Search results for: heart sound classification.
1264 Investigation of Behavior on the Contact Surface of the Tire and Ground by CFD Simulation
Authors: M. F. Sung, Y.D. Kuan, R.J. Shyu, S.M. Lee
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Tread design has evolved over the years to achieve the common tread pattern used in current vehicles. However, to meet safety and comfort requirements, tread design considers more than one design factor. Tread design must consider the grip and drainage, and the manner in which to reduce rolling noise, which is one of the main factors considered by manufacturers. The main objective of this study was the application the computational fluid dynamics (CFD) technique to simulate the contact surface of the tire and ground. The results demonstrated an air-Pumping and large pressure drop effect in the process of contact surface. The results also revealed that the pressure can be used to analyze sound pressure level (SPL).
Keywords: Air-pumping, computational fluid dynamics, sound pressure level, tire.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23761263 Design and Implementation of a Counting and Differentiation System for Vehicles through Video Processing
Authors: Derlis Gregor, Kevin Cikel, Mario Arzamendia, Raúl Gregor
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This paper presents a self-sustaining mobile system for counting and classification of vehicles through processing video. It proposes a counting and classification algorithm divided in four steps that can be executed multiple times in parallel in a SBC (Single Board Computer), like the Raspberry Pi 2, in such a way that it can be implemented in real time. The first step of the proposed algorithm limits the zone of the image that it will be processed. The second step performs the detection of the mobile objects using a BGS (Background Subtraction) algorithm based on the GMM (Gaussian Mixture Model), as well as a shadow removal algorithm using physical-based features, followed by morphological operations. In the first step the vehicle detection will be performed by using edge detection algorithms and the vehicle following through Kalman filters. The last step of the proposed algorithm registers the vehicle passing and performs their classification according to their areas. An auto-sustainable system is proposed, powered by batteries and photovoltaic solar panels, and the data transmission is done through GPRS (General Packet Radio Service)eliminating the need of using external cable, which will facilitate it deployment and translation to any location where it could operate. The self-sustaining trailer will allow the counting and classification of vehicles in specific zones with difficult access.Keywords: Intelligent transportation systems, object detection, video processing, road traffic, vehicle counting, vehicle classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16241262 Classifying Bio-Chip Data using an Ant Colony System Algorithm
Authors: Minsoo Lee, Yearn Jeong Kim, Yun-mi Kim, Sujeung Cheong, Sookyung Song
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Bio-chips are used for experiments on genes and contain various information such as genes, samples and so on. The two-dimensional bio-chips, in which one axis represent genes and the other represent samples, are widely being used these days. Instead of experimenting with real genes which cost lots of money and much time to get the results, bio-chips are being used for biological experiments. And extracting data from the bio-chips with high accuracy and finding out the patterns or useful information from such data is very important. Bio-chip analysis systems extract data from various kinds of bio-chips and mine the data in order to get useful information. One of the commonly used methods to mine the data is classification. The algorithm that is used to classify the data can be various depending on the data types or number characteristics and so on. Considering that bio-chip data is extremely large, an algorithm that imitates the ecosystem such as the ant algorithm is suitable to use as an algorithm for classification. This paper focuses on finding the classification rules from the bio-chip data using the Ant Colony algorithm which imitates the ecosystem. The developed system takes in consideration the accuracy of the discovered rules when it applies it to the bio-chip data in order to predict the classes.Keywords: Ant Colony System, DNA chip data, Classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14681261 Automatic Moment-Based Texture Segmentation
Authors: Tudor Barbu
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An automatic moment-based texture segmentation approach is proposed in this paper. First, we describe the related work in this computer vision domain. Our texture feature extraction, the first part of the texture recognition process, produces a set of moment-based feature vectors. For each image pixel, a texture feature vector is computed as a sequence of area moments. Then, an automatic pixel classification approach is proposed. The feature vectors are clustered using an unsupervised classification algorithm, the optimal number of clusters being determined using a measure based on validation indexes. From the resulted pixel classes one determines easily the desired texture regions of the image.
