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8th International Conference on Ubi-Media Computing (UMEDIA)

作者： Wang, Xiaoye Chai, Xiaorui Hsu, Ching-Hsien Xiao, Yingyuan Li, Yukun Tianjin Univ Technol Tianjin Key Lab Intelligence Comp & Novel Softwar Tianjin 300191 Peoples R China Tianjin Univ Technol Minist Educ Key Lab Comp Vis & Syst Tianjin 300191 Peoples R China Chung Hua Univ Dept Comp Sci & Informat Engn Hsinchu Taiwan

ISBN: (纸本)9781467382700

Mining based on opinions can extract useful information from users' comments. After doing cluster and analysis on the information, users can get a detailed understanding of the commodity, then determine to buy the commodity or not. In this paper, firstly, we extract evaluation objects and evaluation words, then cluster the evaluation objects. Next based on SO-PMI algorithm, judge the polarity of evaluation words and determine their polarity intensity values, then use k-means clustering algorithm to cluster the evaluation words. Last, for every kind of target evaluation object, make a count on the proportion of each kind evaluation word, and show the result to users in an intuitive way. This paper uses noun phrase pattern to match comments to extract evaluation objects and put forward the thematic words extraction algorithm. On judging the evaluation words' polarity, this paper establishes an emotional seed dictionary for each target object. The method of establishing dictionary for every attribute can eliminate the influence that less-correlation evaluation words have on the polarity judgment.

关键词： Opinion mining polarity judgment k-means clustering algorithm SO-PMI algorithm thematic words extraction algorithm

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Unsupervised Subjects Classification Using Insulin and Glucose Data for Insulin Resistance Assessment 20

Unsupervised Subjects Classification Using Insulin and Gluco...

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20th SYMPOSIUM ON SIGNAL PROCESSING, IMAGES AND COMPUTER VISION (STSIVA)

作者： Altuve, Miguel Severeyn, Erika Wong, Sara Pontifical Bolivarian Univ Fac Elect Engn Bucaramanga Colombia Univ Simon Bolivar Appl Biophys & Bioengn Grp Caracas Venezuela Univ Cuenca DEET Investigador Prometeo Cuenca Ecuador

ISBN: (纸本)9781467394611

In this paper, the k-means clustering algorithm is employed to perform an unsupervised classification of subjects based on unidimensional observations (HOMA-IR and the Matsuda indexes separately) and multidimensional observations (insulin and glucose samples obtained from the oral glucose tolerance test). The goal is to explore if the clusters obtained could be used to predict or diagnose insulin resistance or are related to the profiles of the population under study: metabolic syndrome, marathoners and sedentaries. Using two and three clusters, three classification experiments were carried out: i) using the HOMA-IR index as unidimensional observations, ii) using the Matsuda index as unidimensional observations, and iii) using five insulin and five glucose samples as multidimensional observations. The results show that using the HOMA-IR index the clusters are related to insulin resistance but when multidimensional observations are used in the classification process the clusters could be used to predict the insulin resistance or other related diseases.

关键词： diseases medical computing pattern classification pattern clustering unsupervised learning HOMA-IR index k-means clustering algorithm Matsuda index disease glucose data insulin data insulin resistance assessment unsupervised subject classification clustering algorithms Diseases Immune system Indexes Insulin Sugar Unsupervised learning Unsupervised learning maladjustment clustering algorithms medical computing Insulin Insulin Resistance Pattern recognition Insulins Cluster Analysis Immune System sugar Prediabetic State

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Real-time fault detection approach of software under big data environment

Real-time fault detection approach of software under big dat...

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2015 International Conference on Automation,Mechanical Control and Computational Engineering(AMCCE 2015)

作者： Jian xianrui Chengdu vocational and technical college

ISBN: (纸本)9781510803084

For large data environment, the traditional k-means clustering algorithm flaw in the software in real-time fault detection process, we propose a k-means clustering software intelligent real-time fault detection method is improved. The k-means clustering algorithm and particle swarm optimization combined during the iterative process, the combination of k-means to optimize the upcoming PSO algorithm offspring individuals use k-means clustering to obtain local optimal solution calculates and uses these individuals continue to participate in an iterative process, so that the algorithm can improve the convergence speed, avoid falling into local optimal solution, to obtain accurate software fault signal characteristics. Experimental results show that the use of kmeans tilt feature extraction software, intelligent real-time fault detection algorithm can effectively improve the accuracy of fault detection, and achieved satisfactory results. On the condition of big data, traditional k-mean cluster algorithm showed some flaws of software fault detection in realtime way. In this research, an improved k-mean cluster method was proposed for the purpose of testing software fault in intellectual and real-time way. This improved method combined k-mean cluster algorithm with Particle cluster algorithm. k-mean cluster were used in the optimizing procedure among the iterative process. Individuals from Particle cluster algorithm were computed by using k-mean algorithm and obtained the locally optimal solution, and then these individuals continue to participate in the iterative processing. This improved converging rate of the algorithm, avoided falling into the locally optimal solution and finally got the accurate features of software fault signal. Results showed that this testing method with using the tilt feature of k-mean algorithm improved detection accuracy effectively.

