جستجوی استاد راهنمای دانشگاهی

Milan Marjanovićمشاهده پروفایل

استادیار

• Thermal Engineering
• Thermoenergetics
• Renewable Energy
• +۷ مورد دیگر

Milan Marjanović is an Assistant Professor at the Department of Mechanical Engineering, Faculty of Technical Sciences in Čačak, University of Kragujevac. Holding an M.Sc. in Mechanical Engineering, he teaches Thermodynamics, Applied Thermodynamics, Renewable Energy Sources, and Machine Elements. His research focuses on Thermal Engineering, Thermoenergetics, and Renewable Energy systems. Born 1990 in Užice Completed primary/secondary education in Požega Faculty of Mechanical Engineering and Civil Engineering, Kraljevo (2012) Master's in Energy Engineering (2014) Research spans biomass combustion optimization, solar energy systems, and hydraulic simulation tools. Active in academic projects like the national PRIZMA 2023 initiative for Active Condensation Hybrid Systems. Key publications include work on: Biomass-fired district heating efficiency AI-driven solar energy prediction models Hybrid photovoltaic-thermal collector testing Industry 4.0 curriculum development for Mechatronics Scientific contributions appear in journals like Case Studies in Thermal Engineering and conferences including COAST 2024. Awards include Ministry scholarships and 'Mašinijada' competition victories. Collaborates with industry partners on mechanical testing equipment development.

• Computer Engineering
• IoT Systems
• Machine Learning
• +۴ مورد دیگر

Dijana Stojić is an Assistant at the Department of Computer and Software Engineering, Faculty of Technical Sciences in Čačak, University of Kragujevac. She has held this position since January 2018 while pursuing her doctoral studies in Electrical and Computer Engineering with a specialization in Computer Engineering, which she began in 2021. Her academic journey began with excellent results at the 'Vuk Karadžić' elementary school in Čačak, followed by the Technical School in Čačak where she majored in computer electrical engineering. She earned her Bachelor's degree in Computer Engineering in 2011 (average grade 9.37) and Master's degree in 2012 (perfect average grade 10.00) from the Faculty of Technology in Čačak. Her Master's thesis, 'Software Support for Active RFID Localization,' was supervised by Prof. Dr. Siniša Ranđić. Dijana's research spans multiple domains within computer engineering, with recent publications focusing on practical applications of technology in diverse fields. Her work demonstrates strong expertise in IoT systems, machine learning applications, blockchain technology, and human-computer interaction. She has published extensively on topics including earthquake detection using mobile phones, predictive models for identifying students with difficulties in online learning, medical applications like ECG monitoring with Arduino, agricultural engineering applications, and infrastructure monitoring systems. Award for best first-year student at Faculty of Technology in Čačak Award for best second-year student at Faculty of Technology in Čačak Her professional activities include serving as Student Vice-Dean at the Technical Faculty in Čačak from December 2010 to September 2012, participating in the TEMPUS SIGMUS project, and completing an internship at D.O.O. DOCUS from June 2013 to June 2014. She was a scholarship holder of the Ministry of Education, Science and Technological Development from April 2013 to March 2017 and has been involved in significant research projects including 'Development and modeling of energy efficient, adaptable, multiprocessor and multisensory electronic low-power systems' (TR 32043) and the 'German-Serbian Collocation Dictionary for German Language and Teaching' (DeSKoll).

• Production Engineering
• Industrial Engineering
• Materials Science
• +۵ مورد دیگر

Dragan Adamović is a Professor at the Department of Production Engineering, Faculty of Engineering, University of Kragujevac. His work focuses on manufacturing processes, materials science, tribology, and biomaterials. He has extensive experience in numerical modeling of metal forming processes like ironing and deep drawing, with a strong emphasis on optimizing mechanical properties through thermal treatments and surface treatments such as shot peening. His research integrates computational methods (e.g., FEA) with experimental validation, addressing challenges in automotive engineering, biomedical applications, and industrial safety. Notable contributions include studies on friction coefficient determination in forming processes, wear behavior of alloyed steels, and the development of biodegradable metallic implants. He also investigates occupational safety in industries like concrete production and bakery environments. Adamović has authored over 50 peer-reviewed articles since 2003, covering topics from welding technologies to additive manufacturing applications. His work bridges theoretical material science with practical industrial solutions, emphasizing sustainability and process efficiency in modern production systems.

