Automatic Feature Engineering-Based Optimization Method for Car Loan Fraud Detection by Jian Yang, Zixin Tang, Zhenkai Guan, Wenjia Hua, Mingyu Wei, Chunjie Wang, Chenglong Gu

“…Problems like feature dimension explosion, low interpretability, long training time, and low detection accuracy are solved by compressing abstract and uninterpretable features to limit the depth of DFS algorithm. …”
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Soybean Weed Detection Based on RT-DETR with Enhanced Multiscale Channel Features by Hua Yang, Yanjie Lyu, Yunpeng Jiang, Feng Jiang, Taiyong Deng, Lihao Yu, Yuanhao Qiu, Hao Xue, Junying Guo, Zhaoqi Meng

“…To solve the missed and wrong detection problems of the object detection model in identifying soybean companion weeds, this paper proposes an enhanced multi-scale channel feature model based on RT-DETR (EMCF-RTDETR). …”
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Airport Clearance Detection Based on Vision Transformer and Multi-Scale Feature Fusion by Yutong Chen, Yufen Liu, Zhixiong Guo, Qiang Gao

“…To overcome the defects in detection, this paper proposes an airport clearance detection algorithm based on Vision Transformer and multi-scale feature fusion to address the problems of poor real-time performance, low accuracy, and large parameter quantity in existing airport clearance detection systems. …”
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Multimodal Fake News Detection Incorporating External Knowledge and User Interaction Feature by Lifang Fu, Shuai Liu

“…In terms of the propagation chain, the research tends to emphasize only the single chain from the previous communication node, ignoring the intricate communication chain and the mutual influence relationship among users. To address these problems, this paper proposes a multimodal fake news detection model, A-KWGCN, based on knowledge graph and weighted graph convolutional network (GCN). …”
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Partial feature reparameterization and shallow-level interaction for remote sensing object detection by Minh Tai Pham Nguyen, Quoc Duy Nam Nguyen, Hoang Viet Anh Le, Minh Khue Phan Tran, Tadashi Nakano, Thi Hong Tran

“…To address these problems, this study introduces an efficient one-stage object detector that is designed mainly for detecting objects on remote sensing images, which consists of several innovations. …”
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Object detection model design for tiny road surface damage by Chenguang Wu, Min Ye, Hongwei Li, Jiale Zhang

“…Firstly, a backbone applied to road surface damage feature extraction is designed to solve the problems of feature loss and insufficient extraction of tiny damage during feature extraction. …”
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Digital visual media forensics by Chun-hui FENG, Zheng-quan XU, Xing-hui ZHENG, Li JIANG

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DETECTIVE STORY: TO THE PROBLEM OF VARIABILITY OF THE MAIN EVENT AND CHARACTERS (BY THE CASE OF A. SARAKHOV’S STORIES) by I. A. KAZHAROVA

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UAV Small Target Detection Model Based on Dual Branches and Adaptive Feature Fusion by Guogang Wang, Mingxing Gao, Yunpeng Liu

“…In order to solve the problem of small and dense targets in drone aerial images, a small target detection model based on dual branches and adaptive feature fusion is proposed. …”
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Explainable one-class feature extraction by adaptive resonance for anomaly detection in quality assurance. by Hootan Kamran, Dionne Aleman, Chris McIntosh, Tom Purdie

“…Unlike its predecessors, our method enhances anomaly detection for RT plan QA without compromising on interpretability-a critical feature in healthcare applications, where understanding and trust in automated decisions are paramount. …”
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OW-YOLO: An Improved YOLOv8s Lightweight Detection Method for Obstructed Walnuts by Haoyu Wang, Lijun Yun, Chenggui Yang, Mingjie Wu, Yansong Wang, Zaiqing Chen

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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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Automatic detection of fake reviews at marketplaces using expert-based features and consumers’ reactions by A. N. Borodulina, E. V. Mikhalkova

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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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