Efficient and Effective NDVI Time-Series Reconstruction by Combining Deep Learning and Tensor Completion by Ang Li, Menghui Jiang, Dong Chu, Xiaobin Guan, Jie Li, Huanfeng Shen

“…Considering the temporal continuity and spatial correlation of NDVI time-series data, we combine long short-term memory with a convolution (LSTM-Conv) structure and utilize residual learning and dense connection strategies to mine the spatiotemporal features in depth. …”
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A MFR Work Modes Recognition Method Based on Dual-Scale Feature Extraction by Zhiyuan Li, Xuan Fu, Chengjian Mo, Jianlong Tang, Ronghua Guo, Wenbo Li

“…Then, a structure composed of convolutional neural network (CNN) and long short-term memory (LSTM) is followed to extract the deep time-series features at the internal-segment scale of segments, and the features of each segment are concatenated in the time dimension. …”
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Using Deep Learning in Forecasting the Production of Electricity from Photovoltaic and Wind Farms by Michał Pikus, Jarosław Wąs, Agata Kozina

“…We designed deep learning models, including recursive neural networks (RNNs), which are mainly based on long short-term memory (LSTM) networks; gated recurrent units (GRUs), convolutional neural networks (CNNs), temporal fusion transforms (TFTs), and combined architectures. …”
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A Novel Multimodal Deep Learning Approach With Loss Function for Detection of Sleep Apnea Events by Alireza Fakhim Babaei, Jafar Tanha, Mohammad Ali Balafar, Seyedehsan Roshan

“…Our method utilizes a convolutional neural network (CNN) with gated recurrent units (GRU) and an attention mechanism to capture both spatial and temporal information. …”
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A Residual-Corrected Hybrid ARIMA–CNN–LSTM Framework for High-Accuracy Tobacco Sales Forecasting in Regulated Markets by Shiyu Huang, Lili Zhou

“…The ARIMA model is reliable in learning linear or regular relationships, while the deep learn, such as convolutional neural network (CNN) and long short-term memory network (LSTM), is superior when capturing and learning nonlinear relationships. …”
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CBR-Net: A Multisensory Emotional Electroencephalography (EEG)-Based Personal Identification Model with Olfactory-Enhanced Video Stimulation by Rui Ouyang, Minchao Wu, Zhao Lv, Xiaopei Wu

“…The model includes a convolutional neural network (CNN) for spatial feature extraction, Bi-LSTM for temporal modeling, residual connections, and a fully connected classification module. …”
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High-performance traffic volume prediction: An evaluation of RNN, GRU, and CNN for accuracy and computational trade-offs by Pranolo Andri, Saifullah Shoffan, Bella Utama Agung, Wibawa Aji Prasetya, Bastian Muhammad, Hardiyanti P Cicin

“…This study addresses these challenges by evaluating the effectiveness of three deep learning architectures— Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN)—in forecasting hourly traffic volume on Interstate 94. …”
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Milling Machine Fault Diagnosis Using Acoustic Emission and Hybrid Deep Learning with Feature Optimization by Muhammad Umar, Muhammad Farooq Siddique, Niamat Ullah, Jong-Myon Kim

“…Gaussian filtering is applied to enhance the clarity of these scalograms, effectively reducing noise while maintaining essential features. A convolutional neural network (CNN) based on the VGG16 architecture is utilized for spatial feature extraction, followed by a bidirectional long short-term memory (BiLSTM) network to capture the temporal dependencies of the scalograms. …”
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HAMF: A Novel Hierarchical Attention-Based Multi-Modal Fusion Model for Parkinson’s Disease Classification and Severity Prediction by Anitha Rani Palakayala, P. Kuppusamy, D. Kothandaraman, Gunakala Archana, Jaideep Gera

“…An accuracy of 94.2 % was achieved, thus improving by 4–5 %, compared to the existing methodologies. Temporal Convolutional Network (TCN) which can capture long-range temporal dependencies, was used in the longitudinal severity estimation task, achieving a Mean Squared Error (MSE) of 0.12 in disease progression forecasting. …”
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Research on Bearing Fault Diagnosis Method for Varying Operating Conditions Based on Spatiotemporal Feature Fusion by Jin Wang, Yan Wang, Junhui Yu, Qingping Li, Hailin Wang, Xinzhi Zhou

