This page has only limited features, please log in for full access.
Ján Paralič received an M.Sc. degree in Technical Cybernetics in 1992 (studying one year at the Vienna University of Technology) and a Ph.D. degree in Artificial Intelligence in 1998 from the Faculty of Electrical Engineering, Technical University of Košice (TUKE), Slovakia. In 2004 he was promoted to Associate Professor in Artificial Intelligence from the Technical University in Kosice, Slovakia, and in 2011 he was appointed Full Professor in Business Information Systems. He serves as Deputy's head at the Dept. of Cybernetics and Artificial Intelligence, TUKE and leads the Data Science research team of researchers and doctoral students in the field of (big) data analytics, medical data science, but also text data analysis and knowledge-based approaches.
Project Goal: This project aims to prepare a predictive model for the identification of potentially risky patients and, subsequently, in this group of patients, the indication of the coronary angiography based on the model using machine learning with a significant number of subjects examined.
Current Stage: Collecting data, implementing decision support system for cardiologists.
Project Goal: The main objective of the project is to design and verify new adaptive methods for analyzing big data in a dynamic environment, able to extract new knowledge and to integrate them with the knowledge model of the environment.
Current Stage: The project is in its final phase, validating and disseminating the project results.
Today, there are many parameters used for cardiovascular risk quantification and to identify many of the high-risk subjects; however, many of them do not reflect reality. Modern personalized medicine is the key to fast and effective diagnostics and treatment of cardiovascular diseases. One step towards this goal is a better understanding of connections between numerous risk factors. We used Factor analysis to identify a suitable number of factors on observed data about patients hospitalized in the East Slovak Institute of Cardiovascular Diseases in Košice. The data describes 808 participants cross-identifying symptomatic and coronarography resulting characteristics. We created several clusters of factors. The most significant cluster of factors identified six factors: basic characteristics of the patient; renal parameters and fibrinogen; family predisposition to CVD; personal history of CVD; lifestyle of the patient; and echo and ECG examination results. The factor analysis results confirmed the known findings and recommendations related to CVD. The derivation of new facts concerning the risk factors of CVD will be of interest to further research, focusing, among other things, on explanatory methods.
Zuzana Pella; Dominik Pella; Ján Paralič; Jakub Vanko; Ján Fedačko. Analysis of Risk Factors in Patients with Subclinical Atherosclerosis and Increased Cardiovascular Risk Using Factor Analysis. Diagnostics 2021, 11, 1284 .
AMA StyleZuzana Pella, Dominik Pella, Ján Paralič, Jakub Vanko, Ján Fedačko. Analysis of Risk Factors in Patients with Subclinical Atherosclerosis and Increased Cardiovascular Risk Using Factor Analysis. Diagnostics. 2021; 11 (7):1284.
Chicago/Turabian StyleZuzana Pella; Dominik Pella; Ján Paralič; Jakub Vanko; Ján Fedačko. 2021. "Analysis of Risk Factors in Patients with Subclinical Atherosclerosis and Increased Cardiovascular Risk Using Factor Analysis." Diagnostics 11, no. 7: 1284.
(1) Objectives: We aimed to identify clusters of physical frailty and cognitive impairment in a population of older primary care patients and correlate these clusters with their associated comorbidities. (2) Methods: We used a latent class analysis (LCA) as the clustering technique to separate different stages of mild cognitive impairment (MCI) and physical frailty into clusters; the differences were assessed by using a multinomial logistic regression model. (3) Results: Four clusters (latent classes) were identified: (1) highly functional (the mean and SD of the “frailty” test 0.58 ± 0.72 and the Mini-Mental State Examination (MMSE) test 27.42 ± 1.5), (2) cognitive impairment (0.97 ± 0.78 and 21.94 ± 1.95), (3) cognitive frailty (3.48 ± 1.12 and 19.14 ± 2.30), and (4) physical frailty (3.61 ± 0.77 and 24.89 ± 1.81). (4) Discussion: The comorbidity patterns distinguishing the clusters depend on the degree of development of cardiometabolic disorders in combination with advancing age. The physical frailty phenotype is likely to exist separately from the cognitive frailty phenotype and includes common musculoskeletal diseases.
