Comparative Analysis of Hybrid Deep Learning Models for Electricity Load Forecasting During Extreme Weather by Altan Unlu, Malaquias Peña

“…Case Study 1 conducts CNN-Recurrent (RNN, LSTM, GRU, BiRNN, BiGRU, and BiLSTM) models with fully connected dense layers, which combine convolution and recurrent neural networks to capture both spatial and temporal dependencies in the data. …”
Get full text

PatchOut: A novel patch-free approach based on a transformer-CNN hybrid framework for fine-grained land-cover classification on large-scale airborne hyperspectral images by Renjie Ji, Kun Tan, Xue Wang, Shuwei Tang, Jin Sun, Chao Niu, Chen Pan

Get full text

A physical state prediction method based on reduce order model and deep learning applied in virtual reality by Pengbo Yu, Qiyu Liu, Qiyu Liu, Qiyu Liu, Qiyu Liu, Siyun Yi, Ming Zhu, Yangheng Hu, Gexiang Zhang, Gexiang Zhang

“…This method firstly integrates data dimensionality reduction and temporal convolutional network (TCN) to pre-capture time-series data from numerical simulation results, and then employs Kolmogorov–Arnold Networks (KAN) to approximate nonlinear characteristics to improved Long Short-Term Memory (LSTM) network, thereby predict time-series simulation data accurately to achieves realistic and responsive dynamic displays. …”
Get full text

Spatiotemporal hybrid deep learning for estimating and analyzing carbon stocks: a case study in Jiangsu province, China by Lizhi Miao, Jvmin Wang, Kaiwen Wu, Heng Xu, Xiying Sun, Gang Lu, Mei-Po Kwan

“…This research applies GCN (Graph Convolutional Network) to extract spatial features and BiLSTM (Bidirectional Long Short Term Memory Network) to capture temporal features, considering the impact of various factors on carbon stocks. …”
Get full text

Evaluation method of hydrophobicity of composite insulators based on improved Mask R-CNN by SHENG Fei, CAO Liu, LIU Yulong, HUANG Jie, HUANG Yaqian, ZHU Yanqing

“…In this paper, the classification problem is transformed into the target detection problem, and the improved mask region-based convolutional neural network (Mask R-CNN) algorithm is used to evaluate the hydrophobicity level of composite insulators. …”
Get full text

Acoustic cues for person identification using cough sounds by Van-Thuan Tran, Ting-Hao You, Wei-Ho Tsai

“…The proposed architecture, CoughCueNet, is a convolutional recurrent neural network designed to capture both spatial and temporal patterns in cough sounds. …”
Get full text

A spatial hierarchical learning module based cellular automata model for simulating urban expansion: case studies of three Chinese urban areas by Xiaoyong Tan, Min Deng, Kaiqi Chen, Yan Shi, Bingbing Zhao, Qinghao Liu

“…We redefine the neighborhood structure and introduce lightweight convolutional neural networks to capture the complex spatio-temporal interaction in neighborhood effects. …”
Get full text

A dual-branch deep learning model based on fNIRS for assessing 3D visual fatigue by Yan Wu, Yan Wu, Yan Wu, TianQi Mu, SongNan Qu, XiuJun Li, XiuJun Li, XiuJun Li, Qi Li, Qi Li, Qi Li

“…Given the time-series nature of fNIRS data and the variability of fatigue responses across different brain regions, a dual-branch convolutional network was constructed to separately extract temporal and spatial features. …”
Get full text

A New Lightweight Hybrid Model for Pistachio Classification Using Transformers and EfficientNet by Muhammet Cakmak

“…In recent years, Vision Transformers (ViTs) have gained prominence as a highly effective method for image classification, often outperforming traditional Convolutional Neural Networks (CNNs). However, their relatively slow processing speed limits their practical use, particularly in real-time applications. …”
Get full text

Enhancing microgrid forecasting accuracy with a TCNN-TLS framework: A novel approach to mitigating uncertainty in renewable energy and load predictions by Md. Omer Faruque, Md. Majharul Islam, Md Jakaria Talukder, Arif Mia, Shahriar Tasnim, Md. Alamgir Hossain, S.M. Muyeen

“…The employed settings contain a temporal convolutional neural network (TCNN) optimized with a pelican optimization algorithm (POA) to enhance its hyper-parameter selection. …”
Get full text

A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images by Jiang Xie, Jinzhu Wei, Huachan Shi, Zhe Lin, Jinsong Lu, Xueqing Zhang, Caifeng Wan