“…Then, a parallel processing mechanism of the Convolutional Neural Network (SECNN) based on the Squeeze-and-Excitation module and the Bidirectional Long Short-Term Memory network (BiLSTM) is employed to dynamically adjust weights, capturing high-dimensional spatiotemporal features. …”
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A novel hybrid TCN-TE-ANN model for high-precision solar irradiance prediction by Murat Isik

“…While numerous studies have explored various methodologies for solar radiation prediction, challenges remain in achieving high accuracy across diverse geographic locations and temporal resolutions. This study presents a novel hybrid model combining temporal convolutional networks (TCN), Transformer encoders (TE), and artificial neural networks (ANN) to predict global horizontal irradiance (GHI) with high precision. …”
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Ultra-Short-Term Power Forecasting Method for Wind-Solar-Hydro Integration Based on Improved GRU-CNN by Xiaogang WU, Jie YAN, Chang GE, Yajie TANG, Chouwei NI, Qingfeng JI

“…To this end, an integrated ultra-short-term power forecasting method is proposed based on gated recurrent units (GRUs) and convolutional neural networks (CNNs), which can consider the temporal and spatial correlation characteristics of heterogeneous energy sources. …”
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Enhancing energy consumption forecasting for electric vehicle charging stations with Time Series Dense Encoder (TiDE) by Amril Nazir, Abdul Khalique Shaikh, Aftab Ahmed Khan, Abdul Salam Shah, Nadia Khalique

“…The transformers have handled time series forecasting better with larger datasets like the Temporal Fusion Transformer and the Temporal Convolutional Network. …”
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SmartRipen: LSTM-GRU feature selection& XGBoost-CNN for fruit ripeness detection by Archana Ganesh Said, Bharti Joshi

“…A novel Convolutional XGBoost Network (CXGBN) combines CNN's completely connected layers with XGBoost classifications for enhanced efficiency. …”
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A novel AI-driven approach to greenwashing: breakthroughs in the future fit between domain-specific Islamic enterprises with varying developmental progress and ESG landscapes by Mahdi Ghaemi Asl

“…The analysis incorporates 11 sectoral investment indices from emerging and developed countries, comprising 22 international evolving investments. Temporal convolutional networks are leveraged to evaluate long-term memory under varying market conditions. …”
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Video-Based Plastic Bag Grabbing Action Recognition: A New Video Dataset and a Comparative Study of Baseline Models by Pei Jing Low, Bo Yan Ng, Nur Insyirah Mahzan, Jing Tian, Cheung-Chi Leung

“…The second approach leverages a multiple-frame <i>convolutional neural network</i> (CNN) to exploit temporal and spatial patterns in the video data. …”
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Electric Vehicle Charging Demand Prediction Model Based on Spatiotemporal Attention Mechanism by Yang Chen, Zeyang Tang, Yibo Cui, Wei Rao, Yiwen Li

“…The accurate estimation and prediction of charging demand are crucial for the planning of charging infrastructure, grid layout, and the efficient operation of charging networks. To address the shortcomings of existing methods in utilizing the spatial interdependencies among urban regions, this paper proposes a forecasting approach that integrates dynamic time warping (DTW) with a spatial–temporal attention graph convolutional neural network (ASTGCN). …”
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SIAT: Pedestrian trajectory prediction via social interaction-aware transformer by Chengdong Wang, Jianming Wang, Wenbo Gao, Lei Guo

“…This paper introduces the Social Interaction-Aware Transformer (SIAT), a novel approach that leverages a Transformer encoder to process pedestrian embedding features and a Graph Convolutional Network (GCN) to construct a social graph for extracting spatial interaction features. …”
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TransFusion: Generating long, high fidelity time series using diffusion models with transformers by Md Fahim Sikder, Resmi Ramachandranpillai, Fredrik Heintz

“…In the past, standalone Recurrent and Convolutional Neural Network-based Generative Adversarial Networks (GAN) were used to synthesize time-series data. …”
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Cloud-based configurable data stream processing architecture in rural economic development by Haohao Chen, Fadi Al-Turjman

“…Methodology The RL-CNN-GRU framework combines reinforcement learning (RL), convolutional neural network (CNN), and gated recurrent unit (GRU) to improve agricultural price predictions using multidimensional time series data, including historical prices, weather, soil conditions, and other influencing factors. …”
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