Sanja Bekić; František Babič; Viera Pavlišková; Ján Paralič; Thomas Wittlinger; Ljiljana Majnarić. Clusters of Physical Frailty and Cognitive Impairment and Their Associated Comorbidities in Older Primary Care Patients. Healthcare 2021, 9, 891 .
AMA StyleSanja Bekić, František Babič, Viera Pavlišková, Ján Paralič, Thomas Wittlinger, Ljiljana Majnarić. Clusters of Physical Frailty and Cognitive Impairment and Their Associated Comorbidities in Older Primary Care Patients. Healthcare. 2021; 9 (7):891.
Chicago/Turabian StyleSanja Bekić; František Babič; Viera Pavlišková; Ján Paralič; Thomas Wittlinger; Ljiljana Majnarić. 2021. "Clusters of Physical Frailty and Cognitive Impairment and Their Associated Comorbidities in Older Primary Care Patients." Healthcare 9, no. 7: 891.
The paper presents a new approach to effectively support the adaptation phases in the case-based reasoning (CBR) process. The use of the CBR approach in DSS (Decision Support Systems) can help the doctors better understand existing knowledge and make personalized decisions. CBR simulates human thinking by reusing previous solutions applied to past similar cases to solve new ones. The proposed method improves the most challenging part of the CBR process, the adaptation phase. It provides diagnostic suggestions together with explanations in the form of decision rules so that the physician can more easily decide on a new patient’s diagnosis. We experimentally tested and verified our semi-automatic adaptation method through medical data representing patients with cardiovascular disease. At first, the most appropriate diagnostics is presented to the doctor as the most relevant diagnostic paths, i.e., rules—extracted from a decision tree model. The generated rules are based on existing patient records available for the analysis. Next, the doctor can consider these results in two ways. If the selected diagnostic path entirely covers the actual new case, she can apply the proposed diagnostic path to diagnose the new case. Otherwise, our system automatically suggests the minimal rules’ modification alternatives to cover the new case. The doctor decides if the suggested modifications can be accepted or not.
Ľudmila Pusztová; František Babič; Ján Paralič. Semi-Automatic Adaptation of Diagnostic Rules in the Case-Based Reasoning Process. Applied Sciences 2020, 11, 292 .
AMA StyleĽudmila Pusztová, František Babič, Ján Paralič. Semi-Automatic Adaptation of Diagnostic Rules in the Case-Based Reasoning Process. Applied Sciences. 2020; 11 (1):292.
Chicago/Turabian StyleĽudmila Pusztová; František Babič; Ján Paralič. 2020. "Semi-Automatic Adaptation of Diagnostic Rules in the Case-Based Reasoning Process." Applied Sciences 11, no. 1: 292.
One of the most fundamental phenomena heavily influencing the digital society is Big Data. It is crucial not only to collect and analyze vast amounts of data but do it in an intelligent way. We believe that in order to do so, there needs to be a suitable interplay between the knowledge already known in the given application domain (background knowledge) and the knowledge inductively gained from data utilizing various data analysis techniques. We call it a knowledge-based approach to data analysis or intelligent data analysis. In this chapter, we will focus on two main types of the knowledge-based approach to data analysis. We start with the introduction of the semantic modelling of data analytics processes, which can efficiently cover an explicit form of background knowledge. The main focus here will be on the conceptualization of domain knowledge shared between the domain expert and data scientist and modelling of data mining workflows in order to achieve reproducibility and reusability. The second situation is typical for medical application, where the prevalent amount of background knowledge tends to stay tacit. In such a situation, the human-in-the-loop approach is a way how to perform data analysis intelligently. For both of these types of knowledge-based data analysis, specific case studies are presented to show how intelligent data analysis works in practice.
Peter Bednár; Ján Paralič; František Babič; Martin Sarnovský. Knowledge-Based Approaches to Intelligent Data Analysis. Advances in Intelligent Systems and Computing 2020, 75 -97.
AMA StylePeter Bednár, Ján Paralič, František Babič, Martin Sarnovský. Knowledge-Based Approaches to Intelligent Data Analysis. Advances in Intelligent Systems and Computing. 2020; ():75-97.
