TY  - JOUR
AU  - Čisar, Petar
AU  - Maravić Čisar, Sanja
AU  - Popović, Brankica
AU  - Kuk, Kristijan
AU  - Vuković, Igor
PY  - 2022
UR  - http://jakov.kpu.edu.rs/handle/123456789/1446
AB  - This paper deals with the application of artificial immune networks in continuous
function optimizations. The performance of the immunological algorithms is analyzed using
the Optimization Algorithm Toolkit. It is shown that the CLIGA algorithm has, by far, the
fastest convergence and the best score - in terms of the number of required iterations, for the
analyzed continuous function. Also, based on the test results, it was concluded, that the lowest
total number of iterations for the defined run time was achieved with the opt-IA algorithm,
followed by the CLONALG and CLIGA algorithms.
PB  - Budapest : Óbuda University
T2  - Acta Polytechnica Hungarica
T1  - Application of Artificial Immune Networks in Continuous Function Optimizations
VL  - 19
IS  - 7
SP  - 153
EP  - 164
DO  - 10.12700/APH.19.7.2022.7.8
ER  - 

@article{
author = "Čisar, Petar and Maravić Čisar, Sanja and Popović, Brankica and Kuk, Kristijan and Vuković, Igor",
year = "2022",
abstract = "This paper deals with the application of artificial immune networks in continuous
function optimizations. The performance of the immunological algorithms is analyzed using
the Optimization Algorithm Toolkit. It is shown that the CLIGA algorithm has, by far, the
fastest convergence and the best score - in terms of the number of required iterations, for the
analyzed continuous function. Also, based on the test results, it was concluded, that the lowest
total number of iterations for the defined run time was achieved with the opt-IA algorithm,
followed by the CLONALG and CLIGA algorithms.",
publisher = "Budapest : Óbuda University",
journal = "Acta Polytechnica Hungarica",
title = "Application of Artificial Immune Networks in Continuous Function Optimizations",
volume = "19",
number = "7",
pages = "153-164",
doi = "10.12700/APH.19.7.2022.7.8"
}

Čisar, P., Maravić Čisar, S., Popović, B., Kuk, K.,& Vuković, I.. (2022). Application of Artificial Immune Networks in Continuous Function Optimizations. in Acta Polytechnica Hungarica
Budapest : Óbuda University., 19(7), 153-164.
https://doi.org/10.12700/APH.19.7.2022.7.8

Čisar P, Maravić Čisar S, Popović B, Kuk K, Vuković I. Application of Artificial Immune Networks in Continuous Function Optimizations. in Acta Polytechnica Hungarica. 2022;19(7):153-164.
doi:10.12700/APH.19.7.2022.7.8 .

Čisar, Petar, Maravić Čisar, Sanja, Popović, Brankica, Kuk, Kristijan, Vuković, Igor, "Application of Artificial Immune Networks in Continuous Function Optimizations" in Acta Polytechnica Hungarica, 19, no. 7 (2022):153-164,
https://doi.org/10.12700/APH.19.7.2022.7.8 . .

TY  - JOUR
AU  - Vuković, Igor
AU  - Kuk, Kristijan
AU  - Čisar, Petar
AU  - Banđur, Miloš
AU  - Banđur, Đoko
AU  - Milić, Nenad
AU  - Popović, Brankica
PY  - 2021
UR  - http://jakov.kpu.edu.rs/handle/123456789/1531
AB  - Moodle is a widely deployed distance learning platform that provides numerous opportunities to enhance the learning process. Moodle’s importance in maintaining the continuity of education in states of emergency and other circumstances has been particularly demonstrated in the context of the COVID-19 virus’ rapid spread. However, there is a problem with personalizing the learning and monitoring of students’ work. There is room for upgrading the system by applying data mining and different machine-learning methods. The multi-agent Observer system proposed in our paper supports students engaged in learning by monitoring their work and making suggestions based on the prediction of their final course success, using indicators of engagement and machine-learning algorithms. A novelty is that Observer collects data independently of the Moodle database, autonomously creates a training set, and learns from gathered data. Since the data are anonymized, researchers and lecturers can freely use them for purposes broader than that specified for Observer. The paper shows how the methodology, technologies, and techniques used in Observer provide an autonomous system of personalized assistance for students within Moodle platforms.
PB  - Basel : MDPI
T2  - Electronics
T1  - Multi-Agent System Observer: Intelligent Support for Engaged E-Learning
VL  - 10
IS  - 12
SP  - 1370
DO  - 10.3390/electronics10121370
ER  - 

