LDoS attack detection method based on traffic classification prediction by Liang Liu, Yue Yin, Zhijun Wu, Qingbo Pan, Meng Yue

“…The experimental results show that the global LDoS attack traffic detection method based on the Hurst index and GBDT algorithm achieves better detection results under different attack rates.…”
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A Method of Abnormal Behavior Detection for Safety Site Surveillance by Wenjing Wang, Yangyang Zhang, QingE Wu

“…For the complex background in the image, a multiframe differential superposition algorithm is proposed to denoise the target image; a feature extraction method is given to extract features for the target image, and then a more complete image with target features is obtained after filtering; a normal behavior model is established to extract the motion information of the target from a single frame of the image; an abnormal detection method is proposed to determine whether it belongs to abnormal behavior. …”
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A novel transformer-based approach for cardiovascular disease detection by Nimra Noor, Muhammad Bilal, Saadullah Farooq Abbasi, Omid Pournik, Theodoros N. Arvanitis

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Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification by Thitaree Tanprasert, Chalermpol Saiprasert, Suttipong Thajchayapong

“…If an anomaly is detected, the algorithm proceeds to find relevant features in the input signals and use neural networks to identify drivers. …”
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An optimization-inspired intrusion detection model for software-defined networking by Hui Xu, Longtan Bai, Wei Huang

“…This paper proposes an enhanced spider wasp optimizer (ESWO) algorithm for feature dimensionality reduction of intrusion detection datasets and constructs a new intrusion detection model (IDM), namely ESWO-IDM, for SDN. …”
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The analysis of fraud detection in financial market under machine learning by Jing Jin, Yongqing Zhang

“…Therefore, this paper proposes a financial fraud detection model based on Stacking ensemble learning algorithm, which integrates many basic learners such as logical regression (LR), decision tree (DT), random forest (RF), Gradient Boosting Tree (GBT), support vector machine (SVM) and neural network (NN), and introduces feature importance weighting and dynamic weight adjustment mechanism to improve the model performance. …”
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An AI framework for counterattack detection and decision-making evaluation in football by Jiangyan Yang, Huanmin Ge, Yixiong Cui

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A Method for Detecting Tomato Maturity Based on Deep Learning by Song Wang, Jianxia Xiang, Daqing Chen, Cong Zhang

“…The algorithm employs several technical means to improve detection accuracy and efficiency. …”
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Sysmon event logs for machine learning-based malware detection by Riki Mi’roj Achmad, Dyah Putri Nariswari, Baskoro Adi Pratomo, Hudan Studiawan

“…In this research, we employed various machine learning algorithms, both classification (supervised learning) and outlier detection (unsupervised learning) approaches, such as Naive Bayes, Decision Tree, Random Forest, Support Vector Machine (SVM) for supervised learning, and Isolation Forest, Local Outlier Factor (LOF), and One-Class SVM for unsupervised learning. …”
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Enhancing DDoS Attack Classification through SDN and Machine Learning: A Feature Ranking Analysis by Aymen AlAwadi, Kawthar Rasoul ALesawi

“…We reduced the feature up to 5 effective features without compromising the classification accuracy. …”
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Precision Detection of Infrared Small Target in Ground-to-Air Scene by Xiaona Dong, Huilin Jiang, Yansong Song, Keyan Dong

“…In recent years, most target detection methods usually use the statistical features of a rectangular window to represent the contrast between the target and the background. …”
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Fault detection method for distribution network based on edge computing by YANG Hao, ZHAO Huan, XUE Rong, YU Yubin, WEI Enwei, HUANG Bing

“…Based on the edge side, combined with multi-dimensional S-transform fusion algorithm and phase modulus transformation matrix method, the relevant fault features of distribution network are extracted. …”
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Myocarditis Detection Using Proximal Policy Optimization and Mutual Learning by Asadi Srinivasulu, Sivaram Rajeyyagari

“…The model employs multiple convolutional neural networks (CNNs) to extract feature vectors from images for classification. To address class imbalance, a proximal policy optimization (PPO)-based algorithm is utilized, significantly improving the training process by preventing abrupt policy shifts and stabilizing them. …”
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Deep learning-based object detection and robotic arm grasping by ZHANG Lei, ZHANG Senhui, YAN Song, YUAN Yuan

“…For the grasping task, a single-stage grasping pose detection algorithm was designed. Firstly, considering the interference present in unstructured environments, RGB-D images were selected as the input data for the grasping network, and GG-CNN was chosen as the backbone network. …”
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Broiler Behavior Detection and Tracking Method Based on Lightweight Transformer by Haixia Qi, Zihong Chen, Guangsheng Liang, Riyao Chen, Jinzhuo Jiang, Xiwen Luo

“…The FasterNet network based on partial convolution (PConv) was used to replace the Resnet18 backbone network to reduce the computational complexity of the model and to improve the speed of model detection. In addition, we propose a new cross-scale feature fusion network to optimize the neck network of the original model. …”
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Detection of DRFM Deception Jamming Based on Diagonal Integral Bispectrum by Dianxing Sun, Ao Li, Hao Ding, Jifeng Wei

“…Simulations and experimental results show that the correct detection rate reaches 92% at a jamming-to-signal ratio (JSR) and SNR of 0 dB, validating the effectiveness of the algorithm.…”
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Use of satellite data for detecting icebergs and evaluating the iceberg threats by I. A. Bychkova, V. G. Smirnov

“…Te algorithms of the iceberg detection, the procedure of the false target identifcation, and determination the horizontal dimensions of the icebergs and their location are described. …”
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An Effective Detection Approach for Phishing URL Using ResMLP by S. Remya, Manu J. Pillai, Kajal K. Nair, Somula Rama Subbareddy, Yong Yun Cho

“…Traditional blacklists struggle to identify dynamic URLs, necessitating advanced detection mechanisms. In this study, we propose an effective approach utilizing residual pipelining for phishing URL detection. …”
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Field Obstacle Detection and Location Method Based on Binocular Vision by Yuanyuan Zhang, Kunpeng Tian, Jicheng Huang, Zhenlong Wang, Bin Zhang, Qing Xie

“…The improved model incorporates the Large Separable Kernel Attention (LSKA) module to enhance the extraction of field obstacle features. Additionally, the use of a Poly Kernel Inception (PKI) Block reduces model size while improving obstacle detection across various scales. …”
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Detecting unknown vulnerabilities in smart contracts using opcode sequences by Peiqiang Li, Guojun Wang, Xiaofei Xing, Xiangbin Li, Jinyao Zhu

“…Next, we employ an n-gram model and a vector weight penalty mechanism to extract the opcode sequence features. We then use machine learning algorithms to detect unknown vulnerabilities based on the similarity principle. …”
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