Community detection algorithm of hybrid node analysis and edge analysis in complex networks by Kun DENG, Qingfeng JIANG, Xingyan LIU

“…The community detection of hybrid node analysis and edge analysis in complex networks (CDHNE), a novel community detection algorithm, was proposed aiming at the problem that both edge community detection and node-based community detection algorithms had corresponding shortcomings in the process of detecting communities, which affected the quality of complex network community detection.The relatively stable characteristics of the edge in the networks were firstly used by the algorithm to construct a more accurate community structure through edge community detection at the early stage of algorithm execution.Then, after the formation of the edge communities, the flexible characteristics of the node were used to accurately detect the boundary of edge communities, so as to more accurately detect the community structure in the complex networks.In the computer-generated network experiments, when the community structure of the network gradually became fuzzy, the number of overlapping nodes and the number of communities to which the overlapping nodes belonged kept increasing.Compared to traditional algorithms, the accuracy of community detection and overlapping nodes detection were improved by an average of 10% and 15%, respectively, by the CDHNE algorithm.In the real network experiments, the tightness of the community structure detected by the CDHNE algorithm was better.Especially when facing large-scale networks with more than 100 000 nodes, the detection task was completed by the CDHNE algorithm with high quality, and the EQ value reached 0.412 1.The experimental results show that the CDHNE algorithm has advantages in operational stability and handling large-scale networks.…”
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Anomaly detection algorithm based on Gaussian mixture variational auto encoder network by Huahua CHEN, Zhe CHEN, Chunsheng GUO, Na YING, Xueyi YE, Jianwu ZHANG

“…Anomalous data, which deviates from a large number of normal data, has a negative impact and contains a risk on various systems.Anomaly detection can detect anomalies in the data and provide important support for the normal operation of various systems, which has important practical significance.An anomaly detection algorithm based on Gaussian mixture variational auto encoder network was proposed, in which a variational autoencoder was built to extract the features of the input data based on Gaussian mixture distribution, and using this variational autoencoder to construct a deep support vector network to compress the feature space and find the minimum hyper sphere to separate the normal data and the abnormal data.Anomalies can be detected by the score from the Euclidean distance from the feature of data to the center of the hypersphere.The proposed algorithm was evaluated on the benchmark datasets MNIST and Fashion-MNIST, and the corresponding average AUC are 0.954 and 0.937 respectively.The experimental results show that the proposed algorithm achieves preferable effects.…”
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A New Algorithm for Detecting X-Ray Shots in Cyg X-1 by Jin Qin, Hua Feng, Lian Tao

“…Compared with previous techniques, our algorithm allows us to detect shots with lower amplitudes and shorter time separations. …”
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Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling by Miao Peng, Sue Bai, Yang Lu

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Belt conveyor idler fault detection algorithm based on improved YOLOv5 by Cen Pan, Qing Tao, Hao Pei, Biao Wang, Wei Liu

“…Experimental results demonstrate that the enhanced YOLOv5 algorithm achieves a 95.3% mAP on the self-constructed infrared image dataset, surpassing the original algorithm by 2.7%. …”
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Haar Cascade Classifier and Adaboost Algorithm for Face Detection with the Viola-Jones Method by Mohammad Saichu Nidom

“…The conclusion of this study emphasizes the importance of combining methods in developing a more robust and efficient face detection system. The implications of this research can be applied to create more effective security and facial recognition applications and pave the way for further study in optimizing object detection algorithms. …”
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An AI-Based Horticultural Plant Fruit Visual Detection Algorithm for Apple Fruits by Bin Yan, Xiameng Li, Rongshan Yan

“…The detection algorithm proposed in the study can be extended to the intelligent measurement of apple biological and physical characteristics, including for size measurement, shape analysis, and color analysis. …”
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Outlier detection algorithm based on fast density peak clustering outlier factor by Zhongping ZHANG, Sen LI, Weixiong LIU, Shuxia LIU

