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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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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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A novel framework for segmentation of uterus fibroids in ultrasound images using machine learning models

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2022年第1-2期41卷 22-31页

作者： Dilna, k. T. Anitha, J. Hemanth, D. Jude Coll Engn & Technol Dept ECE Payyanur India Karunya Inst Technol & Sci Dept ECE Coimbatore India

A tumour of non-cancerous structure that appears in uterus during child-bearing years is uterine fibroids. Thus, it is necessary to design a fibroid detection system for the fibroid ablation. Various methods developed for the detection of fibroids are easily affected by the image artefacts as they do not take into consideration the spatial information and have lower efficiency problems for fibroid segmentation. This paper puts forward a method for segmentation for fibroid detection. The proposed segmentation model overcomes the drawbacks of existing methodologies of fibroid detection in all stages. Here, the speckle noise existing in the noisy input image can be removed by using IGDT-DWT method and EMD-GCLAHE method. After contrast enhancement, the segmentation of the contrast-enhanced image is done using a novel clustering algorithm namely PC-k-mean algorithm. The proposed segmentation algorithm effectively detects the fibroids, which is experimentally proved by comparing it with existing classifiers.

关键词： uterus fibroid ultrasound scanned images discrete wavelet transform DWT 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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E-Mail Spam Detection Based on Part of Speech Tagging 2

E-Mail Spam Detection Based on Part of Speech Tagging

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2nd International Conference on knowledge Based Engineering and Innovation (kBEI)

作者： Parsaei, Mohammad Reza Salehi, Mohammad Shiraz Univ Technol Sch Comp Sci & IT Shiraz Iran

ISBN: (纸本)9781467365062

Ever since the emails became well-known tools in communication field, the problem of spams was associated with them. One of the most significant methods for filtering such junk email is diagnostic of those e-mails by applying some especial technics named as Data-Mining. In the presented paper, a new approach based on this strategy that how frequently words are repeated is proposed in which the key words in the evidence are found by usage of their repetition number (frequency). The key sentences, those with the key words, of the incoming e-mails have to be tagged and thereafter the grammatical roles of the entire words in the sentence need to be determined, finally they will be put together in a vector in order to indicate the similarity between the received emails. The proposed paper takes advantage of an extraordinary algorithm called k-mean algorithm to classify the received e-mails. It is worthwhile to note that the so-called k-mean algorithm follows some simple and understandable rules which are too easy to work with and this stands as a great privilege for this paper. The precision of the applied algorithm in diagnostic of the e-mails is 83 percent.

关键词： k-mean algorithm Spam e-mail data mining pus tagging vector model

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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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Improvements in LEACH based on k-means and Gauss algorithms

Improvements in LEACH based on K-means and Gauss algorithms

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International Conference on Advanced Wireless Information and Communication Technologies (AWICT)

作者： Rabiaa, Elkamel Noura, Baccar Adnene, Cherif Natl Engn Sch Carthage InnovCom Lab Tunis Tunisia Natl Engn Sch Tunis InnovCom Lab Tunis Tunisia Fac Sci Tunis Signal Proc Lab Tunis Tunisia

Several methods have been developed for routing in wireless sensor networks;the most popular is LEACH protocol. It owes its popularity to its simplicity and ability to handle large data sets. However, the main limitation of this method is its dependency to probabilistic calculations. In this paper, we propose a new improved version of the LEACH protocol. Our main contribution consists on the integration of an improved unsupervised algorithm which is the k-means on clusters and the use of the Gaussian elimination algorithm for choosing the Cluster Head. Based on Davies-Bouldin index (DBI), the k-means finds the optimal number of clusters k. The Davies-Bouldin index makes calculations much less complex, which is a potential purpose in clustering. Thus, DBI based k-means offers more simple parallel distributed computing of k and allows faster auto-classification of sensor nodes. In this manner a balanced energy clusters is created and distributed energy consumption is guaranteed. Simulations in a wireless sensor network show that our new approach greatly reduces the computation time thanks to k-means and minimize energy consumption thanks to Gauss. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： WSN Clustering k-mean algorithm optimal number of cluster Energy efficiency

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Research on Water Quality Comprehensive Evaluation of Water Supply Network Using SOM

Research on Water Quality Comprehensive Evaluation of Water ...

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International Conference on Networking, Sensing and Control

作者： Wu, Wenyan Chang, kui Gao, Jinliang Zhang, Min Li, Nana Yuan, Yixing Staffordshire Univ Fac Comp Engn & Technol POB 333 Stafford ST18 0AD Staffs England Harbin Inst Technol Sch Municipal & Environm Engn Harbin 150090 Peoples R China

ISBN: (纸本)9781424434916

water quality comprehensive evaluation of water supply network was present in this paper. It is on the basis of microcosmic model in water supply network. The water quality indicators, by which the states of water quality are expressed, are selected as the input vector for the comprehensive evaluation model. The Self-Organizing Feature Map (SOM) and k-means arithmetic are adopted in the model. Since there are some problems using single indicator grade evaluation method, thus a comprehensive water quality evaluation method is proposed in this paper. The objective water quality evaluation model of water supply network is developed. In comparison to the single-evaluation indicator, both the single water quality monitoring data and the overall data are all considered in the model using SOM, which reflect the water quality distribution in the water supply network objectively.

关键词： Water supply network Water quality evaluation indicator Self-organizing Map (SOM) k-mean algorithm

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Wave venation in downward gas-liquid two-phase flow (Part II, cluster analysis for huge waves and disturbance waves and characteristics of those waves)

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Heat Transfer - Asian Research 1996年第8期25卷 511-521页

作者： Mori, koji kaji, Masuo kondo, Yoshiyuki Sekoguchi, kotohiko Nakazatomi, Masao Shimizu, Hideo Department of Mechanical Engineering Osaka University Japan Department of Control Engineering Ube Technical College Japan

Wave velocities and wave widths were determined using wave-vein analysis for a wide range of air and water flow rates. Cluster analysis by a k-mean algorithm was applied to the discrimination between huge and disturbance waves for the present experimental conditions. Individual waves discriminated by cluster analysis reasonably correspond to those recognized from the relation between wave velocity and wave width. Appearance flow conditions for liquid slug, huge and disturbance waves were clarified. The characteristics of wave velocity, wave width, and maximum liquid holdup for huge and disturbance waves are discussed. Comparison between liquid slug, huge wave and disturbance wave flow parameters reveals that there exist distinct differences in wave width of these waves. © 1997 Scripta Technica, Inc.

关键词： Cluster analysis Disturbance wave Huge wave k-mean algorithm Liquid slug Maximum liquid holdup Wave velocity Wave width

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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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