Chicago/Turabian StylePeter Bednár; Ján Paralič; František Babič; Martin Sarnovský. 2020. "Knowledge-Based Approaches to Intelligent Data Analysis." Advances in Intelligent Systems and Computing , no. : 75-97.
Physical frailty, cognitive impairment, and symptoms of anxiety and depression frequently co-occur in later life, but, to date, each has been assessed separately. The present study assessed their patterns in primary care patients aged ≥60 years. This cross-sectional study evaluated 263 primary care patients aged ≥60 years in eastern Croatia in 2018. Physical frailty, cognitive impairment, anxiety and depression, were assessed using the Fried phenotypic model, the Mini-Mental State Examination (MMSE), the Geriatric Anxiety Scale (GAS), and the Geriatric Depression Scale (GDS), respectively. Patterns were identified by latent class analysis (LCA), Subjects were assorted by age, level of education, and domains of psychological and cognitive tests to determine clusters. Subjects were assorted into four clusters: one cluster of relatively healthy individuals (61.22%), and three pathological clusters, consisting of subjects with mild cognitive impairment (23.95%), cognitive frailty (7.98%), and physical frailty (6.85%). A multivariate, multinomial logistic regression model found that the main determinants of the pathological clusters were increasing age and lower mnestic functions. Lower performance on mnestic tasks was found to significantly determine inclusion in the three pathological clusters. The non-mnestic function, attention, was specifically associated with cognitive impairment, whereas psychological symptoms of anxiety and dysphoria were associated with physical frailty. Clustering of physical and cognitive performances, based on combinations of their grades of severity, may be superior to modelling of their respective entities, including the continuity and non-linearity of age-related accumulation of pathologic conditions.
Ljiljana Trtica Majnarić; Sanja Bekić; František Babič; Ľudmila Pusztová; Ján Paralič. Cluster Analysis of the Associations among Physical Frailty, Cognitive Impairment and Mental Disorders. Medical Science Monitor 2020, 26, e924281-1 -e924281-12.
AMA StyleLjiljana Trtica Majnarić, Sanja Bekić, František Babič, Ľudmila Pusztová, Ján Paralič. Cluster Analysis of the Associations among Physical Frailty, Cognitive Impairment and Mental Disorders. Medical Science Monitor. 2020; 26 ():e924281-1-e924281-12.
Chicago/Turabian StyleLjiljana Trtica Majnarić; Sanja Bekić; František Babič; Ľudmila Pusztová; Ján Paralič. 2020. "Cluster Analysis of the Associations among Physical Frailty, Cognitive Impairment and Mental Disorders." Medical Science Monitor 26, no. : e924281-1-e924281-12.
Objective: Hepatitis E infection is one of the most frequent acute hepatitis in the world. Currently five human genotypes with different geographical distributions and distinct epidemiologic patterns are identified. In Slovakia, only rare case...
Zuzana Paraličová; Monika Halánová; Ivan Schréter; Zuzana Kalinová; Martin Novotný; Jakub Sekula; Ján Paralič; Pavol Kristian. Seroprevalence of hepatitis E among hospitalized patients in Slovakia: first report. Central European Journal of Public Health 2020, 28, 70 -73.
AMA StyleZuzana Paraličová, Monika Halánová, Ivan Schréter, Zuzana Kalinová, Martin Novotný, Jakub Sekula, Ján Paralič, Pavol Kristian. Seroprevalence of hepatitis E among hospitalized patients in Slovakia: first report. Central European Journal of Public Health. 2020; 28 (1):70-73.
Chicago/Turabian StyleZuzana Paraličová; Monika Halánová; Ivan Schréter; Zuzana Kalinová; Martin Novotný; Jakub Sekula; Ján Paralič; Pavol Kristian. 2020. "Seroprevalence of hepatitis E among hospitalized patients in Slovakia: first report." Central European Journal of Public Health 28, no. 1: 70-73.
