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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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Exploring Users' Preference on Mobile Based on Customer Features 32

Exploring Users' Preference on Mobile Based on Customer Feat...

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32nd International Conference on Conceptual Modeling (ER)

作者： Yu, Gang Wang, Zhiyan Xue, Jiang S China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Guangdong Peoples R China

ISBN: (纸本)9783319141398;9783319141381

Users' mobile model chosen can indicate their telecommunication network demand and consumption habit. The study of users' preference on mobile model has count for much meaning for mobile operators' business strategy and mobile model product sale. This paper looks for customer features as the basis for users' preference judgment from the basic features such as customer attributes, consumption and service usage, that most related to mobile model characteristics, using a genetic algorithm iteration method and k-means clustering algorithm. Our method performs well on how to judge user's preference on mobile model.

关键词： telecommunication customer feature feature selection mobile model characteristic k-means clustering algorithm Adaptive genetic algorithm

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Image Segmentation algorithm for Disease Detection of Wheat Leaves

Image Segmentation Algorithm for Disease Detection of Wheat ...

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International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Niu, Xiaojing Guo, Shihui Wang, Meili Zhang, Hongming Chen, Xianqiang He, Dongjian Northwest A&F Univ Coll Informat Engn Yangling Shaanxi Peoples R China Bournemouth Univ Media Sch Natl Ctr Comp Animat Bournemouth Dorset England Northwest A&F Univ Mech & Elect Engn Yangling Shaanxi Peoples R China

ISBN: (纸本)9781479963812

Wheat diseases are harmful to wheat production, but there are few segmentation algorithms that can effectively identify common diseases of wheat leaves. This paper proposes an automatic and efficient solution with k-means clustering. Firstly, the colour image is transformed to Lab colour space from RGB. clustering is then done by taking the absolute difference between each pixel and the clustering centre in Lab colour space. Unlike traditional methods, our method does not need manual setting for threshold value and is not affected by the selected channel. Our results show that the segmentation accuracy rates for three common diseases (powdery mildew, leaf rust and stripe rust) is more than 90%, which proves the efficacy of our method.

关键词： wheat disease k-means clustering algorithm segmentation algorithm

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A Study of Spore Image Classification Based on Feature Extraction

A Study of Spore Image Classification Based on Feature Extra...

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International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Yue, Lulu Yang, Ming Peng, Li Southwest Univ Sch Comp & Informat Sci Chongqing 400715 Peoples R China

ISBN: (纸本)9783038351153

Fungal diseases are the major diseases of agricultural production, and have brought tremendous impact to it. Identification of spore morphology plays an important role in the identification of fungi. This paper uses the microscopy images of two kinds of fungal spore and utilizes the technology of image analysis and recognition to classify them. We firstly get the underlying feature descriptors of these two kinds of microscopy images by RGB SIFT (Scale Invariant Feature Transform), then create the visual word dictionary using k-means clustering algorithm, at finally we use LDA, kNN and SVM to classify these two kinds of images. The results indicate that the classification of spore image based on feature extraction is feasible. In our future work, we will conduct the classification of related species and highly similar spore images.

关键词： RGB SIFT (Scale Invariant Feature Transform) k-means clustering algorithm Latent Dirichlet Allocation k Nearest Neighbor algorithm Support Vector Machine

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Multifactorial user models for personalized mobile-assisted language learning

Multifactorial user models for personalized mobile-assisted ...

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kES International Conference on Smart Digital Futures (SDF)

作者： Troussas, Christos Virvou, Maria Alepis, Efthimios Univ Piraeus Piraeus Greece

ISBN: (纸本)9781614994053;9781614994046

In this paper, we present our multilingual mobile-assisted language learning system which incorporates multifactorial clustering. This means that it provides machine learning techniques for clustering of multiple user characteristics. Multifactorial classification is conducted by the k-means clustering algorithm which takes as input seven important users' characteristics in order to initialize the process. The clustering is conducted by k-means clustering algorithm, which takes as input multiple user characteristics, in order to initialize the process. The aforementioned characteristics tend to influence the educational procedure. k-means algorithm creates clusters based on data from empirical studies. After determining in which cluster each new student belongs, the system can reason about this specific student, adapting its behavior to the user's needs. The resulting adaptation emerges from the analysis of each cluster's characteristics that derive from each cluster as a superset of already existing user models. The communication between the system and its potential users as students is accomplished through the use of web services.

关键词： k-means clustering algorithm mobile learning multifactorial clustering multilingual mobile assisted language learning user classification

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An Approach of Lane Detection Based on Inverse Perspective Mapping 17

An Approach of Lane Detection Based on Inverse Perspective M...

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IEEE 17th International Conference on Intelligent Transportation Systems (ITSC)

作者： Wang, Jun Mei, Tao kong, Bin Wei, Hu Chinese Acad Sci Inst Intelligent Machines Hefei 230031 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China

ISBN: (纸本)9781479960781

Urban lane detection is an essential task for unmanned vehicle system. This paper describes an approach of lane detection algorithm based on Inverse Perspective Mapping, first using overall optimal threshold method to obtain binary image for reducing noise;next using Inverse Perspective Mapping to transform binary image space to top view space;then using k-means clustering algorithm to analysis linear discriminant for reducing interference affect;finally, fitting lane discontinuous on the top view space according road models. Experimental results are presented to demonstrate the effectiveness and superiority of the urban lane detection algorithm.

关键词： binary image overall optimal threshold method Inverse Perspective Mapping k-means clustering algorithm road models

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Application on query of Distributed Database Based on Improved Genetic algorithm

Application on query of Distributed Database Based on Improv...

