Enconv1d Model Based on Pseudolite System for Long-Tunnel Positioning by Changgeng Li, Yuting Zhang, Changshui Liu

“…The model employs the encoder module from the Transformer to capture multi-step time constraints while introducing a multi-scale one-dimensional convolutional neural network module (1D CNN) to assist the encoder module in learning spatial features and finally outputs the localization results of the Enconv1d model after the dense layer integration. …”
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Prediction of post-Schroth Cobb angle changes in adolescent idiopathic scoliosis patients based on neural networks and surface electromyography by Shuguang Yin, Jiangang Chen, Peng Yan

“…A systematic Schroth exercise training program was designed. sEMG data from specific muscles and Cobb angle measurements were collected. A neural network model integrating Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM) layers, and feature vectors was constructed. …”
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Identification of low-count, low-resolution gamma spectral radionuclide using 2DCNN-BiLSTM neural network by Shu-Xin Zeng, Rui Shi, Guang Yang, Xian-Guo Tuo, Xiong Zeng, Ya-Nan Shang, Zhou Wang, Heng Zhang

“…This method extracts spatial features from gamma spectrum gray images using two-dimensional convolution operations and analyzes temporal features with a bidirectional long short-term memory neural network, leveraging spatiotemporal dependencies for nuclide classification. …”
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Retail Demand Forecasting: A Comparative Analysis of Deep Neural Networks and the Proposal of LSTMixer, a Linear Model Extension by Georgios Theodoridis, Athanasios Tsadiras

“…This study analyzes, optimizes, trains and tests such forecasters, namely the Temporal Fusion Transformer and the Temporal Convolutional Network, alongside the recently proposed Time-Series Mixer, to accurately forecast retail demand given a dataset of historical sales in 45 stores with their accompanied features. …”
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Enhancing Model Robustness in Noisy Environments: Unlocking Advanced Mono-Channel Speech Enhancement With Cooperative Learning and Transformer Networks by Wei Hu, Yan Wu

“…To tackle this challenge head-on, this paper introduces a novel rapid speech enhancement network that harnesses the combined strengths of Convolutional Neural Networks (ConvNets) and Transformers. …”
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Socializing AI: Integrating Social Network Analysis and Deep Learning for Precision Dairy Cow Monitoring—A Critical Review by Sibi Chakravathy Parivendan, Kashfia Sailunaz, Suresh Neethirajan

“…We describe the transition from manual, observer-based assessments to automated, scalable methods using convolutional neural networks (CNNs), spatio-temporal models, and attention mechanisms. …”
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Predicting the Evolution of the Supercontinuum Generation With CNN-LSTM Model by Yi Feng, Ruiyuan Liu, Xinyue Chang, Xiangzhen Huang, Yuan He, Ning Li, Tiantian Zhou, Chujun Zhao

“…We propose a hybrid deep learning model, namely convolutional neural network–long short-term memory (CNN-LSTM) approach to investigate the evolution of the supercontinuum (SC) generation numerically. …”
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Multi-Head Graph Attention Adversarial Autoencoder Network for Unsupervised Change Detection Using Heterogeneous Remote Sensing Images by Meng Jia, Xiangyu Lou, Zhiqiang Zhao, Xiaofeng Lu, Zhenghao Shi

“…The MHGAN employs a bidirectional adversarial convolutional autoencoder network to reconstruct and perform style transformation of heterogeneous images. …”
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MAK-Net: A Multi-Scale Attentive Kolmogorov–Arnold Network with BiGRU for Imbalanced ECG Arrhythmia Classification by Cong Zhao, Bingwei Lai, Yongzheng Xu, Yiping Wang, Haorong Dong

“…To address these limitations, we introduce MAK-Net, a hybrid deep learning framework that combines: (1) a four-branch multiscale convolutional module for comprehensive feature extraction across diverse waveform morphologies; (2) an efficient channel attention mechanism for adaptive weighting of clinically salient segments; (3) bidirectional gated recurrent units (BiGRU) to capture long-range temporal dependencies; and (4) Kolmogorov–Arnold Network (KAN) layers with learnable spline activations for enhanced nonlinear representation and interpretability. …”
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Diagnosis of Power Transformer On-Load Tap Changer Mechanical Faults Based on SABO-Optimized TVFEMD and TCN-GRU Hybrid Network by Shan Wang, Zhihu Hong, Qingyun Min, Dexu Zou, Yanlin Zhao, Runze Qi, Tong Zhao

“…The decomposed components undergo dual-stage temporal processing: A Temporal Convolutional Network (TCN) extracts multi-scale dependencies via dilated convolution architecture, followed by Gated Recurrent Unit (GRU) layers capturing dynamic temporal patterns. …”
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Development of Bimodal Emotion Recognition System Based on Skin Temperature and Heart Rate Variability Using Hybrid Neural Networks by Sayat Orynbassar, Duygun Erol Barkana, Evan Yershov, Madiyar Nurgaliyev, Ahmet Saymbetov, Batyrbek Zholamanov, Gulbakhar Dosymbetova, Ainur Kapparova, Nursultan Koshkarbay, Nurzhigit Kuttybay, Askhat Bolatbek, Kymbat Kopbay, Dinara Almen