@article{
author = "Vuković, Igor and Kuk, Kristijan and Čisar, Petar and Banđur, Miloš and Banđur, Đoko and Milić, Nenad and Popović, Brankica",
year = "2021",
abstract = "Moodle is a widely deployed distance learning platform that provides numerous opportunities to enhance the learning process. Moodle’s importance in maintaining the continuity of education in states of emergency and other circumstances has been particularly demonstrated in the context of the COVID-19 virus’ rapid spread. However, there is a problem with personalizing the learning and monitoring of students’ work. There is room for upgrading the system by applying data mining and different machine-learning methods. The multi-agent Observer system proposed in our paper supports students engaged in learning by monitoring their work and making suggestions based on the prediction of their final course success, using indicators of engagement and machine-learning algorithms. A novelty is that Observer collects data independently of the Moodle database, autonomously creates a training set, and learns from gathered data. Since the data are anonymized, researchers and lecturers can freely use them for purposes broader than that specified for Observer. The paper shows how the methodology, technologies, and techniques used in Observer provide an autonomous system of personalized assistance for students within Moodle platforms.",
publisher = "Basel : MDPI",
journal = "Electronics",
title = "Multi-Agent System Observer: Intelligent Support for Engaged E-Learning",
volume = "10",
number = "12",
pages = "1370",
doi = "10.3390/electronics10121370"
}

Vuković, I., Kuk, K., Čisar, P., Banđur, M., Banđur, Đ., Milić, N.,& Popović, B.. (2021). Multi-Agent System Observer: Intelligent Support for Engaged E-Learning. in Electronics
Basel : MDPI., 10(12), 1370.
https://doi.org/10.3390/electronics10121370

Vuković I, Kuk K, Čisar P, Banđur M, Banđur Đ, Milić N, Popović B. Multi-Agent System Observer: Intelligent Support for Engaged E-Learning. in Electronics. 2021;10(12):1370.
doi:10.3390/electronics10121370 .

Vuković, Igor, Kuk, Kristijan, Čisar, Petar, Banđur, Miloš, Banđur, Đoko, Milić, Nenad, Popović, Brankica, "Multi-Agent System Observer: Intelligent Support for Engaged E-Learning" in Electronics, 10, no. 12 (2021):1370,
https://doi.org/10.3390/electronics10121370 . .

TY  - JOUR
AU  - Vuković, Igor
AU  - Čisar, Petar
AU  - Kuk, Kristijan
AU  - Banđur, Miloš
AU  - Popović, Brankica
PY  - 2021
UR  - http://jakov.kpu.edu.rs/handle/123456789/1522
AB  - In a criminal investigation, along with processing forensic evidence, different investigative techniques are used to identify the perpetrator of the crime. It includes collecting and analyzing unconstrained face images, mostly with low resolution and various qualities, making identification difficult. Since police organizations have limited resources, in this paper, we propose a novel method that utilizes off-the-shelf solutions (Dlib library Histogram of Oriented Gradients-HOG face detectors and the ResNet faces feature vector extractor) to provide practical assistance in unconstrained face identification. Our experiment aimed to establish which one (if any) of the basic image enhancement techniques should be applied to increase the effectiveness. Results obtained from three publicly available databases and one created for this research (simulating police investigators’ database) showed that resizing the image (especially with a resolution lower than 150 pixels) should always precede enhancement to improve face detection accuracy. The best results in determining whether they are the same or different persons in images were obtained by applying sharpening with a high-pass filter, whereas normalization gives the highest classification scores when a single weight value is applied to data from all four databases.
PB  - Kaunas : Kauno Technologijos Universitetas
T2  - Elektronika Ir Elektrotechnika
T1  - Influence of Image Enhancement Techniques on Effectiveness of Unconstrained Face Detection and Identification
VL  - 27
IS  - 5
SP  - 49
EP  - 58
DO  - 10.5755/j02.eie.29081
ER  - 