Intrusion detection systems (IDS) present a critical component of network infrastructures. Machine learning models are widely used in the IDS to learn the patterns in the network data and to detect the possible attacks in the network traffic. Ensemble models combining a variety of different machine learning models proved to be efficient in this domain. On the other hand, knowledge models have been explicitly designed for the description of the attacks and used in ontology-based IDS. In this paper, we propose a hierarchical IDS based on the original symmetrical combination of machine learning approach with knowledge-based approach to support detection of existing types and severity of new types of network attacks. Multi-stage hierarchical prediction consists of the predictive models able to distinguish the normal connections from the attacks and then to predict the attack classes and concrete attack types. The knowledge model enables to navigate through the attack taxonomy and to select the appropriate model to perform a prediction on the selected level. Designed IDS was evaluated on a widely used KDD 99 dataset and compared to similar approaches.
Martin Sarnovsky; Jan Paralic. Hierarchical Intrusion Detection Using Machine Learning and Knowledge Model. Symmetry 2020, 12, 203 .
AMA StyleMartin Sarnovsky, Jan Paralic. Hierarchical Intrusion Detection Using Machine Learning and Knowledge Model. Symmetry. 2020; 12 (2):203.
Chicago/Turabian StyleMartin Sarnovsky; Jan Paralic. 2020. "Hierarchical Intrusion Detection Using Machine Learning and Knowledge Model." Symmetry 12, no. 2: 203.
This article presents a contribution to the general public education about the risks which people are facing on the web and how to prevent them. We first briefly characterize and present a typology of antisocial behavior on the web, analyze and compare some of the existing tools. Next, we present the design and implementation of a website to inform internet users about the possibilities of detecting antisocial behavior. A user study showed that the web application fulfills its purpose and is well accepted by the users.
Jan Paralic; J. Kapcala. Support of General Public Education about Prevention of Antisocial Behaviour on the Web. 2019 17th International Conference on Emerging eLearning Technologies and Applications (ICETA) 2019, 600 -605.
AMA StyleJan Paralic, J. Kapcala. Support of General Public Education about Prevention of Antisocial Behaviour on the Web. 2019 17th International Conference on Emerging eLearning Technologies and Applications (ICETA). 2019; ():600-605.
Chicago/Turabian StyleJan Paralic; J. Kapcala. 2019. "Support of General Public Education about Prevention of Antisocial Behaviour on the Web." 2019 17th International Conference on Emerging eLearning Technologies and Applications (ICETA) , no. : 600-605.
Human urine is one of most accessible body fluids and its collection does not burden patients. Wide spectrum of compounds present in urine with a close relation to metabolism determines a large diagnostic potential. Presented work is focused on information extraction from autofluorescent data analysis of urine. Simple modified synchronous autofluorescent spectrum of urine sample – human fluorescent profile of urine (HFPU) seems to be powerful in mining distinct characters and could be meaningful tool for classification of patients into subgroups according to specific fluorescent characteristics without exact knowledge of urine composition. This could be beneficial in broad range of applications on urine autofluorescence. Offered concept of autofluorescent fingerprints evaluation was tested on 119 urine samples. In this study was found that patients with hypertension have lower intensity of fluorescence at the wavelength range 380–450 nm compare to healthy.
Anna Birková; Július Oboril; Richard Kréta; Beáta Čižmárová; Beáta Hubková; Zuzana Šteffeková; Ján Genči; Ján Paralič; Mária Mareková. Human fluorescent profile of urine as a simple tool of mining in data from autofluorescence spectroscopy. Biomedical Signal Processing and Control 2019, 56, 101693 .
AMA StyleAnna Birková, Július Oboril, Richard Kréta, Beáta Čižmárová, Beáta Hubková, Zuzana Šteffeková, Ján Genči, Ján Paralič, Mária Mareková. Human fluorescent profile of urine as a simple tool of mining in data from autofluorescence spectroscopy. Biomedical Signal Processing and Control. 2019; 56 ():101693.
Chicago/Turabian StyleAnna Birková; Július Oboril; Richard Kréta; Beáta Čižmárová; Beáta Hubková; Zuzana Šteffeková; Ján Genči; Ján Paralič; Mária Mareková. 2019. "Human fluorescent profile of urine as a simple tool of mining in data from autofluorescence spectroscopy." Biomedical Signal Processing and Control 56, no. : 101693.