Keywords: Image segmentation, moment-based texture analysis, automatic classification, validity indexes.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 23791260 Effects of a Methanol Fraction of the Leaves of Leonotis leonurus on the Blood Pressure and Heart Rate of Normotensive Male Wistar Rats
Authors: K. Obikeze, P. Mugabo, I. Green, D. Dietrich, A. Burger
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Leonotisleonurus a shrub indigenous to Southern Africa is widely used in traditional medicine to treat a variety of conditions ranging from skin diseases and cough to epileptic fits and ‘heart problems’. Studies on the aqueous extract of the leaves have indicated cycloxegenase enzyme inhibitory activity and an antihypertensive effect. Five methanol leaf extract fractions (MLEa - MLEe) of L. leonurus were tested on anaesthetized normotensive male Wistar rats (AWR) and isolated perfused working rat hearts (IWH). Fraction MLEc (0.01mg/kg – 0.05mg/kg) induced significant increases in BP and HR in AWR and positive chronotropic and inotropic effects in IWH (1.0mg/ml – 5.0mg/ml). Pre-administration of atenolol (2.0mg/kg) and prazosin (60μg/kg) significantly inhibited MLEc effect on HR and MAP respectively in vivo, while atenolol (7.0mg/ml) pre-perfusion significantly inhibited MLEc effect in vitro. The hypertensive effect of MLEc is probably via β1agonism. Results also indicate the presence of multiple cardioactive compounds in L. leonurus.
Keywords: Cardiovascular effect, in vitro, in vivo, isolated perfused working heart, Leonotis leonurus, rat.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20921259 Optimal ECG Sampling Frequency for Multiscale Entropy-Based HRV
Authors: Manjit Singh
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Multiscale entropy (MSE) is an extensively used index to provide a general understanding of multiple complexity of physiologic mechanism of heart rate variability (HRV) that operates on a wide range of time scales. Accurate selection of electrocardiogram (ECG) sampling frequency is an essential concern for clinically significant HRV quantification; high ECG sampling rate increase memory requirements and processing time, whereas low sampling rate degrade signal quality and results in clinically misinterpreted HRV. In this work, the impact of ECG sampling frequency on MSE based HRV have been quantified. MSE measures are found to be sensitive to ECG sampling frequency and effect of sampling frequency will be a function of time scale.Keywords: ECG, heart rate variability, HRV, multiscale entropy, sampling frequency.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13521258 Fuzzy Wavelet Packet based Feature Extraction Method for Multifunction Myoelectric Control
Authors: Rami N. Khushaba, Adel Al-Jumaily
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The myoelectric signal (MES) is one of the Biosignals utilized in helping humans to control equipments. Recent approaches in MES classification to control prosthetic devices employing pattern recognition techniques revealed two problems, first, the classification performance of the system starts degrading when the number of motion classes to be classified increases, second, in order to solve the first problem, additional complicated methods were utilized which increase the computational cost of a multifunction myoelectric control system. In an effort to solve these problems and to achieve a feasible design for real time implementation with high overall accuracy, this paper presents a new method for feature extraction in MES recognition systems. The method works by extracting features using Wavelet Packet Transform (WPT) applied on the MES from multiple channels, and then employs Fuzzy c-means (FCM) algorithm to generate a measure that judges on features suitability for classification. Finally, Principle Component Analysis (PCA) is utilized to reduce the size of the data before computing the classification accuracy with a multilayer perceptron neural network. The proposed system produces powerful classification results (99% accuracy) by using only a small portion of the original feature set.Keywords: Biomedical Signal Processing, Data mining andInformation Extraction, Machine Learning, Rehabilitation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17371257 Integration of Support Vector Machine and Bayesian Neural Network for Data Mining and Classification
Authors: Essam Al-Daoud
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Several combinations of the preprocessing algorithms, feature selection techniques and classifiers can be applied to the data classification tasks. This study introduces a new accurate classifier, the proposed classifier consist from four components: Signal-to- Noise as a feature selection technique, support vector machine, Bayesian neural network and AdaBoost as an ensemble algorithm. To verify the effectiveness of the proposed classifier, seven well known classifiers are applied to four datasets. The experiments show that using the suggested classifier enhances the classification rates for all datasets.Keywords: AdaBoost, Bayesian neural network, Signal-to-Noise, support vector machine, MCMC.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20201256 Modified Fuzzy ARTMAP and Supervised Fuzzy ART: Comparative Study with Multispectral Classification
Authors: F.Alilat, S.Loumi, H.Merrad, B.Sansal
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In this article a modification of the algorithm of the fuzzy ART network, aiming at returning it supervised is carried out. It consists of the search for the comparison, training and vigilance parameters giving the minimum quadratic distances between the output of the training base and those obtained by the network. The same process is applied for the determination of the parameters of the fuzzy ARTMAP giving the most powerful network. The modification consist in making learn the fuzzy ARTMAP a base of examples not only once as it is of use, but as many time as its architecture is in evolution or than the objective error is not reached . In this way, we don-t worry about the values to impose on the eight (08) parameters of the network. To evaluate each one of these three networks modified, a comparison of their performances is carried out. As application we carried out a classification of the image of Algiers-s bay taken by SPOT XS. We use as criterion of evaluation the training duration, the mean square error (MSE) in step control and the rate of good classification per class. The results of this study presented as curves, tables and images show that modified fuzzy ARTMAP presents the best compromise quality/computing time.
