جستجوی استاد راهنمای دانشگاهی

• Machine Learning
• Optimization Algorithms
• Computer Networks
• +۳ مورد دیگر

Dušan Marković is an academic affiliated with Singidunum University, specifically within the Faculty of Informatics and Computing's Department of Computer Science. His primary role is as a Researcher, focusing on interdisciplinary research areas including machine learning, optimization algorithms, and cybersecurity. He has contributed to both theoretical advancements and practical applications in these fields. His research interests span optimization algorithms (e.g., firefly, monarch butterfly, moth search), their applications in machine learning (e.g., medical data classification), environmental impact assessment in agricultural systems, and educational technology (e.g., virtualization in learning environments). His work often bridges computational methods with real-world challenges in healthcare, urban planning, and disaster management. Notable contributions include improving medical data analysis through hybrid algorithms, assessing thermal comfort in urban areas, and developing cybersecurity measures for network infrastructures. While no specific awards are listed, his active publication record indicates sustained academic engagement. No grants or advising activities are explicitly documented here. He is part of Singidunum University's academic community, contributing to both teaching and research in informatics and computing disciplines.

• Cybersecurity
• Machine Learning
• Web Development
• +۳ مورد دیگر

Nikola Savanović is a researcher and academic at Singidunum University, specializing in cybersecurity, machine learning, and web technologies. He holds a PhD in Advanced Security Systems (2024) from Singidunum University, following a Master's in Contemporary Information Technologies (2016) and a Bachelor's in Informatics and Computing (2013), both from the same institution. He also completed high school in Computer-Aided Design at Politehnika School for New Technologies (2003-2007). His research focuses on intrusion detection systems, machine learning optimization for cybersecurity applications, and adaptive web technologies. He has contributed to educational technology through virtual learning environments and game-based learning. His mathematical work includes fixed point theory in b-metric spaces. He has authored/co-authored multiple books on web design, multimedia systems, and digital marketing. Publications highlight interdisciplinary work: hybrid CNN-XGBoost models for medical diagnostics, metaheuristic algorithms for software defect detection, and cybersecurity innovations in healthcare IoT systems. His articles span computer science, mathematics, and data science, emphasizing practical applications in education and healthcare. Despite no listed awards, his extensive publication record and teaching roles reflect significant academic contributions. He actively collaborates on university projects and advises students in informatics and computing disciplines, though no formal student list is provided.

• Swarm Intelligence
• Optimization Algorithms
• Machine Learning
• +۳ مورد دیگر

Eva Tuba is a researcher at Singidunum University , Serbia. Her work focuses on swarm intelligence , metaheuristic optimization , and machine learning applications across domains like medical imaging, wireless sensor networks, and digital forensics. Research Interests: Swarm optimization algorithms (bat, firefly, fireworks), machine learning, image/signal processing, cloud computing, and explainable AI. Publications: 15+ recent papers in journals such as Journal of King Saud University , IEEE Transactions , and Springer volumes, often in collaboration with researchers like Milena Tuba and Nebojsa Bacanin. Her academic contributions include hybridized algorithms for convolutional neural network tuning , medical diagnostics , and environmental monitoring . While she has no listed awards, her work spans both theoretical and applied computational intelligence.

• Artificial Intelligence
• Machine Learning
• Optimization Algorithms
• +۳ مورد دیگر

Aleksandar Petrović is a researcher and academic at Singidunum University, currently finishing his doctoral studies in Intelligent Software Engineering. He holds a B.Sc. and M.Sc. in Informatics and Computing from Singidunum University. His work focuses on artificial intelligence, machine learning, and optimization algorithms applied to cybersecurity, healthcare, and agriculture. He has published over 50 scientific papers in top-tier journals and conferences. Education : Bachelor of Science (2017-2021): Informatics and Computing, Singidunum University Master of Science (2021-2022): Software and Information Engineering, Singidunum University Doctoral Studies (2022-): Intelligent Software Engineering, Singidunum University Research Interests : Petrović specializes in metaheuristic optimization techniques for machine learning models, with applications in: Cybersecurity (e.g., intrusion detection, SQL injection prevention) Healthcare (e.g., ECG anomaly detection, Parkinson’s disease analysis) Agriculture (e.g., crop yield forecasting, weed detection) Key Research Trends : His recent work emphasizes hybrid models combining recurrent neural networks (RNNs), attention mechanisms, and evolutionary algorithms (e.g., Salp Swarm, Firefly). He frequently explores real-world challenges like renewable energy forecasting and generative adversarial networks (GANs) for synthetic data generation. Advising & Grants : Petrović collaborates on projects funded by Singidunum University and international partners, focusing on AI-driven solutions for smart cities and healthcare. His lab develops tools like Singibot (AI chatbot) and YOLOv8-based surveillance systems. Labs/Teams : Participates in the Singidunum AI Research Group, working on cross-disciplinary projects involving machine learning, robotics, and IoT security.

