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Design of Evolutionary Algorithm Based Energy Efficient clustering Approach for Vehicular Adhoc Networks

Computer Systems Science & Engineering 2023年第7期46卷 687-699页

作者： VDinesh SSrinivasan Gyanendra Prasad Joshi Woong Cho Department of Electronics and Communication Engineering Kongu Engineering CollegePerundurai638060India Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha University602105ChennaiIndia Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

In a vehicular ad hoc network(VANET),a massive quantity of data needs to be transmitted on a large scale in shorter time durations.At the same time,vehicles exhibit high velocity,leading to more vehicle disconnections.Both of these characteristics result in unreliable data communication in VANET.A vehicle clustering algorithm clusters the vehicles in groups employed in VANET to enhance network scalability and connection reliability.clustering is considered one of the possible solutions for attaining effectual interaction in VANETs.But one such difficulty was reducing the cluster number under increasing transmitting nodes.This article introduces an Evolutionary Hide Objects Game Optimization based Distance Aware clustering(EHOGO-DAC)Scheme for VANET.The major intention of the EHOGO-DAC technique is to portion the VANET into distinct sets of clusters by grouping vehicles.In addition,the DHOGO-EAC technique is mainly based on the HOGO algorithm,which is stimulated by old games,and the searching agent tries to identify hidden objects in a given space.The DHOGO-EAC technique derives a fitness function for the clustering process,including the total number of clusters and Euclidean distance.The experimental assessment of the DHOGO-EAC technique was carried out under distinct aspects.The comparison outcome stated the enhanced outcomes of the DHOGO-EAC technique compared to recent approaches.

关键词： Vehicular networks clustering evolutionary algorithm fitness function distance metric

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Reverse-Nearest-Neighbor-Based clustering by Fast Search and Find of Density Peaks

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Chinese Journal of Electronics 2023年第6期32卷 1341-1354页

作者： ZHANG Chunhao XIE Bin ZHANG Yiran College of Computer and Cyber Security Hebei Normal University Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University

clustering by fast search and find of density peaks(CFSFDP) has the advantages of a novel idea, easy implementation, and efficient clustering. It has been widely recognized in various fields since it was proposed in Science in 2014. The CFSFDP algorithm also has certain limitations, such as non-unified sample density metrics defined by cutoff distance, the domino effect for the assignment of remaining samples triggered by unstable assignment strategy, and the phenomenon of picking wrong density peaks as cluster centers. We propose reverse-nearest-neighbor-based clustering by fast search and find of density peaks(RNN-CFSFDP) to avoid these shortcomings. We redesign and unify the sample density metric by introducing reverse nearest neighbor. The newly defined local density metric and the K-nearest neighbors of each sample are combined to make the assignment process more robust and alleviate the domino effect. A cluster fusion algorithm is proposed, which further alleviates the domino effect and effectively avoids the phenomenon of picking wrong density peaks as cluster centers. Experimental results on publicly available synthetic data sets and real-world data sets show that in most cases, the proposed algorithm is superior to or at least equivalent to the comparative methods in clustering performance. The proposed algorithm works better on manifold data sets and uneven density data sets.

关键词： Density peaks Reverse nearest neighbor clustering Cluster fusion

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Internet of Things Enabled Energy Aware Metaheuristic clustering for Real Time Disaster Management

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Computer Systems Science & Engineering 2023年第5期45卷 1561-1576页

作者： Riya Kumarasamy Santhanaraj Surendran Rajendran Carlos Andres Tavera Romero Sadish Sendil Murugaraj Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Center for Artificial Intelligence and Research(CAIR) Chennai Institute of TechnologyChennai600069India COMBA R&D Laboratory Faculty of EngineeringUniversidad Santiago de CaliCali76001Colombia Department of Emerging Technologies Guru Nanak Institute of TechnologyIbrahipatnamTelangana501506India

