Finger drawing on smartphone screens enables early Parkinson's disease detection through hybrid 1D-CNN and BiGRU deep learning architecture. by Zhaohui Zhu, E Wu, Pengfei Leng, Jiajun Sun, Mingming Ma, Zhigeng Pan

“…Our hybrid model combined multi-scale convolutional feature extraction (using parallel 1D-Convolutional branches) with bidirectional temporal pattern recognition (via gated recurrent unit [GRU] networks) to analyze movement abnormalities and detect the disease.…”
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Applying SSVEP BCI on Dynamic Background by Junkai Li, Boxun Fu, Fu Li, Wenkai Gu, Youshuo Ji, Yang Li, Tiejun Liu, Guangming Shi

“…Furthermore, we proposed Multi-scale Temporal-Spatial Global average pooling Neural Network (MTSGNN), an end-to-end network for decoding SSVEP signals evoked by the post-modulation paradigm. …”
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Wavelet-Enhanced Deep Learning Ensemble for Accurate Stock Market Forecasting: A Case Study of Nifty 50 Index by Priya Singh, Manoj Jha, Harshita Patel

“…This research proposes an ensemble model that integrates Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and Temporal Convolutional Networks (TCN) for effective stock market prediction. …”
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Artificial intelligence-driven cybersecurity: enhancing malicious domain detection using attention-based deep learning model with optimization algorithms by Fatimah Alhayan, Asma Alshuhail, Ahmed Omer Ahmed Ismail, Othman Alrusaini, Sultan Alahmari, Abdulsamad Ebrahim Yahya, Monir Abdullah, Samah Al Zanin

“…Furthermore, the hybrid model of a temporal convolutional network and bi-directional long short-term memory with squeeze-and-excitation Attention (TCN-BiLSTM-SEA) model is employed for the classification process. …”
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Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms by D. Ciani, C. Fanelli, B. Buongiorno Nardelli

“…To address these issues, we developed and tested different deep learning methodologies, specifically convolutional neural network (CNN) models that were originally proposed for single-image super resolution. …”
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MS3OSD: A Novel Deep Learning Approach for Oil Spills Detection Using Optical Satellite Multisensor Spatial-Spectral Fusion Images by Kai Du, Yi Ma, Zhongwei Li, Rongjie Liu, Zongchen Jiang, Junfang Yang

“…The framework uses parallel branches, including a convolutional neural network and a vision transformer, to extract surrounding spatial features and central spectral features from the fused data. …”
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DenseNet-ABiLSTM: Revolutionizing Multiclass Arrhythmia Detection and Classification Using Hybrid Deep Learning Approach Leveraging PPG Signals by K. Saranya, U. Karthikeyan, A. Saran Kumar, Ayodeji Olalekan Salau, Ting Tin Tin

“…The model uses 1D convolutional kernels to acquire multiscale conceptual features, followed by BiLSTM to understand temporal relationships among features. …”
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Scene Text Detection and Recognition Using Maximally Stable Extremal Region by Golda Jeyasheeli P, Athinarayanan B, Manish T, Mohamad Umar M

“…Our CRNN architecture consists of convolutional and recurrent layers, which enable us to capture both spatial and temporal features of the text. …”
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Precision irrigation with AI-driven optimization of plant electrophysiology by Yiting Chen, Devon Scott, Hieu Trung Tran, Yan Sum Shirley Yip, Soomin Shin, Woo Soo Kim

“…Our system integrates EP sensors, real-time signal acquisition and processing, and a convolutional neural network (CNN)-based predictive model to optimize irrigation conditions. …”
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Multi-Dimensional Anomaly Detection and Fault Localization in Microservice Architectures: A Dual-Channel Deep Learning Approach with Causal Inference for Intelligent Sensing by Suchuan Xing, Yihan Wang, Wenhe Liu

“…This paper proposes a dual-channel deep learning framework that integrates Temporal Convolutional Networks with Variational Autoencoders to address these challenges. …”
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Enhancing urban air quality prediction using time-based-spatial forecasting framework by Shrikar Jayaraman, Nathezhtha T, Abirami S, Sakthivel G

