Zlatko Baracskai is a Bosnian-Hungarian experimental musician, sound programmer, and academic. He currently serves as a Senior Lecturer at the University of West of England in the Faculty of Computer Science and Creative Technologies, while also holding a Visiting Professor position at the Faculty of Music in Belgrade's Music Direction Study Program since 2020. His academic journey includes a sonology degree from the Institute of Sonology in Den Haag and a PhD in contemporary composition from the University of Birmingham (2011). Research interests span Electronic music composition Algorithmic sound processing Experimental audio technologies Neural network applications in music Asymmetrical rhythm development His publications demonstrate technical expertise in audio algorithm design and neural networks for sound manipulation, with conference papers presented at AES, ICMC, and IEEE SpliTech. Notable projects include Coca-Cola Beatbox Pavilion (2012) and De-Fuse Interactive Installation (2005).
Milan R. Dincic is an Associate Professor at the University of Niš, Faculty of Electronic Engineering, Department of Telecommunications. He holds a PhD in Metrology and Measurement Technology (2017) and a Master’s degree in Telecommunications (2012), both from the same institution. His academic career includes research on quantization techniques, signal processing, and metrology applications. Dr. Dincic’s research focuses on optimizing quantizers for measurement signals, including Gaussian and Laplacian distributions. He has contributed to adaptive quantization, lossless coding algorithms, and hybrid systems integrating codecs like ITU-T G.711. His work bridges theoretical signal processing with practical applications in telecommunications and sensor systems. His publications (26 journal articles) emphasize quantizer design, image sampling, and compression techniques. He co-authored the textbook "Sensors in Vehicles" (2014). Current projects include national/international collaborations in metrology and measurement technology. His lab focuses on advancing signal processing methodologies for precision measurement systems.
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.
Angelina Njegus is a Full Professor at Singidunum University , where she has worked since 2005. She holds a Ph.D. in Business Studies and has extensive experience in both academia and industry, including consulting for IBM. Her research focuses on machine learning algorithms , deep learning , and Big Data Analytics , with applications in pattern recognition , tourism technology , and blockchain systems . Bachelor's: Faculty of Organizational Sciences, Information Systems (1994) Master's: Faculty of Organizational Sciences, Industrial Engineering (1999) Doctoral: Faculty of Business Studies (2003) Her recent publications (2023-2025) demonstrate expertise in metaheuristic optimization for machine learning, audio-visual emotion recognition , and cryptocurrency applications in tourism . She pioneered the Virtual University platform (MTVU) and contributes to agile methodologies in software development. Collaborations include projects with researchers from IEEE, Springer, and international institutions. Research interests span AI ethics in human resources , IoT integration in tourism , and security risks in cloud computing . Her work appears in journals like Complex & Intelligent Systems and conferences including ICPR and FG 2017 . She has authored books on software design patterns and information systems in tourism .
Миона В. Андрејевић Стошовић is a full Professor at the Department of Electronics, Faculty of Electronic Engineering, University of Niš, Serbia. Appointed to her current rank in 2023, she has maintained continuous academic affiliation with the same institution since completing her education. Education: PhD in Electronics (2006, University of Niš) MSc in Electronics (2003, University of Niš) BSc in Electronics (2000, University of Niš) Her research spans electronics with specialization in signal processing , power systems , and renewable energy applications . Recent work integrates neural networks for geotechnical and hydrological modeling, demonstrating interdisciplinary innovation. She actively contributes to photovoltaic system optimization and harmonic pollution detection in power grids. Analysis of her publications reveals a strong focus on filter design (40% of publications), renewable energy systems (30%), and neural network applications (20%), with consistent contributions to high-impact electronics journals since 2014. Administrative Roles: Head of the Department of Electronics. Currently participates in 2 national research projects with no international projects listed.
