ASHM-YOLOv9: A Detection Model for Strawberry in Greenhouses at Multiple Stages by Yan Mo, Shaowei Bai, Wei Chen

“…The squeeze-and-excitation (SE) network was added to increase the network’s capacity for characteristic derivation and its ability to fuse features. Haar wavelet downsampling (HWD) was applied to optimize the Adaptive Downsampling module (Adown) of the initial model, thereby increasing the precision of object detection. …”
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Enhanced Credit Card Fraud Detection Using Deep Hybrid CLST Model by Madiha Jabeen, Shabana Ramzan, Ali Raza, Norma Latif Fitriyani, Muhammad Syafrudin, Seung Won Lee

“…In this research, a combined deep learning model that incorporates a convolutional neural network (CNN), long-short-term memory (LSTM), and fully connected output layer is proposed to enhance the accuracy of fraud detection, particularly in addressing the class imbalance problem. …”
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Enhanced intrusion detection model based on principal component analysis and variable ensemble machine learning algorithm by Ayuba John, Ismail Fauzi Bin Isnin, Syed Hamid Hussain Madni, Farkhana Binti Muchtar

“…Several IDS models have various security problems, such as low detection accuracy and high false alarms, which can be caused by the network traffic dataset's excessive dimensionality and class imbalance in the creation of IDS models. …”
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Detecting subsurface diseases on airport road surface based on an improved SSD algorithm. by Mengmeng Pan, Huiguang Chen, Lipeng Yang, XianRong Jiang

“…Facing the radar data of underground hidden diseases of airport roads with low recognition and high noise intensity, it is inefficient to recognize the diseases by manual identification, and it is difficult to achieve accurate differentiation and localization of the diseases by the existing detection methods. We analyze and propose an improved algorithm EFA-SSD (Enhanced Feature Aggregation SSD) for automatic detection of airport road subsurface diseases, which solves the problems of strong noise background conditions, severe interference of morphological features of different types of subsurface diseases, and low target recognition. …”
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Low scaling factor Seam Carving tamper detection algorithm with hybrid attention by ZHAO Jie, CHANG Haochan, WU Bin

“…The existing seam carving tamper detection algorithms have the problems of low detection accuracy and weak robustness for the case of low scaling factor. …”
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The Contact State Monitoring for Seal End Faces Based on Acoustic Emission Detection by Xiaohui Li, Pan Fu, Kan Chen, Zhibin Lin, Erqing Zhang

“…In the acoustic emission (AE) detection for mechanical seal, the main difficulty is to reduce the background noise and to classify the dispersed features. …”
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Machine learning for Internet of things anomaly detection under low-quality data by Shangbin Han, Qianhong Wu, Yang Yang

“…Therefore, practitioners may not know which algorithm to choose due to the lack of review and evaluation of anomaly detection methods under low-quality data. To address this problem, we give a detailed review and evaluation of six supervised anomaly detection methods, as well as release the core code of feature extractor for pcap format traffic traces and anomaly detection methods for reuse. …”
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Transfer learning and single-polarized SAR image preprocessing for oil spill detection by Nataliia Kussul, Yevhenii Salii, Volodymyr Kuzin, Bohdan Yailymov, Andrii Shelestov

“…We investigated the effectiveness of various neural network architectures and encoders for this task, focusing on scenarios with limited training data. The research problem centered on enhancing feature extraction from single-channel SAR data to improve oil spill detection performance.Our methodology involved developing a novel preprocessing pipeline that converts single-channel SAR data into a three-channel RGB representation. …”
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Single-Frame Infrared Target Detection Based on Fast Content-Related Modeling by Zipeng Zhang, Xidong Zhao, Wenzheng Wang, Yuqi Han, Chenwei Deng, Zhuokai Li, Linbo Tang

“…To solve these problems, a single-frame infrared target detection method based on fast content-related modeling is proposed to combine global and local features of infrared images, describing the common features of varying scenes robustly and enhancing the distinction between targets and backgrounds. …”
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Reconstruction of the object inhomogeneity parameters from near-field measurements in microwave tomography problem using neural networks by A.V. Medvedev, M.Yu. Medvedik