E-mail marketing is one of the main channels of communication with existing and potential customers. The Open Rate metric is as one of the primary indicators of email campaign success. There are many features of e-mail communication affecting the behavior of individual recipients. Understanding and properly setting these features imply the success of email marketing campaigns. One of these features is the time to send the e-mail. In this paper, we present a methodology for predicting suitable email sending time. Analyzing the available data collected from e-mail communications between companies and customers creates a space for applying data mining methods to data. The proposed methodology for the generation of prediction models to determine the optimal time to send an email has been implemented and evaluated on a real dataset with very promising results.
Ján Paralič; Tomáš Kaszoni; Jakub Mačina. Predicting Suitable Time for Sending Marketing Emails. Advances in Intelligent Systems and Computing 2019, 189 -196.
AMA StyleJán Paralič, Tomáš Kaszoni, Jakub Mačina. Predicting Suitable Time for Sending Marketing Emails. Advances in Intelligent Systems and Computing. 2019; ():189-196.
Chicago/Turabian StyleJán Paralič; Tomáš Kaszoni; Jakub Mačina. 2019. "Predicting Suitable Time for Sending Marketing Emails." Advances in Intelligent Systems and Computing , no. : 189-196.
This paper reviews the Case-based reasoning (CBR) approach and its usability in the medicine and presents a new concept on how to improve its adaptation phase. We use the CBR as a supporting method for decision support like diseases diagnostics or therapy identification. We investigated existing approaches, studies, and research works to solve one of the most critical problems in the CBR cycle - adaptation, which is often done manually by the experts in the relevant field. Based on the findings and our experiences with medical diagnostics through suitable data analytical methods, we proposed a new solution to solve this challenge. This approach is based on a comparison of the stored decision rules with the new one related to the current case. This comparison can result in three alternative states: (1) case base contains a similar case, and relevant rule can be applied. (2) The new case is very different from the stored ones, so the input from participated experts is needed, and a new rule will be stored. (3) The new case is partially similar satisfying adaptability conditions, in such a situation we adopt related decision rule to the new conditions under the supervision of the expert. We plan to experimentally test and verify this concept within available medical samples from our previous experiments.
Ľudmila Pusztová; Frantisek Babic; Ján Paralič; Zuzana Paraličová. How to Improve the Adaptation Phase of the CBR in the Medical Domain. Transactions on Petri Nets and Other Models of Concurrency XV 2019, 168 -177.
AMA StyleĽudmila Pusztová, Frantisek Babic, Ján Paralič, Zuzana Paraličová. How to Improve the Adaptation Phase of the CBR in the Medical Domain. Transactions on Petri Nets and Other Models of Concurrency XV. 2019; ():168-177.
Chicago/Turabian StyleĽudmila Pusztová; Frantisek Babic; Ján Paralič; Zuzana Paraličová. 2019. "How to Improve the Adaptation Phase of the CBR in the Medical Domain." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 168-177.
This article about Active learning in data science education deals with methods and tools to support active learning of data science, in particular within the Knowledge Discovery subject at the Technical University of Kosice, Slovakia. One of the results of the work presented in this article is an implemented web application to support active learning in data science. The article involves a brief overview of the methods supporting the active learning which may be used also for teaching data science. The final web application which was proposed and tested by the users, was created by RShiny application. The supporting application is helpful mainly for the students as an interactive mean of active learning in data science. The result of the article is available and it was applied in practice. This work contributes to the effective learning of the users (learners) as well as it contributes to better understanding, comprehension and application of the acquired knowledge and experiences of the students in practice by means of active learning.
V. Konkol'Ová; J. Paralic. Active Learning in Data Science Education. 2018 16th International Conference on Emerging eLearning Technologies and Applications (ICETA) 2018, 285 -290.
AMA StyleV. Konkol'Ová, J. Paralic. Active Learning in Data Science Education. 2018 16th International Conference on Emerging eLearning Technologies and Applications (ICETA). 2018; ():285-290.
Chicago/Turabian StyleV. Konkol'Ová; J. Paralic. 2018. "Active Learning in Data Science Education." 2018 16th International Conference on Emerging eLearning Technologies and Applications (ICETA) , no. : 285-290.