“…The TBS employs Convolutional Neural Networks (CNNs) to capture spatial dependencies based on normalized latitude and longitude coordinates of the cities. …”
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GL-ST: A Data-Driven Prediction Model for Sea Surface Temperature in the Coastal Waters of China Based on Interactive Fusion of Global and Local Spatiotemporal Information by Ning Song, Jie Nie, Qi Wen, Yuchen Yuan, Xiong Liu, Jun Ma, Zhiqiang Wei

“…These data-driven techniques often utilize classic convolutional networks (CONV) and long short-term memory networks (LSTM) to extract spatial and temporal features. …”
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Redefining Urban Traffic Dynamics With TCN-FL Driven Traffic Prediction and Control Strategies by K. M. Karthick Raghunath, C. Rohith Bhat, Venkatesan Vinoth Kumar, Velmurugan Athiyoor Kannan, T. R. Mahesh, K. Manikandan, N. Krishnamoorthy

“…In this study, we have introduced a traffic prediction and handling system that utilizes Temporal Convolutional Networks (TCNs) combined with Federated Learning (FL) to deal with urban traffic effectively. …”
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Spatial Mismatch Between Transportation Development and Tourism Spatial Vitality in Yunnan Province in the Context of Urban–Rural Integration by Juhua Gao, Xingwu Duan, Qinglong Wang, Zijiang Yang, Ronghua Zhong, Xiaodie Yuan, Xiong He

“…Using Weibo check-in big data and OpenStreetMap transportation network data, we apply Convolutional Long Short-Term Memory (ConvLSTM) networks and bivariate spatial autocorrelation analysis to examine this relationship. …”
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On the Hybrid Algorithm for Retrieving Day and Night Cloud Base Height from Geostationary Satellite Observations by Tingting Ye, Zhonghui Tan, Weihua Ai, Shuo Ma, Xianbin Zhao, Shensen Hu, Chao Liu, Jianping Guo

“…The algorithm first utilizes a convolutional neural network-based model to extract cloud top height (CTH) and cloud water path (CWP) from the AHI infrared observations. …”
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Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique by Moritz Schneider, Kevin Seeser-Reich, Armin Fiedler, Udo Frese

“…This study systematically tests several machine-learning architectures for near-fall detection using the Prev-Fall dataset, which consists of high-resolution inertial measurement unit (IMU) data from 110 workers. Convolutional neural networks (CNNs), residual networks (ResNets), convolutional long short-term memory networks (convLSTMs), and InceptionTime models were trained and evaluated over a range of temporal window lengths using a neural architecture search. …”
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Infilling of missing rainfall radar data with a memory-assisted deep learning approach by J. Meuer, L. M. Bouwer, L. M. Bouwer, F. Kaspar, R. Lehmann, W. Karl, T. Ludwig, C. Kadow

“…We propose a deep convolutional neural network enhanced with a memory component to better account for temporal changes in precipitation fields. …”
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Channel-Dependent Multilayer EEG Time-Frequency Representations Combined with Transfer Learning-Based Deep CNN Framework for Few-Channel MI EEG Classification by Ziang Liu, Kang Fan, Qin Gu, Yaduan Ruan

“…By adopting a deep convolutional neural network with EfficientNet as the backbone and utilizing pre-trained weights from natural image datasets for transfer learning, the framework can simultaneously learn temporal, spatial, and channel features embedded in the CDML-EEG-TFR. …”
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Manifold and spatiotemporal learning on multispectral unoccupied aerial system imagery for phenotype prediction by Fared Farag, Trevis D. Huggins, Jeremy D. Edwards, Anna M. McClung, Ahmed A. Hashem, Jason L. Causey, Emily S. Bellis

“…We observed similar performance on a held‐out growing season for a spatiotemporal model (a three‐dimensional convolutional neural network) trained on raw images compared to simpler workflows using dimension reduction of manually extracted features from temporal imagery (i.e., vegetation indices and image texture properties). …”
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Unlocking transcranial FUS-EEG feature fusion for non-invasive sleep staging in next-gen clinical applications by Suneet Gupta, Praveen Gupta, Bechoo Lal, Aniruddha Deka, Hirakjyoti Sarma, Sheifali Gupta

“…The proposed framework integrates two one-dimensional convolutional neural networks (1D-CNNs) to extract sleep-relevant features from EEG and EOG signals, followed by an adaptive feature fusion module that dynamically assigns weights based on feature significance. …”
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