• Machine Learning
• Optimization Algorithms
• Computer Vision
• +۳ مورد دیگر

Ana Vesić is affiliated with Singidunum University, holding a Master's degree in Media and Human-Centered Computing from the Technical University of Vienna (2022) and a Bachelor's in Software and Data Engineering from Singidunum University (2018-2022). Her research focuses on machine learning, optimization algorithms, and their applications in healthcare, agriculture, and computer vision. She has authored multiple peer-reviewed articles on topics such as AI-driven medical image classification, agricultural automation, and emotion detection systems. Education: Master's in Media and Human-Centered Computing, Technical University of Vienna (2022) Bachelor's in Software and Data Engineering, Singidunum University (2018–2022) Research Interests: Her work bridges AI and real-world challenges, emphasizing optimization techniques for machine learning models (e.g., Animal Migration Optimization, Multi-verse Optimizer) and their deployment in healthcare diagnostics, agricultural sustainability, and behavioral analysis. Her projects include developing tools like EAGRO for precision farming and analyzing hidden emotional states through facial recognition. Awards: Helmut-Veith stipend award (2021) for promoting excellence in women in computer science (Vienna University of Technology) Grants & Collaborations: Her research has been supported by stipends and industry collaborations, including work with the Development and Testing Center (DTC) in Belgrade and the Serbian Games Association (SGA). She has presented at international conferences such as IEEE ZINC, Sinteza, and INFUS. Labs & Teams: She contributes to Singidunum University's research initiatives in applied informatics and AI, with a focus on interdisciplinary projects merging technology and societal needs.

• Information Systems
• Data Warehousing
• Database Management
• +۵ مورد دیگر

Dr. Nataša Goјgić is a Professor of Applied Studies at the Department of Information Technologies, Faculty of Technical Sciences Čačak, University of Kragujevac. Her academic career spans decades of teaching and research in information systems, databases, and technology-enhanced education. Military Technical Faculty in Zagreb (Mechanical Engineering) Master's in Information Systems at Technical Faculty Čačak PhD in Information Systems Integration and Quality Management Dr. Goјgić's research focuses on information systems , data warehousing , and educational technology . Her publications reveal strong interests in: Business Intelligence and OLAP systems Regression algorithms in healthcare E-learning platforms and teaching methodologies Quality Management Systems (QMS) integration Database monitoring and financial technology She has pioneered the use of information systems in academic quality assurance and developed teaching materials for database systems. Her work bridges industrial engineering and modern information technologies.

• Quality Management
• Casting Process Optimization
• Machine Learning Applications
• +۳ مورد دیگر

Aleksandar Jovičić is an Assistant Professor at the Department of Mechatronics, Faculty of Technical Sciences, University of Kragujevac in Čačak, Serbia. His academic responsibilities include teaching undergraduate courses in Quality Management, Measurement and Control, and Production Metrology, as well as graduate courses in Quality Management Systems and Risk Management. Education includes: PhD in Mechanical Engineering (2016, University of Kragujevac) MA in Quality Management (2009, University of Belgrade) MA in Mechanical Engineering (2018, University of Kragujevac) Dual BA degrees in Technics/Informatics (2008) and Mechanical Engineering (2006) Research focuses on industrial process optimization through computational methods, with particular expertise in: Machine learning applications in foundry processes and quality control Sustainable manufacturing and circular economy implementation CAD/CAE/CAM integration for casting improvement Quality management systems and production metrology Publications demonstrate strong cross-disciplinary trends, bridging manufacturing engineering with artificial intelligence and environmental sustainability. Recent works (2019-2025) predominantly explore Industry 4.0 technologies in metallurgical processes, predictive modeling for quality control, and sustainable resource management in industrial settings. Research grants include: Implementation of Computational Intelligence in Foundries In Serbia (2018-2019) EIT RawMaterials project RIS - DustREC-II (2023) He has also contributed to industrial partnerships with metal processing enterprises like IKL Industrijski kombinat livnica d.o.o.