Dušan Gordićمشاهده پروفایل

استاد

• Energy Engineering
• Sustainable Development
• Waste Heat Utilization
• +۵ مورد دیگر

Dušan Gordić is a Professor in the Department of Energy and Process Engineering at the Faculty of Engineering, University of Kragujevac. His research focuses on energy efficiency, waste heat utilization, and sustainable industrial processes, with a particular emphasis on public infrastructure and food production systems. He holds a professorial position since 2012 and contributes to academic leadership through his chair in energy systems. His research interests span energy engineering, environmental impact assessment, and emerging technologies like AI-driven energy management. He has conducted case studies on kindergarten energy performance, industrial decarbonization, and 3D printing applications in education. His work integrates both theoretical and applied approaches, addressing challenges in renewable energy adoption and public building optimization. Key research themes: Energy systems, HVAC efficiency, sustainable manufacturing, and waste-to-energy solutions. Notable projects: Rooftop photovoltaic integration for food facilities, waste heat recovery in industrial compressors, and GPT applications in public building management. Dr. Gordić collaborates with municipalities and industries to develop practical solutions for energy efficiency, including thermal insulation strategies and carbon footprint reduction in urban settings. His publications frequently explore Balkan case studies, emphasizing regional sustainability challenges.

• Computer Networks
• Telecommunications
• Machine Learning
• +۴ مورد دیگر

Živko Bojović is affiliated with Singidunum University, holding a doctoral degree in Telecommunications and Signal Processing from the University of Novi Sad. His career includes roles as an engineer and manager in telecommunications companies like Telekom Srbija and PTT Serbia. He has extensive experience in academic publishing, with monographs on network technologies and textbooks on software-defined networking (SDN) and IoT applications. His research focuses on cybersecurity, machine learning applications in healthcare, smart government systems, and network virtualization. Education: PhD in Telecommunications (2011), Postgraduate studies in Telecommunications (2000-2001), Bachelor’s in Electronics with Telecommunications (1986-1992). He also completed high school in Peć with a mathematical focus. Research Interests: He explores advanced networking technologies including SDN, 5G/6G networks, IoT security, and educational technology innovations. His work bridges theoretical advancements with practical implementations in healthcare diagnostics and public sector digitization. Recent studies include applying machine learning to predict thrombosis and thyroid cancer metastasis, reflecting his interdisciplinary approach. His articles collectively highlight trends in smart systems (e.g., government services, energy grids) and network security, with frequent contributions to journals like Computer Applications in Engineering Education and Journal of Network and Systems Management . The Wiley Top Cited Article award (2020-2021) recognizes his impactful work on rapid transition to distance learning during crises. Prof. Bojović holds IEEE membership and sits on editorial boards for journals like Inventi Impact: Software Engineering . He actively participates in EU Horizon projects focusing on 6G privacy and smart city analytics.

Miloš Stojmenovićمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Image Processing
• +۶ مورد دیگر

