Localization of the epileptogenic foci using Support Vector Machine

Epileptic foci localization is a crucial step in planning surgical treatment of medically intractable epilepsy. The solution to this problem can be determined by the detection of the earliest time of seizure onset in electroencephalographic (EEG) recordings. This study presents the application of support vector machine (SVM) for localization of the focus region at the epileptic seizure on the basis of EEG signals. We used intracranial EEG recordings from patients suffering from pharmacoresistant focal-onset epilepsy. We have been investigating a localization of the focus region at the epileptic seizure based on SVM to detect the onset of seizure activity in EEG data. The SVM is trained on sets of intracranial EEG recordings from patients suffering from pharmacoresistant focal-onset epilepsy. The performance of SVM is measured by using accuracy obtained from a fit between the target value and network output. Our EEG based localization of the focus region at the epileptic seizure approach achieves 97.4% accuracy with using 10-fold cross validation. Therefore, our method can be successfully applied to localization of the epileptogenic foci

Parallelization of genetic algorithms using Hadoop Map/Reduce

In this paper we present parallel implementation of genetic algorithm using map/reduce programming paradigm. Hadoop implementation of map/reduce library is used for this purpose. We compare our implementation with implementation presented in [1]. These two implementations are compared in solving One Max (Bit counting) problem. The comparison criteria between implementations are fitness convergence, quality of final solution, algorithm scalability, and cloud resource utilization. Our model for parallelization of genetic algorithm shows better performances and fitness convergence than model presented in [1], but our model has lower quality of solution because of species problem

Usage of Simplified Fuzzy ARTMAP for improvement of classification performances

This study presents a simplified fuzzy ARTMAP (SFAM) for different classification applications. The proposed SFAM model is synergy of fuzzy logic and adaptive resonance theory (ART) neural networks. SFAM is supervised network consisting of two layers (Fuzzy ART and Inter ART) that build constant classification groups in answer to series of input patterns. Fuzzy ART layer takes a series of input patterns and relate them to output classes. Inter ART layer functions in such a way that it raises the vigilance parameter of fuzzy ART layer. By combining this two layers, SFAM is capable to perform classification very efficiently and giving very high performances. Lastly, the SFAM model is applied to different simulations. The simulation results obtained for the three different datasets: Iris, Wisconsin breast cancer and wine dataset prove that SFAM model has better performance results than other models for these classification applications

A novel approach to Hurst analysis of motor vibration data in aging processes

Determining age in motors and rotating machinery in general is a serious question. Using the vibration signals collected during motor aging is challenging due to nonlinear and non-stationary nature of these signals. This paper presents a study on applicability of Hurst analysis in motor vibration data collected in an artificial aging process, which at first seems to suggest inapplicability of Hurst Exponents in motor age determinations. Conclusions drawn from the straightforward application of Hurst exponent calculation are used to propose a novel method for Hurst analysis of such signals, named Higuchi-Hurst-Hilbert Vibration Decomposition (H³VD), using Higuchi’s algorithm for Hurst exponent calculation and Feldman’s Hilbert Vibration Decomposition overcoming the shortcomings of the original approach and delivering results that were expected based on previous research on frequency domain features of motor vibration in artificial aging processes. Features of this approach have been presented on both Fractional Gaussian noise model and the real vibration data. Non-accelerated bearing aging vibration data was used to demonstrate practical applicability of this new method

Detection of congestive heart failures using C4.5 Decision Tree

Automatic electrocardiogram (ECG) heart beat classification is significant for diagnosis of heart failures.  The purpose of this study is to evaluate the effect of C4.5 decision tree method in creating the model that will detect and separate normal and congestive heart failures (CHF) on the long-term ECG time series. The research was conducted in two stages: feature extraction using autoregressive (AR) module and classification by applying C4.5 decision tree method. The ECG signals were obtained from BIDMC Congestive heart failure database and classified by applying different experiments. The experimental results showed that the proposed method reached 99.86% classification accuracy (sensitivity 99.77%, specificity 99.93%, area under the ROC curve 0.998) and has potential in detecting the congestive heart failures

Comparison of Machine Learning Methods for Electricity Demand Forecasting in Bosnia and Herzegovina

Electricity demand forecasting is one of the most important components in the power system analysis. Furthermore, it is difficult and complicated process to forecast energy consumption. This study deals with modeling of the electrical energy consumption in Bosnia and Herzegovina in order to forecast future consumption of electrical loads based on temperature variables using machine learning methods. We used three different  machine learning methods for analyzing short term forecasting. The methods were trained using historical load data, collected from JP Elektroprivreda electrical power utility in BiH, and also considering weather data which is known to have a big impact on the use of electric power. Comparing the results it was seen that prediction for 500 hours is pretty good in range from 92,92% for reactive power till 98.84% for active power. Four different parameters were analyzed mean absolute error, root mean squared error, relative absolute error and root relative square error. The best results for apparent power were gotten with linear regression and are presented as for mean absolute error 9.84, root mean squared error 13.62, relative absolute error 14.06%, root relative squared error 14.39%. It is also seen from the results that,  the short term power consumption can be predicted which is important for maintaining of the voltage at the consumer side

Malicious Web Sites Detection using C4.5 Decision Tree

The technology advancement poses the challenge to the cybercriminals for doing various online criminal acts, such as identity theft, extortion of money or simply, viruses and worms spreading. The common aim of the online criminals is to attract visitors to the Web site, which can be easily accessed by clicking on the URL. Blacklisting seems not to be the successful way of marking Web sites with the “bad” content, considering that many malicious Web sites are not blacklisted. The aim of this paper is to evaluate the ability of C4.5 decision tree classifier in detecting malicious Web sites, based on the features that characterize URLs. The classifier is evaluated through several performance evaluation criteria, namely accuracy, sensitivity, specificity and area under the ROC curve. C4.5 decision tree classifier achieved significant success in malicious Web sites detection, considering all four criteria (accuracy 96.5, sensitivity 96.4, specificity 96.5 and area under the curve 0.958)