GMTBLC: a deep learning-based bi-modal network traffic classification method by WEI Debin, JIANG Qinlong, WEN Jinglong, WANG Xinrui

“…Simultaneously, session images were processed by the spatio-temporal feature extraction (SFE) module, of which the spatial features of packets were extracted by a convolutional neural network with residual connections, and temporal features of packets were extracted by a bi-directional long short-term memory network. …”
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Noise Suppressed Image Reconstruction for Quanta Image Sensors Based on Transformer Neural Networks by Guanjie Wang, Zhiyuan Gao

“…The proposed neural network integrates convolutional neural networks and Transformers. …”
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Lightweighting the prediction process of urban states with parameter sharing and dilated operations by Peixiao Wang, Haolong Yang, Hengcai Zhang, Shifen Cheng, Feng Lu, Zeqiang Chen

“…In this study, we present a lightweight parameter-shared dilated convolutional network (PSDCN) to address these challenges. …”
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Long Term Predictability of Southern Ocean Surface Nutrients Using Explainable Neural Networks by Gian Giacomo Navarra, Curtis Deutsch, Antonios Mamalakis, Andrew Margolskee, Graeme MacGilchrist

“…We employ a deep learning approach, specifically a Temporal Convolution Attention Neural Network (TCANN), to conduct multi‐year forecasting of surface NO3− based on oceanic physical drivers. …”
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Integrating Kolmogorov–Arnold Networks with Time Series Prediction Framework in Electricity Demand Forecasting by Yuyang Zhang, Lei Cui, Wenqiang Yan

“…This study introduces a time series forecasting model based on Kolmogorov–Arnold Networks (KANs), integrated with three advanced neural network architectures, Temporal Convolutional Network (TCN), Bidirectional Long Short-Term Memory (BiLSTM), and Transformer, to forecast UK electricity demand. …”
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CSL-SFNet for Cooperative Spectrum Sensing in Cognitive Satellite Network with GEO and LEO Satellites by Kai Yang, Shengbo Hu, Xin Zhang, Tingting Yan, Manqin Zhu

“…Thus, we propose a novel CSS scheme, which uses convolutional neural networks (CNNs), self-attention (SA) modules, long short-term memory networks (LSTMs), and soft fusion networks, called CSL-SFNet. …”
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The Cable Fault Diagnosis for XLPE Cable Based on 1DCNNs-BiLSTM Network by Qianyu Wang, Dong Cao, Shuyuan Zhang, Yuzan Zhou, Lina Yao

“…In this paper, a hybrid classification model combining the one-dimensional convolutional neural network (1D-CNN) and the bidirectional long short-term memory network (BiLSTM) is proposed for the classification of cable short-circuit faults to improve the accuracy of fault diagnosis. …”
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Developments in Deep Learning Artificial Neural Network Techniques for Medical Image Analysis and Interpretation by Olamilekan Shobayo, Reza Saatchi

“…This article explores recent developments in deep learning techniques applied to medical imaging, including convolutional neural networks (CNNs) for classification and segmentation, recurrent neural networks (RNNs) for temporal analysis, autoencoders for feature extraction, and generative adversarial networks (GANs) for image synthesis and augmentation. …”
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A Novel Dual-Stream Attention-Based Hybrid Network for Solar Power Forecasting by Rafiq Asghar, Michele Quercio, Lorenzo Sabino, Assia Mahrouch, Francesco Riganti Fulginei

“…This research introduces a novel dual-steam hybrid model that uses Bidirectional Long-Short Term Memory (BiLSTM) and Convolutional Neural Networks (CNN) to predict PV power production. …”
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Efficient Joint Transmit and Receive Beam Alignment via Sequential CNN LSTM Networks by Takumi Yoshida, Koji Ishibashi, Hiroki Iimori, Paulo Valente Klaine, Szabolcs Malomsoky

“…To address this issue at the minimum performance degradation, a sequential convolutional neural network (CNN)–long short-term memory (LSTM) network is presented to capture the spatio-temporal correlation of the channel associated with UE movements. …”
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Multiscale fusion enhanced spiking neural network for invasive BCI neural signal decoding by Yu Song, Yu Song, Liyuan Han, Tielin Zhang, Bo Xu, Bo Xu