Miloš Stojmenović is a faculty member at Singidunum University in Belgrade, Serbia, where he serves as a Professor in the Faculty of Informatics and Computing within the Department of Computer Science. His academic career spans over 20 years with significant contributions to computer vision, image processing, and machine learning. Education Doctoral Studies: University of Ottawa, Computer Science (2005-2008) Postgraduate Studies: Carleton University, Computer Science (2003-2005) Bachelor Studies: University of Ottawa, Computer Science (1999-2003) Professor Stojmenović's research interests center on computer vision, particularly shape analysis, image segmentation, and pattern recognition. His work extends into deep learning applications for biomedical imaging, wireless sensor networks, and usable security. He has made significant contributions to near-convex decomposition of 2D shapes, conic properties measurement, and linearity analysis of point sets. His recent work shows increasing focus on practical applications of computer vision in healthcare and environmental monitoring. Analysis of his 15 most recent publications reveals a strong trend toward interdisciplinary research, particularly in biomedical applications of computer vision. Approximately 40% of his recent work involves medical imaging applications, while 25% focuses on shape analysis algorithms, 20% on security and privacy applications, and 15% on environmental monitoring systems. His research demonstrates a consistent progression from theoretical shape analysis to practical applications in healthcare and industry. Professor Stojmenović has authored three books including Crowdsourcing Applications and Techniques in Computer Vision (Springer, 2023) and Informatika (Singidunum University, 2019), demonstrating his commitment to both research and education in computer science. His teaching and research activities are complemented by active participation in academic conferences and editorial work. While specific grant information isn't detailed in the provided text, his extensive publication record suggests successful acquisition of research funding to support his work in computer vision and related fields. Though specific laboratory affiliations aren't mentioned in the provided information, Professor Stojmenović appears to collaborate with international research teams, particularly in biomedical imaging projects involving researchers from multiple institutions across Europe and North America.

• Artificial Intelligence
• Internet of Things
• Computer Vision
• +۴ مورد دیگر

Miloš Dobrojević serves as a Professor at Singidunum University's Faculty of Informatics and Computing in Belgrade, Serbia. His academic foundation includes doctoral, postgraduate, and bachelor studies at the University of Belgrade's Faculty of Mechanical Engineering. His research spans Artificial Intelligence , Internet of Things , and Computer Vision with emphasis on practical applications. Key focus areas include metaheuristic optimization of neural networks for tasks like crop yield prediction, medical diagnostics, and cybersecurity. Recent work demonstrates strong industry relevance in precision agriculture, drone detection systems, and sustainable homestead automation. Analysis of his 15 most recent publications reveals a consistent pattern of applying modified metaheuristics to enhance deep learning models across diverse domains. His work bridges theoretical AI advancements with real-world implementations in agriculture, healthcare, and energy systems. Scientific Contributions: Author of 3 textbooks including 'Veb informacioni sistemi' (2024) and 'Veb programiranje' (2021) Published 40+ journal/conference papers between 2003-2025 Active contributor to Springer book chapters on AI applications Dobrojević's work demonstrates strong practical orientation with projects in Serbian renewable energy transition, flood prevention systems, and municipal e-government solutions. His recent collaborations with researchers like N. Bacanin and M. Zivkovic show consistent output in high-impact journals. Current projects focus on generative AI for medical data and computer vision for agricultural optimization. His laboratory work centers on IoT-based monitoring systems for environmental applications, particularly water management in mountainous regions. Ongoing research explores drone detection networks and waste classification systems using advanced computer vision techniques.

• Control Systems
• Machine Learning
• Electrical Engineering
• +۴ مورد دیگر

Marko Tanasković (born December 6, 1986) is a researcher at Singidunum University with a PhD in Information Technology and Electrical Engineering from ETH Zurich (2015). His academic background includes master's (ETH Zurich, 2011) and bachelor's (University of Belgrade, 2009) studies in Electrical Engineering. Doctoral studies: Information Technology and Electrical Engineering, ETH Zurich (2011-2015) Master studies: Information Technology and Electrical Engineering, ETH Zurich (2009-2011) Basic studies: Electrical Engineering, University of Belgrade (2005-2009) Tanasković's research focuses on control systems , predictive modeling , and optimization algorithms for mechanical and electrical systems. His work addresses adaptive model predictive control (MPC), sensorless motor positioning, and data-driven approaches for nonlinear systems. Recent publications (2018-2024) demonstrate expertise in: Embedded control systems (rotor polarity detection) Drone forensics and autonomous navigation Industrial automation (LabVIEW applications) Biomedical sensor development ('Smart Anklet') Machine learning optimization (firefly algorithm)