Keywords: Neural Networks, fuzzy ART, fuzzy ARTMAP, Remote sensing, multispectral Classification.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13641255 Isolation and Classification of Red Blood Cells in Anemic Microscopic Images
Authors: Jameela Ali Alkrimi, Loay E. George, Azizah Suliman, Abdul Rahim Ahmad, Karim Al-Jashamy
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Red blood cells (RBCs) are among the most commonly and intensively studied type of blood cells in cell biology. Anemia is a lack of RBCs is characterized by its level compared to the normal hemoglobin level. In this study, a system based image processing methodology was developed to localize and extract RBCs from microscopic images. Also, the machine learning approach is adopted to classify the localized anemic RBCs images. Several textural and geometrical features are calculated for each extracted RBCs. The training set of features was analyzed using principal component analysis (PCA). With the proposed method, RBCs were isolated in 4.3secondsfrom an image containing 18 to 27 cells. The reasons behind using PCA are its low computation complexity and suitability to find the most discriminating features which can lead to accurate classification decisions. Our classifier algorithm yielded accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor (K-NN) algorithm, support vector machine (SVM), and neural network RBFNN, respectively. Classification was evaluated in highly sensitivity, specificity, and kappa statistical parameters. In conclusion, the classification results were obtained within short time period, and the results became better when PCA was used.
Keywords: Red blood cells, pre-processing image algorithms, classification algorithms, principal component analysis PCA, confusion matrix, kappa statistical parameters, ROC.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 31991254 Operational risks Classification for Information Systems with Service-Oriented Architecture (Including Loss Calculation Example)
Authors: Irina Pyrlina
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This article presents the results of a study conducted to identify operational risks for information systems (IS) with service-oriented architecture (SOA). Analysis of current approaches to risk and system error classifications revealed that the system error classes were never used for SOA risk estimation. Additionally system error classes are not normallyexperimentally supported with realenterprise error data. Through the study several categories of various existing error classifications systems are applied and three new error categories with sub-categories are identified. As a part of operational risks a new error classification scheme is proposed for SOA applications. It is based on errors of real information systems which are service providers for application with service-oriented architecture. The proposed classification approach has been used to classify SOA system errors for two different enterprises (oil and gas industry, metal and mining industry). In addition we have conducted a research to identify possible losses from operational risks.
Keywords: Enterprise architecture, Error classification, Oil&Gas and Metal&Mining industries, Operational risks, Serviceoriented architecture
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16041253 Locating Center Points for Radial Basis Function Networks Using Instance Reduction Techniques
Authors: Rana Yousef, Khalil el Hindi
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The behavior of Radial Basis Function (RBF) Networks greatly depends on how the center points of the basis functions are selected. In this work we investigate the use of instance reduction techniques, originally developed to reduce the storage requirements of instance based learners, for this purpose. Five Instance-Based Reduction Techniques were used to determine the set of center points, and RBF networks were trained using these sets of centers. The performance of the RBF networks is studied in terms of classification accuracy and training time. The results obtained were compared with two Radial Basis Function Networks: RBF networks that use all instances of the training set as center points (RBF-ALL) and Probabilistic Neural Networks (PNN). The former achieves high classification accuracies and the latter requires smaller training time. Results showed that RBF networks trained using sets of centers located by noise-filtering techniques (ALLKNN and ENN) rather than pure reduction techniques produce the best results in terms of classification accuracy. The results show that these networks require smaller training time than that of RBF-ALL and higher classification accuracy than that of PNN. Thus, using ALLKNN and ENN to select center points gives better combination of classification accuracy and training time. Our experiments also show that using the reduced sets to train the networks is beneficial especially in the presence of noise in the original training sets.
