A deep learning based framework for enhanced reference evapotranspiration estimation: evaluating accuracy and forecasting strategies by Suman Saurabh Sarkar, Jatin Bedi, Sushma Jain

“…This study first evaluated the performances of three deep learning sequential models—Long short-term memory (LSTM), Neural Basis Expansion Analysis for Time Series (N-BEATS) and, Temporal Convolutional Network model (TCN), for predicting daily ET o possessing temporal characteristics. …”
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Diverse behavior clustering of students on campus with macroscopic attention by Wanghu Chen, Zongjuan Wu, Siqi Zeng, Hongle Guo, Jing Li

“…A campus behavior clustering approach based on MA qualities is then proposed to reveal the impact of MA on academic performance, which utilizes a Temporal Convolutional Network (TCN) to extract temporal features. …”
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Development and Training Strategy of Badminton Action Recognition System Under the Background of Artificial Intelligence by Hongjun Ma, Fan Zhang, Ni Liang

“…In the experimental section, the performance of three mainstream models—Spatial-Temporal Graph Convolutional Network (ST-GCN), Vision-Attention Transformer for Real-time Motion Recognition (VATRM), and Multi-Modal Network for Sports Action Recognition (MM-Net)—is compared from two dimensions: recognition performance and computational efficiency. …”
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Machine learning for base transceiver stations power failure prediction: A multivariate approach by Sofia Ahmed, Tsegamlak Terefe, Dereje Hailemariam

“…We employ a combination of deep learning architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and hybrid CNN-LSTM models, to achieve accurate and timely predictions of BTS power failures. …”
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An Improved Fault Diagnosis Method and Its Application in Compound Fault Diagnosis for Paper Delivery Structure Coupling by Fu Liu, Haopeng Chen, Yan Wang

“…To overcome these limitations, a multihead self-attention mechanism-enhanced empirical mode decomposition (EEMD)–convolutional neural network (CNN)–bidirectional long short-term memory (BiLSTM) model is proposed. …”
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Improving trend prediction of agricultural futures price using image encoding and attention mechanisms by Dabin Zhang, Huiqiang Xie, Huanling Hu, Zehui Yu

“…By transforming one-dimensional time series data into image representations, the model employs Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) to extract rich visual patterns and temporal dependencies from the encoded images. …”
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Metal interdiffusion enhanced WOx/CuOx heterojunction optoelectronic memristive synapses for face recognition application by Hans Juliano, Stephen Ekaputra Limantoro, Firman M. Simanjuntak, Tseung-Yuen Tseng

“…A convolutional neural network operation is conducted by exploiting the synaptic functions of the device. …”
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A comparative study of multivariate CNN, BiLSTM and hybrid CNN–BiLSTM models for forecasting foreign exchange rate using deep learning by Elysee Nsengiyumva, Joseph K. Mung’atu, Charles Ruranga

“…This study evaluates the forecasting capabilities of multivariate Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and a hybrid CNN-BiLSTM model for predicting daily rate returns of USD, EUR and GBP in Rwanda’s foreign exchange market from 2012 to 2025. …”
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EST-STFM: An Efficient Deep-Learning-Based Spatiotemporal Fusion Method for Remote Sensing Images by Qiyuan Zhang, Xiaodan Zhang, Chen Quan, Tong Zhao, Wei Huo, Yuanchen Huang

“…However, existing methods face the following challenges: 1) traditional approaches rely on linear assumptions; 2) convolutional neural networks in deep learning struggle with capturing global context; 3) generative adversarial networks suffer from mode collapse; and 4) while Transformers excel at modeling global dependencies, they are computationally intensive. …”
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Harnessing Multi-Source Data and Deep Learning for High-Resolution Land Surface Temperature Gap-Filling Supporting Climate Change Adaptation Activities by Katja Kustura, David Conti, Matthias Sammer, Michael Riffler

“…We develop a regression-based convolutional neural network model, trained on ECOSTRESS (ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station) mission data, which performs pixelwise LST predictions using 5 × 5 image patches, capturing contextual information around each pixel. …”
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Modelling on Car-Sharing Serial Prediction Based on Machine Learning and Deep Learning by Nihad Brahimi, Huaping Zhang, Lin Dai, Jianzi Zhang

“…To achieve that, various machine learning models, namely vector autoregression (VAR), support vector regression (SVR), eXtreme gradient boosting (XGBoost), k-nearest neighbors (kNN), and deep learning models specifically long short-time memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), CNN-LSTM, and multilayer perceptron (MLP), were performed on different kinds of features. …”
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Ensemble Voting Method for Phonocardiogram Heart Signal Classification Using FFT Features by Adisaputra Zidha Noorizki, Heri Pratikno, Weny Indah Kusumawati

