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MOMHR: A Dynamic Multi-hop Routing Protocol for WSN Using Heuristic Based Multi-objective Function

WIRELESS PERSONAL COMMUNICATIONS 2020年第2期111卷 883-907页

作者： Vinodhini, R. Gomathy, C. SRM Inst Sci & Technol ECE Dept Chennai 603203 Tamil Nadu India

Wireless sensor networks (WSNs) are the networks which mainly focuses on the applications and are composed of considerable sensor nodes. The use of energy in a valuable way is considered as a feature for the design structure of WSNs. In the WSNs, the nodes power sources are limited. Moreover, because of this, there is a must for a different approach regarding the energy availability and this is mainly for long distance communication, for this multi-hop (MH) systems are chosen. Even though MH decreases the energy cost used by all node along the path, however, to obtain the best routing path among nodes is yet an interesting subject. In this article, we present a multi-objective multi-hop routing (MOMHR) protocol for optimal data routing to gain the network lifetime. In the first phase, the k-means algorithm is applied to split the nodes into k clusters. Next, the artificial bee colony optimisation algorithm is applied to obtain the best possible CH within each cluster then using a multi-objective functions finally the multi-hop routing protocol finds a multihop path with minimum communication cost from the node to the base station. Our proposed method is simulated in MATLAB platform and compared with two recent protocols such as low energy adaptive clustering hierarchy and energy efficient centroid-based routing protocol. The execution of the proposed MOMHR protocols using multi-objective function is evaluated using metrics such as energy efficiency and network lifetime.

关键词： Wireless sensor networks Multi-hop routing Ant bee colony optimisation k-mean algorithm Multi-objective function

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Research on the Model of Missing Information Completion of Telecom Customers Based on Factor Analysis and Data Mining 5

Research on the Model of Missing Information Completion of T...

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IEEE 5th International Conference on Computer and Communication Systems (ICCCS)

作者： Zeng Rui Yin Hongli Cai Jinyan Yunnan Normal Univ Sch Informat Sci & Technol Kunming Yunnan Peoples R China

ISBN: (数字)9781728161365

ISBN: (纸本)9781728161365

The key problem that must be solved in the analysis and prediction of customer churn in telecom companies is the data completion of customer missing. In this paper, a model based on factor analysis and data mining is proposed to complete customer missing data. This model first completes the factors generated by the missing data, and then completes the missing data. In factor completion, the improved k-mean algorithm is used to effectively solve the problem of initial value and k value selection, and the Euclidean distance is improved to achieve effective clustering of factors and factor completion. The missing data value is obtained by factor reverse reasoning. The model is trained with real historical data and tested to verify that the model is effective.

关键词： factor analysis Euclidean distance k-mean algorithm factor rotation factor score function

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Customer Market Segmentation using Machine Learning algorithm 6

Customer Market Segmentation using Machine Learning Algorith...

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6th International Conference on Trends in Electronics and Informatics (ICOEI)

作者： Regmi, Swesh Raj Meena, Jasraj kanojia, Utkarsh kant, Vishesh Delhi Technol Univ Dept Informat Technol Delhi India

ISBN: (纸本)9781665483285;9781665483292

Everything is becoming more mechanized in today's times, especially in this age of data. Data is the world's new electricity. Many firms can benefit from this information in their marketing efforts. Every company wanted to grow and be better than the competition, so they employed innovative ideas and invested in product development, but only to keep their customers and expand their market. Customers are segmented in today's commercial world. They analyze their consumers' behaviors and purchasing patterns and can assist them in generating profit. Customer segmentation refers to the process of categorizing a company's customers into groups based on their commonalities. Client segmentation is the process of determining how to interact with consumers in different groups to amplify the value of each customer to the company. Unsupervised machine learning is used to segment customers. There are many algorithms that can partition customers based on their behavior and other parameters, and our study's main goal is to cluster customers into various clusters based on their behavior patterns. This research study has compared five clustering algorithms and selected the best of them for further research.