@article{
author = "Vuković, Igor and Čisar, Petar and Kuk, Kristijan and Banđur, Miloš and Popović, Brankica",
year = "2021",
abstract = "In a criminal investigation, along with processing forensic evidence, different investigative techniques are used to identify the perpetrator of the crime. It includes collecting and analyzing unconstrained face images, mostly with low resolution and various qualities, making identification difficult. Since police organizations have limited resources, in this paper, we propose a novel method that utilizes off-the-shelf solutions (Dlib library Histogram of Oriented Gradients-HOG face detectors and the ResNet faces feature vector extractor) to provide practical assistance in unconstrained face identification. Our experiment aimed to establish which one (if any) of the basic image enhancement techniques should be applied to increase the effectiveness. Results obtained from three publicly available databases and one created for this research (simulating police investigators’ database) showed that resizing the image (especially with a resolution lower than 150 pixels) should always precede enhancement to improve face detection accuracy. The best results in determining whether they are the same or different persons in images were obtained by applying sharpening with a high-pass filter, whereas normalization gives the highest classification scores when a single weight value is applied to data from all four databases.",
publisher = "Kaunas : Kauno Technologijos Universitetas",
journal = "Elektronika Ir Elektrotechnika",
title = "Influence of Image Enhancement Techniques on Effectiveness of Unconstrained Face Detection and Identification",
volume = "27",
number = "5",
pages = "49-58",
doi = "10.5755/j02.eie.29081"
}

Vuković, I., Čisar, P., Kuk, K., Banđur, M.,& Popović, B.. (2021). Influence of Image Enhancement Techniques on Effectiveness of Unconstrained Face Detection and Identification. in Elektronika Ir Elektrotechnika
Kaunas : Kauno Technologijos Universitetas., 27(5), 49-58.
https://doi.org/10.5755/j02.eie.29081

Vuković I, Čisar P, Kuk K, Banđur M, Popović B. Influence of Image Enhancement Techniques on Effectiveness of Unconstrained Face Detection and Identification. in Elektronika Ir Elektrotechnika. 2021;27(5):49-58.
doi:10.5755/j02.eie.29081 .

Vuković, Igor, Čisar, Petar, Kuk, Kristijan, Banđur, Miloš, Popović, Brankica, "Influence of Image Enhancement Techniques on Effectiveness of Unconstrained Face Detection and Identification" in Elektronika Ir Elektrotechnika, 27, no. 5 (2021):49-58,
https://doi.org/10.5755/j02.eie.29081 . .

TY  - CONF
AU  - Vuković, Igor
AU  - Čisar, Petar
AU  - Kuk, Kristijan
AU  - Popović, Brankica
PY  - 2021
UR  - http://eskup.kpu.edu.rs/dar/issue/view/6/4
UR  - http://jakov.kpu.edu.rs/handle/123456789/1290
AB  - Big data algorithms developed for predictive policing are increasingly present in the everyday work of law enforcement. There are various applications of such technologies to pre-dict crimes, potential crime scenes, profiles of perpetrators, and more. In this way, police officers are  provided  with  appropriate  assistance  in  their  work,  increasing  their  efficiency  or  entirely  replacing them in specific tasks. Although technologically advanced, police use force and arrest, so  prediction  algorithms  can  have  significantly  different,  more  drastic  consequences  as  com-pared to those that similar technologies would produce in agriculture, industry, or health. For further development of predictive policing, it is necessary to have a clear picture of the problems it can cause. This paper discusses modern predictive policing from the perspective of challenges that negatively affect its application.
PB  - Belgrade : University of Criminal Investigation and Police Studies
C3  - Thematic conference proceedings of international significance [Elektronski izvor] / International Scientific Conference "Archibald Reiss Days", Belgrade, 9-10 November 2021
T1  - Challenges of contemporary predictive policing
SP  - 513
EP  - 526
UR  - https://hdl.handle.net/21.15107/rcub_jakov_1290
ER  - 

@conference{
author = "Vuković, Igor and Čisar, Petar and Kuk, Kristijan and Popović, Brankica",
year = "2021",
abstract = "Big data algorithms developed for predictive policing are increasingly present in the everyday work of law enforcement. There are various applications of such technologies to pre-dict crimes, potential crime scenes, profiles of perpetrators, and more. In this way, police officers are  provided  with  appropriate  assistance  in  their  work,  increasing  their  efficiency  or  entirely  replacing them in specific tasks. Although technologically advanced, police use force and arrest, so  prediction  algorithms  can  have  significantly  different,  more  drastic  consequences  as  com-pared to those that similar technologies would produce in agriculture, industry, or health. For further development of predictive policing, it is necessary to have a clear picture of the problems it can cause. This paper discusses modern predictive policing from the perspective of challenges that negatively affect its application.",
publisher = "Belgrade : University of Criminal Investigation and Police Studies",
journal = "Thematic conference proceedings of international significance [Elektronski izvor] / International Scientific Conference "Archibald Reiss Days", Belgrade, 9-10 November 2021",
title = "Challenges of contemporary predictive policing",
pages = "513-526",
url = "https://hdl.handle.net/21.15107/rcub_jakov_1290"
}