Keywords: Radial basis function networks, Instance-based reduction, PNN.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16871252 From Type-I to Type-II Fuzzy System Modeling for Diagnosis of Hepatitis
Authors: Shahabeddin Sotudian, M. H. Fazel Zarandi, I. B. Turksen
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Hepatitis is one of the most common and dangerous diseases that affects humankind, and exposes millions of people to serious health risks every year. Diagnosis of Hepatitis has always been a challenge for physicians. This paper presents an effective method for diagnosis of hepatitis based on interval Type-II fuzzy. This proposed system includes three steps: pre-processing (feature selection), Type-I and Type-II fuzzy classification, and system evaluation. KNN-FD feature selection is used as the preprocessing step in order to exclude irrelevant features and to improve classification performance and efficiency in generating the classification model. In the fuzzy classification step, an “indirect approach” is used for fuzzy system modeling by implementing the exponential compactness and separation index for determining the number of rules in the fuzzy clustering approach. Therefore, we first proposed a Type-I fuzzy system that had an accuracy of approximately 90.9%. In the proposed system, the process of diagnosis faces vagueness and uncertainty in the final decision. Thus, the imprecise knowledge was managed by using interval Type-II fuzzy logic. The results that were obtained show that interval Type-II fuzzy has the ability to diagnose hepatitis with an average accuracy of 93.94%. The classification accuracy obtained is the highest one reached thus far. The aforementioned rate of accuracy demonstrates that the Type-II fuzzy system has a better performance in comparison to Type-I and indicates a higher capability of Type-II fuzzy system for modeling uncertainty.
Keywords: Hepatitis disease, medical diagnosis, type-I fuzzy logic, type-II fuzzy logic, feature selection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16471251 A Novel Modified Adaptive Fuzzy Inference Engine and Its Application to Pattern Classification
Authors: J. Hossen, A. Rahman, K. Samsudin, F. Rokhani, S. Sayeed, R. Hasan
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The Neuro-Fuzzy hybridization scheme has become of research interest in pattern classification over the past decade. The present paper proposes a novel Modified Adaptive Fuzzy Inference Engine (MAFIE) for pattern classification. A modified Apriori algorithm technique is utilized to reduce a minimal set of decision rules based on input output data sets. A TSK type fuzzy inference system is constructed by the automatic generation of membership functions and rules by the fuzzy c-means clustering and Apriori algorithm technique, respectively. The generated adaptive fuzzy inference engine is adjusted by the least-squares fit and a conjugate gradient descent algorithm towards better performance with a minimal set of rules. The proposed MAFIE is able to reduce the number of rules which increases exponentially when more input variables are involved. The performance of the proposed MAFIE is compared with other existing applications of pattern classification schemes using Fisher-s Iris and Wisconsin breast cancer data sets and shown to be very competitive.Keywords: Apriori algorithm, Fuzzy C-means, MAFIE, TSK
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19311250 A Hybrid Metaheuristic Framework for Evolving the PROAFTN Classifier
Authors: Feras Al-Obeidat, Nabil Belacel, Juan A. Carretero, Prabhat Mahanti,
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In this paper, a new learning algorithm based on a hybrid metaheuristic integrating Differential Evolution (DE) and Reduced Variable Neighborhood Search (RVNS) is introduced to train the classification method PROAFTN. To apply PROAFTN, values of several parameters need to be determined prior to classification. These parameters include boundaries of intervals and relative weights for each attribute. Based on these requirements, the hybrid approach, named DEPRO-RVNS, is presented in this study. In some cases, the major problem when applying DE to some classification problems was the premature convergence of some individuals to local optima. To eliminate this shortcoming and to improve the exploration and exploitation capabilities of DE, such individuals were set to iteratively re-explored using RVNS. Based on the generated results on both training and testing data, it is shown that the performance of PROAFTN is significantly improved. Furthermore, the experimental study shows that DEPRO-RVNS outperforms well-known machine learning classifiers in a variety of problems.Keywords: Knowledge Discovery, Differential Evolution, Reduced Variable Neighborhood Search, Multiple criteria classification, PROAFTN, Supervised Learning.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14771249 Automatic Voice Classification System Based on Traditional Korean Medicine
Authors: Jaehwan Kang, Haejung Lee
