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A Fast Community detection Algorithm Based on Reconstructing Signed Networks

IEEE SYSTEMS JOURNAL 2022年第1期16卷 614-625页

作者： Xia, Chengyi Luo, Yongping Wang, Li Li, Hui-Jia Tianjin Univ Technol Tianjin Key Lab Intelligence Comp & Novel Softwar Engn Res Ctr Learning Based Intelligent Syst Minist Educ Tianjin 300384 Peoples R China Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China

Signed networks depict the individual cooperative or hostile relationship in a population, which can help to deeply mine the characteristics of complex networks and predict the potential collaboration between individuals by analyzing their interaction within different groups or communities. In this article, first of all, an improved modularity function for signed networks is proposed on the basis of the existing modularity function. Then, a new community detection algorithm for signed networks has also been devised, and time complexity analysis shows that the time required for the algorithm has a linear relationship with the number of nodes in the sparse networks. Meanwhile, the affinity index that can be used to convert directed signed networks into the corresponding undirected signed networks is come up with. Finally, the current algorithm has been applied into several illustrative and realistic networks. The experimental results indicate that the number of communities given by the proposed algorithm is consistent with that of actual communities, and thus, it can be further conducive to identifying the community structure hidden within the real-world systems.

关键词： Indexes Partitioning algorithms Image edge detection Clustering algorithms Oscillators Heuristic algorithms detection algorithms Affinity index community detection community structure modularity network reconstruction signed network

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A Signature Recognition Technique With a Powerful Verification Mechanism Based on CNN and PCA

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IEEE ACCESS 2024年 12卷 40634-40656页

作者： Abosamra, Gibrael Oqaibi, Hadi King Abdulaziz Univ Fac Comp & Informat Technol Dept Comp Informat Syst Jeddah 21589 Saudi Arabia

In this paper, we embed a signature verification mechanism in a previously introduced architecture for signature recognition to detect in-distribution and out-of-distribution random forgeries. In the previous architecture, a CNN was trained on the genuine user training dataset and then used as a feature extraction module. A k-NN algorithm with cosine distance was then used to classify the unknown signatures based on the nearest cosine distance neighbor. This architecture led to higher than 99% accuracy, but without verification, because any unknown signature will converge to one of the identities of the training dataset's users. To add a verification mechanism that differentiates between genuine and random forgeries, we use PCA to select the most discriminating features used in calculating the cosine distance between the training and testing signatures. A fixed parameter thresholding technique based on the training distances is introduced that best differentiates between the genuine and random-user signatures. Moreover, enhancement of the technique is carried out by combining the output of the Softmax layer and the last convolution layer of the ResNet18 model to get a highly discriminative representation of the handwritten signatures. Accordingly, the introduced verification mechanism resulted in very low false positive and negative rates for test signatures from inside and outside the main dataset, with an insignificant decrease in the high identification accuracy. The complete architecture has been tested on three publicly available datasets, showing superior results.

关键词： Training Principal component analysis Feature extraction Forgery Vectors Convolutional neural networks Data models Nearest neighbor methods detection algorithms Handwriting recognition cosine distance false negative rate false positive rate k-Nearest neighbor algorithm out-of-distribution detection signature identification signature verification

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HGNN-QSSA: Heterogeneous Graph Neural Networks With Quantitative Sampling and Structure-Aware Attention

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IEEE ACCESS 2024年 12卷 25512-25524页

作者： Zhao, Qin Miao, Yaru An, Dongdong Lian, Jie Li, Maozhen Shanghai Normal Univ Shanghai Engn Res Ctr Intelligent Educ & Big Data Shanghai 200234 Peoples R China Tongji Univ Key Lab Embedded Syst & Serv Comp Minist Educ Shanghai 200092 Peoples R China Brunel Univ London Dept Elect & Comp Engn London UB8 3PH England