Wireless Sensor Networks(WSNs)are a major element of Internet of Things(IoT)networks which offer seamless sensing and wireless connectivity.Disaster management in smart cities can be considered as a safety critical application.Therefore,it becomes essential in ensuring network accessibility by improving the lifetime of IoT assisted WSN.clustering and multihop routing are considered beneficial solutions to accomplish energy efficiency in IoT networks.This article designs an IoT enabled energy aware metaheuristic clustering with routing protocol for real time disaster management(EAMCR-RTDM).The proposed EAMCR-RTDM technique mainly intends to manage the energy utilization of nodes with the consideration of the features of the disaster region.To achieve this,EAMCR-RTDM technique primarily designs a yellow saddle goatfish based clustering(YSGF-C)technique to elect cluster heads(CHs)and organize clusters.In addition,enhanced cockroach swarm optimization(ECSO)based multihop routing(ECSO-MHR)approach was derived for optimal route selection.The YSGF-C and ECSO-MHR techniques compute fitness functions using different input variables for achieving improved energy efficiency and network lifetime.The design of YSGF-C and ECSO-MHR techniques for disaster management in IoT networks shows the novelty of the work.For examining the improved outcomes of the EAMCR-RTDM system,a wide range of simulations were performed and the extensive results are assessed in terms of different measures.The comparative outcomes highlighted the enhanced outcomes of the EAMCRRTDM algorithm over the existing approaches.

关键词： Internet of things disaster management wireless sensor networks real time applications routing clustering

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clustering-based topical Web crawling using CFu-tree guided by link-context

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Frontiers of Computer Science 2014年第4期8卷 581-595页

作者： Lu LIU College of Computer Science and Technology Jilin University Changchun 130012 China Department of Computer Science University of Illinois at Urbana-Champaign Urbana IL 61801 USA

Topical Web crawling is an established technique for domain-specific information retrieval. However, almost all the conventional topical Web crawlers focus on building crawlers using different classifiers, which needs a lot of labeled training data that is very difficult to label manually. This paper presents a novel approach called clustering-based topical Web crawling which is utilized to retrieve information on a specific domain based on link-context and does not require any labeled training data. In order to collect domain-specific content units, a novel hierarchical clustering method called bottom-up approach is used to illustrate the process of clustering where a new data structure, a linked list in combination with CFu-tree, is implemented to store cluster label, feature vector and content unit. During clustering, four metrics are presented. First, comparison variation （CV） is defined to judge whether the closest pair of clusters can be merged. Second, cluster impurity （CIP） evaluates the cluster error. Then, the precision and recall of clustering are also presented to evaluate the accuracy and comprehensive degree of the whole clustering process. Link-context extraction technique is used to expand the feature vector of anchor text which improves the clustering accuracy greatly. Experimental results show that the performance of our proposed method overcomes conventional focused Web crawlers both in Harvest rate and Target recall.

关键词： topical Web crawling comparison variation （CV） cluster impurity （CIP） CFu-tree link-context clustering

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Online risk‑based portfolio allocation on subsets of crypto assets applying a prototype‑based clustering algorithm

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Financial Innovation 2023年第1期9卷 797-836页

作者： Luis Lorenzo Javier Arroyo Faculty of Statistic Studies Complutense UniversityAvda.Puerta de Hierros/n28040 MadridSpain

Mean-variance portfolio optimization models are sensitive to uncertainty in risk-return estimates,which may result in poor out-of-sample performance.In particular,the estimates may suffer when the number of assets considered is high and the length of the return time series is not sufficiently long.This is precisely the case in the cryptocur-rency market,where there are hundreds of crypto assets that have been traded for a few years.We propose enhancing the mean-variance(MV)model with a pre-selection stage that uses a prototype-based clustering algorithm to reduce the number of crypto assets considered at each investment period.In the pre-selection stage,we run a prototype-based clustering algorithm where the assets are described by variables representing the profit-risk duality.The prototypes of the clustering partition are auto-matically examined and the one that best suits our risk-aversion preference is selected.We then run the MV portfolio optimization with the crypto assets of the selected cluster.The proposed approach is tested for a period of 17 months in the whole cryp-tocurrency market and two selections of the cryptocurrencies with the higher market capitalization(175 and 250 cryptos).We compare the results against three methods applied to the whole market:classic MV,risk parity,and hierarchical risk parity methods.We also compare our results with those from investing in the market index CCI30.The simulation results generally favor our proposal in terms of profit and risk-profit financial indicators.This result reaffirms the convenience of using machine learning methods to guide financial investments in complex and highly-volatile environments such as the cryptocurrency market.

