TY  - JOUR
AU  - Ranđelović, Milan
AU  - Aleksić, Aleksandar
AU  - Radovanović, Radovan
AU  - Stojanović, Vladica
AU  - Čabarkapa, Milan
AU  - Ranđelović, Dragan
PY  - 2022
UR  - https://jakov.kpu.edu.rs/handle/123456789/1633
AB  - In this paper, one multidisciplinary-applicable aggregated model has been proposed and verified. This model uses traditional techniques, on the one hand, and algorithms of machine learning as modern techniques, on the other hand, throughout the determination process of the relevance of model attributes for solving any problems of multicriteria decision. The main goal of this model is to take advantage of both approaches and lead to better results than when the techniques are used alone. In addition, the proposed model uses feature selection methodology to reduce the number of attributes, thus increasing the accuracy of the model. We have used the traditional method of regression analysis combined with the well-known mathematical method Analytic Hierarchy Process (AHP). This approach has been combined with the application of the ReliefF classificatory modern ranking method of machine learning. Last but not least, the decision tree classifier J48 has been used for aggregation purposes. Information on grades of the first-year graduate students at the Criminalistics and Police University, Belgrade, after they chose and finished one of the three possible study modules, was used for the evaluation of the proposed model. To the best knowledge of the authors, this work is the first work when considering mining closed frequent trees in case of the streaming of time-varying data.
PB  - Basel : MDPI
T2  - Mathematics
T1  - One Aggregated Approach in Multidisciplinary Based Modeling to Predict Further Students’ Education
VL  - 10
IS  - 14
SP  - 2381
DO  - 10.3390/math10142381
ER  - 

@article{
author = "Ranđelović, Milan and Aleksić, Aleksandar and Radovanović, Radovan and Stojanović, Vladica and Čabarkapa, Milan and Ranđelović, Dragan",
year = "2022",
abstract = "In this paper, one multidisciplinary-applicable aggregated model has been proposed and verified. This model uses traditional techniques, on the one hand, and algorithms of machine learning as modern techniques, on the other hand, throughout the determination process of the relevance of model attributes for solving any problems of multicriteria decision. The main goal of this model is to take advantage of both approaches and lead to better results than when the techniques are used alone. In addition, the proposed model uses feature selection methodology to reduce the number of attributes, thus increasing the accuracy of the model. We have used the traditional method of regression analysis combined with the well-known mathematical method Analytic Hierarchy Process (AHP). This approach has been combined with the application of the ReliefF classificatory modern ranking method of machine learning. Last but not least, the decision tree classifier J48 has been used for aggregation purposes. Information on grades of the first-year graduate students at the Criminalistics and Police University, Belgrade, after they chose and finished one of the three possible study modules, was used for the evaluation of the proposed model. To the best knowledge of the authors, this work is the first work when considering mining closed frequent trees in case of the streaming of time-varying data.",
publisher = "Basel : MDPI",
journal = "Mathematics",
title = "One Aggregated Approach in Multidisciplinary Based Modeling to Predict Further Students’ Education",
volume = "10",
number = "14",
pages = "2381",
doi = "10.3390/math10142381"
}

Ranđelović, M., Aleksić, A., Radovanović, R., Stojanović, V., Čabarkapa, M.,& Ranđelović, D.. (2022). One Aggregated Approach in Multidisciplinary Based Modeling to Predict Further Students’ Education. in Mathematics
Basel : MDPI., 10(14), 2381.
https://doi.org/10.3390/math10142381

Ranđelović M, Aleksić A, Radovanović R, Stojanović V, Čabarkapa M, Ranđelović D. One Aggregated Approach in Multidisciplinary Based Modeling to Predict Further Students’ Education. in Mathematics. 2022;10(14):2381.
doi:10.3390/math10142381 .

Ranđelović, Milan, Aleksić, Aleksandar, Radovanović, Radovan, Stojanović, Vladica, Čabarkapa, Milan, Ranđelović, Dragan, "One Aggregated Approach in Multidisciplinary Based Modeling to Predict Further Students’ Education" in Mathematics, 10, no. 14 (2022):2381,
https://doi.org/10.3390/math10142381 . .

