Neural decoding of Aristotle tactile illusion using deep learning-based fMRI classification by Eunji Lee, Eunji Lee, Ji-Hyun Kim, Jaeseok Park, Jaeseok Park, Sung-Phil Kim, Taehoon Shin, Taehoon Shin, Taehoon Shin

“…Asynchronous).ResultsSimple fully convolution network (SFCN) achieved the highest classification accuracy of 68.4% for the occurrence of Aristotle illusion vs. …”
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PM2.5 prediction using population-based centrality weight by Hee Joon Choi, Won Kyung Lee, So Young Sohn

“…The proposed weight was applied to two types of deep learning models, the long-and-short term temporal neural network (LSTNet) and temporal-graph convolutional network (T-GCN) to forecast the PM2.5 in 25 districts of Seoul Metropolitan City in Korea for empirical experiments. …”
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Real-Time Volume-Rendering Image Denoising Based on Spatiotemporal Weighted Kernel Prediction by Xinran Xu, Chunxiao Xu, Lingxiao Zhao

“…Next, a dual-input convolutional neural network architecture was designed to predict filtering kernels. …”
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Combined Prediction Method of Short-Term Distance Headway Based on EB-GRA-TCN by Chun Wang, Weihua Zhang, Cong Wu, Heng Hu, Wenjia Zhu

“…To solve the above problems, a DHW prediction model is proposed in this paper by integrating entropy-based grey relation analysis (EB-GRA) and temporal convolutional network (TCN), named as EB-GRA-TCN model. …”
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Bitcoin price direction prediction using on-chain data and feature selection by Ritwik Dubey, David Enke

“…The research then explores advanced neural networks for next day price direction prediction, including the Convolutional Neural Network-Long-Short Term Memory (CNN-LSTM) and the Temporal Convolutional Network (TCN). …”
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A Study on Using Transfer Learning to Utilize Information From Similar Systems for Data-Driven Condition Diagnosis and Prognosis by Marcel Braig, Peter Zeiler

“…Both concepts are implemented with the neural network types multilayer perceptron, 1D convolutional neural network, and temporal convolutional network. …”
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Rolling bearing remaining useful life prediction using deep learning based on high-quality representation by Chenyang Wang, Wanlu Jiang, Lei Shi, Liang Zhang

“…The proposed method integrates a one-dimensional deep convolutional autoencoder (1D-DCAE) for high-quality feature extraction and a multilevel bidirectional long short-term memory (Bi-LSTM) network with a temporal pattern attention (TPA) mechanism to capture temporal dependencies. …”
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Prior-free 3D human pose estimation in a video using limb-vectors by Anam Memon, Qasim Arain, Nasrullah Pirzada, Akram Shaikh, Adel Sulaiman, Mana Saleh Al Reshan, Hani Alshahrani, Asadullah Shaikh

“…The limb direction estimator utilizes a fully convolutional network to model limb direction vectors across a temporal window. …”
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Classification of Satellite Image Time Series and Aerial Images Based on Multiscale Fusion and Multilevel Supervision by H. Kanyamahanga, M. Dorozynski, F. Rottensteiner

“…In this context, it is a challenge to train a classifier given the large difference in resolutions. We utilise convolutions to extract spatial information and consider self-attention in the temporal dimension for SITS. …”
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A lightweight CNN-LSTM hybrid model for land cover classification in satellite imagery by Nowshad Hasan, Md. Saiful Islam

“…However, traditional Convolutional Neural Networks (CNNs) require high computational demand, a large number of parameters, and a long training time for classification tasks. …”
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A Hybrid Deep Learning-Based Load Forecasting Model for Logical Range by Hao Chen, Zheng Dang

“…GCSG transforms time-series device load data into image representations using Gramian Angular Field (GAF) encoding, extracts spatial features via a Convolutional Neural Network (CNN) enhanced with a Squeeze-and-Excitation network (SENet), and captures temporal dependencies using a Gated Recurrent Unit (GRU). …”
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Evaluation of machine learning and deep learning algorithms for fire prediction in Southeast Asia by Aditya Eaturu, Krishna Prasad Vadrevu

“…In this study, we utilize Visible Infrared Imaging Radiometer Suite (VIIRS) satellite-derived fire data alongside six machine learning (ML) and deep learning (DL) models, Simple Persistence, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-Long Short-Term Memory (CNN-LSTM), and Convolutional Long Short-Term Memory (ConvLSTM) to determine the most effective fire prediction model. …”
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Vehicle Motion State Prediction Method Integrating Point Cloud Time Series Multiview Features and Multitarget Interactive Information by Ruibin Zhang, Yingshi Guo, Yunze Long, Yang Zhou, Chunyan Jiang

“…Time sequence high-level abstract combination features in the multiview scene are then extracted by an improved VGG19 network model and are fused with the potential spatiotemporal interaction of the multitarget operation state data extraction features detected by the laser radar by using a one-dimensional convolution neural network. …”
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Posture Monitoring of Patients in Radiotherapy Scenarios Based on Stacked Grayscale 3-Channel Images by Yang Zhang, Ziwen Wei, Zhihua Liu, Xiaolong Wu, Junchao Qian

“…This approach enabled capturing motion information through a large-scale dataset pre-trained 2D convolutional neural network (CNN), eliminating the need for computationally expensive optical flow calculations. …”
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A Deep Learning Model for ERP Enterprise Financial Management System by Hui Zhang

“…The method proposes an improved temporal convolutional network-long and short-term memory network (TCN_LSTM) structure and introduces an optimization algorithm to optimize the parameters of the deep learning model. …”
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Enhancement of Underwater Images through Parallel Fusion of Transformer and CNN by Xiangyong Liu, Zhixin Chen, Zhiqiang Xu, Ziwei Zheng, Fengshuang Ma, Yunjie Wang

“…Subsequently, to extract global features, both temporal and frequency domain features are incorporated to construct the convolutional neural network. …”
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Transforming physical fitness and exercise behaviors in adolescent health using a life log sharing model by Shanshan Wang, Jingwu Liu

“…IntroductionThis study investigates the potential of a deep learning-based Life Log Sharing Model (LLSM) to enhance adolescent physical fitness and exercise behaviors through personalized public health interventions.MethodsWe developed a hybrid Temporal–Spatial Convolutional Neural Network-Bidirectional Long Short-Term Memory (TS-CNN-BiLSTM) model. …”
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Efficient human activity recognition on edge devices using DeepConv LSTM architectures by Haotian Zhou, Xiujun Zhang, Yu Feng, Tongda Zhang, Lijuan Xiong

“…We designed and evaluated three models: a 2D Convolutional Neural Network (2D CNN), a 1D Convolutional Neural Network (1D CNN), and a DeepConv LSTM. …”
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Deep supervised, but not unsupervised, models may explain IT cortical representation. by Seyed-Mahdi Khaligh-Razavi, Nikolaus Kriegeskorte

“…SIFT, GIST, self-similarity features, and a deep convolutional neural network). We compared the representational dissimilarity matrices (RDMs) of the model representations with the RDMs obtained from human IT (measured with fMRI) and monkey IT (measured with cell recording) for the same set of stimuli (not used in training the models). …”
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A Study on the STGCN-LSTM Sign Language Recognition Model Based on Phonological Features of Sign Language by Yuxin Han, Yong Han, Qi Jiang

“…To deal with these challenges, this paper proposes a dual-stream deep learning model built on Spatio-Temporal Graph Convolutional Network-Long Short-Term Memory(STGCN-LSTM), which aims to capture both the local features of sign language and the global spatio-temporal characteristics of sign words. …”
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