Applying a Deep Neural Network and Feature Engineering to Assess the Impact of Attacks on Autonomous Vehicles by Sara Ftaimi, Tomader Mazri

“…By integrating deep neural network technology with feature engineering, the proposed system provides a comprehensive impact assessment. …”
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Enhancing Anomaly Detection in Structured Data Using Siamese Neural Networks as a Feature Extractor by Elizabeth P. Chou, Bo-Cheng Hsieh

“…However, in this study, we introduce a novel approach by leveraging the feature extraction capabilities of SNN, inspired by the powerful representation learning ability of neural networks. …”
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Feature Multi-Scale Enhancement and Adaptive Dynamic Fusion Network for Infrared Small Target Detection by Zenghui Xiong, Zhiqiang Sheng, Yao Mao

“…In response to these issues, we propose a deep learning model called the Feature Multi-Scale Enhancement and Adaptive Dynamic Fusion Network (FMADNet). …”
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VSS-SpatioNet: a multi-scale feature fusion network for multimodal image integrations by Zeyu Xiang

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FastADnet: Fast Anomaly Detection via Core-Feature Centered Cluster Reconstruction Network by Daeyeob Na, Youngjoon Yoo

“…By leveraging these features that represent local data distributions and their adjacent patch-features, our network effectively enhances anomaly detection along with reduced inference time. …”
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Remote sensing image semantic segmentation network based on multi-scale feature enhancement fusion by Feiting Wang, Yuan Zhang, Qiongqiong Hu, Yu Zhu

“…To address this issue, we propose a multi-scale feature enhancement network (MFENet) to improve the segmentation accuracy of small-scale objects in HRRSIs. …”
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Performance Analysis of Eye Movement Event Detection Neural Network Models with Different Feature Combinations by Birtukan Adamu Birawo, Pawel Kasprowski

“…Various combinations of these features have been used as input to the networks. The performance of the proposed method was evaluated across all feature combinations and compared to state-of-the-art feature sets. …”
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A two-branch multiscale spectral-spatial feature extraction network for hyperspectral image classification by Aamir Ali, Caihong Mu, Zeyu Zhang, Jian Zhu, Yi Liu

“…This paper presents a two-branch multiscale spectral-spatial feature extraction network (TBMSSN) for HSI classification. …”
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MFCANet: Multiscale Feature Context Aggregation Network for Oriented Object Detection in Remote-Sensing Images by Honghui Jiang, Tingting Luo, Hu Peng, Guozheng Zhang

“…Our proposed model, named the Multi-scale Feature Context Aggregation Network (MFCANet), was evaluated on four challenging remote sensing datasets (MAR20, SRSDD, HRSC, and DIOR-R). …”
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FlexiNet: An Adaptive Feature Synthesis Network for Real-Time Ego Vehicle Speed Estimation by Abdalrahaman Ibrahim, Kyandoghere Kyamakya, Wolfgang Pointner

“…To address these challenges, we propose FlexiNet, a novel adaptive feature synthesis network that leverages monocular camera data to perform real-time speed estimation. …”
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Interpersonal Relationship Detection Using Multi-Head Graph Attention Networks With Multi-Feature Fusion by Simge Akay, Duygu Cakir, Nafiz Arica

“…This paper presents a novel Multi-Head Graph Attention Network (MHF-GAT) with Multi-Feature Fusion for interpersonal relationship detection (IRD) from images. …”
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Classification of Pulmonary Nodules Using Multimodal Feature‐Driven Graph Convolutional Networks with Specificity Proficiency by Renjie Xu, Zhanlue Liang, Dan Wang, Rui Zhang, Jiayi Li, Lingfeng Bi, Kai Zhang, Weimin Li

“…Compared with radiomics and clinical feature‐based machine learning methods, whether a graph convolutional neural network (GCNN) based on radiomics and clinical features improve the performance in distinguishing benign and malignant pulmonary nodules is not well studied. …”
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Equipment Maintenance Support Effectiveness Evaluation Based on Improved Generative Adversarial Network and Radial Basis Function Network by Zhen Li, Jianping Hao, Cuijuan Gao

“…In this paper, a neural network evaluation model based on an improved generative adversarial network (GAN) and radial basis function (RBF) network is proposed to amplify maintenance support samples. …”
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Multi-Level Intertemporal Attention-Guided Network for Change Detection in Remote Sensing Images by Shuo Liu, Qinyu Zhang, Yuhang Zhang, Xiaochen Niu, Wuxia Zhang, Fei Xie

“…To address this issue, we proposed a Multi-level Intertemporal Attention-guided Network (MIANet) for CD. Firstly, an Intertemporal Fusion Attention Unit (IFAU) is proposed to facilitate early feature interaction, which helps eliminate irrelevant changes. …”
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Scalable SHAP-Informed Neural Network by Jarrod Graham, Victor S. Sheng

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Measurement error evaluation method for voltage transformers in distribution networks based on self-attention and graph convolutional networks by Xiujuan Zeng, Tong Liu, Huiqin Xie, Dajiang Wang, Jihong Xiao

“…This paper uses the connection relationship between distribution transformers and voltage transformers to predict the secondary voltage of voltage transformers through the secondary voltage of transformers, constructing a voltage transfer characteristic model between the two to achieve accurate evaluation of voltage transformer errors. To address the challenge of extracting complex nonlinear features from multivariate electrical data, a combined model of a self-attention mechanism and a graph convolutional network (GCN) is proposed. …”
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PLL-VO： An Efficient and Robust Visual Odometry Integrating Point-Line Features and Neural Networks by L. Zhao, Y. Yang, D. Ma, X. Lin, W. Wang

“…After selecting keyframes based on point feature counts and line feature overlap angles, we integrate convolutional neural networks (CNNs) and graph neural networks (GNNs) to enhance sparse matching, thereby improving both accuracy and computational efficiency. …”
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Landslide susceptibility assessment using lightweight dense residual network with emphasis on deep spatial features by Shenghua Xu, Zhuolu Wang, Jiping Liu, Xinrui Ma, Tingting Zhou, Qing Tang

“…In addressing issues such as limited training samples, inadequate utilization of spatially effective features, and high computational costs associated with existing methods, we propose a landslide susceptibility assessment method (DS-DRN), which uses a lightweight dense residual network with emphasis on deep spatial features. …”
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A cross-stage features fusion network for building extraction from remote sensing images by Xiaolong Zuo, Zhenfeng Shao, Jiaming Wang, Xiao Huang, Yu Wang

“…The deep learning-based building extraction methods produce different feature maps at different stages of the network, which contain different information features. …”
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InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks by Mahmood Kalemati, Mojtaba Zamani Emani, Somayyeh Koohi

“…In this paper, we introduce InceptionDTA, a novel drug-target binding affinity prediction model that leverages CharVec, an enhanced variant of Prot2Vec, to incorporate both biological context and categorical features into protein sequence encoding. InceptionDTA utilizes a multi-scale convolutional architecture based on the Inception network to capture features at various spatial resolutions, enabling the extraction of both local and global features from protein sequences and drug SMILES. …”
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