“…Initially, the MFSNN employs temporal convolutional networks and channel attention mechanisms to extract spatiotemporal features from raw data. …”
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Federal Deep Learning Approach of Intrusion Detection System for In-Vehicle Communication Network Security by In-Seop Na, Anandakumar Haldorai, Nithesh Naik

“…It utilizes a federal learning framework which employs Convolutional Neural Networks as well as Long-Term Short Memory. …”
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Cross-Layer Analysis of Machine Learning Models for Secure and Energy-Efficient IoT Networks by Rashid Mustafa, Nurul I. Sarkar, Mahsa Mohaghegh, Shahbaz Pervez, Ovesh Vohra

“…We integrate layer-specific ML models, such as long short-term memory networks for temporal anomaly detection and decision trees for application-layer validation, along with adaptive speck encryption for the dynamic adjustment of cryptographic overheads. …”
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AI Techniques and Applications for Online Social Networks and Media: Insights From BERTopic Modeling by Prema Nedungadi, G. Veena, Kai-Yu Tang, Remya R. K. Menon, Raghu Raman

“…Advanced AI techniques, such as Dynamic Memory Networks and Temporal Convolutional Networks, have shown strong capabilities in tracking opinion dynamics and combating misinformation. …”
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Evolving Hybrid Deep Neural Network Models for End-to-End Inventory Ordering Decisions by Thais de Castro Moraes, Jiancheng Qin, Xue-Ming Yuan, Ek Peng Chew

“…<i>Methods:</i> Addressing this gap, we introduce novel E2E deep learning frameworks that combine Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for resolving single-period inventory ordering decisions, also termed the Newsvendor Problem (NVP). …”
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Direction of Arrival Estimation Algorithm for Underwater Distributed Sources Based on Deep Neural Network by Yinian LIANG, Jie LI, Fangjiong CHEN, Fei JI, Hua YU

“…The proposed method is compared with four traditional subspace-based methods and one deep convolutional neural network algorithm, and the results show that the root mean square error of the proposed method under the coherently distributed source case is 0.42° lower than that of other methods under different signal-to-noise ratios(SNRs) and snapshots; under the incoherently distributed source case, the RMSE of the proposed method is 0.04° lower than that of other methods with SNR greater than 0 dB and snapshots greater than 600. …”
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MTFSR: Multitemporal and Spatial Feature Reconstruction Denoising Network for Remote Sensing Change Detection by YeKai Cui, Peng Duan, Jinjiang Li

“…With the widespread application of convolutional neural networks (CNNs) in remote sensing (RS) technologies, change detection (CD) has attracted increasing attention in environmental monitoring research. …”
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Feature Fusion Graph Consecutive-Attention Network for Skeleton-Based Tennis Action Recognition by Pawel Powroznik, Maria Skublewska-Paszkowska, Krzysztof Dziedzic, Marcin Barszcz

“…The FFCGAN model obtained very high results for accuracy, precision, recall, and F1-score, outperforming the commonly applied networks for action recognition, such as the Spatial-Temporal Graph Convolutional Network or its modifications. …”
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Development of a data-driven neural network model for electron thermal transport in NSTX by H. Chung, C.Y. Lee, G.J. Choi, S.M. Kaye, B.P. LeBlanc, J.W. Berkery, Y.-S. Na

“…The model incorporates both convolutional NNs and recurrent NNs, allowing it to simultaneously account for the spatial and temporal non-localities and multi-scale features of turbulent transport, which have been considered only in a limited manner in conventional models. …”
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Reinforcement Learning With Deep Features: A Dynamic Approach for Intrusion Detection in IoT Networks by Mohamad Khayat, Ezedin Barka, Mohamed Adel Serhani, Farag Sallabi, Khaled Shuaib, Heba M. Khater

“…Moreover, deep learning models, including long short-term memory networks and convolutional neural networks (CNNs), were used to automatically extract high-level features from raw data such as network logs or sensor readings. …”
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