Inquiry (E-Journal - Faculty of Business and Administration, International University of Sarajevo)
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Application of Discrete-Time Markov Models
Get PDFThis paper introduced a general class of mathematical models, Markov chain models, which are appropriate for modeling of phenomena in the physical life, medicine, engineering and social sciences. Application of Markov chains are quite common and have become a standard tool of decision making. What matters in predicting the future of the system is its present state, and not the path by which the system got to its present state. Two methods are presented that exemplify the flexibility of this approach: the regular Markov chain and absorbing Markov chain. The long-term trend in absorbing Markov chains depends on the initial state. In addition, changing the initial state can change the final result. This property distinguishes absorbing Markov chains from regular Markov chains, where the final result is independent of the initial state. The problems are formulated by using the Wolfram Mathematical Programming System
Reconstructing Macroeconomics, A Perspective from Statistical Physics and Combinatorial Stochastic Processes: Book Review
Get PDFProfessors Aoki and Yoshikawa adopted a variety of concepts from statistical physics and combinatorial stochastic processes to various problems in economics (such as labor markets, real growth, consumption, unemployment, financial market, productivity difference, etc.). In order to analyze these phenomena, they depart from the standard methods of model construction and analysis in mainstream economics and use methods that generally fall into two broad categories. One deals with stochastic dynamics, and the other with the formation of clusters and random combinatorial analysis. The authors build their new macroeconomics on the observation that the huge number of heterogeneous agents act stochastically different based on individual insights, tastes, goals, etc. In this approach the properties of economic systems are described by macroscopic dynamical equations of motion that are nonlinear partial differential equations, such as backward and forward Kolmogorov equations (known as a Chapman-Kolmogorov equation and Fokker-Planck equations)
Study Of HbA1c As A Reliable Indicator For Metabolic Syndrome In Non Diabetic Patients
Get PDFWith the metabolic syndrome and diabetes mellitus increase in the recent decade, the importance of early detection of insulin resistance is essential. However, a simple method is not currently available for precise measurements. Therefore the aim of this study was to elucidate the association of HbA1c with metabolic syndrome as a constellation of cardiovascular risk factors. The study population consisted of 45 subjects with metabolic syndrome and 45 free of metabolic syndrome (control group).Total cholesterol, triglycerides, glucose, HbA1c, body mass index (BMI), waist circumference (WC), systolic and diastolic blood pressure were measured in both groups. HbA1c levels are found much more in MS group than the control group, 8,7% and 6,2%, respectively. The sensitivity and specificity of HbA1c is significantly higher in metabolic syndrome patients, 86,7% and 46,67%, respectively. Additionally, subjects with metabolic syndrome exhibited significantly higher blood glucose, triglyceride, systolic/diastolic blood pressure and cholesterol. Our results suggest that HbA1c may be a marker for metabolic syndrome and may identify in a certain degree insulin resistance subjects
JAVA Implementation for Triangulation of Convex Polygon Based on Lukasiewicz’s Algorithm and Binary Trees
No full textTriangulation of the polygon is one of the fundamental algorithms in computational geometry. This paper describes one method of recording triangulations of a convex polygon. The notation that is obtained is expressed in the form of binary records. A presented method of storing triangulations is based on Lukasiewicz's algorithm and binary trees. Particularly, we present the implementation for creating binary records for triangulation polygons. As evaluation of presented method we provide Java implementation for binary notation for triangulation polygons. Java application displays a correlation between the binary notation and the graphical representation
Denver Groups Classification of Human Chromosomes Using CANN Teams
Get PDFUnbanded human chromosome can be classified into seven Denver Groups (A-G) based on their lengths and the ratio of the length of the shorter arm to the whole length of the chromosome, which is called the centromere index (CI). In this article, the novel artificial neural network committee machines technique (CANNT) developed earlier, is applied to the Denver Groups and the correct classification rate in Denver Groups Classification of Human Chromosomes raised from 96%, to a level of 98%
Analysis of cancer incidence and mortality in Bosnia and Herzegovina and comparison with Slovenia, Croatia and Serbia
Get PDFElectricity 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
Committee Machine Networks to Diagnose Cardiovascular Diseases
Get PDFA parallel committee machines technique for neural network systems with back propagation together with a majority voting scheme is presented in this paper. Previous research with regards to predict the presence of cardiovascular diseases has shown accuracy rates up to 72.9% but it comes with a cost of reduced prediction accuracy of the minority class. The designed neural network system in this article presents a significant increase of robustness and it is shown that by majority voting of the parallel networks, recognition rates reach to > 90 in the V.A. Medical Center, Long Beach and Cleveland Clinic Foundation data set
Localization of the epileptogenic foci using Support Vector Machine
Get PDFEpileptic 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
Fuzzy Analysis of Breast Cancer Disease using Fuzzy c-means and Pattern Recognition
Get PDFBreast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. The automatic diagnosis of breast cancer is an important, real-world medical problem. In this article is introduced a new approach for diagnosis of breast cancer. The proposed approach uses Fuzzy c-means (FCM) algorithm and pattern recognition method. Algorithm has been applied to breast cancer clinic instances obtained from the University of Wisconsin. Using FCM algorithm clinic instances are grouped into two clusters, one with benign instances and other with malign instances. Further, input data are divided in train data and test data and success of each is evaluated. In pattern recognition method each input test data is assigned to one of the clusters obtained from the process of FCM classification. The proposed system has showed that the recommended system has a high accuracy
Denver Groups Classification of Human Chromosomes Using CANN Teams Supplemented by a Nearest Neighbor Technique CANNT-S
No full textClassification of the human chromosomes based on their lengths and the centromeric index is performed such that chromosomes are classified into seven Denver Groups (A-G). In this article, the novel artificial neural network committee machines technique (CANNT) developed earlier is modified to take into account mixed signals in winning teams of CANNT, and the correct classification rate in Denver Groups Classification of Human Chromosomes raised from 96%, to a level of 97.1%