Leveraging Edge Intelligence for Solar Energy Management in Smart Grids by Trong-Minh Hoang, Tuan-Anh Pham, van-Nhan Nguyen, Duc-Thang Doan, Nhu-Ngoc Dao

“…This paper introduces an edge intelligence-driven hybrid deep learning model that integrates Temporal Convolutional Networks (TCN) and Gated Recurrent Units (GRU) for precise solar energy prediction. …”
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Deep learning-based surface deformation tracking with interferometric fringes: A case study in Taiwan by Shih-Teng Chang, Shih-Yuan Lin, Yu-Ching Lin

“…A Fringe-Labeling Model (FLM) was developed to identify deformation regions, followed by a Fringe-Detection Model (FDM) using Faster Region-based Convolutional Neural Networks (Faster R-CNN) to classify deformation magnitudes. …”
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Deep Transfer Learning for Lip Reading Based on NASNetMobile Pretrained Model in Wild Dataset by Ashwaq Waleed Abdul Ameer, Pedram Salehpour, Mohammad Asadpour

“…The proposed framework involves a process that extracts features from video frames in a time sequence, employing methods such as Convolutional Neural Networks (CNN), CNN-Gated Recurrent Units (CNN-GRU), Temporal CNN, and Temporal PoinWise. …”
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Machine learning-based analysis of defensive strategies in basketball using player movement data by Jianlong Li

“…This research aims to develop a hybrid machine learning model combining Long-Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) to classify and analyze defensive basketball strategies, specifically identifying switch and trap plays. …”
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Irrigated rice-field mapping in Brazil using phenological stage information and optical and microwave remote sensing by Andre Dalla Bernardina Garcia, MD Samiul Islam, Victor Hugo Rohden Prudente, Ieda Del’Arco Sanches, Irene Cheng

“…We applied a modified version of the Fusion Adaptive Patch Network (FAPNET), named as Patch Layer Adaptive Network (PLANET) convolutional neural network (CNN) to obtain binary rice mapping, which was evaluated using the traditional Mean Intersection over Union (MIoU) and Dice coefficient. …”
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LSTM and TCN application for airport surface distress detection by Ludek Cicmanec, Adam Kubica, Jiri Maslan

“…In our work, we investigated the capacity of neural networks with a Long Short-Term Memory (LSTM) layer with normalized data weighted and not weighted, bidirectional LSTM, and Temporal Convolutional Networks (TCNs). …”
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Evaluating the Impact of Frequency Decomposition Techniques on LSTM-Based Household Energy Consumption Forecasting by Maissa Taktak, Faouzi Derbel

“…Our approach employs computationally efficient convolution-based filters—uniform and binomial—with varying window sizes to separate these components for specialized processing. …”
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Input-output optics as a causal time series mapping: A generative machine learning solution by Abhijit Sen, Bikram Keshari Parida, Kurt Jacobs, Denys I. Bondar

“…Using both the transverse and nonintegrable Ising models as examples, we show that not only can temporal convolutional networks capture the input/output mapping generated by the system but can also be used to characterize the complexity of the mapping. …”
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TCN-Based DDoS Detection and Mitigation in 5G Healthcare-IoT: A Frequency Monitoring and Dynamic Threshold Approach by Mirza Akhi, Ciaran Eising, Lubna Luxmi Dhirani

“…., Distributed Denial of Service (DDoS)) in 5G networks. This research develops a monitoring frequency-based detection and dynamic threshold mitigation method using Temporal Convolutional Networks (TCNs) in 5G H-IoT environments. …”
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Estimation of lower limb torque: a novel hybrid method based on continuous wavelet transform and deep learning approach by Shu Xu, Tao Wang, Zenghui Ding, Yu Wang, Tongsheng Wan, Dezhang Xu, Xianjun Yang, Ting Sun, Meng Li

“…The proposed method combines time-frequency domain analysis through continuous wavelet transform (CWT) with a hybrid architecture comprising multi-head self-attention (MHSA), bidirectional long short-term memory (Bi-LSTM), and a one-dimensional convolutional residual network (1D Conv ResNet). This integration enhances feature extraction, noise suppression, and temporal dependency modeling, particularly for non-stationary and nonlinear signals in dynamic environments. …”
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Time-Distributed Vision Transformer Stacked With Transformer for Heart Failure Detection Based on Echocardiography Video by Mgs M. Luthfi Ramadhan, Adyatma W. A. Nugraha Yudha, Muhammad Febrian Rachmadi, Kevin Moses Hanky Jr Tandayu, Lies Dina Liastuti, Wisnu Jatmiko