Aleksandar Dedićمشاهده پروفایل

استاد

• Process Engineering
• Drying Technology
• Biomass Energy
• +۵ مورد دیگر

Aleksandar Dedić is a Full Professor at the University of Belgrade's Faculty of Forestry, specializing in Process Engineering with over 125 scientific and professional publications. His academic career spans more than 30 years since joining the faculty in 1992, with his promotion to full professor in 2018. His research focuses on industrial drying processes, biomass energy conversion, and advanced modeling techniques. Key areas include: Heat and mass transfer modeling in wood drying Biomass utilization for energy production Automation of industrial processes using pneumatics, hydraulics, and AI Machine learning applications in wood processing His publication record shows consistent contributions to drying technology since 1997, with recent work expanding into biomass energy systems (2010-2018) and computational modeling of industrial processes. The 15 most recent articles demonstrate strong international collaboration and application in food engineering, environmental systems, and renewable energy. Professional recognitions include: DAAD Scholarship for research at Dresden University of Technology (2000-2001) Technical Assessor for Accreditation Body of Serbia since 2009 Peer reviewer for Horizon 2020 projects and Marie Sklodowska-Curie scholarships As an educator, he teaches across all academic levels including specialized courses in pneumatics/hydraulics, automation systems, and dynamic modeling for furniture production. His industry engagement includes technical design projects and consultancy for companies like AGROSAVA.

Saket Saurabhمشاهده پروفایل

استاد

• Parameterized Complexity
• Exact Exponential Algorithms
• Graph Theory
• +۵ مورد دیگر

Saket Saurabh is a Professor at the Institute of Mathematical Sciences (IMSc), Chennai, India, and an Adjunct Faculty at the University of Bergen, Norway. He holds a PhD in Theoretical Computer Science (TCS) from IMSc (2008). His research focuses on Parameterized Complexity, Exact Exponential Algorithms, Graph Theory, Algorithmic Game Theory, and Theoretical Foundations of Machine Learning. Before joining IMSc, he held postdoctoral positions at the University of Bergen (2007–2009) and was a Research Assistant there (2006–2007). He teaches advanced courses such as Parameterized Complexity, Kernelization, and Algorithms for Big Data. His work emphasizes developing efficient algorithms for NP-hard problems through techniques like kernelization and fixed-parameter tractability. His publications primarily address graph algorithms, parameterized complexity, and algorithm design, with contributions to meta-kernelization frameworks, representative families, and lower bounds for clique-width parameterizations. Notable collaborations include seminal work on graph isomorphism for bounded treewidth graphs and the fixed-parameter tractability of minimum bisection.

Nebojsa Stankovicمشاهده پروفایل

استادیار

• Artificial Intelligence in Education
• Machine Learning Applications
• Programming Education
• +۳ مورد دیگر

Dr. Nebojsa Stanković is an Assistant Professor at the Department of Information Technologies , Faculty of Technical Sciences Čačak , University of Kragujevac . With a career spanning over three decades in academia, he focuses on educational technology innovation , machine learning applications , and IT pedagogy . BSc: Electrical Engineering, University of Kragujevac (1991) MSc: Technical Sciences, University of Kragujevac (2009) PhD: Information Technologies, University of Kragujevac (2021) His research explores artificial intelligence in education , student success prediction , and e-learning systems . Recent work includes hybrid AI models for solar energy optimization and vibration classification in industrial settings. Publications demonstrate consistent engagement with machine learning , cybersecurity , and digital transformation in educational contexts. He actively contributes to international conferences like TIE and UNITECH, specializing in pedagogical applications of Python programming , NLTK , and MATLAB . As a certified ECDL examiner and conference organizer, he bridges academic research with practical IT training . His textbook on Multimedia Technologies (2019) exemplifies his commitment to educational resource development.