“…In this study, a novel convolutional neural network model with bimodal layer-wise feature fusion module (BLFFM) and temporal hybrid attention module (THAM) is proposed, which uses multistage bimodal ultrasound images as input for early prediction of the efficacy of neoadjuvant chemotherapy in locally advanced breast cancer (LABC) patients. …”
Get full text

Real-Time Quality Monitoring and Anomaly Detection for Vision Sensors in Connected and Autonomous Vehicles by Elena Politi, Charalampos Davalas, Christos Chronis, George Dimitrakopoulos, Dimitrios Michail, Iraklis Varlamis

“…On this basis we adopt a two-stage approach to validate the performance of the proposed methods against a baseline Convolutional Neural Network (CNN) in a controlled low-criticality environment, as well as in more complex real-world scenarios. …”
Get full text

A Welding Defect Detection Model Based on Hybrid-Enhanced Multi-Granularity Spatiotemporal Representation Learning by Chenbo Shi, Shaojia Yan, Lei Wang, Changsheng Zhu, Yue Yu, Xiangteng Zang, Aiping Liu, Chun Zhang, Xiaobing Feng

“…A MobileNetV2 backbone network integrated with a Temporal Shift Module (TSM) is designed to progressively capture the short-term dynamic features of the molten pool and integrate temporal information across both low-level and high-level features. …”
Get full text

A Hierarchical and Self-Evolving Digital Twin (HSE-DT) Method for Multi-Faceted Battery Situation Awareness Realisation by Kai Zhao, Ying Liu, Yue Zhou, Wenlong Ming, Jianzhong Wu

“…The model integrates a Transformer–Convolutional Neural Network (Transformer-CNN) architecture to process historical and real-time data, capturing dynamic state variations with high precision. …”
Get full text

An RIS-Assisted Integrated Deep Learning Framework for MIMO-OFDM-IM by Md Abdul Aziz, Md Habibur Rahman, Mohammad Abrar Shakil Sejan, Rana Tabassum, Hyoung-Kyu Song

“…InDeep leverages one-dimensional convolutional neural network (1D-CNN) layers for feature extraction, followed by bidirectional gated recurrent unit (Bi-GRU) networks to capture temporal dependencies in the received signal. …”
Get full text

Real-Time Fault Diagnosis of Mooring Chain Jack Hydraulic System Based on Multi-Scale Feature Fusion Under Diverse Operating Conditions by Yujia Liu, Wenhua Li, Haoran Ye, Shanying Lin, Lei Hong

“…Firstly, the model incorporates a convolutional neural network (CNN) layer to extract localized spatial features from multivariate time-series data, effectively identifying fault patterns over the associated short intervals. …”
Get full text

Deep learning-based mapping of total suspended solids in rivers across South Korea using high resolution satellite imagery by JunGi Moon, SungMin Suh, SangJin Jung, Sang-Soo Baek, JongCheol Pyo

“…We found that the convolutional neural network (CNN) model was more accurate than traditional regression and other models, with a Nash-Sutcliffe efficiency (NSE) of 0.758. …”
Get full text

An RNN-CNN-Based Parallel Hybrid Approach for Battery State of Charge (SoC) Estimation Under Various Temperatures and Discharging Cycle Considering Noisy Conditions by Md. Shahriar Nazim, Md. Minhazur Rahman, Md. Ibne Joha, Yeong Min Jang

“…To address this issue, this work proposes a new hybrid method that integrates a gated recurrent unit (GRU), temporal convolution network (TCN), and attention mechanism. …”
Get full text

Video Coding Based on Ladder Subband Recovery and ResGroup Module by Libo Wei, Aolin Zhang, Lei Liu, Jun Wang, Shuai Wang

“…By using multi-layer convolution operations along with feature map compression and recovery, the ResGroup module enhances the network’s expressive capability and effectively reduces computational complexity. …”
Get full text

Lightweight Deep Learning Model for Fire Classification in Tunnels by Shakhnoza Muksimova, Sabina Umirzakova, Jushkin Baltayev, Young-Im Cho

“…This model integrates MobileNetV3 for spatial feature extraction, Temporal Convolutional Networks (TCNs) for temporal sequence analysis, and advanced attention mechanisms, including Convolutional Block Attention Modules (CBAMs) and Squeeze-and-Excitation (SE) blocks, to prioritize critical features such as flames and smoke patterns while suppressing irrelevant noise. …”
Get full text