Decision Boundaries and Classification Performance Of SVM And KNN Classifiers For 2-Dimensional Dataset

Support Vector Machines (SVM) and K-Nearest Neighborhood (k-NN) are two most popular classifiers in machine learning. In this paper, we intend to study the generalization performance of the two classifiers by visualizing the decision boundary of each classifier when subjected to a two-dimensional (2-D) dataset. Four different sets of database comprising of 2-D datasets namely the eigenpostures of human (EPHuman), the breast cancer (BCancer), the Swiss roll (SRoll) and Twinpeaks (Tpeaks) were used in this study. Results obtained confirmed SVM classifier superb generalization performance since it contributed the lower classification error rate when compared to the k-NN classifier during the training for binary classification of all 2-D datasets. This is evident and can be clearly visualized through the plots depicting the decision boundaries of the binary classification task

CLASSIFICATION OF FEATURE SELECTION BASED ON ARTIFICIAL NEURAL NETWORK

Pattern recognition (PR) is the central in a variety of engineering applications. For this reason, it is indeed vital to develop efficient pattern recognition systems that facilitate decision making automatically and reliably. In this study, the implementation of PR system based on computational intelligence approach namely artificial neural network (ANN) is performed subsequent to selection of the best feature vectors. A framework to determine the best eigenvectors which we named as ‘eigenpostures’ of four main human postures specifically, standing, squatting/sitting, bending and lying based on the rules of thumb of Principal Component Analysis (PCA) has been developed. Accordingly, all three rules of PCA namely the KG-rule, Cumulative Variance and the Scree test suggest retaining only 35 main principal component or ‘eigenpostures’. Next, these ‘eigenpostures’ are statistically analyzed via Analysis of Variance (ANOVA) prior to classification. Thus, the most relevant component of the selected eigenpostures can be determined. Both categories of ‘eigenpostures’ prior to ANOVA as well as after ANOVA served as inputs to the ANN classifier to verify the effectiveness of feature selection based on statistical analysis. Results attained confirmed that the statistical analysis has enabled us to perform effectively the selection of eigenpostures for classification of four types of human postures

Using Support Vector Machine for Prediction Dynamic Voltage Collapse in an Actual Power System

Abstract—This paper presents dynamic voltage collapse prediction on an actual power system using support vector machines. Dynamic voltage collapse prediction is first determined based on the PTSI calculated from information in dynamic simulation output. Simulations were carried out on a practical 87 bus test system by considering load increase as the contingency. The data collected from the time domain simulation is then used as input to the SVM in which support vector regression is used as a predictor to determine the dynamic voltage collapse indices of the power system. To reduce training time and improve accuracy of the SVM, the Kernel function type and Kernel parameter are considered. To verify the effectiveness of the proposed SVM method, its performance is compared with the multi layer perceptron neural network (MLPNN). Studies show that the SVM gives faster and more accurate results for dynamic voltage collapse prediction compared with the MLPNN. Keywor ds —Dynamic voltage collapse, prediction, artificial neural network, support vector machines

Support Vector Regression Based S-transform for Prediction of Single and Multiple Power Quality Disturbances

This paper presents a novel approach using Support Vector Regression (SVR) based S-transform to predict the classes of single and multiple power quality disturbances in a three-phase industrial power system. Most of the power quality disturbances recorded in an industrial power system are non-stationary and comprise of multiple power quality disturbances that coexist together for only a short duration in time due to the contribution of the network impedances and types of customers’ connected loads. The ability to detect and predict all the types of power quality disturbances encrypted in a voltage signal is vital in the analyses on the causes of the power quality disturbances and in the identification of incipient fault in the networks. In this paper, the performances of two types of SVR based S-transform, the non-linear radial basis function (RBF) SVR based S-transform and the multilayer perceptron (MLP) SVR based S-transform, were compared for their abilities in making prediction for the classes of single and multiple power quality disturbances. The results for the analyses of 651 numbers of single and multiple voltage disturbances gave prediction accuracies of 86.1% (MLP SVR) and 93.9% (RBF SVR) respectively. Keywords: Power Quality, Power Quality Prediction, S-transform, SVM, SV

Performance Evaluation of Voltage Stability Indices for Dynamic Voltage Collapse Prediction

The research presents a study in evaluating the performance of several voltage stability index has been proposed and it is named as the power transfer stability index. The proposed index is then compared with other known voltage stability indices such as the voltage collapse prediction index, the line index and power margin. To evaluate and compare the effectiveness of these indices in predicting proximity to voltage collapse, simulation are carried out using the WSCC 9 bus test system. Simulation test result show that the proposed power transfer ability index and the voltage collapse prediction index give a better prediction of dynamic voltage collapse comapared to the power margin and the line index

Algorithms Development in Detection of the Gelatinization Process during Enzymatic âDodolâ Processing

