identifies bottlenecks in production facilities and conducts cycle time studies using an end-to-end pipeline. …”
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