Block-chain abnormal transaction detection method based on adaptive multi-feature fusion by Huijuan ZHU, Jinfu CHEN, Zhiyuan LI, Shangnan YIN

“…Aiming at the problem that the performance of intelligent detection models was limited by the representation ability of original data (features), a residual network structure ResNet-32 was designed to automatically mine the intricate association relationship between original features, so as to actively learn the high-level abstract features with rich semantic information.Low-level features were more transaction content descriptive, although their distinguishing ability was weaker than that of the high-level features.How to integrate them together to obtain complementary advantages was the key to improve the detection performance.Therefore, multi feature fusion methods were proposed to bridge the gap between the two kinds of features.Moreover, these fusion methods can automatically remove the noise and redundant information from the integrated features and further absorb the cross information, to acquire the most distinctive features.Finally, block-chain abnormal transaction detection model (BATDet) was proposed based on the above presented methods, and its effectiveness in the abnormal transaction detection is verified.…”
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Network intrusion detection method based on VAE-CWGAN and fusion of statistical importance of feature by Taotao LIU, Yu FU, Kun WANG, Xueyuan DUAN

“…Considering the problems of traditional intrusion detection methods limited by the class imbalance of datasets and the poor representation of selected features, a detection method based on VAE-CWGAN and fusion of statistical importance of features was proposed.Firstly, data preprocessing was conducted to enhance data quality.Secondly, a VAE-CWGAN model was constructed to generate new samples, addressing the problem of imbalanced datasets, ensuring that the classification model no longer biased towards the majority class.Next, standard deviation, difference of median and mean were used to rank the features and fusion their statistical importance for feature selection, aiming to obtain more representative features, which made the model can better learn data information.Finally, the mixed data set after feature selection was classified through a one-dimensional convolutional neural network.Experimental results show that the proposed method demonstrates good performance advantages on three datasets, namely NSL-KDD, UNSW-NB15, and CIC-IDS-2017.The accuracy rates are 98.95%, 96.24%, and 99.92%, respectively, effectively improving the performance of intrusion detection.…”
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A Block Object Detection Method Based on Feature Fusion Networks for Autonomous Vehicles by Qiao Meng, Huansheng Song, Gang Li, Yu’an Zhang, Xiangqing Zhang

“…Nowadays, automatic multi-objective detection remains a challenging problem for autonomous vehicle technologies. …”
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Remote Sensing Image Change Detection Based on Multi-Level Diversity Feature Fusion by Honggang Xie, Wanjie Ma

“…Therefore, this paper proposes a remote sensing image change detection method based on multi-level and multi-diversity feature fusion. …”
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A novel feature extractor based on constrained cross network for detecting sleep state by Chenlei Tian, Fei Song

“…This study explores an improved feature extractor based on the Constrained Cross Network to enhance the accuracy of the sleep-wake binary classification problem. …”
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MTFSR: Multitemporal and Spatial Feature Reconstruction Denoising Network for Remote Sensing Change Detection by YeKai Cui, Peng Duan, Jinjiang Li

“…To address these issues, this article proposes a multitemporal and spatial feature reconstruction denoising network for remote sensing change detection (MTFSR). …”
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Refined Anchor-Free Model With Feature Enhancement Mechanism for Ship Detection in Infrared Images by Yunlong Gao, Chuan Wu, Ming Ren, Yang Feng

“…Besides, the complex surroundings, including inshore buildings and thick clouds, put higher difficulties for ship detection tasks. In order to handle the above problems, we propose a refined anchor-free model with feature enhancement mechanism for ship detection in infrared images. …”
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Cross-Level Adaptive Feature Aggregation Network for Arbitrary-Oriented SAR Ship Detection by Lu Qian, Junyi Hu, Haohao Ren, Jie Lin, Xu Luo, Lin Zou, Yun Zhou

“…In response to these challenges, this study introduces a new detection approach called a cross-level adaptive feature aggregation network (CLAFANet) to achieve arbitrary-oriented multi-scale SAR ship detection. …”
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Microblog hot topic detection method based on meaningful string clustering by Min HE, Li-hong WANG, Pan DU, Jin ZHANG, Xue-qi CHENG