关键词： Fintech Mean-variance Cryptocurrency Electronic market Portfolio allocation model clustering

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clustering Reference Images Based on Covisibility for Visual Localization

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Computers, Materials & Continua 2023年第5期75卷 2705-2725页

作者： Sangyun Lee Junekoo Kang Hyunki Hong School of Integrative Engineering Chung-Ang UniversityHeukseok-ro 84Dongjak-kuSeoul06973Korea School of Software Chung-Ang UniversityHeukseok-ro 84Dongjak-kuSeoul06973Korea

In feature-based visual localization for small-scale scenes,local descriptors are used to estimate the camera pose of a query image.For large and ambiguous environments,learning-based hierarchical networks that employ local as well as global descriptors to reduce the search space of database images into a smaller set of reference views have been introduced.However,since global descriptors are generated using visual features,reference images with some of these features may be erroneously selected.In order to address this limitation,this paper proposes two clustering methods based on how often features appear as well as their covisibility.For both approaches,the scene is represented by voxels whose size and number are computed according to the size of the scene and the number of available 3Dpoints.In the first approach,a voxel-based histogram representing highly reoccurring scene regions is generated from reference images.A meanshift is then employed to group the most highly reoccurring voxels into place clusters based on their spatial proximity.In the second approach,a graph representing the covisibility-based relationship of voxels is built.Local matching is performed within the reference image clusters,and a perspective-n-point is employed to estimate the camera pose.The experimental results showed that camera pose estimation using the proposed approaches was more accurate than that of previous methods.

关键词： Visual localization deep learning voxel representation clustering covisibility meanshift graph structure

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clustering approach based on hierarchical expansion for community detection of scientific collaboration network

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High Technology Letters 2016年第4期22卷 419-425页

作者：李晓慧 Zheng Yanning Institute of Scientific and Technical Information of China Beijing 100038P.R.China

This study presents a clustering algorithm based on hierarchical expansion to solve the problem of community detection in scientific collaboration network. The characteristics of achievements information related to scientific and technological domains are analyzed,and then an ontology that represents their latent collaborative relations is built to detect clusters from the collaboration network. A case study is conducted to collect a data set of research achievements in the electric vehicle field and better clustering results are obtained. A hierarchical recommendation framework that enriches the domain ontologies and retrieves more relevant information resources is proposed in the last part of this paper. This work also lays out a novel insight into the exploitation of scientific collaboration network to better classify achievements information.

关键词： scientific collaboration network clustering achievements information recommender systems

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clustering-Aided Supervised Malware Detection with Specialized Classifiers and Early Consensus

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计算机、材料和连续体（英文） 2023年第4期75卷 1235-1251页

作者： Murat Dener Sercan Gulburun Information Security Engineering Graduate School of Natural and Applied SciencesGazi UniversityAnkara06560Turkey

One of the most common types of threats to the digital world is malicious software.It is of great importance to detect and prevent existing and new malware before it damages information assets.Machine learning approaches are used effectively for this purpose.In this study,we present a model in which supervised and unsupervised learning algorithms are used together.clustering is used to enhance the prediction performance of the supervised classifiers.The aim of the proposed model is to make predictions in the shortest possible time with high accuracy and f1 score.In the first stage of the model,the data are clustered with the k-means algorithm.In the second stage,the prediction is made with the combination of the classifier with the best prediction performance for the related cluster.While choosing the best classifiers for the given clusters,triple combinations of ten machine learning algorithms(kernel support vector machine,k-nearest neighbor,naive Bayes,decision tree,random forest,extra gradient boosting,categorical boosting,adaptive boosting,extra trees,and gradient boosting)are used.The selected triple classifier combination is positioned in two stages.The prediction time of the model is improved by positioning the classifier with the slowest prediction time in the second stage.The selected triple classifier combination is positioned in two tiers.The prediction time of the model is improved by positioning the classifier with the highest prediction time in the second tier.It is seen that clustering before classification improves prediction performance,which is presented using Blue Hexagon Open Dataset for Malware Analysis(BODMAS),Elastic Malware Benchmark for Empowering Researchers(EMBER)2018 and Kaggle malware detection datasets.The model has 99.74%accuracy and 99.77%f1 score for the BODMAS dataset,99.04%accuracy and 98.63%f1 score for the Kaggle malware detection dataset,and 96.77%accuracy and 96.77%f1 score for the EMBER 2018 dataset.In addition,the tiered positioning of classifiers shortened the average pre