Heterogeneous information networks provide abundant structural and semantic information. Two main strategies for leveraging this data include meta-path-based and meta-path-free methods. The effectiveness of the former heavily depends on the quality of manually defined meta-paths, which may lead to the instability of the model. However, the existing meta-path-free methods lack of neighbor screening during aggregating, and there is also an overemphasis on attribute information. To address these issues, we propose the Heterogeneous Graph Neural Network model by incorporating Quantitative Sampling and Structure-aware Attention. We introduce a Quantitative Sampling Module that calculates the similarity between neighbors of the target nodes and target nodes, enabling us to select the top k nodes with the strongest relevance to the target node based on this measure, and incorporate a Structure-aware Attention Module during the aggregation of neighbor information. This module combines both structural and attribute information to aggregate the neighbor information effectively. By implementing these improvements, our proposed model exhibits superior performance compared to several state-of-the-art methods on two real-world datasets.

关键词： Graph neural networks Social networking (online) Correlation Prediction algorithms Task analysis Stacking Semantics Information systems Heterogeneous networks detection algorithms Heterogeneous information network community detection quantitative sampling structure-aware attention

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A Comprehensive Analysis of the Machine Learning algorithms in IoT IDS Systems

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IEEE ACCESS 2024年 12卷 46785-46811页

作者： Ozdogan, Erdal Gazi Univ IT Dept TR-06560 Ankara Turkiye

In this study, machine learning algorithms in IoT IDS (Internet of Things Intrusion detection System) systems are comprehensively compared from various aspects. Accuracy, precision, and training time are evaluated. The effects of data preprocessing techniques including normalization, outlier removal, standardization, and regularization on the datasets are examined. Furthermore, the impact of dataset balancing, considering both balanced and imbalanced scenarios, on machine learning performance is investigated. The contribution of feature selection on the four different datasets is also analyzed. Based on findings, it is observed that certain preprocessing operations provide significant advantages in various ML algorithms, whereas others have very low impact, and their performance varies depending on the dataset and feature selection. The aim of this study is to facilitate the complexity and lengthiness of machine learning processes and algorithm selection, providing insights for future academic research. By addressing this objective, an effort is made to shed light on simplifying the utilization of machine learning algorithms. The challenges arising from the complexity of machine learning processes in IoT IDS systems are addressed by this study. This contribution can greatly benefit researchers in their academic endeavors. This multifaceted approach proves beneficial when comparing the methods under consideration, fostering a scientific discourse on their efficacy within contexts.

关键词： detection algorithms IoT attack detection IoT IDS ML algorithm comparison machine learning preprocessing machine learning in IoT IDS

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Ellipse Encoding for Arbitrary-Oriented SAR Ship detection Based on Dynamic Key Points

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Gao, Fei Huo, Yiyang Sun, Jinping Yu, Tao Hussain, Amir Zhou, Huiyu Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Taiyuan Satellite Launch Ctr Xinzhou 36300 Peoples R China Edinburgh Napier Univ Cyber & Big Data Res Lab Edinburgh EH11 4BN Midlothian Scotland Univ Leicester Sch Comp & Math Sci Leicester LE1 7RH Leics England

In recent years, there has been growing interest in developing oriented bounding box (OBB)-based deep learning approaches to detect arbitrary-oriented ship targets in synthetic aperture radar (SAR) images. However, most existing OBB-based detection methods suffer from boundary discontinuity problems for bounding box angle prediction and key point regression challenges. In this article, we present a novel OBB-based detection algorithm that utilizes ellipse encoding to effectively exploit the geometric and scattering properties of ship targets. Specifically, the ship contour is fit by an OBB inscribed ellipse that is encoded as a set of distances between dynamic key points on the bow and target center. By combining the bow angle interval and the decoding process, the negative impact of the boundary discontinuity problem is avoided. In addition, we propose an elliptical Gaussian distribution heatmap and a pooling strategy termed double-peak max-pooling (DPM) to deal with the challenge of separating densely distributed ships in inshore scenes. The former can enhance the heatmap's ship-side score gap between neighboring ship targets, while the latter can solve the problem of target center responses being suppressed after max-pooling. Simulation experiments conducted on the benchmark Rotating SAR Ship detection Dataset (RSSDD) and Rotated Ship detection Dataset in SAR Images (RSDD-SAR) demonstrate the superior performance of our method for ship target detection compared to several state-of-the-art OBB-based algorithms. Ablation experiments show that elliptical Gaussian distribution heatmap and DPM can further improve the inshore detection performance.

