Rolling Bearing Life Prediction Based on Improved Transformer Encoding Layer and Multi-Scale Convolution by Zhuopeng Luo, Zhihai Wang, Xiaoqin Liu, Yingming Yang

“…Next, to further extract local temporal features within the bearing’s life cycle, a multi-scale convolution module is proposed to reinforce the local information across the entire lifespan. …”
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Low-Voltage Biological Electric Shock Fault Diagnosis Based on the Attention Mechanism Fusion Parallel Convolutional Neural Network/Bidirectional Long Short-Term Memory Model by Meijin Lin, Yuliang Luo, Senjie Chen, Zhirong Qiu, Zibin Dai

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Computationally Efficient Single Layer Transformer Convolutional Encoder for Accurate Price Prediction of Agriculture Commodities by Caceja Elyca Anak Bundak, Mohd Amiruddin Abd Rahman, Nurin Syazwina Mohd Haniff, Nur Syaiful Afrizal, Khairul Adib Yusof, Muhammad Khalis Abdul Karim, Md Shuhazlly Mamat, Romi Fadillah Rahmat

“…In STCE, the fully connected Convolutional Neural Network (CNN) layer is used in the transformer to get the first temporal features and record long-range dependencies with Multi-Head Attention. …”
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A Convolutional Neural Network–Long Short-Term Memory–Attention Solar Photovoltaic Power Prediction–Correction Model Based on the Division of Twenty-Four Solar Terms by Guodong Wu, Diangang Hu, Yongrui Zhang, Guangqing Bao, Ting He

“…Firstly, given that the meteorological data from the same festival is more representative of the climate state at the current prediction moment, the sample data are grouped according to the 24 festival time nodes. Secondly, a convolutional neural network–long short-term memory (CNN-LSTM) PV power prediction model based on an Attention mechanism is proposed. …”
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An explainable and efficient deep learning framework for EEG-based diagnosis of Alzheimer's disease and frontotemporal dementia by Waqar Khan, Muhammad Shahbaz Khan, Sultan Noman Qasem, Sultan Noman Qasem, Wad Ghaban, Faisal Saeed, Muhammad Hanif, Jawad Ahmad

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Edge caching strategy based on multi-agent deep reinforcement learning in cloud-edge-end scenarios by WANG Haiyan, CHANG Bo, LUO Jian

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Sleep Staging Using Compressed Vision Transformer With Novel Two-Step Attention Weighted Sum by Hyounggyu Kim, Moogyeong Kim, Wonzoo Chung

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Pedestrian Trajectory Prediction Based on Dual Social Graph Attention Network by Xinhai Li, Yong Liang, Zhenhao Yang, Jie Li

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A Deep Learning Method for Improving Community Multiscale Air Quality Forecast: Bias Correction, Event Detection, and Temporal Pattern Alignment by Ioannis Stergiou, Nektaria Traka, Dimitrios Melas, Efthimios Tagaris, Rafaella-Eleni P. Sotiropoulou

“…Addressing these limitations, this study introduces a hybrid deep learning model that integrates convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) for ozone forecast bias correction. …”
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Research on an hourly heat load forecasting model for district heating systems based on heterogeneous model fusion by Tieliu Jiang, Wenyue Liu, Jianqing Lin, Xu Jin, Zhongyan Liu

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Learning and Generation of Drawing Sequences Using a Deep Network for a Drawing Support System by Homari Matsumoto, Atomu Nakamura, Shun Nishide

“…We developed an encoder–decoder model based on convolutional neural networks to predict the next frame from a current input image. …”
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Hybrid Neural Network Models to Estimate Vital Signs from Facial Videos by Yufeng Zheng

“…The hybrid model integrates convolutional neural network (CNN), convolutional long short-term memory (convLSTM), and video vision transformer (ViViT) architectures to ensure comprehensive analysis. …”
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A Novel 3D Convolutional Neural Network-Based Deep Learning Model for Spatiotemporal Feature Mapping for Video Analysis: Feasibility Study for Gastrointestinal Endoscopic Video Cla... by Mrinal Kanti Dhar, Mou Deb, Poonguzhali Elangovan, Keerthy Gopalakrishnan, Divyanshi Sood, Avneet Kaur, Charmy Parikh, Swetha Rapolu, Gianeshwaree Alias Rachna Panjwani, Rabiah Aslam Ansari, Naghmeh Asadimanesh, Shiva Sankari Karuppiah, Scott A. Helgeson, Venkata S. Akshintala, Shivaram P. Arunachalam

“…As a feasibility use case, this study focuses on gastrointestinal (GI) endoscopic video classification. A 3D convolutional neural network (CNN) is developed to classify upper and lower GI endoscopic videos using the hyperKvasir dataset, which contains 314 lower and 60 upper GI videos. …”
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Gearbox Fault Diagnosis Under Noise and Variable Operating Conditions Using Multiscale Depthwise Separable Convolution and Bidirectional Gated Recurrent Unit with a Squeeze-and-Exc... by Xiaoteng Ma, Kejia Zhai, Nana Luo, Yehui Zhao, Guangming Wang

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Enhanced analog circuit fault diagnosis via continuous wavelet transform and dual-stream convolutional fusion by Zhiwen Hou, Jingrui Liu, Sijiu Yu

“…To overcome the limitations of traditional methods, this study proposes a novel analog circuit fault diagnosis method based on Continuous Wavelet Transform (CWT) and Dual-Stream Convolutional Neural Network (DSCNN). The method uses CWT to convert raw fault waveform data into two-dimensional time–frequency images and employs a one-dimensional convolutional neural network (1D-CNN) to extract temporal features and a two-dimensional convolutional neural network (2D-CNN) to extract image features, achieving feature fusion. …”
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Wind Power Forecasting Based on Multi-Graph Neural Networks Considering External Disturbances by Xiaoyin Xu, Zhumei Luo, Menglong Feng

“…The framework adopts a three-component architecture consisting of (1) a multi-graph convolutional network using both geographical proximity and power correlation graphs to capture heterogeneous spatial dependencies between wind farms, (2) an attention-enhanced LSTM network that weights temporal features differentially based on their predictive significance, and (3) a specialized Conv2D mechanism to identify and isolate external disturbance patterns. …”
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Dual-Branch Deep Learning with Dynamic Stage Detection for CT Tube Life Prediction by Zhu Chen, Yuedan Liu, Zhibin Qin, Haojie Li, Siyuan Xie, Litian Fan, Qilin Liu, Jin Huang

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Spatiotemporal Multivariate Weather Prediction Network Based on CNN-Transformer by Ruowu Wu, Yandan Liang, Lianlei Lin, Zongwei Zhang

“…Therefore, we designed a spatiotemporal coupled prediction network based on convolution and Transformer for weather prediction from the perspective of multivariate spatiotemporal fields. …”
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Data-Driven Optimized Load Forecasting: An LSTM-Based RNN Approach for Smart Grids by Muhammad Asghar Majeed, Sotdhipong Phichaisawat, Furqan Asghar, Umair Hussan

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StApneaNet: A Deep Learning-Based Automatic Sleep Stage Adaptive Apnea Detection Network Using Single Channel EEG Signal by Suvasish Saha, Shaikh Anowarul Fattah, Mohammad Saquib

“…In the joint model, multi-band EEG signals are used as input to a multi-kernel CNN block which gives time sequential inter-band related features through causal convolution. A residual squeeze and excitation based channel attention mechanism is then applied to the output feature channels which are further processed through a bi-directional long short term memory (Bi-LSTM) layer along with a temporal attention block. …”
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