An Interpretable Siamese Attention Res-CNN for Fingerprint Spoofing Detection by Chengsheng Yuan, Zhenyu Xu, Xinting Li, Zhili Zhou, Junhao Huang, Ping Guo

“…This paper proposes a new fingerprint liveness detection method based on Siamese attention residual convolutional neural network (Res-CNN) that offers an interpretative perspective to this challenge. …”
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Predicting noncoding RNA and disease associations using multigraph contrastive learning by Si-Lin Sun, Yue-Yi Jiang, Jun-Ping Yang, Yu-Han Xiu, Anas Bilal, Hai-Xia Long

“…The third step is to use an encoder with a Graph Convolutional Network (GCN) architecture to extract embedding vectors. …”
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Modeling Temperature in the Ecuadorian Paramo Through Deep Learning by Marco Javier Castelo Cabay, Jose Antonio Piedra-Fernandez, Rosa Maria Ayala

“…Six neural network architectures were evaluated: Long short-term memory (LSTM), bidirectional LSTM, LSTM with attention mechanism, gated recurrent unit (GRU), convolutional neural network (CNN), and CNN-LSTM. At the Airport Ambato station, the LSTM with attention mechanism was the most effective, achieving an RMSE of 0.82 and an R-squared of 0.66. …”
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Neural network analysis of mortality risk predictors in patients after acute coronary syndrome by D. A. Shvets, A. Yu. Karasev, M. V. Smolyakov, S. V. Povetkin, V. I. Vishnevsky

“…To solve the classification problems, two types of neural network architectures were used: Multilayer Perceptron (MLP) and Convolutional Neural Networks (CNN). The ratio in the examples for learning and validation was 340/60. …”
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A Novel AI-Based Integrated Cybersecurity Risk Assessment Framework and Resilience of National Critical Infrastructure by Sardar Muhammad Ali, Abdul Razzaque, Muhammad Yousaf, Sardar Sadaqat Ali

“…For feature selection, we used Forward Feature Elimination (FFE), Backward Feature Elimination (BFE), and Recursive Feature Elimination (RFE) to identify the most relevant features. We trained three ML classifiers: Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbors (KNN), along with three DL models: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN). …”
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Prediction of 123I-FP-CIT SPECT Results from First Acquired Projections Using Artificial Intelligence by Wadi’ Othmani, Arthur Coste, Dimitri Papathanassiou, David Morland

“…In this study we aimed to develop a Convolutional Neural Network (CNN) able to predict the outcome of the full examination based on the first acquired projection, and reliably detect normal patients. …”
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Short-term wind power forecasting method for extreme cold wave conditions based on small sample segmentation by Lin Lin, Jinhao Xu, Jianfei Liu, Hao Zhang, Pengchen Gao

“…Then, a cold wave power loss extraction method based on Graph Convolutional Networks (GCN) and Bidirectional Gated Recurrent Units (BiGRU) is introduced for the entire time period. …”
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Toward Adaptive Unsupervised and Blind Image Forgery Localization with ViT-VAE and a Gaussian Mixture Model by Haichang Yin, KinTak U, Jing Wang, Wuyue Ma

“…Most image forgery localization methods rely on supervised learning, requiring large labeled datasets for training. …”
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A novel attention-based deep learning model for improving sentiment classification after the case of the 2023 Kahramanmaras/Turkey earthquake on Twitter by Serpil Aslan, Muhammed Yildirim

“…Twitter has emerged as one of the most widely used platforms for sharing information and updates. …”
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AI-Based Forecasting in Renewable-Rich Microgrids: Challenges and Comparative Insights by Martins Osifeko, Josiah Lange Munda

“…Classical ML models outperformed most DL architectures, including Transformer and Convolutional Neural Network (CNN)-LSTM, which underperformed despite their complexity. …”
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Mathematical Modeling of Cyberattack Defense Mechanism Using Hybrid Transfer Learning With Snow Ablation Optimization Algorithm in Critical Infrastructures by Mohamad Khairi Ishak

“…Industrial control methods are one of the most vital aspects of the cybersecurity of critical infrastructures. …”
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A Deep Learning Framework for Chronic Kidney Disease stage classification by Gayathri Hegde M, P Deepa Shenoy, Venugopal KR, Arvind Canchi

“…To evaluate the proposed method, eight DL models — Feedforward Neural Network, Recurrent Neural Network, Deep Neural Network, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU) and Bidirectional GRU were trained on selected features using different FS methods, as well as complete dataset. …”
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Photoplethysmogram (PPG)-Based Biometric Identification Using 2D Signal Transformation and Multi-Scale Feature Fusion by Yuanyuan Xu, Zhi Wang, Xiaochang Liu

“…Next, a novel Lightweight Multi-Scale Feature Fusion (LMSFF) module is proposed, which addresses the limitation of single-scale feature extraction in existing methods by employing parallel multi-scale convolutional operations. Finally, cross-stage feature fusion is implemented, overcoming the limitations of traditional feature fusion methods. …”
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Deep Learning-Based Prediction of Pitch Response for Floating Offshore Wind Turbines by Ruifeng Chen, Ke Zhang, Min Luo, Ye An, Lixiang Guo

“…This model integrates convolutional neural networks (CNNs) and gated recurrent units (GRUs), effectively extracting the coupling relationships among various input features and capturing the temporal dependencies to enhance predictive accuracy. …”
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Real-Time Common Rust Maize Leaf Disease Severity Identification and Pesticide Dose Recommendation Using Deep Neural Network by Zemzem Mohammed Megersa, Abebe Belay Adege, Faizur Rashid

“…Maize is one of the most widely grown crops in Ethiopia and is a staple crop around the globe; however, common rust maize disease (CRMD) is becoming a serious problem and severely impacts yields. …”
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Automatic Paddy Planthopper Detection and Counting Using Faster R-CNN by Siti Khairunniza-Bejo, Mohd Firdaus Ibrahim, Marsyita Hanafi, Mahirah Jahari, Fathinul Syahir Ahmad Saad, Mohammad Aufa Mhd Bookeri

“…The datasets were subjected to data augmentation and utilised to train four convolutional object detection models based on transfer learning. …”
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Predicting wheat yield using deep learning and multi-source environmental data by Muhammad Ashfaq, Imran Khan, Dilawar Shah, Shujaat Ali, Muhammad Tahir

“…The framework employs three leading deep learning models—convolutional neural networks (CNN), recurrent neural networks (RNN), and artificial neural networks (ANN)—trained on detrended yield data from 2017 to 2022. …”
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Deep Learning Frontiers in 3D Object Detection: A Comprehensive Review for Autonomous Driving by Ambati Pravallika, Mohammad Farukh Hashmi, Aditya Gupta

“…We offer a thorough examination of techniques, including deep learning frameworks such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to assess their advantages and drawbacks in 3D object detection. …”
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High-Quality Sample Generation for Power System Transient Stability Assessment Based on Data-Driven Methods by Baoqin Li, Pengfei Fan, Qixin Chen, Rong Li, Kaijun Lin

“…Finally, a deep convolutional generative adversarial network (DCGAN) is constructed to mitigate the class imbalance problem. …”
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Enhanced Brain Tumor Classification Using MobileNetV2: A Comprehensive Preprocessing and Fine-Tuning Approach by Md Atiqur Rahman, Mohammad Badrul Alam Miah, Md. Abir Hossain, A. S. M. Sanwar Hosen

“…<b>Background:</b> Brain tumors are among the most difficult diseases to deal with in modern medicine due to the uncontrolled cell proliferation, which causes grave damage to the nervous system. …”
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