Небојша Пјевалица is a Regular Professor at the Department of Computer Communications, Faculty of Technical Sciences, University of Novi Sad. He holds a BSc in Electrical Engineering (Electronics and Telecommunications) from the same faculty (1995) and an MSc in Digital Signal Processing (2000). Since 1997, he has been employed at the Faculty, starting as an assistant and advancing to his current position. His research focuses on FPGA design, embedded systems, and automotive technologies. Notable projects include work on ADC converters with phase-locked loops, arithmetic processors for Sigma-Delta modulators, and automotive communication systems. Education: BSc (1995), MSc (2000) Research interests include digital signal processing, FPGA-based embedded solutions, and advanced communication protocols. He collaborated with NASA on engineering education projects and RT-RK Institute on FPGA design. His industry projects span automotive electronics, sensor systems, and motor control applications. Grants/Projects: Multiple national science ministry projects and industry collaborations in automotive and communication sectors. Labs/Teams: Active contributor to the RT-RK Research Institute’s FPGA design initiatives.
Dr. Milan Vesković is an Assistant Professor at the Department of Computer and Software Engineering, Faculty of Technical Sciences in Čačak, University of Kragujevac, Serbia. He has been employed at the Faculty since October 2007, progressing from research associate to his current position. His academic journey includes teaching courses in mechatronics, electronics, radio systems, and electronic components and assemblies. Graduated from Faculty of Technical Sciences in Novi Sad, March 11, 2002 (Electrical Engineering and Computer Science) Master's degree from Technical Faculty in Čačak, December 2009 (Electromagnetism) PhD from Faculty of Technical Sciences in Čačak, April 2018 (Electronics) Dr. Vesković's research spans multiple domains within electrical engineering and computer science. His primary interests include the application of numerical methods in electromagnetism (particularly the method of fictitious sources) for solving electrostatic conductor problems, signal processing techniques in electronics, and the application of electronic components in education, ecology, and hardware/software development. His work demonstrates a strong interdisciplinary approach, bridging theoretical electrical engineering concepts with practical applications in computing and environmental sustainability. His recent publications reveal a growing interest in emerging technologies including nanotechnology applications, waste management systems, educational technology (particularly Micro:bit applications), and optimization algorithms for energy management. There's also a clear trend toward interdisciplinary research combining electrical engineering principles with computer science, environmental science, and educational methodologies. Dr. Vesković has served as a UNIDO Consultant for Cleaner Production (CP) Programme following his successful engagement with the "Cleaner Production 2011" project in collaboration with UNIDO and the city of Čačak. As an educator, Dr. Vesković has contributed significantly to curriculum development and teaching in electrical engineering and computer science. His research has been supported through various academic and industry collaborations, particularly in the areas of cleaner production methods and educational technology implementation. He has been actively involved in numerous international conferences and has published 59 papers in domestic and international journals and conference proceedings. His work spans multiple laboratories and research groups within the Faculty of Technical Sciences, particularly those focused on electronics, computer engineering, and interdisciplinary applications of technology in environmental and educational contexts.
Dr. Đorđe Damnjanović is an Assistant Professor at the Department of Computer and Software Engineering within the Faculty of Technical Sciences in Čačak , part of the University of Kragujevac . He specializes in digital signal processing, wavelet transform applications, and remote laboratory development for engineering education. Born: July 17, 1986, Belgrade Education: B.Sc. (2010), M.Sc. (2011), Ph.D. (2022) in Electrical and Computer Engineering His research focuses on audio signal analysis using wavelets, human-computer interaction , and remote experimentation systems. He has led projects like the NeReLa Tempus Network and contributed to blockchain consensus mechanisms . Publications span biomedical signal processing, noise reduction, and educational technology. Recent work includes 2025 studies on vehicle vibration analysis and 2024 papers on mobile earthquake detection. He teaches Signals and Systems , Digital Signal Processing , and Human-Computer Interaction at graduate and postgraduate levels.
