Accurate hourly AQI prediction using temporal CNN-LSTM-MHA+GRU: A case study of seasonal variations and pollution extremes in Visakhapatnam, India by Sreenivasulu T, Mokesh Rayalu G

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STIDNet: Spatiotemporally Integrated Detection Network for Infrared Dim and Small Targets by Liuwei Zhang, Zhitao Zhou, Yuyang Xi, Fanjiao Tan, Qingyu Hou

“…In this work, a spatiotemporally integrated detection network (STIDNet) is proposed for IRDSTD. In the network, a spatial saliency feature generation module (SSFGM) employs a U-shaped network to extract deep features from the spatial dimension of the input image in a frame-by-frame manner and splices them based on the temporal dimension to obtain an airtime feature tensor. …”
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A Deep Neural Network-Based Approach to Media Hotspot Discovery by Pan Luo

“…To address this problem, this paper proposes a method to discover current hotspots by combining deep neural networks with text data. First, the text data features are extracted based on the graphical convolutional neural network, and the temporal correlation of numerical information is modeled using gated recurrent units, and the numerical feature vectors are fused with the text feature vectors. …”
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BN-SNN: Spiking neural networks with bistable neurons for object detection. by Siddiqui Muhammad Yasir, Hyun Kim

“…Spiking neural networks (SNNs) are emerging as a promising evolution in neural network paradigms, offering an alternative to conventional convolutional neural networks (CNNs). …”
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A dual path graph neural network framework for dementia diagnosis by Denghui Zhang, Chenxuan Zhu

“…Abstract Dementia typically results from damage to neural pathways and the consequent degeneration of neuronal connections. Graph neural networks (GNNs) have been widely employed to model complex brain networks. …”
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Cloud-edge collaborative data anomaly detection in industrial sensor networks. by Tao Yang, Xuefeng Jiang, Wei Li, Peiyu Liu, Jinming Wang, Weijie Hao, Qiang Yang

“…The latter based on GCRL is developed by inserting Long Short-Term Memory network (LSTM) into Graph Convolutional Network (GCN), which can effectively extract the spatial and temporal features of the sensor data for anomaly detection. …”
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An Effective Deep Neural Network Architecture for EEG-Based Recognition of Emotions by Khadidja Henni, Neila Mezghani, Amar Mitiche, Lina Abou-Abbas, Amel Benazza-Ben Yahia

“…The purpose of this study is to investigate a novel end-to-end deep learning method of emotion recognition using EEG data, which prefaces a combination of two-dimensional (2D) convolutional network (CNN) and Long short-term memory network (LSTM) by an autoencoder. …”
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A Hybrid Deep Learning-Based Network for Photovoltaic Power Forecasting by Altaf Hussain, Zulfiqar Ahmad Khan, Tanveer Hussain, Fath U Min Ullah, Seungmin Rho, Sung Wook Baik

“…Firstly, data preprocessing is performed to normalize, remove the outliers, and deal with the missing values prominently. Next, the temporal features are extracted using deep sequential modelling schemes, followed by the extraction of spatial features via convolutional neural networks. …”
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WSN intrusion detection method using improved spatiotemporal ResNet and GAN by Yang Jing

“…Then, an improved spatiotemporal residual network model is designed, in which the spatial and temporal features of the data are extracted and fused through multi-scale one-dimensional convolution modules and gated loop unit modules, and identity maps are added based on the idea of residual networks to avoid network degradation and other issues. …”
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Multi-label Bird Species Classification Using Transfer Learning Network by Xue HAN, Jianxin PENG

“…The long short-term memory network (LSTM) was further employed to extract temporal features. …”
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Comparative Analysis of Deep Learning Models for Intrusion Detection in IoT Networks by Abdullah Waqas, Sultan Daud Khan, Zaib Ullah, Mohib Ullah, Habib Ullah

“…We conducted a comparative analysis of three widely used DL models—Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Bidirectional LSTM (biLSTM)—across four benchmark IoT intrusion detection datasets: BoTIoT, CiCIoT, ToNIoT, and WUSTL-IIoT-2021. …”
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RoPT: Route-Planning Model with Transformer by Zuyun Xiong, Yan Wang, Yuxuan Tian, Lijuan Liu, Shunzhi Zhu

“…This model is based on the fusion of Graph Convolutional Networks (GCNs) and a Transformer, which uses GCNs for capturing complex spatial dependencies between the current intersection and the destination in a road network. …”
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Dynamic Optimization of Recurrent Networks for Wind Speed Prediction on Edge Devices by Laeeq Aslam, Runmin Zou, Ebrahim Shahzad Awan, Sayyed Shahid Hussain, Muhammad Asim, Samia Allaoua Chelloug, Mohammed A. ELAffendi

“…To address this gap, we propose a framework that co-optimizes the discrete hyperparameter spaces of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Temporal Convolutional Network (TCN) models under strict memory constraints. …”
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Crop Classification Using Time-Series Sentinel-1 SAR Data: A Comparison of LSTM, GRU, and TCN with Attention by Yuta Tsuchiya, Rei Sonobe

“…A time series of 16 scenes, acquired at 12-day intervals from 25 April to 22 October 2024, was used to classify six crop types: beans, beetroot, grassland, maize, potato, and winter wheat. Three temporal models—long short-term memory (LSTM), bidirectional gated recurrent unit (Bi-GRU), and temporal convolutional network (TCN)—were evaluated with and without an attention mechanism. …”
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Safeguarding the Integrity of Online Social Networks (OSN): Leveraging the Efficacy of Conv-LSTM-Based Siamese Network to Predict Hate Speech in Low Resource Hindi-English Code-Mix... by Shankar Biradar, Sunil Saumya, Sanjana Kavatagi

“…While most existing studies focus on monolingual data, our work addresses hate speech detection in Hindi-English code-mixed text. We propose a Convolution-LSTM network that incorporates spatial and temporal features. …”
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PDSDC: Progressive Spatiotemporal Difference Capture Network for Remote Sensing Change Detection by YeKai Cui, Peng Duan, Jinjiang Li

“…For high-level semantic features, a dynamic graph convolutional attention network is constructed, which dynamically establishes topological associations between features through a learnable adjacency matrix, optimizing global semantic consistency through a channel recalibration mechanism. …”
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The spatial–temporal evolution characteristics and influencing factors of coordinated development in the Yellow River Basin: Based on the perspective of flood-sediment transport, e... by Ni Geng, Guiliang Tian, Hengquan Zhang

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Video-Based Sign Language Recognition via ResNet and LSTM Network by Jiayu Huang, Varin Chouvatut

“…In sign language recognition tasks, traditional convolutional neural networks used to extract spatio-temporal features from sign language videos suffer from insufficient feature extraction, resulting in low recognition rates. …”
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Listening deeper: neural networks unravel acoustic features in preterm infant crying by Yuta Shinya, Taiji Ueno, Masahiko Kawai, Fusako Niwa, Seiichi Tomotaki, Masako Myowa

“…To address this, we employed a convolutional neural network to assess whether whole Mel-spectrograms of infant crying capture gestational age (GA) variations (79 preterm infants; 52 term neonates). …”
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A novel twin time series network for building energy consumption predicting. by Zhixin Sun, Han Cui, Xiangxiang Mei, Hailei Yuan

“…To overcome these issues, the study proposes Twin Time-Series Networks (T2SNET), which incorporates a time-embedding layer and a Temporal Convolutional Network (TCN) to extract patterns from Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), along with an adaptive fusion gate to combine energy consumption and meteorological data. …”
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