TY  - JOUR
AU  - Ranđelović, Milan
AU  - Nedeljković, Slobodan
AU  - Jovanović, Mihailo
AU  - Čabarkapa, Milan
AU  - Stojanović, Vladica
AU  - Aleksić, Aleksandar
AU  - Ranđelović, Dragan
PY  - 2020
UR  - https://jakov.kpu.edu.rs/handle/123456789/1653
AB  - One of the essential activities for sustainable local economic development is continuous improvement of business environment which can be carried out through the business-friendly certification as objective benchmarking process, which is influenced by many factors - criteria that could be analyzed using multi-criteria decision-making methods. Determining criteria weights is the most important task regarding these methods for which a number of methodologies based on different approaches were developed. These methodologies could be generally divided into two groups: subjective and objective. Shortly, these methodologies quantify given preferences using knowledge of experts if they are subjective or using calculations from available data if they are objective. Methodologies from these two groups give different results in a wide range of values. Therefore, it is useful to create composite indicators using aggregation of both approaches in order to reduce the influence of their bad individual characteristics and, therefore, achieve a balanced symmetrical approach. The purpose of this paper is constructing one efficient model that solves a problem of the planning of sustainable local economic development in the Republic of Serbia. Our approach uses the aggregation of the entropy method, as one objective approach, and the analytical hierarchy process, as a subjective approach, in executing business-friendly certification process. The implementation of the proposed approach has been demonstrated as a part of a business-to-government (B2G) platform called “Multi-Criteria Support System for Analysis of the Local Economic Environment” in the City of Niš.
PB  - Basel : MDPI
T2  - Symmetry
T1  - Use of Determination of the Importance of Criteria in Business-Friendly Certification of Cities as Sustainable Local Economic Development Planning Tool
VL  - 12
IS  - 2
SP  - 425
DO  - 10.3390/sym12030425
ER  - 

@article{
author = "Ranđelović, Milan and Nedeljković, Slobodan and Jovanović, Mihailo and Čabarkapa, Milan and Stojanović, Vladica and Aleksić, Aleksandar and Ranđelović, Dragan",
year = "2020",
abstract = "One of the essential activities for sustainable local economic development is continuous improvement of business environment which can be carried out through the business-friendly certification as objective benchmarking process, which is influenced by many factors - criteria that could be analyzed using multi-criteria decision-making methods. Determining criteria weights is the most important task regarding these methods for which a number of methodologies based on different approaches were developed. These methodologies could be generally divided into two groups: subjective and objective. Shortly, these methodologies quantify given preferences using knowledge of experts if they are subjective or using calculations from available data if they are objective. Methodologies from these two groups give different results in a wide range of values. Therefore, it is useful to create composite indicators using aggregation of both approaches in order to reduce the influence of their bad individual characteristics and, therefore, achieve a balanced symmetrical approach. The purpose of this paper is constructing one efficient model that solves a problem of the planning of sustainable local economic development in the Republic of Serbia. Our approach uses the aggregation of the entropy method, as one objective approach, and the analytical hierarchy process, as a subjective approach, in executing business-friendly certification process. The implementation of the proposed approach has been demonstrated as a part of a business-to-government (B2G) platform called “Multi-Criteria Support System for Analysis of the Local Economic Environment” in the City of Niš.",
publisher = "Basel : MDPI",
journal = "Symmetry",
title = "Use of Determination of the Importance of Criteria in Business-Friendly Certification of Cities as Sustainable Local Economic Development Planning Tool",
volume = "12",
number = "2",
pages = "425",
doi = "10.3390/sym12030425"
}

Ranđelović, M., Nedeljković, S., Jovanović, M., Čabarkapa, M., Stojanović, V., Aleksić, A.,& Ranđelović, D.. (2020). Use of Determination of the Importance of Criteria in Business-Friendly Certification of Cities as Sustainable Local Economic Development Planning Tool. in Symmetry
Basel : MDPI., 12(2), 425.
https://doi.org/10.3390/sym12030425

Ranđelović M, Nedeljković S, Jovanović M, Čabarkapa M, Stojanović V, Aleksić A, Ranđelović D. Use of Determination of the Importance of Criteria in Business-Friendly Certification of Cities as Sustainable Local Economic Development Planning Tool. in Symmetry. 2020;12(2):425.
doi:10.3390/sym12030425 .

Ranđelović, Milan, Nedeljković, Slobodan, Jovanović, Mihailo, Čabarkapa, Milan, Stojanović, Vladica, Aleksić, Aleksandar, Ranđelović, Dragan, "Use of Determination of the Importance of Criteria in Business-Friendly Certification of Cities as Sustainable Local Economic Development Planning Tool" in Symmetry, 12, no. 2 (2020):425,
https://doi.org/10.3390/sym12030425 . .