This paper approaches the problem of processing medical records with the aim of data analysis from a larger set of domain specific medical data. We solve this problem in cooperation with doctors from Department of Cardiology of the East Slovak Institute of Cardiovascular Disease. For this purpose, we designed, implemented and tested first prototype of a software system able to read and process medical records about cardiological patients and provide anonymized data for further analysis.
Zuzana Pella; Peter Milkovic; Jan Paralic. Application for Text Processing of Cardiology Medical Records. 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA) 2018, 169 -174.
AMA StyleZuzana Pella, Peter Milkovic, Jan Paralic. Application for Text Processing of Cardiology Medical Records. 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA). 2018; ():169-174.
Chicago/Turabian StyleZuzana Pella; Peter Milkovic; Jan Paralic. 2018. "Application for Text Processing of Cardiology Medical Records." 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA) , no. : 169-174.
Metabolic syndrome (MS) represents an important risk factor for the development of cardiovascular diseases, as well as type 2 diabetes mellitus, which as one of a few clinical syndromes affects more than 25% of the world population. The diagnosis is often associated with various negative activities like little physical exercise, poor diet, stress, genetic predisposition, and excessive alcohol consumption. The aim of this paper is to provide a literature review of the current state of the art in the area of MS diagnosis by means of data mining methods. We structure our literature review by means of the CRISP-DM methodology, which is typically used to organize the analytical process. The reviewed problem was most often approached as a binary classification problem and frequently used methods have been decision trees, neural networks and logistic regression. Some of the authors applied also suitable statistical methods like Welch’s t-test, Pearson’s chi-squared test. Mostly, the size of analyzed data samples was more than one thousand patients.
Ľudmila Pusztová; František Babič; Ján Paralič. Data Analytics for Metabolic Syndrome Diagnostics. VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014 2018, 311 -314.
AMA StyleĽudmila Pusztová, František Babič, Ján Paralič. Data Analytics for Metabolic Syndrome Diagnostics. VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014. 2018; ():311-314.
Chicago/Turabian StyleĽudmila Pusztová; František Babič; Ján Paralič. 2018. "Data Analytics for Metabolic Syndrome Diagnostics." VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014 , no. : 311-314.
There is potential for medical research on the basis of routine data used from general practice electronic health records (GP eHRs), even in areas where there is no common GP research platform. We present a case study on menopausal women with hypertension and metabolic syndrome (MS). The aims were to explore the appropriateness of the standard definition of MS to apply to this specific, narrowly defined population group and to improve recognition of women at high CV risk. We investigated the possible uses offered by available data from GP eHRs, completed with patients interview, in goal of the study, using a combination of methods. For the sample of 202 hypertensive women, 47-59 years old, a data set was performed, consisted of a total number of 62 parameters, 50 parameters used from GP eHRs. It was analysed by using a mixture of methods: analysis of differences, cutoff values, graphical presentations, logistic regression and decision trees. The age range found to best match the emergency of MS was 51-55 years. Deviations from the definition of MS were identified: a larger cut-off value of the waist circumference measure (89 vs 80 cm) and parameters BMI and total serum cholesterol perform better as components of MS than the standard parameters waist circumference and HDL-cholesterol. The threshold value of BMI at which it is expected that most of hypertensive menopausal women have MS, was found to be 25.5. The other best means for recognision of women with MS include triglycerides above the threshold of 1.7 mmol/L and information on statins use. Prevention of CVD should focus on women with a new onset diabetes and comorbidities of a long-term hypertension with anxiety/depression. The added value of this study goes beyond the current paradigm on MS. Results indicate characteristics of MS in a narrowly defined, specific population group. A comprehensive view has been enabled by using heterogenoeus data and a smart combination of various methods for data analysis. The paper shows the feasibility of this research approach in routine practice, to make use of data which would otherwise not be used for research.
Šefket Šabanović; Majnarić Trtica Ljiljana; František Babič; Michal Vadovsky; Ján Paralič; Aleksandar Vcev; Andreas Holzinger. Metabolic syndrome in hypertensive women in the age of menopause: a case study on data from general practice electronic health records. BMC Medical Informatics and Decision Making 2018, 18, 24 .