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This paper introduces an automatic voice classification system for the diagnosis of individual constitution based on Sasang Constitutional Medicine (SCM) in Traditional Korean Medicine (TKM). For the developing of this algorithm, we used the voices of 309 female speakers and extracted a total of 134 speech features from the voice data consisting of 5 sustained vowels and one sentence. The classification system, based on a rule-based algorithm that is derived from a non parametric statistical method, presents 3 types of decisions: reserved, positive and negative decisions. In conclusion, 71.5% of the voice data were diagnosed by this system, of which 47.7% were correct positive decisions and 69.7% were correct negative decisions.Keywords: Voice Classifier, Sasang Constitution Medicine, Traditional Korean Medicine, SCM, TKM.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13891248 Principal Component Analysis for the Characterization in the Application of Some Soil Properties
Authors: Kamolchanok Panishkan, Kanokporn Swangjang, Natdhera Sanmanee, Daoroong Sungthong
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The objective of this research is to study principal component analysis for classification of 67 soil samples collected from different agricultural areas in the western part of Thailand. Six soil properties were measured on the soil samples and are used as original variables. Principal component analysis is applied to reduce the number of original variables. A model based on the first two principal components accounts for 72.24% of total variance. Score plots of first two principal components were used to map with agricultural areas divided into horticulture, field crops and wetland. The results showed some relationships between soil properties and agricultural areas. PCA was shown to be a useful tool for agricultural areas classification based on soil properties.Keywords: soil organic matter, soil properties, classification, principal components
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 41141247 DeClEx-Processing Pipeline for Tumor Classification
Authors: Gaurav Shinde, Sai Charan Gongiguntla, Prajwal Shirur, Ahmed Hambaba
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Health issues are significantly increasing, putting a substantial strain on healthcare services. This has accelerated the integration of machine learning in healthcare, particularly following the COVID-19 pandemic. The utilization of machine learning in healthcare has grown significantly. We introduce DeClEx, a pipeline which ensures that data mirrors real-world settings by incorporating gaussian noise and blur and employing autoencoders to learn intermediate feature representations. Subsequently, our convolutional neural network, paired with spatial attention, provides comparable accuracy to state-of-the-art pre-trained models while achieving a threefold improvement in training speed. Furthermore, we provide interpretable results using explainable AI techniques. We integrate denoising and deblurring, classification and explainability in a single pipeline called DeClEx.
Keywords: Machine learning, healthcare, classification, explainability.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 661246 Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor
Authors: Samir Brahim Belhaouari
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By taking advantage of both k-NN which is highly accurate and K-means cluster which is able to reduce the time of classification, we can introduce Cluster-k-Nearest Neighbor as "variable k"-NN dealing with the centroid or mean point of all subclasses generated by clustering algorithm. In general the algorithm of K-means cluster is not stable, in term of accuracy, for that reason we develop another algorithm for clustering our space which gives a higher accuracy than K-means cluster, less subclass number, stability and bounded time of classification with respect to the variable data size. We find between 96% and 99.7 % of accuracy in the lassification of 6 different types of Time series by using K-means cluster algorithm and we find 99.7% by using the new clustering algorithm.Keywords: Pattern recognition, Time series, k-Nearest Neighbor, k-means cluster, Gaussian Mixture Model, Classification
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19651245 Predicting Groundwater Areas Using Data Mining Techniques: Groundwater in Jordan as Case Study
Authors: Faisal Aburub, Wael Hadi
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Data mining is the process of extracting useful or hidden information from a large database. Extracted information can be used to discover relationships among features, where data objects are grouped according to logical relationships; or to predict unseen objects to one of the predefined groups. In this paper, we aim to investigate four well-known data mining algorithms in order to predict groundwater areas in Jordan. These algorithms are Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbor (kNN) and Classification Based on Association Rule (CBA). The experimental results indicate that the SVMs algorithm outperformed other algorithms in terms of classification accuracy, precision and F1 evaluation measures using the datasets of groundwater areas that were collected from Jordanian Ministry of Water and Irrigation.Keywords: Classification, data mining, evaluation measures, groundwater.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 25951244 Analyzing Transformation of 1D-Functions for Frequency Domain based Video Classification
Authors: Kahraman Ayyildiz, Stefan Conrad
