Minimising Security Deviations in Software-Defined Networks Using Deep Learning by mohammed alghaloom

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NPP estimation by fusing geodetector and deep spatio-temporal networks by Xiaohui He, Chenqiao Yuan, Panle Li, Xijie Cheng, Mengjia Qiao, Xiaoyu He, Nan Yang, Guangsheng Zhou, Jiandong Shang

“…To address this limitation, we propose a novel approach named integrate geographic with deep spatio-temporal networks (IGDSNet). Specially, the IGDSNet uses the geodetector to explore geographic mechanism of NPP and then introduces the spatio-temporal long- and short-term memory networks (ST-LSTM) to obtain the deep spatio-temporal feature of NPP. …”
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Deep Complex Gated Recurrent Networks-Based IoT Network Intrusion Detection Systems by Engy El-Shafeiy, Walaa M. Elsayed, Haitham Elwahsh, Maazen Alsabaan, Mohamed I. Ibrahem, Gamal Farouk Elhady

“…Convolutional neural networks (CNN) are used for spatial feature extraction and superfluous data are filtered to improve computing efficiency. …”
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Comparative Analysis of Fine-Tuning I3D and SlowFast Networks for Action Recognition in Surveillance Videos by T. Gopalakrishnan, Naynika Wason, Raguru Jaya Krishna, Vamshi Krishna B, N. Krishnaraj

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Forensic of video object removal tamper based on 3D dual-stream network by Lizhi XIONG, Mengqi CAO, Zhangjie FU

“…In order to solve the problems of inaccurate temporal detection and location of the object removal tampered video， a video tamper forensics method based on 3D dual-stream network was proposed.Firstly， the spatial rich model （SRM） layer was used to extract the high-frequency information from video frames.Secondly， the improved 3D convolution （C3D） network was used as the feature extractor of the dual-stream network to extract the high-frequency information and low-frequency information from the high-frequency frame and the original video frame respectively.Finally， through compact bilinear pooling （CBP） layer， two sets of different feature vectors were fused into one set of feature vectors for classification prediction.The experimental results demonstrate that the classification accuracy of the proposed method in all video frames has an advantage in SYSU-OBJFORG dataset， which makes the temporal detection and location of object removal tampered video more accurate.…”
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Research on Spaceborne Neural Network Accelerator and Its Fault Tolerance Design by Yingzhao Shao, Junyi Wang, Xiaodong Han, Yunsong Li, Yaolin Li, Zhanpeng Tao

“…To meet the high-reliability requirements of real-time on-orbit tasks, this paper proposes a fault-tolerant reinforcement design method for spaceborne intelligent processing algorithms based on convolutional neural networks (CNNs). This method is built on a CNN accelerator using Field-Programmable Gate Array (FPGA) technology, analyzing the impact of Single-Event Upsets (SEUs) on neural network computation. …”
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A Deep Learning–Based Multimodal F10.7 Prediction with Mamba by Dong Zhao, Fengping Dou, Xueshang Feng, Xin Huang, Sai Ma, Long Xu

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Hybrid Attention and Multiscale Module for Alzheimer's Disease Classification by WANG Yuanjun

“…The method leverages image data and a convolutional neural network to enhance the model's attention to the hippocampus, amygdala, and temporal lobe through the introduction of a hybrid attention mechanism. …”
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A comprehensive framework for multi-modal hate speech detection in social media using deep learning by R. Prabhu, V. Seethalakshmi

“…Hence, this research proposes a novel Multi-modal Hate Speech Detection Framework (MHSDF) that combines Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to analyze complex, heterogeneous data streams. …”
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Ada-GCNLSTM: An adaptive urban crime spatiotemporal prediction model by Miaoxuan Shan, Chunlin Ye, Peng Chen, Shufan Peng

“…We then incorporate a memory network based on long short-term memory network to capture the underlying relationships between temporal features. …”
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Classification and Physical Characteristic Analysis of Fermi-GBM Gamma-Ray Bursts Based on Deep Learning by Jia-Ming Chen, Ke-Rui Zhu, Zhao-Yang Peng, Li Zhang

“…We propose a new classification method based on convolutional neural networks and adopt a sample including 3774 GRBs observed by Fermi-GBM to address the T _90 overlap problem. …”
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ScTCN-LightGBM: a hybrid learning method via transposed dimensionality-reduction convolution for loading measurement of industrial material by Zihua Chen, Runmei Zhang, Zhong Chen, Yu Zheng, Shunxiang Zhang

“…Second, we design a sided-composited temporal convolutional network that combines a novel transposed dimensionality-reduction convolution residual block with the conventional residual block. …”
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Ultra-short-term Multi-region Power Load Forecasting Based on Spearman-GCN-GRU Model by Junying WU, Xin LU, Hong LIU, Bin ZHANG, Shouliang CHAI, Yunchun LIU, Jianan WANG

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Short-term Wind Power Forecasting Based on BWO‒VMD and TCN‒BiGRU by LU Jing, ZHANG Yanru, WANG Rui

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Study on Photovoltaic Plant Site Selection Models Based on Geographic and Environmental Features by RAO Zhi, YANG Zaimin, YANG Xiongping, LI Jiaming, YANG Ping, WEI Zhichu

Subjects: “…global horizontal irradiance prediction|site selection of photovoltaic power stations|environmental features|geographic features|model of temporal convolutional network （tcn）|model of informer…”
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A Novel Electrical Load Forecasting Model for Extreme Weather Events Based on Improved Gated Spiking Neural P Systems and Frequency Enhanced Channel Attention Mechanism by Yuanshuo Guo, Jun Wang, Yan Zhong, Tao Wang, Zeyuan Sui

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A multi-dimensional data-driven ship roll prediction model based on VMD-PCA and IDBO-TCN-BiGRU-Attention by Huifeng Wang, Jianchuan Yin, Jianchuan Yin, Nini Wang, Lijun Wang, Lijun Wang

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BAM-SLDK: biologically inspired attention mechanism with spiking learnable delayed kernel synapses by Mario Chacón-Falcón, Alberto Patiño-Saucedo, Luis Camuñas-Mesa, Teresa Serrano-Gotarredona, Bernabé Linares-Barranco

“…Spiking neural networks are emerging as an alternative neural network model due to their biological plausibility, energy efficiency, and built-in ability to learn from temporal dynamics. …”
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STGAT: Spatial–Temporal Graph Attention Neural Network for Stock Prediction by Ruizhe Feng, Shanshan Jiang, Xingyu Liang, Min Xia

“…Additionally, deep learning methods, especially temporal convolution networks and graph attention networks, have been introduced in this area and have achieved significant improvements in both stock price prediction and portfolio optimization. …”
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Prediction of crystalline structure evolution during solidification of aluminum at different cooling rates using a hybrid neural network model by Rafi B. Dastagir, Shorup Chanda, Farsia K. Chowdhury, Shahereen Chowdhury, K. Arafat Rahman

“…By combining the temporal pattern descriptors of LSTMs with the feature extraction potential of convolutional neural networks (CNN), the hybrid Conv1D-LSTM model achieves higher accuracy in predicting crystal structural evolution curves, in contrast to the performance of standalone LSTM and CNN models. …”
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