“…Specifically, most existing studies use a convolutional neural network that only captures the local context of an image hindering it from learning the global context of an image. …”
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Deep-Learning-Driven Insights into Nitrogen Leaching for Sustainable Land Use and Agricultural Practices by Caixia Hu, Jie Li, Yaxu Pang, Lan Luo, Fang Liu, Wenhao Wu, Yan Xu, Houyu Li, Bingcang Tan, Guilong Zhang

“…A machine learning (ML) model for predicting nitrate leaching was then developed, with the random forest (RF) model outperforming the support vector machine (SVM), extreme gradient boosting (XGBoost), and convolutional neural network (CNN) models, achieving an R² of 0.75. …”
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Leveraging Prior Knowledge in Semi-Supervised Learning for Precise Target Recognition by Guohao Xie, Zhe Chen, Yaan Li, Mingsong Chen, Feng Chen, Yuxin Zhang, Hongyan Jiang, Hongbing Qiu

“…The architecture employs a Convolutional Block Attention Module (CBAM) for localized feature refinement, a lightweight New Transformer Encoder for global context modeling, and a novel TriFusion Block to synergize spectral–temporal–spatial features through parallel multi-branch fusion, addressing the limitations of single-modality extraction. …”
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EvapoDeep: A Dual Deep Learning Framework Utilizing GNSS Data for Evapotranspiration Modeling and Predictive Analysis by Saeed Ebrahimi, Saeid Haji-Aghajany, Yazdan Amerian, Melika Tasan

“…This DET₀ is then modeled using an advanced Convolutional Neural Network-based deep learning method. …”
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Detection of Marine Oil Spill from PlanetScope Images Using CNN and Transformer Models by Jonggu Kang, Chansu Yang, Jonghyuk Yi, Yangwon Lee

“…While oil spill detection has traditionally relied on synthetic aperture radar (SAR) images, the combined use of optical satellite sensors alongside SAR can significantly enhance monitoring capabilities, providing improved spatial and temporal coverage. The advent of deep learning methodologies, particularly convolutional neural networks (CNNs) and Transformer models, has generated considerable interest in their potential for oil spill detection. …”
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Deep learning for single-site solar irradiance forecasting using multi-station data by Daniel Guatibonza, Gabriel Narvaez, Luis Felipe Giraldo

“…This study examines the integration of data from multiple stations for solar irradiance forecasting at a single site using advanced deep learning models, such as long-term memory (LSTM), deep modular attention (DeepMap), and graph convolutional networks (GC-LSTM). The research addresses an important gap: the statistical evaluation of the contribution of neighboring data to improving forecast accuracy in solar PV applications. …”
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Deep FS: A Deep Learning Approach for Surface Solar Radiation by Fatih Kihtir, Kasim Oztoprak

“…Time series analysis was conducted using Convolutional Neural Networks (CNNs), with results demonstrating superior performance compared to traditional methodologies across standard performance metrics. …”
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Comparison of Machine Learning and Deep Learning Models Performance in predicting wind energy by Saswati Rakshit, Anal Ranjan Sengupta

“…This comprehensive analysis includes nine ML models—Linear Regression, Random Forests (RF), Gradient Boosting Machines (GBM), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), AdaBoost, XGBoost, Support Vector Regression (SVR), and Neural Networks—as well as Four time-series forecasting models—ARIMA, Temporal Convolutional Networks (TCNs), Long Short-Term Memory (LSTM) networks and GRU. …”
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The Role of Time in Facial Dynamics and Challenges in Automatic Emotion Recognition (2019–2024) by Williams Contreras-Higuera, Lucrezia Crescenzi-Lanna

“…While deep learning (DL) techniques like convolutional neural networks (CNNs) and long short-term memory (LSTM) networks offer significant advances, they face challenges such as gradient vanishing and overfitting, particularly in long and complex sequences. …”
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A Survey of Deep Learning-Driven 3D Object Detection: Sensor Modalities, Technical Architectures, and Applications by Xiang Zhang, Hai Wang, Haoran Dong

“…Regarding technical architectures, the paper examines structured representation optimization in traditional convolutional networks, spatiotemporal modeling breakthroughs in bird’s-eye view (BEV) methods, voxel-level modeling advantages of occupancy networks for irregular objects, and dynamic scene understanding capabilities of temporal fusion architectures. …”
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