Vuković, I., Čisar, P., Kuk, K.,& Popović, B.. (2021). Challenges of contemporary predictive policing. in Thematic conference proceedings of international significance [Elektronski izvor] / International Scientific Conference "Archibald Reiss Days", Belgrade, 9-10 November 2021
Belgrade : University of Criminal Investigation and Police Studies., 513-526.
https://hdl.handle.net/21.15107/rcub_jakov_1290

Vuković I, Čisar P, Kuk K, Popović B. Challenges of contemporary predictive policing. in Thematic conference proceedings of international significance [Elektronski izvor] / International Scientific Conference "Archibald Reiss Days", Belgrade, 9-10 November 2021. 2021;:513-526.
https://hdl.handle.net/21.15107/rcub_jakov_1290 .

Vuković, Igor, Čisar, Petar, Kuk, Kristijan, Popović, Brankica, "Challenges of contemporary predictive policing" in Thematic conference proceedings of international significance [Elektronski izvor] / International Scientific Conference "Archibald Reiss Days", Belgrade, 9-10 November 2021 (2021):513-526,
https://hdl.handle.net/21.15107/rcub_jakov_1290 .

TY  - CONF
AU  - Vuković, Igor
AU  - Popović, Brankica
AU  - Čisar, Petar
PY  - 2020
UR  - https://jakov.kpu.edu.rs/handle/123456789/1618
AB  - Services distributed over the Internet are ranging from entertaining and informative to those whose availability must not be interrupted because it affects the quality of life, but also safety and health. Due to its importance, the global computer network is a desirable target, attacks are continually taking place, and the damage is more than considerable. Among the many types of attacks, one of the most effective, given the relationship between the damage done and the challenge to be prevented, detect and control, are DDoS attacks. This paper discusses the phases, components, categories, and types of DDoS attacks and emphasizes detection approaches. The standout approach and one that can answer the complexity of detecting DDoS attacks is the classification with artificial intelligence techniques. This work shows why artificial intelligence represents the starting point for further research in information security.
PB  - Belgrade : University of Criminal Investigation and Police Studies
C3  - Thematic conference proceedings of international significance [Elektronski izvor]
T1  - Application of artificial intelligence in detection of DDos attacks
VL  - 10
SP  - 557
EP  - 566
UR  - https://hdl.handle.net/21.15107/rcub_jakov_1618
ER  - 

@conference{
author = "Vuković, Igor and Popović, Brankica and Čisar, Petar",
year = "2020",
abstract = "Services distributed over the Internet are ranging from entertaining and informative to those whose availability must not be interrupted because it affects the quality of life, but also safety and health. Due to its importance, the global computer network is a desirable target, attacks are continually taking place, and the damage is more than considerable. Among the many types of attacks, one of the most effective, given the relationship between the damage done and the challenge to be prevented, detect and control, are DDoS attacks. This paper discusses the phases, components, categories, and types of DDoS attacks and emphasizes detection approaches. The standout approach and one that can answer the complexity of detecting DDoS attacks is the classification with artificial intelligence techniques. This work shows why artificial intelligence represents the starting point for further research in information security.",
publisher = "Belgrade : University of Criminal Investigation and Police Studies",
journal = "Thematic conference proceedings of international significance [Elektronski izvor]",
title = "Application of artificial intelligence in detection of DDos attacks",
volume = "10",
pages = "557-566",
url = "https://hdl.handle.net/21.15107/rcub_jakov_1618"
}

Vuković, I., Popović, B.,& Čisar, P.. (2020). Application of artificial intelligence in detection of DDos attacks. in Thematic conference proceedings of international significance [Elektronski izvor]
Belgrade : University of Criminal Investigation and Police Studies., 10, 557-566.
https://hdl.handle.net/21.15107/rcub_jakov_1618

Vuković I, Popović B, Čisar P. Application of artificial intelligence in detection of DDos attacks. in Thematic conference proceedings of international significance [Elektronski izvor]. 2020;10:557-566.
https://hdl.handle.net/21.15107/rcub_jakov_1618 .

Vuković, Igor, Popović, Brankica, Čisar, Petar, "Application of artificial intelligence in detection of DDos attacks" in Thematic conference proceedings of international significance [Elektronski izvor], 10 (2020):557-566,
https://hdl.handle.net/21.15107/rcub_jakov_1618 .