关键词： Marine vehicles Feature extraction detection algorithms Synthetic aperture radar Radar polarimetry Heating systems Scattering Arbitrary-oriented boundary discontinuity ellipse encoding ship detection synthetic aperture radar (SAR)

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LOVD: Land Vehicle detection in Complex Scenes of Optical Remote Sensing Image

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Yan, Puti Liu, Xinrui Wang, Feng Yue, Chengfei Wang, Xin Harbin Inst Technol Dept Aerosp Engn Harbin 150001 Peoples R China Harbin Inst Technol Shenzhen Inst Space Sci & Appl Technol ISSAT Shenzhen 518055 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China

Nowadays, there is a growing body of research about object detection in remote sensing data. However, the detection algorithms for small targets in remote sensing areas are inadequate, largely because of the unavailability of high-quality datasets. Most remote sensing datasets are comprehensive, which means that they include bridges, airplanes, and lots of other common categories. Compared with other categories, the number and diversity of weak objects, such as vehicles, are quite insufficient. These limitations greatly affect the detection of small targets in remote sensing images. In order to promote the development of algorithms for the detection of small targets in remote sensing images and also allow access to remote sensing data, we have established a large-scale dataset for the detection of vehicle targets in optical remote sensing images and called it LOVD. It contains 1196 pictures and 541751 instances, covering 13 categories. For the dataset, we have proposed in this article: 1) it is the largest one in terms of vehicle category and the total number of vehicle instances;2) it contains images with various backgrounds in different weather and scenarios;and 3) all targets are marked by oriented bounding boxes (OBBs), and two label formats are provided. Finally, we test the state-of-the-art detection algorithms on our dataset and provide a benchmark for OBB detection.

关键词： Remote sensing Meteorology Training Optical imaging Object detection Detectors detection algorithms Dataset remote sensing vehicle detection

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Label Propagation Algorithm Joint Multilayer Neighborhood Overlap and Historic Label Similarity for Community detection

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IEEE SYSTEMS JOURNAL 2022年第2期16卷 2626-2634页

作者： Zhang, Siyang Yu, Siyu E, Xinhua Huo, Ru Sui, Ziheng Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Peking Univ Fac Informat Technol Sch Software & Microelect Beijing 102600 Peoples R China

Community detection is an extremely important technology for today's rapidly evolving data mining and exploratory analysis. Many community detection algorithms have been proposed and applied. Among them, the label propagation algorithm (LPA) is widely used due to its closeness to linear time complexity and its simple characteristics. However, the results of detecting the community were found to be random when using LPA. Based on previous improvements to the LPA, this article proposes a new LPA (NOHLPA) that joins multilayer neighborhood overlap and historical label similarity. The NOHLPA considers both the node update order and label selection rules. We cited the label entropy as the basis for node update order and defined multilayer neighborhood overlap and historical label similarity for calculating node preferences to devise more appropriate label selection rules. We test the NOHLPA on five real datasets and compare NOHLPA with different combinations of algorithms. The experimental results show that the NOHLPA effectively improves the accuracy of community partitioning while ensuring stability.

关键词： Entropy Nonhomogeneous media Internet Time complexity Social networking (online) Heuristic algorithms detection algorithms Community detection historical label similarity label propagation multilayer neighborhood overlap

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Infrared Small UAV Target detection Algorithm Based on Enhanced Adaptive Feature Pyramid Networks

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IEEE ACCESS 2022年 10卷 115988-115995页

作者： Zheng Lu Peng Yueping Ye Zecong Jiang Rongqi Zhou Tongtong Engn Univ PAP Xian 710086 Peoples R China Engn Univ PAP Coll Informat Engn Xian 710086 Peoples R China

