Innovative Expert-Based Tools for Spatiotemporal Shallow Landslides Mapping: Field Validation of the GOGIRA System and Ex-MAD Framework in Western Greece by Michele Licata, Francesco Seitone, Efthimios Karymbalis, Konstantinos Tsanakas, Giandomenico Fubelli

“…ExMAD applied a pre-trained U-Net convolutional neural network for automated temporal trend detection of landslide events. …”
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A Hybrid Framework for Photovoltaic Power Forecasting Using Shifted Windows Transformer-Based Spatiotemporal Feature Extraction by Ping Tang, Ying Su, Weisheng Zhao, Qian Wang, Lianglin Zou, Jifeng Song

“…Therefore, this paper proposes a hybrid framework based on shifted windows Transformer (Swin Transformer), convolutional neural network, and long short-term memory network to comprehensively extract spatiotemporal feature information, including global spatial, local spatial, and temporal features, from ground-based sky images and PV power data. …”
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Advances to IoT security using a GRU-CNN deep learning model trained on SUCMO algorithm by Amit Sagu, Nasib Singh Gill, Preeti Gulia, Noha Alduaiji, Piyush Kumar Shukla, Mohd Asif Shah

“…This paper proposes a hybrid deep learning model that combines Convolutional Neural Network (CNN) and Gated Recurrent Units (GRUs) to classify the IoT security threats. …”
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Real-time Jordanian license plate recognition using deep learning by Salah Alghyaline

“…This paper aims to develop an accurate ALPR for Jordanian LPs. Two-stage Convolutional Neural Networks (CNNs) are used in the proposed approach, the CNNs are based on the YOLO3 framework. …”
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Impact of Safety Signage Placement on Evacuation Behavior in Virtual Fire Scenarios Based on EDA Data by Wenqi Song, Xu Feng, Yu Song

“…Three variables are evaluated, signage height (1m, 0.5m, and 0m), spacing (5m and 10m), and presence of active fire, using a hybrid classification model that integrates an im-proved convolutional neural network (CNN), a Transformer-based sequence encoder, and a multi-layer spiking neural network. …”
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Time series prediction based on the variable weight combination of the T-GCN-Luong attention and GRU models by Yushu Guo, Jiacheng Huang, Xuchu Jiang

“…Considering the spatiotemporal features of temperature changes, this paper proposes a variable weight combination model based on a temporal graph convolutional network (T-GCN), Luong attention network (LUA) and gated recurrent unit (GRU) network, which fully utilizes spatiotemporal information to predict future temperature changes more accurately. …”
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Comparison of Machine Learning Methods for Menstrual Cycle Analysis and Prediction by Mutiara Khairunisa, Desak Made Sidantya Amanda Putri, I Gusti Ngurah Lanang Wijayakusuma

“…This study compares three machine learning methods—Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Decision Tree—for analyzing and predicting menstrual cycles. …”
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An Improved CEEMDAN-FE-TCN Model for Highway Traffic Flow Prediction by Heyao Gao, Hongfei Jia, Lili Yang

“…The fuzzy entropy (FE) is then calculated to recombine subsequences, highlighting traffic flow dynamics in different frequencies and improving prediction efficiency. Finally, the Temporal Convolutional Network (TCN) is adopted to predict the recombined subsequences, and the final prediction result is reconstructed. …”
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Enhancing corn industry sustainability through deep learning hybrid models for price volatility forecasting. by Chengjin Yang, Yanzhong Zhai, Zehua Liu

“…The model integrates a three-layer decomposition combined dual-filter time-series denoising method (TLDCF-TSD), a bidirectional time-convolutional enhancement network (BiTCEN), a bidirectional long- and short-term memory network (BiLSTM), and a frequency-enhanced channel attention mechanism (FECAM) to improve prediction accuracy and robustness. …”
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A rapid and efficient method for flash flood simulation based on deep learning by Xinying Wang, Miao Xiao, Yi Liu, Jun Guo, Yangyang Qin, Yunkang Zhang

“…Then, we developed a Temporal Convolutional Network (TCN) model to predict flash floods. …”
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Enhancing Crop Classification in Emilia-Romagna (Italy) Using Transformer-Based Multi-Source Data Fusion with Thermal Observations by Y. Qi, E. Mandanici, M. Helmy, F. Trevisiol, G. Bitelli