“…A neural network approach was used to filter the values obtained after a two-step algorithm. This problem arises in acoustics, electrodynamics, flaw detection, as well as in medical diagnostics. …”

Research on Target-detection and Tracking of Intelligent Mine Trucks in Open-pit Mines by LI Yuanzhengyu, HU Yunqing, LONG Teng, HUANG Wenyu, PAN Wenbo, LI Peijie

“…Among them, feature extraction module uses point cloud gradient and distance feature extraction methods to solve the point cloud segmentation problem of small and irregular-shaped obstacles in changing scenes; target tracking module constructs track information for multi-target tracking, which improves the stability of target tracking; an asynchronous fusion strategy of multi-source sensors is designed for heterogeneous sensor fusion module, which overcomes the problem of heterogeneous sensor fusion, and improves the detection and tracking capabilities of small and various targets in unpaved road and dusty environment of open-pit mines. …”
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Harnessing self-supervised learning to boost malicious traffic detection with enhanced attention by SUN Jianwen, ZHANG Bin, LI Hongyu, CHANG Heyu

“…The existing deep learning-based malicious traffic detection methods generally suffered from three main problems: labeled sample scarcity, inadequate representation of malicious behavior traffic features, and a high false positive rate due to ineffective integration of behavioral association patterns during detection. …”
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Efficient-Deformable-DETR: Enhancing underwater small target detection in complex environments by Xie Haozheng, Xin Guanghong, Kang Shuo, Xie Qiongdan

“…This paper focuses on the problem of small target detection in complex underwater environments and proposes an improved model based on the Deformable-DETR model. …”
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Machine Learning Model for Early Detection of COVID-19 by Heart Rhythm Abnormalities by M. S. Mezhov, V. O. Kozitsin, Iu. D. Katser

“…The article describes the problem of detecting covid anomalies in rhythm strips. …”
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Adversarial Hierarchical-Aware Edge Attention Learning Method for Network Intrusion Detection by Hao Yan, Jianming Li, Lei Du, Binxing Fang, Yan Jia, Zhaoquan Gu

“…Existing graph-based approaches either underestimate edge-level features or fail to balance detection accuracy with adversarial robustness. …”
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Anterior Cruciate Ligament (ACL) Tear Detection Using Hybrid CNN Transformer by Suthir Sriram, Deependra K. Singh, D. V. Sairam, Nivethitha Vijayaraj, Thangavel Murugan

“…For instance, CNNs have a tendency of extracting local features, hence have problems in understanding global context but on the other hand, transformers excel in understanding global information even if it implies losing some details in localization. …”
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Credit card fraud detection in the era of disruptive technologies: A systematic review by Asma Cherif, Arwa Badhib, Heyfa Ammar, Suhair Alshehri, Manal Kalkatawi, Abdessamad Imine

“…The selection of 40 relevant articles is reviewed and categorized according to the topics covered (class imbalance problem, feature engineering, etc.) and the machine learning technology used (modelling traditional and deep learning). …”
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Semantic-Aware Remote Sensing Change Detection with Multi-Scale Cross-Attention by Xingjian Zheng, Xin Lin, Linbo Qing, Xianfeng Ou

“…As deep learning strives, Convolutional Neural Networks (CNNs) have shown their effects in image processing applications. There are two problems in old-school change detection techniques: First, the techniques do not fully use the effective information of the global and local features, which causes their semantic comprehension to be less accurate. …”
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YOLO-DKM: A Flame and Spark Detection Algorithm Based on Deep Learning by Linpo Shang, Xufei Hu, Zijian Huang, Qiang Zhang, Zhiyu Zhang, Xin Li, Yanzuo Chang

“…In the field of computer vision, existing fire detection algorithms often have problems such as low detection accuracy and algorithm error detection. …”
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MAGECODE: Machine-Generated Code Detection Method Using Large Language Models by Hung Pham, Huyen Ha, van Tong, Dung Hoang, Duc Tran, Tuyen Ngoc Le

“…In order to assess the proposed method, we introduce a new dataset comprising more than 45,000 code solutions generated by LLMs for programming problems. The solutions for these programming problems which were obtained from three advanced LLMs (GPT4, Gemini, and Code-bison-32k), were written in Python, Java, and C++. …”
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