Miroslav Bјekićمشاهده پروفایل

استاد

• Electrical Power Engineering
• Electric Micromachines
• Simulation and Modeling of Electromechanical Processes
• +۴ مورد دیگر

Professor Miroslav Bјekić is a Full Professor at the Department of Power Engineering within the Faculty of Technical Sciences Čačak, University of Kragujevac, Serbia. With a career spanning over three decades, he has established himself as a leading expert in electrical power engineering, particularly in the areas of electric machines, drives, and energy efficiency. Education background: Graduated in Electrical Engineering (Industrial Power Engineering) from Technical Faculty Čačak in 1991 Completed Magister studies at Faculty of Electrical Engineering, University of Belgrade in 1995 Obtained PhD in 2006 from Technical Faculty Čačak with dissertation on step motors Professor Bјekić's research focuses on electric micromachines, simulation and modeling of electromechanical processes, and laboratory investigation of electric machines and drives . His work bridges theoretical electrical engineering with practical applications, particularly in energy efficiency. He has developed numerous educational tools and software applications that enhance the teaching of complex electrical engineering concepts through visualization and simulation. His publication record demonstrates a consistent research trajectory with recent work increasingly focusing on educational technology applications (particularly GeoGebra tools for electrical machine instruction), advanced motor control algorithms, and energy efficiency optimization. The interdisciplinary nature of his work connects electrical engineering with building science, robotics, and educational methodology. Scientific recognition: Recipient of 2nd award at international PhD seminar in Ohrid, 2005 Coordinator of multiple national and international research projects Active participant in standardization committees for electrical machinery Professor Bјekić has secured significant research funding through national projects including TR33016 'Research, Development and Implementation of Programs and Measures for Energy Efficiency of Electric Drives' and has participated in international initiatives like TEMPUS projects. He has developed accredited teacher training programs and has modernized laboratory facilities for electric machines and drives through project funding and industry collaboration. He leads the Laboratory for Electrical Machines, Electromotor Drives and Automation (www.empa.tfc.kg.ac.rs), which has evolved into a center for both research and educational innovation, featuring remote laboratory experiments that connect university and secondary vocational school education.

Marko Rosićمشاهده پروفایل

دانشیار

• Electrical Drives
• Induction Motors
• Torque Ripple Reduction
• +۵ مورد دیگر

Dr. Marko Rosić is an Associate Professor at the Department of Power Engineering, Faculty of Technical Sciences Čačak, University of Kragujevac, Serbia. Born on July 31, 1984, in Priboj, he completed his education in electrical engineering, graduating with top honors in 2008. He joined the faculty in 2008 as a Teaching Associate and progressed to Assistant Professor (2016) and Associate Professor (2021). He teaches courses including Electrical Drives, Control of Electrical Drives, and Computer-Aided Design in Power Engineering. His research focuses on: Torque ripple reduction in induction motor drives Energy efficiency in electric drives Magnetic field modeling and hysteresis analysis Educational technology for engineering instruction Control algorithms for electrical machines His recent publications (2021-2025) demonstrate a consistent focus on improving control algorithms for induction motors, advancing magnetic material modeling, developing fault detection systems for DC motors, and creating innovative educational tools like GeoGebra applications for engineering education. This work bridges theoretical research and practical implementations in industrial power systems. Scientific Awards: Most Successful Student Award (during studies) Most Successful Graduate Student in Electrical Engineering Young Researcher Best Paper Award (ETRAN 2013) He has led or contributed to multiple national and international projects including: TR 33016: 'Research and implementation of energy efficiency programs for electric drives' (national) 543667-TEMPUS: 'Building Network of Remote Labs' (international) 'Development of Computer-aided Design curriculum for digital competencies' (project coordinator) He develops laboratory setups including electromagnetic load emulators, DC motor test benches, and remote experimentation platforms. As a steering committee member of the ETRAN society and editor assistant for the Serbian Journal of Electrical Engineering, he actively contributes to the academic community.