关键词： Malware detection ensemble learning classification clustering specialized classifier early consensus

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A Data-Driven clustering Recommendation Method for Single-Cell RNA-Sequencing Data

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Tsinghua Science and Technology 2021年第5期26卷 772-789页

作者： Yu Tian Ruiqing Zheng Zhenlan Liang Suning Li Fang-Xiang Wu Min Li the Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityHunan 410083China the Division of Biomedical Engineering and Department of Mechanical Engineering University of SaskatchewanSKS7N5A9Canada

Recently,the emergence of single-cell RNA-sequencing(scRNA-seq)technology makes it possible to solve biological problems at the single-cell resolution.One of the critical steps in cellular heterogeneity analysis is the cell type identification.Diverse scRNA-seq clustering methods have been proposed to partition cells into clusters.Among all the methods,hierarchical clustering and spectral clustering are the most popular approaches in the downstream clustering analysis with different preprocessing strategies such as similarity learning,dropout imputation,and dimensionality reduction.In this study,we carry out a comprehensive analysis by combining different strategies with these two categories of clustering methods on scRNA-seq datasets under different biological conditions.The analysis results show that the methods with spectral clustering tend to perform better on datasets with continuous shapes in two-dimension,while those with hierarchical clustering achieve better results on datasets with obvious boundaries between clusters in two-dimension.Motivated by this finding,a new strategy,called QRS,is developed to quantitatively evaluate the latent representative shape of a dataset to distinguish whether it has clear boundaries or not.Finally,a data-driven clustering recommendation method,called DDCR,is proposed to recommend hierarchical clustering or spectral clustering for scRNA-seq data.We perform DDCR on two typical single cell clustering methods,SC3 and RAFSIL,and the results show that DDCR recommends a more suitable downstream clustering method for different scRNA-seq datasets and obtains more robust and accurate results.

关键词： single-cel RNA-sequencing(sc RNA-seq) cel ular heterogeneity cel type identification data latent shape clustering

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A clustering-based flexible weighting method in AdaBoost and its application to transaction fraud detection

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Science China(Information Sciences) 2021年第12期64卷 69-79页

作者： Chaofan YANG Guanjun LIU Chungang YAN Changjun JIANG Department of Computer Science Tongji University Shanghai Electronic Transactions and Information Service Collaborative Innovation Center Tongji University

AdaBoost is a famous ensemble learning method and has achieved successful applications in many fields. The existing studies illustrate that AdaBoost easily suffers from noisy points, resulting in a decline of classification performance. The main reason is that it increases the weights of all misclassified samples(especially noisy points) in the same way so that the influence of noisy points can hardly be weakened. In this paper, the clustering algorithm is used to dynamically decide noisy points in the process of iterations.More precisely, we compute a misclassification degree for every cluster in every iteration that is used to decide if a misclassified sample is a noisy point or not in the current iteration. Furthermore, we propose a flexible method to update the weights of the misclassified samples. The experimental results on 22 public datasets show that our method achieves better results than the state-of-the-art methods including AdaBoost,AdaCoast, LogitBoost, and SPLBoost. We also apply our method to the transactions fraud detection, and the experiments on our real big dataset of transactions also illustrate its good performance.

关键词： ensemble learning AdaBoost clustering misclassification degree transaction fraud detection

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