Computer vision systems have found wide application in foods processing industry to perform quality evaluation. The systems enable to replace human inspectors for the evaluation of a variety of quality attributes. This paper describes the implementation of the Fast Fourier Transform and Kalman filtering algorithms to detect the glutinous rice flour slurry (GRFS) gelatinization in an enzymatic âdodol. processing. The onset of the GRFS gelatinization is critical in determining the quality of an enzymatic âdodol.. Combinations of these two algorithms were able to detect the gelatinization of the GRFS. The result shows that the gelatinization of the GRFS was at the time range of 11.75 minutes to 14.75 minutes for 24 batches of processing. This paper will highlight the capability of computer vision using our proposed algorithms in monitoring and controlling of an enzymatic âdodol. processing via image processing technology

Classification-based fast transient stability assessment of power systems using LS-SVM with enhanced feature reduction techniques

This paper presents fast transient stability assessment of a large 87-bus Malaysia test system using a new method called the least squares support vector machine (LS-SVM) with incorporation of feature reduction techniques. The investigated power system is divided into smaller areas depending on the coherency of the areas when subjected to disturbances. By doing this, the amount of data sets collected for the respective areas is reduced. Transient stability of the power system is first determined based on the generator relative rotor angles obtained from time domain simulations carried out by considering three phase faults at different loading conditions. The data collected are then used as inputs to the LS-SVM. The developed LS-SVM is used as a classifier to determine whether the power system is stable or unstable. The performance of the LS-SVM is enhanced by employing feature reduction techniques to reduce the number of features. It can be concluded that the LS-SVM with the incorporation of feature reduction techniques reduces the time taken to train the LS-SVM and improved the accuracy of the classification results

An improved method in transient stability assessment of a power system using probabilistic neural network

This paper presents transient stability assessment of electrical power system using probabilistic neural network (PNN) and principle component analysis. Transient stability of a power system is first determined based on the generator relative rotor angles obtained from time domain simulation outputs. Simulations were carried out on the IEEE 9-bus test system considering three phase faults on the system. The data collected from the time domain simulations are then used as inputs to the PNN in which PNN is used as a classifier to determine whether the power system is stable or unstable. To verify the effectiveness of the proposed PNN method, it is compared with the multi layer perceptron neural network. Results show that the PNN gives faster and more accurate transient stability assessment compared to the multi layer perceptron neural network in terms of classification results

Kesan modul pendidikan kesihatan dalam meningkatkan kepuasan hidup kalangan perawat keluarga yang merawat pesakit strok kali pertama di rumah

The involvement of family member as “unpaid caregivers” negatively affected the caregivers’ psychological, physical and social health either while the stroke survivors are in hospitals or at home. The objective of this study was to determine the effect of the health education module on life satisfaction among family caregivers of first ever stroke survivors at home, health progress of first ever stroke patient and predictors of life satisfaction among family caregiver. A quasi-experimental pre and post intervention design with purposive sampling was used in this study. Life satisfaction of family caregivers was measured using the Bakas Caregiving Outcome Scale (BCOS), whereas the health status perception of stroke patients was measured by Barthel’s Index (BI) and EQ-5D. A repeated measures Split Plot two-way ANOVA and multiple linear regression test was used to test the hypothesis. The assumption of SPANOVA test was met when the Levene’s and Box test are not significant (p > .05). Huynh-Feldt Epsilon value was used when Mauchly Sphericity test is significant [p < .05]. This research involved 52 family caregivers of intervention group, while 50 family caregivers were in the non intervention group of hospitalized first ever stroke patients from two tertiary hospitals in Kelantan. Data collection was atpreintervention visit (hospital) and post intervention home visits (1 month, 3 month and 6 month). The findings showed that majority of the family caregivers are among the aged of 20 to 60 years old, female, unemployed and wife or children of the first-ever stroke patient. The SPANOVA findings showed that there was a significant interaction between repeated measure time and family caregiver groups [F (2.45, 245.73) = 25.76, p < .001, η2 = .21] and main effect between the groups of family caregivers [F (1, 100) = 65.980, p < .001, η2 = .39] on the family caregivers life satisfaction score. The findings also showed that there was a significant interaction between repeated measure time and first ever stroke patient groups [F (2.09, 209.58) = 9.29, p < .000, η2 = .85] and main effect between groups of first ever stroke patient [F (1, 100) = 23.20, p < .000, η2= .19] on the score of health perception status of stroke patient. A large effect size was evident. Significant predictors of life satisfaction of family caregivers were combination of caregiver’s health and presence of chronic diseases [F (2, 49) = 34.807, p 61 years) [F (1, 50) = 9.436, p <. 05] and physical function ability [F (1, 50) = 9.363, p < .05]. Families are responsible for their disabled sick family members in hospital or at home because of the social values and religion. A structured health educational module with skills manual developed in this study has proven to improve life satisfaction of the family caregivers and health status perception of the first-ever stroke patients. This health educational module is suggested to be a guidance for nurses and become a tool in the improvement of existing health education modules for the benefit of the family caregivers, stroke patients, and rehabilitation centers