关键词： k-means clustering algorithm feature extraction real-time software fault detection

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Machine Learning Techniques with Probability Vector for Cooperative Spectrum Sensing in Cognitive Radio Networks

Machine Learning Techniques with Probability Vector for Coop...

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IEEE Wireless Communications and Networking Conference

作者： Yingqi Lu Pai Zhu Donglin Wang Michel Fattouche Department of Electrical and Computer Engineering University of Calgary Carnegie Mellon University Department of Electrical and Computer Engineering New York Institute of Technology

ISBN: (纸本)9781467398152

We study cooperative spectrum sensing in cognitive radio networks (CRN) using machine learning techniques in this paper. A low-dimensional probability vector is proposed as the feature vector for machine learning based classification, instead of the N-dimensional energy vector in a CRN with a single primary user (PU) and N secondary users (SUs). This proposed method down-converts a high-dimensional feature vector to a constant two-dimensional feature vector for machine learning techniques while keeping the same spectrum sensing performance if not better. Due to its lower dimension, the probability vector based classification is capable of having a smaller training duration and a shorter classification time for testing vectors.

关键词： Cognitive radio network (CRN) Cooperative spectrum sensing Probability vector Energy vector k-means clustering algorithm Support vector machine (SVM)

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Task scheduling scheme based on resource clustering in desktop grids

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2014年第6期27卷 918-930页

作者： Gil, Joon-Min kim, Sungsuk Lee, JongHyuk Catholic Univ Daegu Sch Informat Technol Engn Gyongsan 712702 Gyeongbuk South Korea Seokyeong Univ Dept Comp Sci Seoul 136704 South Korea Samsung Elect Software R&D Ctr Suwon 443742 Gyeonggi South Korea

Desktop grids are platforms for grid computing that incorporate desktop resources into a grid infrastructure. The purpose of this computing paradigm is to process a massive computational tasks by exploiting the donated resources connected over the Internet. In desktop grids, it is important to guarantee fast turnaround time in the presence of dynamic properties, such as volatility and heterogeneity. To achieve this objective, we propose a task scheduling scheme based on resource clustering that can selectively allocate tasks to those resources that are most suitable for the current situation of a desktop grid environment. As a classifier of resources, the k-means clustering algorithm is introduced to classify resources according to their own task execution availability and result-return probability. The experimental results show that our scheduling scheme is more efficient than existing scheduling schemes with respect to reducing both the turnaround time and the quantity of resources consumed. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： task scheduling resource classification k-means clustering algorithm desktop grids

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Electricity Information Big Data Based Load Curve clustering 6

Electricity Information Big Data Based Load Curve Clustering

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China International Conference on Electricity Distribution (CICED)

作者： Zheng, Haiyan Jin, Nong Xiong, Zheng Ji, Cong Fang, Chao Zhong, Chunlin Jiangsu Frontier Elect Technol CO LTD Nanjing 211102 Jiangsu Peoples R China Jiangsu Elect Power Co Nanjing 210024 Jiangsu Peoples R China

ISBN: (纸本)9781479941261

clustering analysis of load curves on basis of electricity information big data is an important basis of load characterisitic and electricity consumption habits analysis of large users. In view of the slow speed of traditional k-means clustering algorithm in the background of big data, a parallel k-means clustering algorithm is proposed to speed up the clustering procedure. Firstly, all the load curves are de-noised by wavelet decomposing in order to reduce the influence of small fluctuations. Secondly, a multi-core parallel technology based k-means clustering algorithm is applied to load curve clustering. Thirdly, more than 40,000 load curves are clustered by the multi-core parallel technology based k-means clustering algorithm. Test results show that the proposed parallel k-means clustering algorithm can speed up clustering procedure effectively.

关键词： big data wavelet denoising load curve cluster k-means clustering algorithm multi-core paralleltechnology

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Study on the Path Optimized method Based on an Improved clustering Ant Colony algorithm for CNC Laser Drilling

Study on the Path Optimized method Based on an Improved Clus...

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International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Guo, Ente Wu, Ting Zhang, Libing Huang, Fengli Jiaxing Univ Coll Mech & Elect Engn Jiaxing 314001 Peoples R China

ISBN: (纸本)9783038351153

In order to improve the machining efficiency of the CNC laser drilling, this paper presents the path optimized method based on an improved ant colony algorithm of the k-means clustering approach. The mathematical model of the path optimization is constructed, and the path optimized method based on the improved clustering ant colony algorithm is designed and actualized. The optimized path method for CNC laser drilling based on the improved clustering ant colony algorithm is tested, and the simulative and experimental result have shown that the proposed method is better performance, and the machining efficiency is greatly improved.

关键词： path optimization laser drilling ant colony algorithm k-means clustering algorithm

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Study on Multi-UAV Task clustering and Task Planning in Cooperative Reconnaissance 6

Study on Multi-UAV Task clustering and Task Planning in Coop...