“…For the problem that peak density clustering algorithm requires human set parameters and high time complexity, an outlier detection algorithm based on fast density peak clustering outlier factor was proposed.Firstly, k nearest neighbors algorithm was used to replace the density peak of density estimate, which adopted the KD-Tree index data structure calculation of k close neighbors of data objects, and then the way of the product of density and distance was adopted to automatic selection of clustering centers.In addition, the centripetal relative distance and fast density peak clustering outliers were defined to describe the degree of outliers of data objects.Experiments on artificial data sets and real data sets were carried out to verify the algorithm, and compared with some classical and novel algorithms.The validity and time efficiency of the proposed algorithm are verified.…”
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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

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A Review of YOLO Algorithm and Its Applications in Autonomous Driving Object Detection by Jiapei Wei, Azizan As'arry, Khairil Anas Md Rezali, Mohd Zuhri Mohamed Yusoff, Haohao Ma, Kunlun Zhang

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Decomposition-Based Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Social Networks by Jingjing Ma, Jie Liu, Wenping Ma, Maoguo Gong, Licheng Jiao

“…In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. …”
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Data-driven network intrusion detection using optimized machine learning algorithms by Dauda Adeite Adenusi, Oladosu Oyebisi Oladimeji, Theopilus Adekunle Oyekola, Korede Solomon Olagunju

“…This study presents a comprehensive evaluation of machine learning approaches for network intrusion detection, comparing the performance of Decision Trees (DT), Random Forest (RF), K-Nearest Neighbors (K-NN), Gradient Boosting (GB), and Logistic Regression (LR) algorithms. …”
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A Lightweight Detection Method for Meretrix Based on an Improved YOLOv8 Algorithm by Zhongxu Tian, Sifan Hou, Xiaoxue Yue, Xuewen Hu

“…To address this issue, this paper proposes a lightweight detection method, YOLOv8-RFD, based on an improved YOLOv8 algorithm, tailored for clam sorting applications. …”
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FB-YOLOv8s: A fire detection algorithm based on YOLOv8s by Yuhang Liu, Chunjuan Bo, Chong Feng

“…However, there exist some problems in traditional detection algorithms of fire, such as low accuracy, high miss rate, and low detection rate of small targets. …”
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Parameter Optimisation of Support Vector Machine using Genetic Algorithm for Cyberbullying Detection by Mohd Qorib Alqowiy, Ema Utami

“…The results demonstrate an accuracy improvement, with the genetic algorithm achieving an accuracy of 86%. This highlights the effectiveness of genetic algorithms in optimizing SVM parameters for cyberbullying detection.…”
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Static Analysis-based Detection of Android Malware using Machine Learning Algorithms by Omar Emad Saied, Karam Hatim Thanoon

“…The rapid growth of Android applications has led to increased security threats, making malware detection a critical concern in cybersecurity. This research proposes a static analysis-based technique that employs machine learning for Android malware detection. …”
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Multisensor-Weighted Fusion Algorithm Based on Improved AHP for Aircraft Fire Detection by Rui Wang, Yahui Li, Hui Sun, Kaixin Yang

“…Comparing the proposed algorithm to the grey fuzzy neural network fusion algorithm and fuzzy logic fusion algorithm in terms of the time consumption for fire detection and sending alarm and the accuracy of fire alarm perspectives, the experiments demonstrate that the proposed fire detection algorithm can detect the fire within 10s and reduce the false alarm rate to less than 0.5%, which verifies the superiority of the algorithm in promptness and accuracy. …”
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Outlier Detection of Photovoltaic Power Generation System Data Based on GMM-IF Algorithm by Xin Li, Guoxin Du, Yuman Li, Zengqiang Ma, Lihu Liu, Minglong Jiang, Feiyu Liu, Tianming Mu

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A new RS steganalysis algorithm of detecting UnderMP3Cover by Jian ZHANG, Rangding WANG, Diqun YAN

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Impact of Right-Hand Polarized Signals in GNSS-R Water Detection Algorithms by Jilun Peng, Estel Cardellach, Weiqiang Li, Serni Ribo, Antonio Rius

“…The water detection algorithm for the dual-polarized HydroGNSS mission was validated using spaceborne left-hand circular polarization (LHCP) data from the cyclone global navigation satellite system. …”
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