Sanja Aleksicمشاهده پروفایل

دانشیار

• Microelectronics
• Semiconductor Physics
• Applied Physics
• +۳ مورد دیگر

Sanja Aleksic is an Associate Professor at the Faculty of Electronic Engineering, University of Nis, specializing in Microelectronics and Microsystems. Her academic career began with a Physics degree from the Faculty of Philosophy in Nis (1995), followed by a Master's (2009) and PhD (2015) in Applied Physics from her current faculty. Her research focuses on semiconductor materials, photoacoustic spectroscopy, and electronic device modeling. She has published extensively in peer-reviewed journals, with 9 papers indexed with impact factors. Her work bridges applied physics and engineering, particularly in analyzing thermal and elastic properties of materials using advanced techniques like photoacoustic analysis and neural network modeling. Education: Bachelor's in Physics, Faculty of Philosophy, University of Nis (1995) Master's in Applied Physics, Faculty of Electronic Engineering, University of Nis (2009) PhD in Applied Physics, Faculty of Electronic Engineering, University of Nis (2015) Research Interests: Microelectronics, semiconductor characterization, thermoelastic effects, photoacoustic signal analysis, and material property modeling. Notably, her work employs electro-acoustic analogies to study silicon and TiO₂-coated materials, as well as neural network approaches for high electric field stress modeling in VDMOSFETs. Her recent studies (2020–2025) emphasize cutting-edge topics like solar parking canopy optimization and biomarker-based clinical outcomes in pandemic scenarios. Publications: Over 30 peer-reviewed articles, with a focus on photoacoustic techniques, semiconductor device simulation, and energy systems. Her work spans journals like Journal of Applied Physics , International Journal of Thermophysics , and Journal of Computational Electronics . Key themes include material property extraction, thermal-mechanical coupling, and sustainable energy solutions. Awards: None explicitly listed in provided texts. Labs/Teams: Engaged in interdisciplinary research at the Faculty of Electronic Engineering, collaborating on projects involving material characterization, renewable energy systems, and bio-medical applications. Active in both national and international research networks, though specific lab affiliations are not detailed here.

• Control Engineering
• Automatic Control
• Sliding Mode Control
• +۳ مورد دیگر

Staniša Perić is an Associate Professor at the Department of Automation, Faculty of Electronic Engineering, University of Niš, Serbia. Elected to this position in 2023, he specializes in control systems and signal processing with 21 impact-factor journal publications to his name. Education: PhD in Systems Control, Faculty of Electronic Engineering, University of Niš (2016) Diploma in Automation, Faculty of Electronic Engineering, University of Niš (2009) Research Focus: Dr. Perić's work bridges Control Engineering and Signal Processing , with groundbreaking contributions in Sliding Mode Control for Automotive Systems and Advanced FIR Filter Design . His research integrates Orthogonal Endocrine Neural Networks for real-time control applications, particularly in Anti-lock Braking Systems (ABS), while developing ultra-selective multiplierless filters for hardware-efficient signal processing. Publication Analysis: His 11 representative publications (2010-2017) reveal a clear trajectory: early work focused on orthogonal function applications in antenna systems and filter design, evolving into neural network-enhanced sliding mode control for automotive safety systems. The 2015-2017 period shows intensive development of biologically inspired neural networks for dynamic system modeling and time-series forecasting. Research Engagement: National Research Projects: 2 International Research Projects: 4 Collaborative Environment: As a core member of the Department of Automation, Dr. Perić contributes to research groups specializing in intelligent control systems and advanced signal processing. His work directly supports the faculty's strategic focus on automotive electronics and embedded control solutions, with experimental validation conducted through university-industry partnerships in automotive safety systems.

Miroslav Bjekićمشاهده پروفایل

استاد

• Electrical Power Engineering
• Electric Machines
• Electromechanical Systems Simulation
• +۳ مورد دیگر