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International Conference on Mechatronics Engineering and Computing Technology (ICMECT)

作者： Chen Fuxing Xie Xusheng Jiangxi Normal Univ Coll Comp Informat Engn Nanchang Peoples R China

ISBN: (纸本)9783038351153

The query cost usually as an important criterion for a distributed database. The genetic algorithm is an adaptive probabilistic search algorithm, but the crossover and mutation probability usually keep a probability in traditional genetic algorithm. If the crossover probability keep a large value, the possibility of damage for genetic algorithm model is greater;In turn, if the crossover probability keep a small value, the search process will transform a slow processing or even stagnating. If the mutation probability keep a small value, a new individual can be reproduced difficultly;In turn, if the mutation probability keep a large value, the genetic algorithm will as a Pure random search algorithm. To solve this problem, proposed a improved genetic algorithm that multiple possibility of crossover and mutation based on k-means clustering algorithm. The experiment results indicate that the algorithm is effective.

关键词： distributed database genetic algorithm distributed query optimization k-means clustering algorithm

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Detecting Anomaly Using the Scene Modeling Based on Time Delayed Statistical Data

Detecting Anomaly Using the Scene Modeling Based on Time Del...

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IEEE International Conference on Information and Automation

作者： Lixin Chen Huiwen Guo Min Wang Yen-Lun Chen Xinyu Wu Wei Feng Key Laboratory of Human-Machine Intelligence-Synergic Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

ISBN: (纸本)9781467391054

We propose a novel approach for the crowd anomaly detection in multiple cameras with non-overlapping view. In this paper, we refer to the activities of crowd in far-field scenes. Firstly, we present a model for learning all of the motion patterns under single camera view, which are regarded as the normal situation. In the surveillance region, we mark the entrances and exits under the single camera view and acquire the crowd flow model by the k-means clustering algorithm. Secondly, we analyze the crowd flow model based on the time delayed statistical data between two camera views. And then we acquire the relative location among the entrances and exits in the different regions. Thirdly, we analyze the crowd transferring probabilistic model on the global scene based on the log-likelihood function and Dirichlet distribution to detect the crowd anomaly. We set up the empirical threshold value of probability P_c. If the probability of detected model is less than P_c, the detected model is marked as the crowd anomaly. Our approach is evaluated on the simulated data set and the real data set in far-field scenes. Experimental results show the anomaly detection is precise.

关键词： Crowd anomaly detection Non-overlapping view k-means clustering algorithm Log-likelihood function Dirichlet distribution

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An unsupervised feature extractionl method for classification of hyperspectral images 22

An unsupervised feature extractionl method for classificatio...

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22nd Iranian Conference on Electrical Engineering, ICEE 2014

作者： Imani, Maryam Ghassemian, Hassan Faculty of Electrical and Computer Engineering Tarbiat Modares University Iran

ISBN: (纸本)9781479944095

Hyperspectral images provide huge volume of spectral information for classification of land cover classes. Feature reduction plays an important role as a pre-processing step in classification of high dimensional data. Because of limited available training samples, unsupervised feature extraction is a proper selection for reduction of feature space. We propose an unsupervised feature extraction method in this paper that is called boundary clustering based feature extraction (BCFE). In the BCFE, at first using a clustering algorithm, data is clustered. We use the k-means clustering algorithm in this paper. After clustering, by training of SVM with using the obtained clusters, boundary samples of clusters are calculated. These boundary samples are used for discriminant analysis in the proposed feature extraction method. The experimental results on two real hyperspectral images show the advantage of BCFE in comparison with the most conventional feature extraction methods such as principal component analysis (PCA) and linear discriminant analysis (LDA). © 2014 IEEE.

关键词： feature extraction hyperspectral imaging image classification support vector machines unsupervised learning BCFE method k-means clustering algorithm LDA PCA SVM training boundary clustering based feature extraction high dimensional data classification hyperspectral image classification land cover class classification linear discriminant analysis principal component analysis spectral information unsupervised feature extraction method Accuracy Educational institutions Feature extraction Hyperspectral imaging Principal component analysis Support vector machines Training extraction-clustering-boundary feature sample-classification-hyperspectral image

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Non-invasive method to analyse the risk of developing diabetic foot

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HEALTHCARE TECHNOLOGY LETTERS 2014年第4期1卷 109-113页

作者： Silva, Rebeca N. Ferreira, Ana C. B. H. Ferreira, Danton D. Barbosa, Bruno H. G. Univ Fed Lavras Dept Engn BR-37200000 Lavras MG Brazil Univ Fed Juiz de Fora Sch Nursing BR-36036900 Juiz De Fora MG Brazil

Foot complications (diabetic foot) are among the most serious and costly complications of diabetes mellitus. Amputation of all or part of a lower extremity is usually preceded by a foot ulcer. To prevent diabetic foot, an automatic non-invasive method to identify patients with diabetes who have a high risk of developing diabetic foot is proposed. To design the proposed method, information concerning social scope and self-care of 153 diabetic patients was presented to the k-means clustering algorithm, which divided the data into two groups: high risk and low risk of developing diabetic foot. In the operational stage, the Euclidian distance from the information vector to the centroids of each group of risk is used as criterion for classification. Both real and simulated data were used to evaluate the method in which promising results were achieved with accuracy of 0.97 + 0.06 for simulated data and 0.68 + 0.16 considering the classification of specialists as the gold standard for real data. The method requires a simple computational processing and can be useful for basic health units to triage diabetic patients helping the health-care team to reduce the number of cases of diabetic foot.

关键词： Euclidian distance k-means clustering algorithm automatic noninvasive method computational processing data classification diabetes mellitus diabetic foot diabetic patients diseases foot complications foot ulcer health care information vector medical diagnostic computing operational stage patient diagnosis pattern classification pattern clustering risk analysis self-care simulated data social scope

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