Dr. Branko Marković serves as an Assistant Professor at the Department of Computer and Software Engineering, Faculty of Technical Sciences in Čačak, University of Kragujevac, Serbia. His academic career spans over two decades, transitioning from industry experience in telecommunications to dedicated academic work following the merger of the College of Technical Vocational Studies with the Faculty in 2020. Dr. Marković earned his Bachelor's degree from the Faculty of Electrical Engineering in Belgrade in 1992, followed by a Master's degree in 2004 and a Doctorate in 2018, all focusing on telecommunications and speech processing. His doctoral research centered on speech processing and recognition, establishing the foundation for his subsequent research trajectory. His primary research focuses on speech processing , with groundbreaking work in whispered speech recognition - a challenging domain due to reduced acoustic energy in whispered speech. His research explores sophisticated methodologies including data augmentation techniques, wavelet transformations, inverse filtering, and various feature extraction approaches. He has made significant contributions to Serbian speech database development, notably the "Phonemes_1.0" database. His work bridges theoretical research with practical applications in assistive technologies, security systems, and human-computer interaction, demonstrating consistent innovation through 2024 publications. Analysis of his publication trends reveals an evolution from fundamental techniques like Dynamic Time Warping (DTW) and Mel-Frequency Cepstral Coefficients (MFCC) toward more advanced approaches involving deep learning and multimodal systems. His recent work emphasizes data augmentation for model robustness and wavelet-based feature extraction, reflecting the field's progression toward more sophisticated machine learning methodologies while maintaining focus on the unique challenges of whispered speech. Dr. Marković teaches across multiple academic levels, covering essential computer engineering subjects including Computer Assembly and Service, Internet Technologies, Computer Networks, Distributed Software Systems, Web Programming, and Computer Systems Design. His educational contributions extend to several textbooks for vocational studies in computer engineering fields. His professional journey includes valuable industry experience at the Mihailo Pupin Institute in Belgrade and Canadian telecommunications companies Bell Sygma, Architel, and Nortel Networks, followed by founding his own company Big engineering before transitioning to academia. This industry-academia trajectory enriches his practical teaching approach and research relevance.
Aleksandar V. Jocic is an Assistant Professor at the Faculty of Electronic Engineering, University of Niš, Serbia, specializing in Metrology and Measurement Technology. He has been affiliated with the Department of Telecommunications since 2010. His academic career began with a Bachelor’s degree from the same faculty in 2007. Education: BSc in Telecommunications, Faculty of Electronic Engineering, University of Niš (2007) Research focuses on signal processing techniques such as DPCM compression, ECG signal analysis, and measurement system optimization. His work integrates statistical methods like Monte Carlo simulations and adaptive quantization algorithms. Key contributions include improving ECG signal compression efficiency, enhancing measurement accuracy in wireless systems, and developing remote monitoring systems with collision avoidance features. Publications emphasize interdisciplinary applications in telecommunications, biomedical engineering, and metrology. He collaborates on national/international projects, including the Power Box measuring module (2013). Active in conferences like SAUM and ETAI, his research bridges theoretical analysis and practical engineering solutions.
Milan Tuba is a Professor of Computer Science and Mathematics at Singidunum University in Belgrade, Serbia. He holds academic roles including former Vice Rector for International Relations at Singidunum University, Head of the Department for Mathematical Sciences at State University of Novi Pazar, and Dean of the Graduate School of Computer Science at John Naisbitt University. His expertise spans multiple institutions, including Vanderbilt University and New York University in the U.S., where he led NSF projects and founded the Microprocessor Lab and VLSI Lab. Education: He earned B.S., M.S., and Ph.D. degrees in Mathematics and Computer Science from the University of Belgrade and New York University. Research Interests: Tuba focuses on nature-inspired optimization algorithms applied to image processing, neural networks, and computer networks. His work integrates metaheuristics with machine learning, addressing challenges in healthcare, energy forecasting, and cloud computing. Recent projects include developing optimized algorithms for medical image analysis, wind energy prediction, and cloud resource scheduling. Scientific Contributions: With over 300 publications (h-index 53), he has authored/co-edited books on topics like target localization and hardware design. His awards include inclusion in Stanford University's top 2% global scientists list (2020–2024). Advising & Leadership: Tuba has mentored dozens of PhD/Master's students across universities in Serbia, Bosnia, and the U.S. He leads research groups and serves on editorial boards for journals and conferences. His labs focus on VLSI design, neural networks, and optimization. Labs/Teams: Founder of Microprocessor Lab and VLSI Lab at U.S. institutions, and currently directs AI-focused research at Singidunum University.
