FFLKCDNet: First Fusion Large-Kernel Change Detection Network for High-Resolution Remote Sensing Images by Bochao Chen, Yapeng Wang, Xu Yang, Xiaochen Yuan, Sio Kei Im

“…This paper proposes a high-resolution remote sensing image change detection model called FFLKCDNet (First Fusion Large-Kernel Change Detection Network) to solve this issue. FFLKCDNet features a Bi-temporal Feature Fusion Module (BFFM) to fuse remote sensing features from different temporal scales, and an improved ResNet network (RAResNet) that combines large-kernel convolution and multi-attention mechanisms to enhance feature extraction. …”
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Causal inference-based graph neural network method for predicting asphalt pavement performance by CHEN Kai;WANG Xiaohe;SHI Xinli;CAO Jinde

“…The local feature extraction module utilizes dilated convolutional neural networks(CNN) with various kernel sizes to extract short-term temporal patterns at different scales. …”
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Deep learning for network security: an Attention-CNN-LSTM model for accurate intrusion detection by Abdullah Mujawib Alashjaee

“…Hence, this study proposes a novel hybrid deep learning model, which associates Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), along with a self-attention method to highlight the utmost informative input features. …”
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SLiTRANet: An EEG-Based Automated Diagnosis Framework for Major Depressive Disorder Monitoring Using a Novel LGCN and Transformer-Based Hybrid Deep Learning Approach by Sagnik de, Anurag Singh, Vivek Tiwari, Harshita Patel, G. N. Vivekananda, Dharmendra Singh Rajput

“…The Stockwell transform (S-transform) is employed to exploit the spectro-temporal information from the EEG and the resulting 2D representation is then fed into customized Linear Graph Convolution Network for MDD detection. …”
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Link Scheduling in Satellite Networks via Machine Learning Over Riemannian Manifolds by Joarder Jafor Sadique, Imtiaz Nasim, Ahmed S. Ibrahim

“…Furthermore, we introduce a separate model, the convolutional neural network (CNN) over Riemannian manifolds, which captures geometric relationships between satellites and users by extracting spatial features from the network topology across all links. …”
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A Multimodal Deep Learning Model Integrating CNN and Transformer for Predicting Chemotherapy-Induced Cardiotoxicity by Ahmed Bouatmane, Abdelaziz Daaif, Abdelmajid Bousselham, Bouchra Bouihi, Omar Bouattane

“…We developed three architectures that integrate Convolutional Neural Networks (CNNs) for feature extraction from TDI images with Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer models to capture temporal dependencies and enhance prediction accuracy. …”
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IT Equipment Automatic Configuration Method Based on Energy Consumption Prediction by CHEN Xiaojiang, HUANG Hongcong, CAI Xuelong, DING Bo

“…To address these problems, this paper proposes an IT equipment energy consumption prediction method based on Long Short-Term Memory Network (LSTM) and Graph Convolutional Neural Network (GCN). …”
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Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism by Wangyong Guo, Shijin Liu, Liguo Weng, Xingyu Liang

“…To achieve accurate and efficient short-term load forecasting, this study proposes a novel power grid load forecasting model that integrates Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), Multi-Head Self-Attention Mechanism (MHSA), Global Attention Mechanism (GAM), and Channel Attention Mechanism (CAM) to achieve efficient and precise short-term load forecasting. …”
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International Natural Uranium Price Prediction Based on TF-CNN-BiLSTM Model by YANG Jingzhe, XUE Xiaogang

“…To address these challenges, this study proposed a novel TF-CNN-BiLSTM model, which synergistically combines the self-attention mechanism of Transformer, the local feature extraction capability of convolutional neural network (CNN), and the bidirectional temporal dependency modeling of bidirectional long short-term memory (BiLSTM). …”

Density Logging Curve Reconstruction Method Based on Bayesian-Optimized CNN-LSTM by LI Hongxi, CHEN Mingjiang, ZHANG Xiankun, YANG Bo, ZHAO Bin, LI Xiansheng, WANG Huanhuan

“…To address this issue, this study proposes a density logging curve reconstruction method based on Bayesian-optimized convolutional neural networks (CNN) combined with long short-term memory networks (LSTM). …”
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Multi-Layer Modeling and Visualization of Functional Network Connectivity Shows High Performance for the Classification of Schizophrenia and Cognitive Performance via Resting fMRI by Duc My Vo, Anees Abrol, Zening Fu, Vince D. Calhoun

“…<b>Background:</b> In functional magnetic resonance imaging (fMRI), functional network connectivity (FNC) captures temporal coupling among intrinsic connectivity networks (ICNs). …”
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A Spatio-Temporal Tensor Graph Neural Network-Based Method for Node-Link Prediction in Port Networks by Zhixin Xia, Zhangqi Zheng, Feiyang Wei, Yongshan Liu, Lu Yu

“…Secondly, for the port network temporal links, this paper introduces an exponential decay model to extract the corresponding temporal features and obtains the temporal edge embedding vectors by convolution method based on the GCN model. …”
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Graph Neural Network Classification in EEG-Based Biometric Identification: Evaluation of Functional Connectivity Methods Using Time-Frequency Metric by Roghaieh Ashenaei, Ali Asghar Beheshti Shirazi

“…To address this, we introduce two novel time-frequency FC metrics: 1) Multi-band Coherency, which integrates features across delta, theta, alpha, and beta bands to enhance robustness and discriminability; and 2) wavelet coherency, addressing the non-stationarity of EEG signals, models phase lead-lag interactions and temporal variations. Integrated with Graph Convolutional Neural Networks (GCNNs), our approach leverages graph-structured FC data for superior classification. …”
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Spatiotemporal Patterns of Intermittent Snow Cover From PlanetScope Imagery Using Deep Learning by Zhaocheng Wang, Jaya Venkatesh Jaya Baskar, Maneesh Sarma Sistla Naga Sai, Bohumil Svoma, Enrique R. Vivoni

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AI-Enhanced Detection of Heart Murmurs: Advancing Non-Invasive Cardiovascular Diagnostics by Maria-Alexandra Zolya, Elena-Laura Popa, Cosmin Baltag, Dragoș-Vasile Bratu, Simona Coman, Sorin-Aurel Moraru

“…This study presents a novel convolutional recurrent neural network (CRNN) model designed for the non-invasive classification of heart murmurs. …”
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UN15: An Urban Noise Dataset Coupled with Time–Frequency Attention for Environmental Sound Classification by Yu Shen, Ge Cao, Huan-Yu Dong, Bo Dong, Chang-Myung Lee

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TCN-LSTM-MHSA model optimized by improved slime mould algorithm for stress prediction of roadway anchor bolts (cables) by QI Junyan, CHE Yuhao, WANG Lei, YUAN Ruifu

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Golden eagle optimized CONV-LSTM and non-negativity-constrained autoencoder to support spatial and temporal features in cancer drug response prediction by Wesam Ibrahim Hajim, Suhaila Zainudin, Kauthar Mohd Daud, Khattab Alheeti

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Speech Emotion Recognition on MELD and RAVDESS Datasets Using CNN by Gheed T. Waleed, Shaimaa H. Shaker

“…This research presents a high-performance SER framework based on a lightweight 1D Convolutional Neural Network (1D-CNN) and a multi-feature fusion technique. …”
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Evaluation of Single-Shot Object Detection Models for Identifying Fanning Behavior in Honeybees at the Hive Entrance by Tomyslav Sledevič

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