关键词： Machine Learning Classification Clustering Customer Segmentation Spending behavior k-mean algorithm

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Integration of Probability and Clustering Based Approaches in the Field of Black Spot Identification

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PERIODICA POLYTECHNICA-CIVIL ENGINEERING 2019年第1期63卷 46-52页

作者： Ghadi, Maen Torok, Arpad Tanczos, katalin Budapest Univ Technol & Econ Fac Transport Engn & Vehicle Engn Dept Automot Technol POB 91 H-1521 Budapest Hungary Budapest Univ Technol & Econ Fac Transport Engn & Vehicle Engn Dept Transport Technol & Econ POB 91 H-1521 Budapest Hungary

The objective of the paper is to define a complex methodology to analyze black spot locations of road infrastructure network combining the benefit of both;Empirical Bayes method and k-mean clustering approach. In the first step, k-mean algorithm is used to define homogeneous accident clusters. The homogeneity is described in three terms: traffic conditions, geometric design of the road and accident characteristics. Then, Empirical Bayes method is applied to define black spots based on the determined clusters. Due to the combination of the introduced methods, a powerful technique is provided that is able to identify high-risk locations and cluster dependent segment length as the output of the model.

关键词： black spots empirical Bayesian k-mean algorithm cluster analysis road safety

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Machine Learning Based Blind Decoding for Space-Time Line Code (STLC) Systems

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2019年第5期68卷 5154-5158页

作者： Joung, Jingon Jung, Bang Chul Chung Ang Univ Sch Elect & Elect Engn Seoul 06974 South Korea Chungnam Natl Univ Dept Elect Engn Daejeon 34134 South Korea

In this correspondence paper, a novel machine learning (clustering) based blind decoding method is proposed for the space-time line code (STLC) systems without the information of modulation size and channels. The number of clusters, which is equivalent to the modulation size, is estimated from the received signals by using a k-mean cluster validation metrics, such as silhouette score and Davies-Bouldin index, and the cluster indices are directly mapped to the transmitted binary information bits. To improve the clustering performance, received signal normalization and the initial centroids of the clusters are designed by exploiting the combined STLC signal structure. From the numerical results, it is verified that the proposed blind decoding method can achieve near-coherent decoding performance with either small-size modulation, low noise at the receiver, or a large number of transmit antennas.

关键词： Machine learning clustering k-mean algorithm blind decoding space-time line code

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A combination of objective functions and hybrid krill herd algorithm for text document clustering analysis

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2018年 73卷 111-125页

作者： Abualigah, Laith Mohammad khader, Ahamad Tajudin Hanandeh, Essam Said Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia Zarqa Univ Dept Comp Informat Syst POB 13132 Zarqa Jordan

krill herd (kH) algorithm is a novel swarm-based optimization algorithm that imitates krill herding behavior during the searching for foods. It has been successfully used in solving many complex optimization problems. The potency of this algorithm is very high because of its superior performance compared with other optimization algorithms. Hence, the applicability of this algorithm for text document clustering is investigated in this work. Text document clustering refers to the method of clustering an enormous amount of text documents into coherent and dense clusters, where documents in the same cluster are similar. In this paper, a combination of objective functions and hybrid kH algorithm, called, MHkHA, is proposed to solve the text document clustering problem. In this version, the initial solutions of the kH algorithm are inherited from the k-mean clustering algorithm and the clustering decision is based on two combined objective functions. Nine text standard datasets collected from the Laboratory of Computational Intelligence are used to evaluate the performance of the proposed algorithms. Five evaluation measures are employed, namely, accuracy, precision, recall, F-measure, and convergence behavior. The proposed versions of the kH algorithm are compared with other well-known clustering algorithms and other thirteen published algorithms in the literature. The MHkHA obtained the best results for all evaluation measures and datasets used among all the clustering algorithms tested.

关键词： Combination of objective functions Hybridization krill herd algorithm k-mean algorithm Text document clustering

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SAR image change detection based on equal weight image fusion and adaptive threshold in the NSST domain

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EUROPEAN JOURNAL OF REMOTE SENSING 2018年第1期51卷 785-794页

作者： Zhou Wenyan Jia Zhenhong Yu, Yinfeng Jie Yang kasabov, Nilola Xinjiang Univ Coll Informat Sci & Engn Urumqi 830046 Peoples R China Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai Peoples R China Auckland Univ Technol Knowledge Engn & Discovery Res Inst Auckland New Zealand

In order to improve the accuracy of change detection and reduce the running time, a change detection method based on equal weight image fusion and adaptive threshold in the NSST domain is proposed. First, the logarithmic transformation is used to transform images and the mean filter is applied to the transformed images. The log-ratio method and the mean ratio method are adopted to generate two kinds of difference images. The final difference image is achieved by equal weight image fusion method. Then, an adaptive threshold denoising method based on non-subsampled shearlet transform (NSST) is used to achieve noise reduction. Finally, the k-means clustering algorithm is utilized to get the change detection results. The experimental results show that the proposed algorithm has better change detection performance than the reference algorithms in visual effect and objective parameters.

