Integrating Explanations into CNNs by Adopting Spiking Attention Block for Skin Cancer Detection by Inzamam Mashood Nasir, Sara Tehsin, Robertas Damaševičius, Rytis Maskeliūnas

“…By building upon previous research on explainability in dermatology, this work introduces a novel soft attention mechanism, called Convolutional Spiking Attention Module (CSAM), to deep neural architectures, which focuses on enhancing critical elements and reducing noise-inducing features. …”
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Reducing overfitting in vehicle recognition by decorrelated sparse representation regularisation by Wanyu Wei, Xinsha Fu, Siqi Ma, Yaqiao Zhu, Ning Lu

“…Abstract Most state‐of‐the‐art vehicle recognition methods benefit from the excellent feature extraction capabilities of convolutional neural networks (CNNs), which allow the models to perform well on the intra‐dataset. …”
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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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Offline Arabic handwritten word recognition: A transfer learning approach by Mohamed Awni, Mahmoud I. Khalil, Hazem M. Abbas

“…In this paper, we examine the performance of three deep convolution neural networks that have been randomly initialized for recognizing Arabic handwritten words. …”
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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

“…In this paper, we propose a cross-machine IFD method based on a residual full convolutional neural network (ResFCN) transfer learning model, which leverages the time-series features of monitoring data. …”
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Forecasting Day-Ahead Electricity Demand in Australia Using a CNN-LSTM Model with an Attention Mechanism by Laial Alsmadi, Gang Lei, Li Li

“…To address this issue, this paper introduces a novel hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks with an attention mechanism designed to forecast day-ahead electricity demand in Australia. …”
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GANs for data augmentation with stacked CNN models and XAI for interpretable maize yield prediction by Ishaan Seshukumar Pothapragada, Sujatha R

“…The predictive framework is based on the ensemble of one-dimensional convolutional neural network (CNN) learning on the features selected, combining three parallel branches (processing features selected by Decision Tree, XGBoost, and Lasso methods), followed by a stacked refinement with residual connections. …”
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Integrated Modeling and Target Classification Based on mmWave SAR and CNN Approach by Chandra Wadde, Gayatri Routhu, Mark Clemente-Arenas, Surya Prakash Gummadi, Rupesh Kumar

“…Subsequently, the reconstructed images were classified using a Convolutional Neural Network (CNN) algorithm in a Python (3.10.14) environment. …”
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Advancing Early Diagnosis: Predicting Mild Cognitive Impairment Progression in Normal Individuals Using Deep Learning on MRI Features by Abdullah Baktash, Yashar Sarbaz, Saeed Meshgini, Reza Afrouzian

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Development of a Baby Cry Identification System Using a Raspberry Pi-Based Embedded System and Machine Learning by Mohcin Mekhfioui, Wiam Fadel, Fatima Ezzahra Hammouch, Oussama Laayati, Marouan Bouchouirbat, Nabil El Bazi, Amal Satif, Tarik Boujiha, Ahmed Chebak

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A hybrid deep learning framework for early detection of diabetic retinopathy using retinal fundus images by Mishmala Sushith, A. Sathiya, V. Kalaipoonguzhali, V. Sathya

“…This paper presents a novel hybrid deep learning framework that combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for diabetic retinopathy (DR) early detection and progression monitoring using retinal fundus images. …”
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Nonlocal and Local Feature-Coupled Self-Supervised Network for Hyperspectral Anomaly Detection by Degang Wang, Longfei Ren, Xu Sun, Lianru Gao, Jocelyn Chanussot

“…Furthermore, an improved center block masked convolution strengthens NL2Net ’s focus on surrounding background features, enhancing the background modeling effect. …”
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Uncertainty CNNs: A path to enhanced medical image classification performance by Vasileios E. Papageorgiou, Georgios Petmezas, Pantelis Dogoulis, Maxime Cordy, Nicos Maglaveras

“…Numerous deterministic deep learning (DL) methods have been developed to serve as reliable medical imaging tools, with convolutional neural networks (CNNs) being the most widely used approach. …”
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Automated depression detection via cloud based EEG analysis with transfer learning and synchrosqueezed wavelet transform by Sara Bagherzadeh, Mohammad Reza Norouzi, Amirhesam Ghasri, Pouya Tolou Kouroshi, Sepideh Bahri Hampa, Fatemeh Farokhshad, Ahmad Shalbaf

“…This system was optimized through a series of experiments to identify the most accurate model. The experiments employed a pre-trained convolutional neural network, ResNet18, fine-tuned on time–frequency synchrosqueezed wavelet transform (SSWT) images derived from EEG signals. …”
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IL-6-Inducing Peptide Prediction Based on 3D Structure and Graph Neural Network by Ruifen Cao, Qiangsheng Li, Pijing Wei, Yun Ding, Yannan Bin, Chunhou Zheng

“…In order to simultaneously consider node weights and information updates, a dual-channel method combining Graph Attention Network (GAT) and Graph Convolutional Network (GCN) is adopted. Finally, the extracted features from both channels are merged for the classification of IL-6-inducing peptides. …”
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Checkpoint data-driven GCN-GRU vehicle trajectory and traffic flow prediction by Deyong Guan, Na Ren, Ke Wang, Qi Wang, Hualong Zhang

“…The method adopts a checkpoint data-driven approach for data collection, combines graph convolutional neural network (GCN) and gated recurrent unit (GRU) models to more effectively learn and extract spatiotemporal correlation features of vehicle trajectories, which significantly improves the accuracy of vehicle trajectory prediction, and uses the output of the trajectory prediction model to forecast traffic flow more accurately. …”
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Efficient and Motion Correction-Free Myocardial Perfusion Segmentation in Small MRI Data Using Deep Transfer Learning From Cine Images: A Promising Framework for Clinical Implement... by German Garcia-Jara, Angel Jimenez-Molina, Esteban Reyes, Nicolas Tapia-Rivas, Cristobal Ramos-Gomez, Jose De Grazia, Matias Sepulveda

“…After pretraining a U-net convolutional neural network, a special fine-tuning scheme optimizes its performance. …”
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A Novel Framework for Improving Soil Organic Carbon Mapping Accuracy by Mining Temporal Features of Time-Series Sentinel-1 Data by Zhibo Cui, Bifeng Hu, Songchao Chen, Nan Wang, Defang Luo, Jie Peng

“…Despite extensive studies using S-1 data for SOC mapping, most focus on either single or multi-date periods without achieving satisfactory results. …”
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CAFU-Net: A Context-Aware Feature Aggregation Network for Lung Nodule Segmentation by Jiachen Hou, Yingqi Lu, Zhougui Ling, Tao Li, Xiangsuo Fan, Yanna Qin, Qingnan Huang

“…The network significantly enhances the accuracy and robustness of pulmonary nodule segmentation by integrating context-aware information based on a gating mechanism, a shared-weight triple attention mechanism, and a dual-scale selective convolution kernel structure. Quantitative and qualitative experiments were conducted on the publicly available LIDC (The Lung Image Database Consortium) dataset and the private MID-FAHGMU (The Fourth Affiliated Hospital of Guangxi Medical University Medical Imaging Department) dataset. …”
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Associations of greenhouse gases, air pollutants and dynamics of scrub typhus incidence in China: a nationwide time-series study by Haoyue Cao, Jianqiang Han, Weiming Hou, Juxiang Yuan

“…During periods of medium to high risk, Convolutional Neural Networks (CNN) showed that environmental factors performed well in predicting the incidence of scrub typhus. …”
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