“…Aiming at the properties of sparse feature, content fragmentation for microblog data, a hot topic detection method was proposed based on meaningful string clustering. …”
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Microblog hot topic detection method based on meaningful string clustering by Min HE, Li-hong WANG, Pan DU, Jin ZHANG, Xue-qi CHENG

“…Aiming at the properties of sparse feature, content fragmentation for microblog data, a hot topic detection method was proposed based on meaningful string clustering. …”
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Behavior Anomaly Detection Based on Multi-modal Feature Fusion and Its Application in English Teaching by Lei Kan, Man Wang

“…In order to improve the teaching quality, this paper proposes a multi-modal feature fusion-based abnormal behavior detection method, aiming at the problems of false detection, missing detection and imbalance of positive and negative samples in the abnormal behavior detection of students in class. …”
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CGA-Net: A CNN-GAT Aggregation Network Based on Metric for Change Detection in Remote Sensing by Huilan Lin, Chunlei Zhao, Rong He, Ming Zhu, Xin Jiang, Yi Qin, Wen Gao

“…Attempting to solve the problems in existing object-level change detection methods, such as ignoring the relationship between dual-branch features, insufficient utilization of feature point information, and unreasonable fusion weight allocation mechanism, this article proposes an object-level change detection network, CGA-Net, based on metric, which combines similarity measurement with the feature extraction and fusion. …”
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Mf-net: multi-feature fusion network based on two-stream extraction and multi-scale enhancement for face forgery detection by Hanxian Duan, Qian Jiang, Xin Jin, Michal Wozniak, Yi Zhao, Liwen Wu, Shaowen Yao, Wei Zhou

“…In order to improve the cross-dataset detection performance of the model, this paper proposes a multi-feature fusion network based on two-stream extraction and multi-scale enhancement. …”
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Target detection of helicopter electric power inspection based on the feature embedding convolution model. by Dakun Liu, Wei Zhou, Linzhen Zhou, Wen Guan

“…This study aims to improve the helicopter electric power inspection process by using the feature embedding convolution (FEC) model to solve the problems of small scope and poor real-time inspection. …”
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A novel feature extraction method based on dynamic handwriting for Parkinson's disease detection. by Huimin Lu, Guolian Qi, Dalong Wu, Chenglin Lin, Songzhe Ma, Yingqi Shi, Han Xue

“…Therefore, to solve the above problems, a new feature extraction approach for PD detection is proposed using handwriting. …”
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A quantitative benchmark of neural network feature selection methods for detecting nonlinear signals by Antoine Passemiers, Pietro Folco, Daniele Raimondi, Giovanni Birolo, Yves Moreau, Piero Fariselli

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Target Detection Algorithm Based on Global Feature Fusion in Parallel Dual Path Backbone by QIU Yunfei, XIN Hao

“…To solve these problems, a target detection algorithm based on global feature fusion in parallel dual path backbone is proposed. …”
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Enhanced Peer-to-Peer Botnet Detection Using Differential Evolution for Optimized Feature Selection by Sangita Baruah, Vaskar Deka, Dulumani Das, Utpal Barman, Manob Jyoti Saikia

“…Differential evolution is a population-based meta-heuristic technique which can be applied to nonlinear and non-differentiable optimization problems owing to its fast convergence and use of few control parameters. …”
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Bi-temporal Gaussian feature dependency guided change detection in remote sensing images by Yi Xiao, Bin Luo, Jun Liu, Xin Su, Wei Wang

“…To better alleviate these problems, we propose a bi-temporal Gaussian distribution feature-dependent (BGFD) network. …”
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Efficient Method for Robust Backdoor Detection and Removal in Feature Space Using Clean Data by Donik Vrsnak, Marko Subasic, Sven Loncaric

“…This method can detect and reconstruct the feature representation of the possible triggers used in attacking the neural network. …”
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