Multistep PV power forecasting using deep learning models and the reptile search algorithm by Sameer Al-Dahidi, Hussein Alahmer, Bilal Rinchi, Abdullah Bani-Abdullah, Mohammad Alrbai, Osama Ayadi, Loiy Al-Ghussain

“…This study investigates the performance of three advanced deep learning models: Temporal Convolutional Network (TCN), Minimal Gated Unit (MGU), and Temporal Fusion Transformer (TFT), applied to one-day-ahead and three-day-ahead PV power forecasting. …”
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Optimizing physical education schedules for long-term health benefits by Liang Tan, Qin Chen, Jianwei Wu, Mingbang Li, Tianyu Liu

“…The developed DL model integrates convolutional neural network (CNN) layers to capture spatial features and long short-term memory (LSTM) layers to extract temporal patterns from demographic and activity-related variables. …”
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A novel hybrid model by integrating TCN with TVFEMD and permutation entropy for monthly non-stationary runoff prediction by Huifang Wang, Xuehua Zhao, Qiucen Guo, Xixi Wu

“…Subsequently, the complexity of each sub-component is evaluated using the permutation entropy (PE), and similar low-frequency components are clustered based on the entropy value to reduce the computational cost. Then, the temporal convolutional network (TCN) model is built for runoff prediction for each high-frequency IMFs and the reconstructed low-frequency IMF respectively. …”
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Modeling Equatorial to Mid‐Latitudinal Global Night Time Ionospheric Plasma Irregularities Using Machine Learning by Ephrem Beshir Seba, Giovanni Lapenta

“…We utilize Random Forest (RF) and a one‐dimensional Convolutional Neural Network (1D‐CNN) model, incorporating data from the Swarm A, B, and C satellites, space weather data from the OMNIWeb data center, as well as zonal and meridional wind model data. …”
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Hybrid CNN-LSTM Model with Custom Activation and Loss Functions for Predicting Fan Actuator States in Smart Greenhouses by Gregorius Airlangga, Julius Bata, Oskar Ika Adi Nugroho, Boby Hartanto Pramudita Lim

“…In this study, we propose a novel hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture to predict fan actuator states based on environmental data. …”
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Oilfield Production Prediction Method Based on Multi-Input CNN-LSTM With Attention Mechanism by Lihui Tang, Zhenpeng Wang, Yajun Gao, Hao Wu, Wenbo Zhang, Xiaoqing Xie

“…To achieve rapid, low-cost, and intelligent oil production prediction, we propose a multi-input deep neural network model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks with an attention mechanism. …”
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Multi-model Approach for Tree Detection and Classification in Wallonia Region (Belgium) by N. Dimarco, B. Bartiaux, L. Andreani, R. Schlögel

“…A Faster R-CNN model trained for tree detection achieved a F1 score of 0.828 and a mAP@50 of 0.827, effectively locating tree crowns under varying illumination and phenological conditions. Meanwhile, a convolutional neural network (CNN) for species classification attained an overall accuracy of 0.937, accurately distinguishing most species and age classes. …”
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Enhancing Stock Price Forecasting with CNN-BiGRU-Attention: A Case Study on INDY by Madilyn Louisa, Gumgum Darmawan, Bertho Tantular

“…The method applied a hybrid model combining a Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and an Attention Mechanism (AM) to address the nonlinear, volatile, and noisy characteristics of stock data. …”
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Breast cancer classification based on hybrid CNN with LSTM model by Mourad Kaddes, Yasser M. Ayid, Ahmed M. Elshewey, Yasser Fouad

“…This paper presents a novel hybrid model of DL models combined a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for binary breast cancer classification on two datasets available at the Kaggle repository. …”
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Linear and Non-Linear Methods to Discriminate Cortical Parcels Based on Neurodynamics: Insights from sEEG Recordings by Karolina Armonaite, Livio Conti, Luigi Laura, Michele Primavera, Franca Tecchio

“…For this study, we used a linear Power Spectral Density (PSD) estimate and three non-linear measures: the Higuchi fractal dimension (HFD), a one-dimensional convolutional neural network (1D-CNN), and a one-shot learning model. …”
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An effectiveness of deep learning with fox optimizer-based feature selection model for securing cyberattack detection in IoT environments by Mimouna Abdullah Alkhonaini