“…This research investigates the classification of PCG signals using Fast Fourier Transform (FFT) features and deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Temporal Convolutional Network (TCN). Hyperparameter tuning, particularly learning rate adjustment, is applied to optimize the performance of the models. …”
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Early prediction of ransomware API calls behaviour based on GRU-TCN in healthcare IoT by Jueun Jeon, Seungyeon Baek, Byeonghui Jeong, Young-Sik Jeong

“…The extracted behaviour features are entered into a hybrid deep learning model that combines the bidirectional gated recurrent unit (Bi-GRU) model and the temporal convolutional network (TCN) model to predict a future 90 s API calls sequence. …”
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Research on interval prediction method of railway freight based on big data and TCN‐BiLSTM‐QR by Chenyang Feng, Yang Lei

“…On the basis of traditional prediction models, this paper introduces the concepts of interval and probability prediction, and proposes a temporal convolutional network (TCN)‐bi‐directional long short‐term memory (BiLSTM) interval prediction method for medium and long‐term railway freight volume. …”
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Enhancing Cognitive Workload Classification Using Integrated LSTM Layers and CNNs for fNIRS Data Analysis by Mehshan Ahmed Khan, Houshyar Asadi, Mohammad Reza Chalak Qazani, Adetokunbo Arogbonlo, Siamak Pedrammehr, Adnan Anwar, Hailing Zhou, Lei Wei, Asim Bhatti, Sam Oladazimi, Burhan Khan, Saeid Nahavandi

“…To address these limitations associated with conventional methods, this paper conducts a comprehensive exploration of the impact of Long Short-Term Memory (LSTM) layers on the effectiveness of Convolutional Neural Networks (CNNs) within deep learning models. …”
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DTC-m6Am: A Framework for Recognizing N6,2′-O-dimethyladenosine Sites in Unbalanced Classification Patterns Based on DenseNet and Attention Mechanisms by Hui Huang, Fenglin Zhou, Jianhua Jia, Huachun Zhang

“…The model then combines densely connected convolutional networks (DenseNet) and temporal convolutional network (TCN). …”
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A Learning Emotion Recognition Model Based on Feature Fusion of Photoplethysmography and Video Signal by Xiaoliang Zhu, Zili He, Chuanyong Wang, Zhicheng Dai, Liang Zhao

“…To address these concerns, our work mainly includes the following: (i) the development of a temporal convolutional network model incorporating channel attention to overcome PPG-based emotion recognition challenges; (ii) the introduction of a network model that integrates multi-scale spatiotemporal features to address the challenges of emotion recognition in spontaneous environmental videos; (iii) an exploration of a dual-mode fusion approach, along with an improvement of the model-level fusion scheme within a parallel connection attention aggregation network. …”
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CH-RUN: a deep-learning-based spatially contiguous runoff reconstruction for Switzerland by B. Kraft, M. Schirmer, W. H. Aeberhard, M. Zappa, S. I. Seneviratne, L. Gudmundsson

“…We test two sequential deep-learning architectures: a long short-term memory (LSTM) model, which is a recurrent neural network able to learn complex temporal features from sequences, and a convolution-based model, which learns temporal dependencies via 1D convolutions in the time domain. …”
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Bayesian Optimized of CNN-M-LSTM for Thermal Comfort Prediction and Load Forecasting in Commercial Buildings by Chi Nghiep Le, Stefan Stojcevski, Tan Ngoc Dinh, Arangarajan Vinayagam, Alex Stojcevski, Jaideep Chandran

“…To address this energy consumption challenge, a predictive model named Bayesian optimisation Convolution Neural Network Multivariate Long Short-term Memory (BO CNN-M-LSTM) is introduced in this research. …”
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Non-intrusive load monitoring based on time-enhanced multidimensional feature visualization by Tie Chen, Yimin Yuan, Jiaqi Gao, Shinan Guo, Pingping Yang

“…By adding a time axis to the V–I trajectory, it integrates the rate of change in voltage and current, power factor, and third harmonic to form a three-dimensional spatiotemporal color V–I trajectory, addressing the gap in dynamic characteristics. The ECA-ResNet34 network model is used for load identification, avoiding the problems of network degradation and training difficulties caused by the excessive depth of traditional convolutional neural networks (CNN), and achieving efficient monitoring of household loads. …”
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