• Wireless Communications
• Antenna Design
• RF Power Amplifiers
• +۳ مورد دیگر

Александар С. Атанасковић is an Assistant Professor at the Department of Telecommunications, Faculty of Electronic Engineering, University of Niš, Serbia, appointed in 2018 after completing his doctorate at the same institution. His academic affiliation spans all educational levels at the university, from undergraduate to doctoral studies. His educational background includes: Doctorate in Telecommunications (2015) Master's Degree in Telecommunications (2004) Bachelor's Degree in Electronics and Telecommunications (1997) His research integrates advanced electromagnetic theory and signal processing with modern computational techniques. Key focus areas include: Computational modeling of conformal and on-body antennas using cylindrical TLM methods Digital linearization techniques for RF power amplifiers, particularly Doherty architectures Deep learning applications in sensor systems for security and safety His work bridges theoretical electromagnetics with practical communication hardware design, emphasizing efficiency and linearity improvements. Analysis of his 10 journal publications (2013-2023) reveals an evolving research trajectory: early work concentrated on RF amplifier linearization through harmonic manipulation, while recent publications (2020-2023) demonstrate increased focus on computational antenna modeling and machine learning applications. The consistent thread is optimizing wireless communication hardware performance through innovative signal processing and electromagnetic simulation techniques. Scientific Awards: No awards listed in source material. Research funding includes active participation in one national and two international projects. While no formal advisees are documented, his faculty position inherently involves graduate student supervision within the Department of Telecommunications' academic programs. His laboratory work appears centered within the Faculty of Electronic Engineering's antenna and RF research facilities, likely collaborating with the Microwave and Antenna Laboratory group on computational electromagnetics projects involving cylindrical TLM approaches and wearable antenna systems.

Xavier Goaocمشاهده پروفایل

استاد

• Computational Geometry
• Discrete Mathematics
• Combinatorial Convexity
• +۵ مورد دیگر

Xavier Goaoc is a Professor of Computer Science at Université de Lorraine, affiliated with the Department of Computer Science & Engineering at École des Mines de Nancy and the Gamble research team (joint between LORIA and INRIA). His research focuses on algorithms and discrete mathematics, particularly discrete and computational geometry, including convex hulls, intersection patterns, topological generalizations, and geometric transversal theory. University: Université de Lorraine School: School of Engineering Department: Department of Computer Science & Engineering Research Team: Gamble (LORIA/INRIA) Emails: xavier.goaoc@loria.fr , xavier.goaoc@univ-lorraine.fr His research spans computational geometry, combinatorial convexity, geometric transversal theory, and random geometric structures. Key topics include homological minors, order types of point sets, and geometric optimization. His 15 most recent publications highlight advancements in computational geometry algorithms, structural complexity, and topological constraints. Notably, his work has received Best Paper Awards at SoCG 2020, 2018, 2016, and 2012. Administrative roles include heading the computer science & engineering department at Mines Nancy and co-chairing the computer science department of the IAEM doctoral school. He is also a member of the Université de Lorraine's ‘pôle AM2I’ council. Teaching activities encompass courses in algorithms, computer architecture, blockchains, and geometric models for vision, with publications and grants reflecting his interdisciplinary impact in computer science and mathematics.

• Cryptology
• Cryptanalysis
• RFID Systems
• +۶ مورد دیگر

Violeta Tomasevic is a Professor at Singidunum University's Faculty of Informatics and Computing, where she has been employed since 2007. She teaches across all academic levels (bachelor's, master's, and doctoral) in computer science and informatics fields, with courses including Computer Organization and Architecture, Advanced Software Architectures, and Expert Systems. Her academic journey began with a strong foundation in electrical engineering from the University of Belgrade. Her educational background includes: Doctoral studies in Cryptanalysis at the Faculty of Electrical Engineering, University of Belgrade (1998-2005) Master's studies in Digital Signal Processing at the Faculty of Electrical Engineering, University of Belgrade (1988-1994) Basic studies in Electronics at the Faculty of Electrical Engineering, University of Belgrade (1983-1988) Tomasevic's research primarily focuses on cryptology, specializing in cryptanalysis, RFID systems security, and multi-criteria decision making. Her work bridges theoretical cryptography with practical applications in information security and business decision support. She has made significant contributions to time-memory trade-off techniques in cryptographic analysis and has extended her expertise into insurance technology applications, particularly regarding cybersecurity threats in modern insurance systems. Her research demonstrates a consistent trajectory from foundational work in expert systems to advanced cryptographic research and practical business applications. Her publication record shows a clear evolution from early work on expert systems and business decision support to specialized cryptographic research, particularly in RFID protocols and stream ciphers like RC4. Recent publications demonstrate her expanding interests into educational data mining and insurance technology applications. The interdisciplinary nature of her work connects computer science with business applications, reflecting Singidunum University's emphasis on practical, industry-relevant research. Tomasevic has participated in numerous research projects throughout her career, including domestic projects funded by the Ministry of Science (BEST, FIDES), commercial projects for domestic and international clients (BASF), and European Commission FP6 projects (PROMETEA, Web4Web). She was also involved in the Joint Project Office cooperation program with the Fraunhofer FIRST Institute from Berlin. She has authored several textbooks including 'Basics of computer architecture and organization' (2019), 'Basics of computer technology' (2012), and 'Application software development' (2012), which support her teaching activities in computer science education at Singidunum University.