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In this paper we illuminate a frequency domain based classification method for video scenes. Videos from certain topical areas often contain activities with repeating movements. Sports videos, home improvement videos, or videos showing mechanical motion are some example areas. Assessing main and side frequencies of each repeating movement gives rise to the motion type. We obtain the frequency domain by transforming spatio-temporal motion trajectories. Further on we explain how to compute frequency features for video clips and how to use them for classifying. The focus of the experimental phase is on transforms utilized for our system. By comparing various transforms, experiments show the optimal transform for a motion frequency based approach.Keywords: action recognition, frequency, transform, motion recognition, repeating movement, video classification
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16931243 Cardiac Function and Morphological Adaptations in Endurance and Resistance Athletes: Evaluation using a new Method
Authors: K. Hosseini, MD., R. Mazaheri, MD., H.R. Khoddami Vishteh, MD., M.A. Mansournia, MD., H. Angoorani, MD
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Background: Tissue Doppler Echocardiography (TDE) assesses diastolic function more accurately than routine pulse Doppler echo. Assessment of the effects of dynamic and static exercises on the heart by using TDE can provides new information about the athlete-s heart syndrome. Methods: This study was conducted on 20 elite wrestlers, 14 endurance runners at national level and 21 non-athletes as the control group. Participants underwent two-dimensional echocardiography, standard Doppler and TDE. Results: Wrestlers had the highest left ventricular mass index, enddiastolic inter-ventricular septum thickness and left ventricular Posterior wall thickness. Runners had the highest Left ventricular end-diastolic volume, LV ejection fraction, stroke volume and cardiac output. In TDE, the early diastolic velocity of mitral annulus to the late diastolic velocity ratio in athletic groups was greater than the controls with no significant difference. Conclusion: In spite of cardiac morphological changes in athletes, TDE shows that cardiac diastolic function won-t be adversely affected.Keywords: Tissue Doppler Echocardiography, Diastolic function, Athlete's heart syndrome, Static exercise, Dynamic exercise
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16161242 Integrating Context Priors into a Decision Tree Classification Scheme
Authors: Kasim Terzic, Bernd Neumann
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Scene interpretation systems need to match (often ambiguous) low-level input data to concepts from a high-level ontology. In many domains, these decisions are uncertain and benefit greatly from proper context. This paper demonstrates the use of decision trees for estimating class probabilities for regions described by feature vectors, and shows how context can be introduced in order to improve the matching performance.Keywords: Classification, Decision Trees, Interpretation, Vision
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 13001241 The Research of Fuzzy Classification Rules Applied to CRM
Authors: Chien-Hua Wang, Meng-Ying Chou, Chin-Tzong Pang
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In the era of great competition, understanding and satisfying customers- requirements are the critical tasks for a company to make a profits. Customer relationship management (CRM) thus becomes an important business issue at present. With the help of the data mining techniques, the manager can explore and analyze from a large quantity of data to discover meaningful patterns and rules. Among all methods, well-known association rule is most commonly seen. This paper is based on Apriori algorithm and uses genetic algorithms combining a data mining method to discover fuzzy classification rules. The mined results can be applied in CRM to help decision marker make correct business decisions for marketing strategies.Keywords: Customer relationship management (CRM), Data mining, Apriori algorithm, Genetic algorithm, Fuzzy classification rules.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16611240 Multi-Level Air Quality Classification in China Using Information Gain and Support Vector Machine
Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li
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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.
Keywords: Machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9481239 An Improved k Nearest Neighbor Classifier Using Interestingness Measures for Medical Image Mining
Authors: J. Alamelu Mangai, Satej Wagle, V. Santhosh Kumar
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The exponential increase in the volume of medical image database has imposed new challenges to clinical routine in maintaining patient history, diagnosis, treatment and monitoring. With the advent of data mining and machine learning techniques it is possible to automate and/or assist physicians in clinical diagnosis. In this research a medical image classification framework using data mining techniques is proposed. It involves feature extraction, feature selection, feature discretization and classification. In the classification phase, the performance of the traditional kNN k nearest neighbor classifier is improved using a feature weighting scheme and a distance weighted voting instead of simple majority voting. Feature weights are calculated using the interestingness measures used in association rule mining. Experiments on the retinal fundus images show that the proposed framework improves the classification accuracy of traditional kNN from 78.57 % to 92.85 %.