TY  - JOUR
AU  - Aleksić, Aleksandar
AU  - Nedeljković, Slobodan
AU  - Jovanović, Mihailo
AU  - Ranđelović, Miloš
AU  - Vuković, Marko
AU  - Stojanović, Vladica
AU  - Radovanović, Radovan
AU  - Ranđelović, Milan
AU  - Ranđelović, Dragan
PY  - 2020
UR  - https://jakov.kpu.edu.rs/handle/123456789/1637
AB  - The main motivation to conduct the study presented in this paper was the fact that due to the development of improved solutions for prediction risk of bleeding and thus a faster and more accurate diagnosis of complications in cirrhotic patients, mortality of cirrhosis patients caused by bleeding of varices fell at the turn in the 21th century. Due to this fact, an additional research in this field is needed. The objective of this paper is to develop one prediction model that determines most important factors for bleeding in liver cirrhosis, which is useful for diagnosis and future treatment of patients. To achieve this goal, authors proposed one ensemble data mining methodology, as the most modern in the field of prediction, for integrating on one new way the two most commonly used techniques in prediction, classification with precede attribute number reduction and multiple logistic regression for calibration. Method was evaluated in the study, which analyzed the occurrence of variceal bleeding for 96 patients from the Clinical Center of Nis, Serbia, using 29 data from clinical to the color Doppler. Obtained results showed that proposed method with such big number and different types of data demonstrates better characteristics than individual technique integrated into it.
PB  - Basel : MDPI
T2  - Mathematics
T1  - Prediction of Important Factors for Bleeding in Liver Cirrhosis Disease Using Ensemble Data Mining Approach
VL  - 8
IS  - 11
SP  - 1887
DO  - 10.3390/math8111887
ER  - 

@article{
author = "Aleksić, Aleksandar and Nedeljković, Slobodan and Jovanović, Mihailo and Ranđelović, Miloš and Vuković, Marko and Stojanović, Vladica and Radovanović, Radovan and Ranđelović, Milan and Ranđelović, Dragan",
year = "2020",
abstract = "The main motivation to conduct the study presented in this paper was the fact that due to the development of improved solutions for prediction risk of bleeding and thus a faster and more accurate diagnosis of complications in cirrhotic patients, mortality of cirrhosis patients caused by bleeding of varices fell at the turn in the 21th century. Due to this fact, an additional research in this field is needed. The objective of this paper is to develop one prediction model that determines most important factors for bleeding in liver cirrhosis, which is useful for diagnosis and future treatment of patients. To achieve this goal, authors proposed one ensemble data mining methodology, as the most modern in the field of prediction, for integrating on one new way the two most commonly used techniques in prediction, classification with precede attribute number reduction and multiple logistic regression for calibration. Method was evaluated in the study, which analyzed the occurrence of variceal bleeding for 96 patients from the Clinical Center of Nis, Serbia, using 29 data from clinical to the color Doppler. Obtained results showed that proposed method with such big number and different types of data demonstrates better characteristics than individual technique integrated into it.",
publisher = "Basel : MDPI",
journal = "Mathematics",
title = "Prediction of Important Factors for Bleeding in Liver Cirrhosis Disease Using Ensemble Data Mining Approach",
volume = "8",
number = "11",
pages = "1887",
doi = "10.3390/math8111887"
}

Aleksić, A., Nedeljković, S., Jovanović, M., Ranđelović, M., Vuković, M., Stojanović, V., Radovanović, R., Ranđelović, M.,& Ranđelović, D.. (2020). Prediction of Important Factors for Bleeding in Liver Cirrhosis Disease Using Ensemble Data Mining Approach. in Mathematics
Basel : MDPI., 8(11), 1887.
https://doi.org/10.3390/math8111887

Aleksić A, Nedeljković S, Jovanović M, Ranđelović M, Vuković M, Stojanović V, Radovanović R, Ranđelović M, Ranđelović D. Prediction of Important Factors for Bleeding in Liver Cirrhosis Disease Using Ensemble Data Mining Approach. in Mathematics. 2020;8(11):1887.
doi:10.3390/math8111887 .

Aleksić, Aleksandar, Nedeljković, Slobodan, Jovanović, Mihailo, Ranđelović, Miloš, Vuković, Marko, Stojanović, Vladica, Radovanović, Radovan, Ranđelović, Milan, Ranđelović, Dragan, "Prediction of Important Factors for Bleeding in Liver Cirrhosis Disease Using Ensemble Data Mining Approach" in Mathematics, 8, no. 11 (2020):1887,
https://doi.org/10.3390/math8111887 . .