• Environmental Chemistry
• Atmospheric Pollution
• Machine Learning Applications
• +۴ مورد دیگر

Gordana Jovanovic is an active Assistant Professor at Singidunum University's Faculty of Informatics and Computing, specializing in environmental chemistry with strong computational focus. Her research bridges atmospheric pollution dynamics and machine learning applications, evidenced by 15+ annual publications in high-impact journals. Research interests emphasize explainable AI applications for environmental systems, particularly: Atmospheric fate modeling of PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) Volatile organic compound (VOC) behavior prediction using XGBoost/SHAP frameworks Marine pollution monitoring in the Adriatic Sea ecosystem Biomonitoring techniques for soil-plant-air pollution transfer Her publication trajectory shows significant methodological evolution from traditional environmental chemistry (2016-2021) to AI-integrated approaches since 2022, with recent work featured in Science of the Total Environment and Chemosphere . She co-authored the 2023 textbook 'Environmental Chemistry' and has contributed book chapters on AI applications in environmental science. Research collaborations span multiple institutions: University of Belgrade (doctoral alma mater and ongoing partnership) Croatian research groups (Adriatic Sea pollution studies) International co-authors from Montenegro, Russia, and Turkey Her advising approach emphasizes practical data science skills, with students typically contributing to conference presentations and journal publications. Current work focuses on advancing explainable AI frameworks for pollution source identification and health risk assessment, aligning with Singidunum University's new Applied Artificial Intelligence programs.

• Information and Communication Technologies in Education
• Digital Business
• Artificial Intelligence in Education
• +۸ مورد دیگر

Marija Marković Blagojević is a researcher at Singidunum University's Faculty of Informatics and Computing, with 17 years of experience in information and communication technologies (ICT) in education and business. She has published over 70 scientific and professional papers, co-authored the textbook Digital Business , and actively contributes to higher education quality assurance through accreditation and self-evaluation projects. Doctoral Studies: Applied Management, University of Privredna Akademija Novi Sad (2013-2018) Master's Studies: Informatics Management, Union University Belgrade (2009-2010) Bachelor's Studies: Industrial Engineering, Union University Belgrade (2002-2004) Her research focuses on ICT in education , artificial intelligence applications , digital transformation , sustainable development , and modern teaching methodologies . She has led projects like Erasmus+ initiatives on AI in adult education and founded the Center for Innovative Learning 'Better ME - Better WE' in Kruševac. Her 15 most recent publications span topics including IoT security , AI in education , and renewable energy forecasting , with a particular emphasis on digital tools' impact on student engagement and workforce adaptation . She has organized lectures on 'Artificial Intelligence in Education' and 'Business Communication in Digital Environments'. She coordinates academic journals and conferences through systems like Moodle and OJS , and has implemented national projects such as the 'Skillful with Skilled ChatGPT' initiative (2024). Her work aligns with Industry 5.0 principles, integrating humanization and sustainability in technological applications.

• Control Theory
• Automotive Engineering
• Quantum Computing
• +۳ مورد دیگر

Uroš Kalabić is a Teaching Professor at Singidunum University in Serbia. He holds a doctoral degree in Electrical and Computer Engineering from the University of Michigan (2015), a Master's degree from the same university (2011), and a Bachelor's degree in Electrical and Computer Engineering from the University of Toronto (2010). His research focuses on advanced control systems and their applications in automotive engineering, aerospace, and quantum computing. Doctoral studies: University of Michigan, Electrical and Computer Engineering (2015) Master's studies: University of Michigan, Electrical and Computer Engineering (2011) Undergraduate studies: University of Toronto, Electrical and Computer Engineering (2010) Kalabić's research interests span stochastic model predictive control , quantum optimization , autonomous traffic systems , and nonlinear control on manifolds . His work includes applications in automotive engine control, spacecraft attitude control, quantum search algorithms, and sustainable transportation schemes. He has published in top journals like AUTOMATICA and the Journal of Dynamic Systems, Measurement, and Control, focusing on control theory and its interdisciplinary applications. His publications reveal expertise in stochastic control , reinforcement learning , quantum computing , and transportation optimization . Notable contributions include constraint-separation principles in MPC, reference governors for probabilistic systems, and deep learning-based traffic management frameworks. Currently, no scientific awards or honors are documented in the provided materials. Similarly, there is no mention of student advising roles, laboratory leadership, or active research grants in the available descriptions.