Keywords: Medical Image Mining, Data Mining, Feature Weighting, Association Rule Mining, k nearest neighbor classifier.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 33081238 The Implementation of the Multi-Agent Classification System (MACS) in Compliance with FIPA Specifications
Authors: Mohamed R. Mhereeg
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The paper discusses the implementation of the MultiAgent classification System (MACS) and utilizing it to provide an automated and accurate classification of end users developing applications in the spreadsheet domain. However, different technologies have been brought together to build MACS. The strength of the system is the integration of the agent technology with the FIPA specifications together with other technologies, which are the .NET widows service based agents, the Windows Communication Foundation (WCF) services, the Service Oriented Architecture (SOA), and Oracle Data Mining (ODM). The Microsoft's .NET widows service based agents were utilized to develop the monitoring agents of MACS, the .NET WCF services together with SOA approach allowed the distribution and communication between agents over the WWW. The Monitoring Agents (MAs) were configured to execute automatically to monitor excel spreadsheets development activities by content. Data gathered by the Monitoring Agents from various resources over a period of time was collected and filtered by a Database Updater Agent (DUA) residing in the .NET client application of the system. This agent then transfers and stores the data in Oracle server database via Oracle stored procedures for further processing that leads to the classification of the end user developers.
Keywords: MACS, Implementation, Multi-Agent, SOA, Autonomous, WCF.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17091237 Terrain Classification for Ground Robots Based on Acoustic Features
Authors: Bernd Kiefer, Abraham Gebru Tesfay, Dietrich Klakow
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The motivation of our work is to detect different terrain types traversed by a robot based on acoustic data from the robot-terrain interaction. Different acoustic features and classifiers were investigated, such as Mel-frequency cepstral coefficient and Gamma-tone frequency cepstral coefficient for the feature extraction, and Gaussian mixture model and Feed forward neural network for the classification. We analyze the system’s performance by comparing our proposed techniques with some other features surveyed from distinct related works. We achieve precision and recall values between 87% and 100% per class, and an average accuracy at 95.2%. We also study the effect of varying audio chunk size in the application phase of the models and find only a mild impact on performance.Keywords: Terrain classification, acoustic features, autonomous robots, feature extraction.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 11321236 Time and Frequency Domain Analysis of Heart Rate Variability and their Correlations in Diabetes Mellitus
Authors: P. T. Ahamed Seyd, V. I. Thajudin Ahamed, Jeevamma Jacob, Paul Joseph K
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Diabetes mellitus (DM) is frequently characterized by autonomic nervous dysfunction. Analysis of heart rate variability (HRV) has become a popular noninvasive tool for assessing the activities of autonomic nervous system (ANS). In this paper, changes in ANS activity are quantified by means of frequency and time domain analysis of R-R interval variability. Electrocardiograms (ECG) of 16 patients suffering from DM and of 16 healthy volunteers were recorded. Frequency domain analysis of extracted normal to normal interval (NN interval) data indicates significant difference in very low frequency (VLF) power, low frequency (LF) power and high frequency (HF) power, between the DM patients and control group. Time domain measures, standard deviation of NN interval (SDNN), root mean square of successive NN interval differences (RMSSD), successive NN intervals differing more than 50 ms (NN50 Count), percentage value of NN50 count (pNN50), HRV triangular index and triangular interpolation of NN intervals (TINN) also show significant difference between the DM patients and control group.Keywords: Autonomic nervous system, diabetes mellitus, frequency domain and time domain analysis, heart rate variability.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 31111235 Sound Insulation between Buildings: The Impact Noise Transmission through Different Floor Configurations
Authors: Abdelouahab Bouttout, Mohamed Amara
Abstract:
The present paper examines the impact noise transmission through some floor building assemblies. The Acoubat software numerical simulation has been used to simulate the impact noise transmission through different floor configurations used in Algerian construction mode. The results are compared with the available measurements. We have developed two experimental methods, i) field method, and ii) laboratory method using Brüel and Kjær equipments. The results show that the different cases of floor configurations need some improvement to ensure the acoustic comfort in the receiving apartment. The recommended value of the impact sound level in the receiving room should not exceed 58 dB. The important results obtained in this paper can be used as platform to improve the Algerian building acoustic regulation aimed at the construction of the multi-storey residential building.Keywords: Impact noise, building acoustic, floor insulation, resilient material.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2703