False traffic information detection based on weak classifiers integration in vehicular ad hoc networks by Xiang-wen LIU, Ya-li SHI, ENGXia F

“…Vehicles report traffic information mutually by self-organized manner in vehicular ad hoc networks (VANET),and the message need to be identified in the open network environment.However,it is very difficult for fast moving ve-hicles to detect a lot of traffic alert information in a short time.To solve this problem,a false traffic message detection method was presented based on weak classifiers integration.Firstly,the effective features of traffic alert information was extended and segmentation rules were designed to divide the information feature set into multiple feature subsets,then the corresponding weak classifiers were used to process feature subsets respectively according to the different character-istics of the subsets' features.Simulation experiments and performance analysis show that the selected weak classifiers integration method reduces the detection time,and because of the application of combined features,the detection rate is better than the test of using only some of the characteristics.…”
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CATransU-Net: Cross-attention TransU-Net for field rice pest detection. by Xuwei Lu, Yunlong Zhang, Congqi Zhang

“…Accurate detection of rice pests in field is a key problem in field pest control. …”
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Research on Region Noise Reduction and Feature Analysis of Total Focus Method Ultrasound Image Based on Branch Pipe Fillet Weld by Yuqin Wang, Yong Li, Yangguang Bu, Shaohua Dong, Haotian Wei, Jingwei Cheng

“…Firstly, a multi-mode, fully focused real-time imaging software system was developed to address the specificity of the detection object; secondly, a phased array detection system based on 64 elements was constructed; finally, a region wavelet denoising method based on TFM images was proposed to solve the problem of artifacts caused by poor coupling; and based on the feature extraction method for a minimum rectangle, we analyzed the size, position, angle, and other information regarding defects. …”
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Quad-Tree-Based Driver Classification Using Deep Learning for Mild Cognitive Impairment Detection by Seyedeh Gol Ara Ghoreishi, Charles Boateng, Sonia Moshfeghi, Muhammad Tanveer Jan, Joshua Conniff, Kwangsoo Yang, Jinwoo Jang, Borko Furht, David Newman, Ruth Tappen, Monica Rosselli, Kelley L. Jackson

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Audio copy-move forgery detection with decreasing convolutional kernel neural network and spectrogram fusion by Canghong Shi, Xin Qiu, Min Wu, Xianhua Niu, Xiaojie Li, Sani M. Abdullahi

“…This dissimilarity makes detecting such tampering a major challenge. To address this problem, we propose a robust audio copy-move forgery detection method using a Decreasing Convolutional Kernel Neural Network (DCKNN), data augmentation, and digital fusion. …”
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A Lightweight Conditional Diffusion Segmentation Network Based on Deformable Convolution for Surface Defect Detection by Jiusheng Chen, Yibo Zhao, Haibing Wang

“…Second, the efficient feature extraction block is proposed to address the problem of modeling varying defects, which is designed with a partial deformable convolutional layer that can fully extract geometric features of the diverse defects to further enhance the modeling power of the proposed network. …”
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The problem of rare tumors in gynecology: a clinical case of benign retroperitoneal sclerosing pelvic pecoma in 49-year-old woman by V. I. Lanchinsky, Yu. V. Chushkov, A. B. Ponomarev, Yu. I. Osmanov, A. I. Ishchenko, N. V. Vorobev

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DC-Mamba: A Novel Network for Enhanced Remote Sensing Change Detection in Difficult Cases by Junyi Zhang, Renwen Chen, Fei Liu, Hao Liu, Boyu Zheng, Chenyu Hu

“…Nevertheless, existing Mamba-based methods lack optimization for complex change areas, making it easy to lose shallow features or local features, which leads to poor performance on challenging detection cases and high-difficulty datasets. …”
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Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection by Guillaume Lemaître, Mojdeh Rastgoo, Joan Massich, Carol Y. Cheung, Tien Y. Wong, Ecosse Lamoureux, Dan Milea, Fabrice Mériaudeau, Désiré Sidibé

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Hyphatia: A Card-Not-Present Fraud Detection System Based on Self-Supervised Tabular Learning by Josue Genaro Almaraz-Rivera, Jose Antonio Cantoral-Ceballos, Juan Felipe Botero, Francisco Javier MunOz, Brian David Martinez

“…The IEEE-CIS dataset has emerged as a strong option for creating and validating smart detection systems against this problem. In this work, we propose a solution, Hyphatia, where Self-Supervised Learning is implemented for tabular data based on SubTab. …”
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EER-DETR: An Improved Method for Detecting Defects on the Surface of Solar Panels Based on RT-DETR by Jiajun Dun, Hai Yang, Shixin Yuan, Ying Tang