Prof. Dr. Vladimir Mladenović is a Full Professor at the Department of Information Technologies , Faculty of Technical Sciences in Čačak , University of Kragujevac , Serbia. A 1975 native of Paraćin, he earned his Dipl. Ing. from the Faculty of Electronic Engineering, University of Niš (2000), followed by an M.Sc. (2005) and Ph.D. in Technical Sciences (2009) from the University of Kragujevac. His career spans industry (IT Manager, Serbian Glass Factory, 2001–2004), secondary education (teacher, 2004–2009), and academia (Professor of Vocational Studies, 2009–2013) before joining the University of Kragujevac. Research Focus: Symbolic software & tools, higher integration in IT systems Signal & multimedia processing, computer vision & AI/ML 5G/IoT architectures, edge computing, data protection Modern communication systems, web & network technologies He directs the Digital Innovation Lab at his faculty and has authored over 100 peer-reviewed publications in leading journals and conferences, with a strong emphasis on cross-layer modeling, deep learning, and IoT applications. He also co-holds 18 Serbian utility patents covering smart IoT systems ranging from greenhouse monitoring to smartwatch data collection, fire detection, and pedestrian safety. Honors & Professional Standing: Licensed Technology-Transfer Engineer – WIPO & Intellectual Property Office of Serbia (2012) Member, Union of Engineers and Technicians of Serbia Teaching Portfolio: Undergraduate: Modern Software Architectures, Multimedia Systems, Data Protection Master: Modern Communication Systems, Web Programming Doctoral: Modern Network Technologies, Applied Computer Vision
Angelina Njeguš is a Full Professor at Singidunum University. She holds a B.Sc. in Information Systems from the Faculty of Organizational Sciences (FON), University of Belgrade (1994), an M.Sc. in Industrial Engineering (FON, 1999), and a Ph.D. in Virtual University System Development from the Faculty of Business Studies, Belgrade (2003). Her expertise spans Information Systems Analysis, Business Intelligence, Data Governance, and Big Data Analytics. Recently, her research focuses on Affective Computing, HCI, and pattern recognition using Deep Learning. She has published over 100 articles and authored seven books, including titles on software design patterns and information systems in tourism. Research Interests: - Advanced analytics and AI-driven systems in hospitality - Cybersecurity in cloud environments - Emotion recognition through multimodal data analysis - Agile methodologies in software development Key Collaborations: - IBM (AI and IDR consulting) - Microsoft/Oracle partnerships in certification and internship programs Her work bridges academia and industry, with contributions to: - Blockchain applications in tourism - Employee satisfaction models using ML - Digital transformation strategies in hospitality
Petar Spalević is a professor at Singidunum University within the School of Electrical Engineering and Computing. With over two decades of academic contributions from 2002 to 2025, he has established himself as a significant researcher in electrical engineering and computer science disciplines. His research expertise spans multiple interconnected domains: Wireless communications and fading channel modeling Free space optical (FSO) transmission systems Machine learning applications for healthcare diagnostics Educational technology and innovative teaching methodologies Dr. Spalević's publication trajectory reveals an evolution from foundational work in signal processing and wireless communications to more contemporary applications integrating machine learning techniques. His recent publications (2023-2025) demonstrate a strong focus on healthcare applications including Parkinson's detection from gait analysis, respiratory condition classification from audio, and ECG anomaly detection using advanced neural network architectures. He maintains parallel research in wireless communications, particularly examining FSO system performance under various atmospheric turbulence conditions. His work bridges theoretical communication engineering with practical applications across healthcare, transportation, and education sectors. This multidisciplinary approach while maintaining technical depth in core engineering principles characterizes his research philosophy.
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.
