Vladimir Brtka

Automated extraction of decision rules for leptin dynamics-A rough sets approach

A significant area in the field of medical informatics is concerned with the learning of medical models from low-level data. The goals of inducing models from data are twofold: analysis of the structure of the models so as to gain new insight into the unknown phenomena, and development of classifiers or outcome predictors for unseen cases. In this paper, we will employ approach based on the relation of indiscernibility and rough sets theory to study certain questions concerning the design of model based on if-then rules, from low-level data including 36 parameters, one of them leptin. To generate easy to read, interpret, and inspect model, we have used ROSETTA software system. The main goal of this work is to get new insight into phenomena of leptin levels while interplaying with other risk factors in obesity.

The synthesis of the rough set model for the better applicability of sagittal abdominal diameter in identifying high risk patients

This paper aims to investigate possible application of the rough set approach to table-organized data in the medical domain, which reveals some relationships among sagittal abdominal diameter, anthropometric parameters and cardiovascular risk factors. When applied to table-organized data, the methodology based on the rough set theory is capable of producing decision rules in the form of If-Then rules. Such rules are suitable for inspection, examination and further analysis. By examination of the selected 30 decision rules, sagittal abdominal diameter could point out a group of obese and preobese patients with high content of visceral fat with different combination and composition of cardiovascular risk factors. These results suggest that sagittal abdominal diameter could be a clinically useful marker for identification of risk factors, combination and structure of total cardiovascular risk by applying different rules in obese and preobese persons.

Evaluation of the virtual network laboratory exercises using a method based on the rough set theory

The development of computer network technologies increased the importance of network professionals education and resulted in extensive research in the field of computer science education. Virtual networking laboratories have been very popular in the past few years, especially for training and education of network professionals. These laboratories are emulated computer network environments, based on virtualization technology, and have a number of advantages comparing to classical laboratories. Even with a large number of developed laboratory platforms, there are a small number of attempts to evaluate these systems and to find out how effective they are. This paper presents an approach to the evaluation of the virtual networking laboratory, primarily by the evaluation of its exercises used for a computer‐networking course at the university. We are going to employ an approach based on the relation of indiscernibility and the rough set theory in order to produce a model based on if–then rules, starting from low‐level data. This model helps to identify the most important parts and eventual mistakes in the evaluated exercises, creating the possibility for their improvement and overall students achievement as well.

A comparison of rule sets induced by techniques based on rough set theory

The main goal of this paper is to investigate the performance of rule sets obtained by different techniques based on the rough set theory. The performance of a rule set is defined as its ability to classify a set of objects into predefined classes. The set of rules contains the if then form rules. The set of objects is represented by flat table (table, organized data) where each object is represented by its attributes. To generate the rule sets we have used Rosetta software system. The performance of rule sets is measured by employment of the confusion matrix.

A Comparison of Rule Sets Generated from Databases by Indiscernibility Relation - A Rough Sets Approach

Rough sets theory (Pawlak 1980s) proved to be an excellent mathematical tool for the task of automated extraction of If-Then rule sets from table-organized data. In this paper, we employ an approach based on the relation of indiscernibility and rough sets theory in comparison to the method based on pure classification. We have used a well-known ROSETTA software system. The main goal of this work is to compare rule set generated by ROSETTA and rule set generated by method based on pure classification. Comparison is conducted on real-life database from domain of medicine including recently discovered protein hormone Leptin.

Automated Extraction of Decision Rules from Medical Databases - A Rough Sets Approach

One of the three goals of this work is to research automated knowledge acquisition process from table-organized data by using the Rough set theory. This theory proved to be an excellent mathematical tool for the task of automated extraction of rule sets from table-organized data. The second goal is to employ a system for automated theorem proving -LogPro, based on ordered linear resolution with marked literals. This approach has shown better performances that the one based on linear resolution with selection function for definite clauses and negation as failure, e.g. Prolog. The rule sets produced by rough sets technique, in form of Prolog-like clauses are generated from real life medical table-organized data including 36 parameters, one of them being protein hormone Leptin. The third goal of this work is to get new insight into phenomena of Leptin levels while interplaying with other risk factors in obesity, especially with Trunk Fat. This is done by employing generated rule sets in LogPro.

Software application for analyzing ZigBee network performance in university courses

Wireless Sensor Networks (WSN) and related technologies have the important impact nowadays. These technologies have variety of applications, and they are key enabling technologies of IoT (Internet of Things). ZigBee standard is the most common WSN technology. Its importance and expansion, as a widely used technology in many different areas, influence the importance of including ZigBee technology in courses at university. This paper presents the software application and related hardware platform designed to be used by the students to measure and analyze the performances of ZigBee network in design and pre-implementation process. The presented application can be used during the classes of Communication Systems and Computer Network Courses for ZigBee traffic and packet analyses. In addition, this application can be used for researching in the field of wireless sensor networks, ZigBee and other similar popular technologies such as Bluetooth Low Energy, IEEE 802.15.4, etc. The supporting platform for this application and experiment for its evaluation are also presented in this paper.

The data visualization technique in e-learning system

Data mining techniques and their applications are widely recognized as powerful tools in various domains. Educational data mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from the educational context. In the domain of education, there are varieties of data of different types collected during the educational process. The main question is: Is it possible to process the collected data with the data mining system and what are main advantages of data mining and e-learning interaction? In this paper, we present an insight into the possible interaction between course management system and data mining techniques. The main goal of this work is to investigate some data mining techniques in order to deliver most appropriate learning object to the learner. In this paper, visualization as a data mining technique is investigated. For this purpose, free data mining tool Weka was used.

Evaluation of the software maintenance tasks based on fuzzy screening

Evaluation of software maintenance tasks can significantly contribute to the efficiency of maintenance management, which is recognized as the most costly part of the software life cycle. This paper presents an approach to evaluation of software maintenance tasks based on fuzzy screening. The construction of data set for the case study is based on the real data extracted from the maintenance repository available in a local very small software company. In the construction of data set participated researchers and experienced software experts from the selected company. Results of the analysis provide evidence that fuzzy screening can be used for evaluation of maintenance tasks based on selected attributes. Further work directions are outlined in the conclusion section.

The representation of indiscernibility relation by graph

The paper presents a new form of indiscernibility relation based on graph. Based on widely accepted definitions of indiscernibility relation and its matrix representation, it has been shown how the indiscernibility relations can be obtained by a graph. The application of the indiscernibility graph enables the partitioning of the universe of objects represented by their attributes. This is in connection with the Rough sets theory (Z. Pawlak, 1980s). The relation between state space, when applied to the indiscernibility graph, is also theoretically discussed. Formal definitions are accompanied by some examples.