Influential nodes recognition of diverse complex network based on deep learning by MA Yulei, GUO Shasha

“…Firstly, multiple centrality indexes were utilized to evaluate the importance of network topology from different perspectives, the weight of each index in different complex networks was decided adaptively through the learnable weight vector. …”
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Recent Advancement in Small Traffic Sign Detection: Approaches and Dataset by R. Suresha, N. Manohar, G. Ajay Kumar, M. Rohit Singh

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Adaptive Resource Scheduling for Dual Connectivity in Heterogeneous IoT Cellular Networks by Wooseong Kim

“…We evaluate proposed algorithms using LTE system level simulator and show that our approach improves network throughput.…”
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Effective Multifocus Image Fusion Based on HVS and BP Neural Network by Yong Yang, Wenjuan Zheng, Shuying Huang

“…In this paper, a novel multifocus image fusion method based on human visual system (HVS) and back propagation (BP) neural network is presented. Three features which reflect the clarity of a pixel are firstly extracted and used to train a BP neural network to determine which pixel is clearer. …”
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Proposal for an SDN-Like Innovative Metro-Access Optical Network Architecture by Tommaso Muciaccia, Vittorio M. N. Passaro

“…The present work introduces a unified metro-access optical network architecture based on some features inspired by SDN models. …”
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Electrical excitability of neuronal networks based on the voltage threshold of electrical stimulation by Chen Meng, Yang Lu, Yan Huang, Xiaoying Lü

“…Abstract Microelectrode arrays (MEAs) have been widely used in studies on the electrophysiological features of neuronal networks. In classic MEA experiments, spike or burst rates and spike waveforms are the primary characteristics used to evaluate the neuronal network excitability. …”
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3D‐TabNetHS: A hyperspectral image classification method based on improved interpretable 3D attentive TabNet by Ning Li, Daozhi Wei, Shucai Huang, Yong Zhang

“…These methods use sequential attention to select appropriate HSI spatial‐spectral features and add a space spectral information extraction (SSE) module composed of a 3D convolutional neural network (3D‐CNN) and fully connected layers to the Attention Transformer module in the original TabNet network to extract spatial‐spectral soft features. …”
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FAULT DIAGNOSIS OF ROLLING BEARINGS BASED ON CHANNEL AND SPATIAL RECONSTRUCTION NETWORKS by ZHOU Tao, YAO DeChen, YANG JianWei

“…Since the fault vibration data collected in real engineering may be accompanied by noise，traditional diagnostic models are difficult to identify fault categories，to address this problem，a rolling bearing fault diagnosis research method based on channel and spatial reconstruction and progressive convolutional neural networks （CSRP-CNN） was proposed.The model utilizes channel and spatial reconstruction convolution （CSConv） to reduce the redundant information of channels and space in fault features，and reduces the complexity and computation to improve the performance； using convolutional block attention module （CBAM），attention enhancement operation was carried out in the channel and spatial dimensions to make the model pay attention to important fault feature information； and progressive convolutional network structure was used in the shallow layer of the network，which will fuse the previous fault feature information fused with the current input to obtain richer feature information.The performance of CSRP-CNN was evaluated by two different datasets of Case Western Reserve University（CWRU）and machinery fault simulator magnum（MFS-MG）.After the noise and ablation tests，it is verified that CSRP-CNN has strong robustness and the effects of CSConv，CBAM and progressive convolutional neural network（PCNN） on the model noise immunity performance.…”
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Fault diagnosis of rolling bearing based on channel and spatial reconstruction networks by ZHOU Tao, YAO Dechen, YANG Jianwei