“…The hybrid neural network, combining a convolutional neural network and a gated recurrent unit (CNN+GRU), was trained on experimental SKT and HRV data collected from individuals experiencing basic emotions such as anger, disgust, fear, happiness, sadness, and surprise. …”
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Advanced Hydro-Informatic Modeling Through Feedforward Neural Network, Federated Learning, and Explainable AI for Enhancing Flood Prediction by Shahariar Hossain Mahir, Md Tanjum An Tashrif, Md Ahsan Karim, Dipanjali Kundu, Anichur Rahman, Md. Amir Hamza, Fahmid Al Farid, Abu Saleh Musa Miah, Sarina Mansor

“…To address this, our research adopts the Federated Learning (FL) framework in an effort to train state-of-the-art deep learning models like Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), Feed-Forward Neural Network (FNN) and Temporal Fusion Transformer-Convolutional Neural Network (TFT -CNN) on a 78-year dataset of rainfall, river flow, and meteorological variables from Sylhet and its upstream regions in Meghalaya and Assam, India. …”
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A Hybrid Deep Learning Model for Network Intrusion Detection System Using Seq2Seq and ConvLSTM-Subnets by S. Hariharan, Y. Annie Jerusha, G. Suganeshwari, S. P. Syed Ibrahim, Uday Tupakula, Vijay Varadharajan

“…This enables the model to leverage the spatial feature extraction capabilities of Convolutional Neural Networks (CNN) alongside the sequential learning strengths of LSTM networks. …”
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A Spatiotemporal-Adaptive-Network-Based Method for Predicting Axial Forces in Assembly Steel Struts with Servo System of Foundation Pits by Weiwei Liu, Jianchao Sheng, Jian Zhou, Jinbo Fu, Wangjing Yao, Kuan Chang, Zhe Wang

“…A self-attention mechanism is then employed to capture the global dependencies within the axial force data, enhancing the feature representation. Concurrently, a convolutional neural network (CNN) is utilized to extract local spatial features. …”
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A lightweight Deeplab V3+ network integrating deep transitive transfer learning and attention mechanism for burned area identification by Lizhi Liu, Ying Guo, Erxue Chen, Zengyuan Li, Yu Li, Yang Liu, Qiang Zhang, Bing Wang

“…The lightweight MobileNet V2 network integrated with Convolutional Block Attention Module (CBAM) is designed as the backbone network to replace the traditional time-consuming Xception of Deeplab V3 +. …”
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Leveraging physics-informed neural networks for efficient modelling of coastal ecosystems dynamics: A case study of Sundarbans mangrove forest by Majdi Fanous, Jonathan M. Eden, Juntao Yang, Simon See, Vasile Palade, Alireza Daneshkhah

“…The model is trained using a small subset of CFD simulation data and validated against a traditional finite element (FE) solver and a Convolutional Neural Network (CNN) baseline. The proposed hybrid PINNs model achieved RMSE values in the range of 10−2 for elevation and velocity and 10−3 for sediment concentration, significantly outperforming the CNN model, particularly in generalising to unseen time steps. …”
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A spatiotemporal model for urban taxi Origin–Destination prediction based on Multi-hop GCN and Hierarchical LSTM by Jiang Rong, Wangtu Xu, Yanjie Wen

“…Specially, DBSTNet models pick-up and drop-off patterns via transposed OD matrices and employs 2 independent branches to separately capture spatial–temporal features. We develop a Multi-hop Spatial-Hierarchical Temporal (MS-HT) block that leverages Chebyshev polynomial-based k-hop Graph Convolutions Networks(GCNs) to extract long-range spatial dependencies, which alleviates over-smoothing resulting from stacked GCNs. …”
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DASD- diagnosing autism spectrum disorder based on stereotypical hand-flapping movements using multi-stream neural networks and attention mechanisms by Theyazn H. H. Aldhyani, Theyazn H. H. Aldhyani, Abdullah H. Al-Nefaie, Abdullah H. Al-Nefaie

“…IntroductionThe early detection and diagnosis of autism spectrum disorder (ASD) remain critical challenges in developmental healthcare, with traditional diagnostic methods relying heavily on subjective clinical observations.MethodsIn this paper, we introduce an innovative multi-stream framework that seamlessly integrates three state-of-the-art convolutional neural networks, namely, EfficientNetV2B0, ResNet50V2, DenseNet121, and Multi-Stream models to analyze stereotypical movements, particularly hand-flapping behaviors automatically. …”
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PD-Net: Parkinson’s Disease Detection Through Fusion of Two Spectral Features Using Attention-Based Hybrid Deep Neural Network by Munira Islam, Khadija Akter, Md. Azad Hossain, M. Ali Akber Dewan

“…To this end, the study proposes a hybrid model that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) for the detection of Parkinson’s disease. …”
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Priority-aware task offloading for LEO satellite edge computing network: a multi-agent deep reinforcement learning-based approach by Juan Chen, Jie Zhong, Zongling Wu, Di Tian, Yujie Chen

“…Furthermore, the agent design adopts an encoder-decoder architecture, combined with self-attention (SA) mechanisms and temporal convolutional network (TCN) technology to extract environmental features, achieving real-time optimization of task scheduling. …”
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