“…Furthermore, the FOFSDL-SCD model utilizes the Fox optimizer algorithm (FOA) method for the feature selection process to select the most significant features from the dataset. Moreover, the temporal convolutional network (TCN) model is employed for classification. …”
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A VMD-TCN-Based Method for Predicting the Vibrational State of Scaffolding in Super High-Rise Building Construction by Ping Zhu, Gen Liu, Jian Wang, Pengfei Wang

“…This study proposes a vibration state prediction model based on Variational Mode Decomposition (VMD) and Temporal Convolutional Network (TCN), referred to as the VMD-TCN model. …”
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An AI explained systematic modular approach for enhanced Electricity Theft Detection for urbanized Smart Grid environment by Muhammad Ammar, Nadeem Javaid, Ali Arishi

“…Finally, the proposed SATBlend in the classification module utilizes AlexNet for feature extraction, ShuffleNet for efficient computation, and a temporal convolutional network for temporal correlation detection to enhance the reliability of advanced ETD systems. …”
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High accuracy indoor positioning system using Galois field-based cryptography and hybrid deep learning by Mohammad Mazyad Hazzazi, Prashant Kumar Shukla, Piyush Kumar Shukla, Fahad Alblehai, Sameer Nooh, Mohd Asif Shah

“…These features can be signal-based, spatial–temporal, motion-based, or environmental. The Deep Spatial–Temporal Attention Network (Deep-STAN) is an innovative hybrid model for location classification that combines Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), Long-Short Term Memory (LSTMs), and attention processes. …”
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Real-Time Coronary Artery Dominance Classification from Angiographic Images Using Advanced Deep Video Architectures by Hasan Ali Akyürek

“…Transformer-based models showed superior accuracy compared to convolution-based methods, highlighting their effectiveness in capturing spatial–temporal patterns in angiographic videos. …”
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A Smartphone-Based Non-Destructive Multimodal Deep Learning Approach Using pH-Sensitive Pitaya Peel Films for Real-Time Fish Freshness Detection by Yixuan Pan, Yujie Wang, Yuzhe Zhou, Jiacheng Zhou, Manxi Chen, Dongling Liu, Feier Li, Can Liu, Mingwan Zeng, Dongjing Jiang, Xiangyang Yuan, Hejun Wu

“…A Temporal Convolutional Network (TCN) was then used to model dynamic patterns in chemical indicators across spoilage stages, combined with a Context-Aware Gated Fusion (CAG-Fusion) mechanism to adaptively integrate image and chemical temporal features. …”
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Leveraging Machine Learning and Remote Sensing for Water Quality Analysis in Lake Ranco, Southern Chile by Lien Rodríguez-López, Lisandra Bravo Alvarez, Iongel Duran-Llacer, David E. Ruíz-Guirola, Samuel Montejo-Sánchez, Rebeca Martínez-Retureta, Ernesto López-Morales, Luc Bourrel, Frédéric Frappart, Roberto Urrutia

“…Employing four advanced machine learning models (recurrent neural network (RNNs), long short-term memory (LSTM), recurrent gate unit (GRU), and temporal convolutional network (TCNs)), the research focuses on the estimation of chlorophyll-a concentrations at three sampling stations within Lake Ranco. …”
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Multiclass Classification of Imagined Speech Vowels and Words of Electroencephalography Signals Using Deep Learning by Nrushingh Charan Mahapatra, Prachet Bhuyan

“…We proposed a novel supervised deep learning model that combined the temporal convolutional networks and the convolutional neural networks with the intent of retrieving information from the EEG signals. …”
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Predictive study of machine learning combined with serum Neuregulin 4 levels for hyperthyroidism in type II diabetes mellitus by Huilan Gu, Ye Lu

“…Additionally, a CNN+LSTM network was employed to extract spatial (thyroid morphology) and temporal (hemodynamics) features from ultrasound sequences. …”
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Short-Term Passenger Flow Prediction Based on Federated Learning on the Urban Metro System by Guowen Dai, Jinjun Tang, Jie Zeng, Yuting Jiang

“…To address these issues, this study proposes a federated learning framework integrating convolutional neural networks (CNNs) and bidirectional gated recurrent units (BIGRU). …”
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