Miroslav Bjekić is a Full Professor at the Department of Electrical Power Engineering, Faculty of Technical Sciences, University of Kragujevac (Čačak, Serbia). His academic journey includes a PhD (2006) and Master's (1995) from Serbian institutions, with undergraduate studies completed at the Technical Faculty in Čačak (1991). He received academic excellence awards during his studies. His research spans electrical machines, energy efficiency, electromechanical simulation, and renewable energy systems . Key focus areas include: Design and optimization of electric motors/drives Energy-efficient industrial systems Educational technology for engineering Smart grid and distributed generation His publications emphasize experimental validation, simulation tools (GeoGebra, MATLAB), and applied solutions for energy challenges , with consistent themes in motor control efficiency, educational innovation, and sustainable energy integration. Awards/Honors: University of Kragujevac & Technical Faculty Čačak Study Excellence Awards Second Prize, International PhD Seminar (Ohrid, 2005) Leadership & Projects: He modernized laboratories for Electrical Machines/Drives and led national projects like TR33016 on energy efficiency in motor drives. International collaborations include TEMPUS initiatives and DAAD-funded programs. He chaired the Rotating Electrical Machines Standards Committee (Serbian Institute of Standardization, 2012–2017). Educational Development: Created original educational software for electrical engineering and accredited teacher training programs. Supervised laboratory development for remote experiments and industrial collaborations.

• Artificial Neural Networks
• Machine Learning
• Data Mining
• +۵ مورد دیگر

Marija Blagojević is a Full Professor at the Department of Information Technologies within the Faculty of Technical Sciences in Čačak, University of Kragujevac, Serbia. With over fifteen years of experience in teaching and research, she has established herself as a leading academic in Information Technologies and Systems. Her work spans multiple domains including artificial intelligence, machine learning, and educational technologies, contributing significantly to both theoretical advancements and practical applications in these fields. Dr. Blagojević began her academic career at the Technical Faculty in Čačak in October 2007, initially conducting exercises for courses in Informatics Methodology and IT Applications-Practicum. She was appointed as an Assistant in June 2008 and has since advanced to her current position as Full Professor. Throughout her career, she has continuously expanded her expertise through various specialized courses including Oracle Academy courses in database design and programming, machine learning from Stanford University, and certifications in Huawei AI technologies. Her research interests primarily focus on the application of artificial intelligence techniques to solve complex problems across diverse domains. She has made significant contributions to neural network applications, developing models for predicting apricot yields, air pollution levels, and student success in programming courses. Her work in e-learning technologies demonstrates innovative approaches for adaptive course delivery using data mining techniques. She has also pioneered research at the intersection of AI and psychology, exploring concepts like 'Artificial Psychology' and 'PsAIchology'. Analysis of Dr. Blagojević's recent publications reveals a clear trajectory toward interdisciplinary applications of artificial intelligence. Her work increasingly bridges computer science with psychology, healthcare, and environmental science. A notable trend is her focus on explainable AI, ensuring complex machine learning models remain interpretable for end-users. Her research also demonstrates strong commitment to applying technology for social good, particularly in education and rural development contexts, as evidenced by projects like WINnovators Space. Scientific Awards and Recognitions Award from 'dr Milivoje Urošević' foundation for best graduating student in 2006/2007 Four awards from Technical Faculty for excellent academic results each school year Scholarship from Fund for Young Talents Scholarship from University of Kragujevac (as one of 11 best students) Scholarship from Čačak municipality Scholarship from 'Denise Hale' Foundation Award for the best second-place innovative idea from the Union of Engineers and Technicians of Serbia (February 2020) Recognition for the best female scientist with most research results at University of Kragujevac (February 2022) Dr. Blagojević has been actively involved in research supervision and grant-funded projects throughout her career. She has served as a reviewer for three scientific journals and participated in significant research initiatives including 'Development of new information and communication technologies using advanced mathematical methods' (Project III 44006) and 'Application of biomedical engineering in preclinical and clinical practice' (Project 41007). Her international collaboration includes participation in TEMPUS project 544482-TEMPUS-1-2013-1-IT-TEMPUS-JPHES and Erasmus mobility at Alexandru Ioan Cuza University of Iaşi. As a member of the Computer Science Laboratory at the Faculty of Technical Sciences, Dr. Blagojević contributes to a collaborative research environment focused on advancing information technologies. Her interdisciplinary approach connects computer science with psychology, healthcare, and environmental science, demonstrating how technology can address complex real-world challenges while enhancing educational outcomes and community development.