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. …”
Get full text

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. …”
Get full text

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). …”
Get full text

VSS-SpatioNet: a multi-scale feature fusion network for multimodal image integrations by Zeyu Xiang

Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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). …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

Insights into gait performance in Parkinson's disease via latent features of deep graph neural networks by Jiecheng Wu, Jiecheng Wu, Ning Su, Xinjin Li, Xinjin Li, Chao Yao, Jipeng Zhang, Xucheng Zhang, Wei Sun

“…However, most of the current methods depend on data preprocessing and feature engineering, often require domain knowledge and laborious human involvement, and require additional manual adjustments when dealing with new tasks.MethodsTo reduce the model's reliance on data preprocessing, feature engineering, and traversal rules, we employed the Spatial-Temporal Graph Convolutional Networks (ST-GCN) model. …”
Get full text

A complex network perspective on spatiotemporal evolution of extreme precipitation over the middle and lower reaches of the Yangtze river by Ziyi Hu, Aixia Feng, Changgui Gu, Peng Zhao, Qiguang Wang

“…In this study, we aimed to analyze the spatiotemporal evolution characteristics of extreme precipitation based on visible graph network and state transition networks at different percentile thresholds, and to analyze and compare with the topology, network type, anomalous year, trend of the change stage, key modes, and the future evolution trend. …”
Get full text

Scalable SHAP-Informed Neural Network by Jarrod Graham, Victor S. Sheng

Get full text

Race estimation with deep networks by Mazida A. Ahmed, Ridip Dev Choudhury, Kishore Kashyap

“…Identifying race, which is a major physical feature in humans, is still a challenging task owing much to the lack of a concrete definition of race and the diversity of population across the globe. …”
Get full text