关键词： Non-subsampled shearlet transform image fusion change detection difference map adaptive threshold k-mean algorithm

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Clustering and multifacility location with constraints via distance function penalty methods and dc programming

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OPTIMIZATION 2018年第11期67卷 1869-1894页

作者： Nguyen Mau Nam Nguyen Thai An Reynolds, Sam Tuyen Tran Portland State Univ Fariborz Maseeh Dept Math & Stat Portland OR 97207 USA Duy Tan Univ Inst Res & Dev Danang Vietnam

This paper is a continuation of our effort in using mathematical optimization involving DC programming in clustering and multifacility location. We study a penalty method based on distance functions and apply it particularly to a number of problems in clustering and multifacility location in which the centers to be found must lie in some given set constraints. We also provide numerical examples to test our method.

关键词： Clustering DC programming Nesterov's smoothing techniques k-mean algorithm 49J52 49J53 90C31

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Healthcare Staff Routing Problem using Adaptive Genetic algorithms with Adaptive Local Search and Immigrant Scheme 2

Healthcare Staff Routing Problem using Adaptive Genetic Algo...

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International Conference on Digital Arts, Media and Technology (ICDAMT) - Digital Economy for Sustainable Growth

作者： Sinthamrongruk, Thepparit Dahal, keshav Satiya, Oranut Vudhironarit, Thishnapha Yodmongkol, Pitipong Univ West Scotland Paisley Renfrew Scotland Chiang Mai Univ Chiang Mai Thailand Phramongkutklao Hosp Bangkok Thailand Phramongkutklao Coll Med Bangkok Thailand

ISBN: (纸本)9781509052103

Healthcare staff routing to provide healthcare service to the patients is one of the real-world scheduling problems similar to multiple travelling salesman problems (MTSP). Healthcare staff members provide daily medical services at patients' homes. The service provider authority has to schedule these staff in an effective and efficient way so that it achieves the minimum total cost. The aim of this study is to propose an Adaptive Local Search based on Genetic algorithm (GA) to solve Healthcare Staff Routing Problem. Two new types of Adaptive Local Searches have been proposed to explore the optimal solutions. Also, Immigrant Scheme has been applied to improve the performance of the proposed GA. With this feature, we make an effort to motivate the GA to replace population occasionally by calling the best GA chromosome when the GA struggles at the local optimal solution. By the proposed algorithm, an effective routing schedule for staff members is generated. Our empirical study demonstrates that the proposed GA with Adaptive Local Search and Immigrant Scheme outperforms its rival methods in terms of the sum of distances.

关键词： adaptive local search adaptive genetic algorithm constructive scheduling healthcare service k-mean algorithm

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A graph-based method for blood vessel segmentation of retinal images 9th

A graph-based method for blood vessel segmentation of retina...

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9th International Conference on Bioinformatics and Biomedical Engineering (iCBBE)

作者： Zhang, J. D. Jiang, W. H. Zhang, C. X. Cui, Y. J. Shenzhen Inst Informat Technol Dept Elect & Commun Shenzhen Peoples R China

ISBN: (纸本)9781315683010;9781138027848

Segmentation of blood vessels from retinal images plays a crucial role for diagnosing eye diseases. In this paper, we propose an automatic segmentation method for blood vessels in retinal images. Firstly, we preprocess the retinal images to enhance vessels and overcome the lighting variation. Then, a graph-based algorithm is exploited to partition the retinal image into multiple regions. Finally, we classify each region as retinal region or non-vessel region with k-mean algorithm. The proposed method is validated on the publicly available DRIVE database, and compared with the state-of-the-art algorithms.

关键词： medical image segmentation retinal images graph-based algorithm k-mean algorithm

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