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6th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Zhao Junwei Zhao Jianjun Naval Aeronaut & Astronaut Univ Grad Students Brigade Yantai Peoples R China Naval Aeronaut & Astronaut Univ Ordnance Sci & Technol Dept Yantai Peoples R China

ISBN: (纸本)9781479949557

For multi-UAV cooperative reconnaissance to enemy's multi-task points, because of multi-task, reasonable clustering is needed and the task clustering model should be established. In this paper, the task planning model is established according to task clustering of each UAV, and the sequence of task execution is determined. Reasonable task clustering optimization index is put forward. Task allocation is proposed based on improved k-means clustering algorithm of simulated annealing. The shortest path task planning is designed using the simulated annealing algorithm, which makes multi-UAV relatively balanced in the assignments, the task group in the group centralized distribution, inter-group distribution scattered and the total cruise time shortest. Simulation results show that the task clustering is well achieved and the optimum task planning program is obtained. The validity of the model and algorithm is verified and the algorithm has certain theoretical and practical value.

关键词： cooperative reconnaissance task clustering Task Planning Task allocation k-means clustering algorithm

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Principal component analysis-based learning for preceding vehicle classification

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IET INTELLIGENT TRANSPORT SYSTEMS 2014年第1期8卷 28-35页

作者： Mangai, Muthulingam Alarmel Gounden, Nanjappagounder Ammasai Natl Inst Technol Dept Elect & Elect Engn Tiruchirappalli Tamil Nadu India

This study presents a new scheme for cluster generation and classification of preceding vehicles from images. The proposed clustering algorithm models the distribution of vehicle images using vehicle' clusters. Non-vehicle' clusters are generated by modelling the distribution of non-vehicle images. The clusters are created using k-means clustering algorithm. Hierarchically related nested eigenspaces are acquired to reassign the patterns of each cluster. An appropriate classifier is obtained to classify the vehicles based on the distance-from-feature-space' measurement. The eigenspaces of vehicle clusters together with non-vehicle clusters are used for classification. The approach of modelling the distribution of vehicle and non-vehicle images and the choice of the classifier used are investigated through experiments thoroughly. Comparison on the performance of the proposed scheme is made with that of MultiClustered Modified Quadratic Discriminant Function approach of categorising the preceding vehicles. The superior performance of the proposed scheme is clearly illustrated through the classification results.

关键词： image classification learning (artificial intelligence) pattern clustering principal component analysis traffic engineering computing principal component analysis-based learning preceding vehicle classification cluster generation vehicle image distribution nonvehicle clusters k-means clustering algorithm nested eigenspaces classifier distance-from-feature-space measurement multiclustered modified quadratic discriminant function approach

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Application of Neural Networks in Early Detection and Diagnosis of Parkinson's Disease

Application of Neural Networks in Early Detection and Diagno...

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International Conference on Cyber and it Service Management (CITSM)

作者： Olanrewaju, Rashidah Funke Sahari, Nur Syarafina Musa, Aibinu A. Hakiem, Nashrul Int Islamic Univ Malaysia Dept Elect & Comp Engn Fac Engn Kuala Lumpur Malaysia Fed Univ Technol Minna Minna Niger State Nigeria UIN Syarif Hidayatullah Jakarta Fac Sci & Technol Dept Informat Engn South Tangerang Indonesia

ISBN: (纸本)9781479979738

Parkinson's disease (PD) is a chronic neurological progressive disorder caused by lack of the chemical dopamine in the brain. Up to today, there is still no cure or prevention for PD, and usually the disease worsens gradually over time. However, this disease can be controlled with some treatment, especially in the early stage. Hence, this study proposes a method in early detection and diagnosis of PD by using the Multilayer Feedforward Neural Network (MLFNN) with Back-propagation (BP) algorithm. This MLFNN with BP algorithm is simulated using MATLAB software. The dataset information used in this study was taken from the Oxford Parkinson's Disease Detection Dataset. The output of the network is classified into healthy or PD by using k-means clustering algorithm. The performance of this classifier was evaluated based on the three parameters;sensitivity, specificity and accuracy. The result shows that network can be used in diagnosis and detection of PD due to the good performance, which is 83.3% for sensitivity, 63.6% for specificity, and 80% for accuracy.

关键词： backpropagation diseases medical computing medical disorders multilayer perceptrons neurophysiology pattern clustering BP algorithm MLFNN Matlab software Oxford Parkinson's disease detection dataset PD detection PD diagnosis PD output Parkinson's disease detection Parkinson's disease diagnosis accuracy parameter back-propagation algorithm chemical dopamine chronic neurological progressive disorder healthy output k-means clustering algorithm multilayer feedforward neural network performance evaluation sensitivity parameter specificity parameter Accuracy Classification algorithms clustering algorithms Diseases Neural networks Neurons Sensitivity Multilayer feedforward neural network MATLAB back propagation maladjustment Parkinson Disease clustering algorithms medical computing medical disorders Neural network Multilayer perceptrons Sorting algorithm Neurophysiology Cluster Analysis Performance Evaluation Early Diagnosis

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