Under the explosive growth of "low-slow-small" unmanned aerial vehicles (UAVs), harassment incidents with "low-slow-small" UAVs, such as illegal surveying and mapping, no-fly zone flights, terrorist attacks, have frequently been occurring due to the low entry barrier to the industry and the lack of market norms and control standards, and severely impair the safety of public life and property. The premise of developing anti-UAV technology is efficient and accurate UAV target detection, which plays a vital role in the follow-up UAV target tracking, attack, and interception. This paper proposes a new DEAX algorithm named enhanced adaptive feature pyramid networks-based Target detection Algorithm for infrared small UAV target detection. Our proposed algorithm improves the original feature pyramid networks in four aspects for the small UAV target detection task. 1) Use channel separation to keep more channels when adjusting the number of channels in convolutional to avoid the loss of helpful feature information;2) Design a feature enhancement module to enhance "target feature" and suppress "non-target feature";3) Alleviate the differences of receptive fields and semantic information among different layers by shared convolution;4) Introduce adaptive feature fusion method into feature fusion and complete the construction of enhanced adaptive feature pyramid networks (EAFPN) to solve the problem of weakening feature expression in cross-scale feature fusion. EAFPN is applied to the single-stage target detection networks. We find the detection accuracy and speed of the algorithm outperform those based on feature pyramid networks (FPN), where the improvement on mAP is 8.9%.

关键词： Feature extraction Object detection Semantics detection algorithms Target tracking Imaging Classification algorithms Object detection feature pyramid feature enhancement

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A Methodology for Forest Fire detection and Notification Using AI and IoT Approaches 2

A Methodology for Forest Fire Detection and Notification Usi...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Borah, Asha Rani Ravi, Nikhil Narayan, Nikhil D'Souza, Prathik Joel New Horizon College of Engineering Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350327533

This study explores how advanced technology has revolutionized the use of drones for forest fire surveillance. This research study explores the historical development of Unmanned Aerial Vehicles (UAVs) in forest monitoring with a focus on control systems and detection algorithms. The drone's capabilities are improved via telemetry integration, sophisticated autopilot systems, and an emphasis on accuracy, autonomy, and real-Time communication. An analysis of object recognition, anomaly detection, and environmental monitoring algorithms in detail demonstrates how important a role these algorithms play in spotting dangers like forest fire. This study presents successful implementations through illustrative case studies, discusses obstacles, and lays forth future plans for utilizing drone technology for the forest fire surveillance in environmental conservation initiatives. © 2024 IEEE.

关键词： Autonomy Control systems Cutting-edge technology detection algorithms Forest surveillance drones Historical evolution Real-Time communication Unmanned Aerial Vehicles

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Improving Plant Disease Classification With Deep-Learning-Based Prediction Model Using Explainable Artificial Intelligence

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IEEE ACCESS 2024年 12卷 100005-100014页

作者： Nigar, Natasha Faisal, Hafiz Muhammad Umer, Muhammad Oki, Olukayode Manappattukunnel Lukose, Jose Univ Engn & Technol Dept Comp Sci RCET Lahore 54000 Pakistan Tech Univ Munich Sch Engn & Design D-80333 Munich Germany TEKHQS INC Irvine CA 92620 USA Walter Sisulu Univ Dept Informat Technol ZA-5117 Mthatha South Africa

Plant diseases can have profound effects on the economy, impacting both local and global scales. These diseases can lead to substantial losses in agricultural productivity, affecting crop yields and quality. In this context, deep learning algorithms are widely acknowledged as effective solutions. However, the use of these black-box approaches raises concerns about trust in interpreting and validating the decisions generated by the models. This study proposes an explainable artificial intelligence (XAI) based plant disease classification system to classify and identify distinct ailments with improved accuracy. The system correctly identifies 38 different plant diseases with accuracy, precision, and recall as 99.69%, 98.27%, and 98.26%, respectively. These predictions are subjected to additional analysis employing the local interpretable model-agnostic explanations (LIME) framework to produce visual explanations aligning with prior beliefs and adhering to established best practices in explanations. This system will serve as a promising avenue for revolutionizing disease detection, fostering informed decision-making, and ultimately contributing to global food security.

关键词： Accuracy Plant diseases Feature extraction Solid modeling Computational modeling Predictive models Explainable AI detection algorithms Plant disease detection deep learning explainable artificial intelligence prediction model prediction model

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