“…To address this problem, a rolling bearing fault diagnosis research method based on channel and spatial reconstruction and progressive convolutional neural networks (CSRP-CNN) was proposed. The model utilized channel and spatial reconstruction convolution （CSConv） to reduce the redundant information of channels and space in fault features, and reduced the complexity and computation to improve the performance; using the convolutional block attention module (CBAM), attention enhancement operation was carried out in the channel and spatial dimensions to make the model pay attention to the important fault feature information; and the progressive convolutional network structure was used in the shallow layer of the network, which would fuse the previous fault feature information with the current input to obtain the richer feature information. …”
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Heartbeat Stars Recognition Based on Recurrent Neural Networks: Method and Validation by Min-Yu Li, Sheng-Bang Qian, Li-Ying Zhu, Wen-Ping Liao, Lin-Feng Chang, Er-Gang Zhao, Xiang-Dong Shi, Fu-Xing Li, Qi-Bin Sun, Ping Li

“…The use of orbital harmonics as features for HBSs proves to be a practical approach that significantly reduces the computational cost of neural networks. …”
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Classifying reservoir facies using attention-based residual neural networks by An Hai Nguyen, Khang Nguyen, Nga Mai

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Post-Disaster Building Damage Segmentation Using Convolutional Neural Networks by Revaldi Rahmatmulya, Agung Teguh Wibowo Almais, Mokhamad Amin Hariyadi

“…The steps undertaken in this research include data preprocessing using patchify and data augmentation. Subsequently, feature extraction is performed using convolution, followed by the training process using a neural network with the proposed architecture. …”
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Generation and optimisation of colour-shaded relief maps using neural networks by Chenglin Bian, Shaomei Li, Jingzhen Ma, Guangzhi Yin, Bowei Wen, Linghui Kong

“…The experimental results suggest that all four types of network-based shaded relief maps models effectively depict the primary terrain features within the mapped area. …”
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High-Relative-Bandwidth Multiband LNA Distortion Elimination With Neural Network by Zheng Xiang Li, Rui Lv

“…This article presents an innovative approach that harnesses neural networks (NN) to eliminate non-linear distortion in low-noise amplifier (LNA) within multi-channel direct sampling receivers (MDSR). …”
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TIER: Temporal Convolutional Network Information Extractor With Conditional Random Field by Huiwen Wu, Xiubo Zhang, Tengyuan Zhou, An Hu

“…We propose TIER, which combines the Temporal Convolutional Network (TCN) and the Conditional Random Field (CRF). …”
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Deep Communication: Exploring End-to-End Wireless with Convolutional Neural Network by Sumaira Mushtaq, Bahman R Alyaeia, Momna Sultan, Janshair Khan

“…In this paper, we propose a new approach to achieving end-to-end wireless communication using convolutional neural networks (CNNs) in the presence of Nakagami fading, Additive white Gaussian noise (AWGN), and multiple-input multiple-output (MIMO) fading channels. …”
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Data-driven contact structures: From homogeneous mixing to multilayer networks. by Alberto Aleta, Guilherme Ferraz de Arruda, Yamir Moreno

“…A key role has also been played by the latest advances in new disciplines like network science. Nonetheless, current models still lack a faithful representation of all possible heterogeneities and features that can be extracted from data. …”
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Anomaly Detection in Network Traffic Using Advanced Machine Learning Techniques by Stephanie Ness, Vishwanath Eswarakrishnan, Harish Sridharan, Varun Shinde, Naga Venkata Prasad Janapareddy, Vineet Dhanawat

“…This study investigates the application of various machine learning models for detecting anomalies in network traffic, specifically focusing on their effectiveness in addressing challenges such as class imbalance and feature complexity. …”
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Hybrid Deep Neural Network with Domain Knowledge for Text Sentiment Analysis by Jawad Khan, Niaz Ahmad, Youngmoon Lee, Shah Khalid, Dildar Hussain

“…Conventional text SA techniques are effective and easy to understand but encounter difficulties when handling sparse data. Deep Neural Networks (DNNs) excel in handling data sparsity but face challenges with high-dimensional, noisy data. …”
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Temporal Relational Graph Convolutional Network Approach to Financial Performance Prediction by Brindha Priyadarshini Jeyaraman, Bing Tian Dai, Yuan Fang

“…This representation is then combined with FinBERT embeddings through a projection layer, enabling a richer feature space. To demonstrate the efficacy of FintechKG, we evaluate its performance using the example task of financial performance prediction. …”
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