“…In response to the shortcomings of existing detection methods in identifying tiny defects and model efficiency, this study innovatively constructed the EER-DETR detection framework: firstly, a feature reconstruction module WDBB with a differentiable branch structure was introduced to significantly enhance the feature retention ability for fine cracks and other small targets; secondly, an adaptive feature pyramid network EHFPN was innovatively designed, which achieved efficient integration of multi-level features through a dynamic weight allocation mechanism, reducing the model complexity by 9.7% while maintaining detection accuracy, solving the industry problem of “precision—efficiency imbalance” in traditional feature pyramid networks; finally, an enhanced upsampling component was introduced to effectively address the problem of detail loss that occurs in traditional methods during image resolution enhancement. …”
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A comparative analysis of fault detection and process diagnosis methods based on a signal processing paradigm by Dorel Aiordăchioaie

“…Seven methods are considered for evaluation: two in the time domain, two in the frequency domain and three in the time–frequency domain. The main problem is to extract and select the right features to use in the classification stage. …”
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Data poisoning attack detection approach for quality of service aware cloud API recommender system by Zhen CHEN, Wenchao QI, Taiyu BAO, Limin SHEN

“…To solve the problem that existing studies usually assumed that the QoS data of cloud API recommender system was reliable, ignoring the data poisoning attack on cloud API recommender system by malicious users in open network environment, a data poisoning attack detection approach based on multi-feature fusion was proposed.Firstly, a user connected network graph was constructed based on the designed similarity function, and users’ neighborhood features were captured using Node2vec.Secondly, sparse auto-encoder was used to mine user QoS deep feature, and user interpretation feature based on QoS data weighted average deviation was designed.Furthermore, a fake user detection model based on support vector machine was established by integrating user neighborhood feature, QoS deep feature, and interpretation feature, the model parameters were learned using grid search and alternating iterative optimization strategy to complete fake user detection.Finally, the effectiveness and superiority of the proposed approach were verified through extensive experiments, realizing the poison attack defense against QoS aware cloud API recommender system at the data side.…”
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Early Fault Detection Method of Rolling Bearing Based on MCNN and GRU Network with an Attention Mechanism by Xiaochen Zhang, Yiwen Cong, Zhe Yuan, Tian Zhang, Xiaotian Bai

“…Aiming at the problem of early fault diagnosis of rolling bearing, an early fault detection method of rolling bearing based on a multiscale convolutional neural network and gated recurrent unit network with attention mechanism (MCNN-AGRU) is proposed. …”
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DCP-YOLOv7x: improved pest detection method for low-quality cotton image by Yukun Ma, Yajun Wei, Minsheng Ma, Zhilong Ning, Minghui Qiao, Uchechukwu Awada

“…In addition, the model detection head part is replaced with a DyHead (Dynamic Head) structure, which dynamically fuses the features at different scales by introducing dynamic convolution and multi-head attention mechanism to enhance the model's ability to cope with the problem of target morphology and location variability.ResultsThe model was fine-tuned and tested on the Exdark and Dk-CottonInsect datasets. …”
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Application of a Latent Diffusion Model to Plant Disease Detection by Generating Unseen Class Images by Noriyuki Mori, Hiroki Naito, Fumiki Hosoi

“…However, these methods require large amounts of labelled data, which are difficult to prepare and preprocess. To overcome this problem, we propose the use of a latent diffusion model for plant disease detection by generating unseen class images. …”
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A Dual-Module System for Copyright-Free Image Recommendation and Infringement Detection in Educational Materials by Yeongha Kim, Soyeon Kim, Seonghyun Min, Youngung Han, Ohyoung Lee, Wongyum Kim

“…However, unauthorized use of images frequently results in legal issues related to copyright infringement. To mitigate this problem, we introduce a dual-module system specifically designed for educators. …”
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TCE-YOLOv5: Lightweight Automatic Driving Object Detection Algorithm Based on YOLOv5 by Han Wang, Zhenwei Yang, Qiaoshou Liu, Qiang Zhang, Honggang Wang

“…Therefore, a lightweight object detection algorithm based on YOLOv5 is proposed to solve the problem of excessive network parameters in automatic driving scenarios. …”
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CM-YOLO: Typical Object Detection Method in Remote Sensing Cloud and Mist Scene Images by Jianming Hu, Yangyu Wei, Wenbin Chen, Xiyang Zhi, Wei Zhang

“…However, the degradation and loss of features caused by the obstruction of cloud and mist elements still pose a challenging problem for this technology. …”
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Global-local graph attention with cyclic pseudo-labels for bitcoin anti-money laundering detection by Meng Li, Lu Jia, XinQiao Su

“…Abstract This paper addresses the problem of detecting money laundering in the Bitcoin network. …”
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