AMA StyleŠefket Šabanović, Majnarić Trtica Ljiljana, František Babič, Michal Vadovsky, Ján Paralič, Aleksandar Vcev, Andreas Holzinger. Metabolic syndrome in hypertensive women in the age of menopause: a case study on data from general practice electronic health records. BMC Medical Informatics and Decision Making. 2018; 18 (1):24.
Chicago/Turabian StyleŠefket Šabanović; Majnarić Trtica Ljiljana; František Babič; Michal Vadovsky; Ján Paralič; Aleksandar Vcev; Andreas Holzinger. 2018. "Metabolic syndrome in hypertensive women in the age of menopause: a case study on data from general practice electronic health records." BMC Medical Informatics and Decision Making 18, no. 1: 24.
František Babič; Jaroslav Olejár; Zuzana Vantová; Ján Paralič. Predictive and Descriptive Analysis for Heart Disease Diagnosis. Position Papers of the 2017 Federated Conference on Computer Science and Information Systems 2017, 11, 155 -163.
AMA StyleFrantišek Babič, Jaroslav Olejár, Zuzana Vantová, Ján Paralič. Predictive and Descriptive Analysis for Heart Disease Diagnosis. Position Papers of the 2017 Federated Conference on Computer Science and Information Systems. 2017; 11 ():155-163.
Chicago/Turabian StyleFrantišek Babič; Jaroslav Olejár; Zuzana Vantová; Ján Paralič. 2017. "Predictive and Descriptive Analysis for Heart Disease Diagnosis." Position Papers of the 2017 Federated Conference on Computer Science and Information Systems 11, no. : 155-163.
The aim of this paper is to apply predictive data mining (DM) techniques in order to predict the average fuel consumption for trucks and drivers resp., to identify the key factors that affect fuel consumption of vehicles and also to identify best practices and driving styles of drivers. For this purpose different models have been proposed to provide an overview of the key factors affecting fuel consumption for individual vehicles and their drivers. Predictive models enabled us to identify main influencing factors and provide recommendations for a logistics company to reduce the fuel consumption. Data were collected from Dynafleet information system of a small transport company. The company is dealing with freight traffic, particularly trucks. We first describe selected projects dealing with similar tasks in this area. Next, we explore and analyze data using CRISP-DM methodology by appropriate methods designed for data mining and then evaluate the results of the experiments.
Miroslava Muchová; Ján Paralič; Michael Nemčík. Using Predictive Data Mining Models for Data Analysis in a Logistics Company. Advances in Intelligent Systems and Computing 2017, 655, 161 -170.
AMA StyleMiroslava Muchová, Ján Paralič, Michael Nemčík. Using Predictive Data Mining Models for Data Analysis in a Logistics Company. Advances in Intelligent Systems and Computing. 2017; 655 ():161-170.
Chicago/Turabian StyleMiroslava Muchová; Ján Paralič; Michael Nemčík. 2017. "Using Predictive Data Mining Models for Data Analysis in a Logistics Company." Advances in Intelligent Systems and Computing 655, no. : 161-170.
This paper presents an application for managing education by creating presence lists. An administration application has also been created in order to allow manipulating with the content of the enumerables in the presence lists. Overviews of possible solutions are presented. Our main motivation was to provide secure and reliable way of evaluating student attendance on specific lectures. In order to accomplish this task, we utilized the NFC technology on a smartphone and student ISIC ID card, the ownership of which is compulsory for every university student at the Technical University of Košice. The application has been implemented for specific conditions at the Technical University of Košice.
Zuzana Vantova; Jan Paralic; Vladimir Gaspar. Mobile application for creating presence lists. 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI) 2017, 223 -228.
AMA StyleZuzana Vantova, Jan Paralic, Vladimir Gaspar. Mobile application for creating presence lists. 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI). 2017; ():223-228.
Chicago/Turabian StyleZuzana Vantova; Jan Paralic; Vladimir Gaspar. 2017. "Mobile application for creating presence lists." 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI) , no. : 223-228.