“…We implemented four deep learning models using TensorFlow: Dense Neural Network (DNN), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Transformer. …”
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EPCNet: Implementing an ‘Artificial Fovea’ for More Efficient Monitoring Using the Sensor Fusion of an Event-Based and a Frame-Based Camera by Orla Sealy Phelan, Dara Molloy, Roshan George, Edward Jones, Martin Glavin, Brian Deegan

“…However, increased resolution results in increased network latency and power consumption. To minimise this latency, Convolutional Neural Networks (CNNs) often have a resolution limitation, requiring images to be down-sampled before inference, causing significant information loss. …”
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Continuous Arabic Sign Language Recognition Models by Nahlah Algethami, Raghad Farhud, Manal Alghamdi, Huda Almutairi, Maha Sorani, Noura Aleisa

“…This study is the first to use the Temporal Convolutional Network (TCN) model for Arabic Sign Language (ArSL) recognition. …”
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Development of Continuous AMSR-E/2 Soil Moisture Time Series by Hybrid Deep Learning Model (ConvLSTM2D and Conv2D) and Transfer Learning for Reanalyses by Visakh Sivaprasad, Mehdi Rahmati, Anne Springer, Harry Vereecken, Carsten Montzka

“…We developed a sophisticated deep learning ConvLSTM model, that combines convolutional long short-term memory (ConvLSTM2D) layers and convolutional neural network (CNN) layers. …”
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Internet of things enabled deep learning monitoring system for realtime performance metrics and athlete feedback in college sports by Yang Hu, Yaxing Li, Benlai Cui, Hao Su, Pan Zhu

“…The proposed work integrates advanced wearable sensor technologies with a hybrid neural network combining Temporal Convolutional Networks, Bidirectional Long Short-Term Memory (TCN + BiLSTM) + Attention mechanisms. …”
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Temperature Prediction and Fault Warning of High-Speed Shaft of Wind Turbine Gearbox Based on Hybrid Deep Learning Model by Min Zhang, Jijie Wei, Zhenli Sui, Kun Xu, Wenyong Yuan

“…Compared to the long short-term memory (LSTM) and convolutional neural network and LSTM hybrid models, the STA architecture reduces the root mean square error of the prediction by approximately 37% and 13%, respectively. …”
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Optimizing Deep Learning Models for Fire Detection, Classification, and Segmentation Using Satellite Images by Abdallah Waleed Ali, Sefer Kurnaz

“…The study evaluated the performance of three distinct models: an autoencoder, a U-Net, and a convolutional neural network (CNN), comparing their effectiveness in predicting wildfire occurrences. …”
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HSTCN-NuSVC: A Homogeneous Stacked Deep Ensemble Learner for Classifying Human Actions Using Smartphones by Sarmela Raja Sekaran, Ying Han Pang, Ooi Shih Yin, Lim Zheng You

“…This work aims to overcome the issues by proposing a lightweight, homogenous stacked deep ensemble model, termed Homogenous Stacking Temporal Convolutional Network with Nu-Support Vector Classifier (HSTCN-NuSVC), for activity classification. …”
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Hierarchical Multi-Scale Decomposition and Deep Learning Ensemble Framework for Enhanced Carbon Emission Prediction by Yinuo Sun, Zhaoen Qu, Zhuodong Liu, Xiangyu Li

“…Each IMF is processed through a hybrid convolutional neural network (CNN)–Transformer architecture: CNNs extract local features and transformers model long-range dependencies via multi-head attention. …”
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Surface and Subsurface Soil Moisture Estimation Using Fusion of SMAP, NLDAS-2, and SOLUS100 Data with Deep Learning by Saman Rabiei, Ebrahim Babaeian, Sabine Grunwald

“…This study developed a convolutional neural network–long short-term memory (ConvLSTM) deep learning model to produce ‘daily’ surface (5 cm) and subsurface (25 cm) SM products (9 km) by integrating SMAP level 3 ancillary data, North American Land Data Assimilation System (NLDAS-2; 12 km) SM, and Soil Landscapes of the United States (SOLUS100) digital maps across the contiguous U.S. …”
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