The Third Serbian International Conference on Applied Artificial Intelligence (SICAAI).​

It is our great pleasure to invite you to the Third Serbian International Conference on Applied Artificial Intelligence (SICAAI).

The Conference will be held in Kragujevac, Serbia, on May 23rd – 24th, 2024. Kragujevac is the fourth largest city in Serbia and the administrative center of the Šumadija District. Today, the city represents a modern industrial and commercial center of the country. It enjoys the status of an education center housing the University of Kragujevac, one of the region’s largest higher education institutions.


Kragujevac has historic significance as the first capital of modern Serbia. It bears an industrial tradition of two centuries and an openness to adopt new ideas and businesses. Kragujevac is also a home of experienced and industrious workers, educated young people, rich cultural heritage, and optimism, above all.

Today, Kragujevac is a leader of economic development of Šumadija district and a community of successful people. Kragujevac supports and represents the central hub of industrial and economic development, administrative, educational, health, cultural and sport activities in the Šumadija district.


AAI2024 will provide an exceptional Serbian and international forum to share the state-of-the-art research knowledge and results on the innovative theories, methodology and applications of artificial intelligence and its sub-domain like deep learning, machine learning in different areas such as medicine, economy, education, law, smart city, government, industry etc. AAI2024 welcomes abstracts in all sub-areas of artificial intelligence. Moreover, the conference aims to provide a platform for researchers and practitioners for both academia and industry to share the information about cutting-edge developments in the field of artificial intelligence.

This conference intends to emphasize a research connection, therefore, the authors are invited to highlight the advantages of information technologies in various domains. Innovative research ideas on how to solve problems using artificial intelligence, both in R&D and real-time applications are welcome. Papers describing the advanced prototypes, systems, methodologies, tools and techniques and general survey papers, which indicates future directions, are also encouraged.

Conference Venue

Rectorate of the University of Kragujevac, address: Liceja Kneževine Srbije A1, ground floor.

Important Dates

  • Submission of scientific contributions (abstracts 1 page or full paper minimum 6 pages) deadline: April 10th, 2024 EXTENDED April 20th, 2024
  • Notification of acceptance deadline: April 30th, 2024
  • Early bird registration fee payment deadline: May 10th, 2024


  • MS1: AI in Energy and Environmental Science
    Organizers: Boban Stojanović, Faculty of Science, University of Kragujevac, Kragujevac, Serbia; Nikola Milivojević, Water Institute Jaroslav Cerni, Belgrade, Serbia; Milan Stojković, The Institute for Artificial Intelligence R&D of Serbia, Novi Sad, Serbia.
  • MS2: AI & IOT for Smart Industry
    Organizers: Milovan Medojević, The Institute for Artificial Intelligence R&D of Serbia, EnergyPulse DOO, Novi Sad, Serbia.
  • MS3: AI in Computer Vision and Remote Sensing
    Organizers: Marko Pavlović, The Institute for Artificial Intelligence R&D of Serbia, Novi Sad, Serbia; Slobodan Ilić, The Institute for Artificial Intelligence R&D of Serbia, Novi Sad, Serbia; Dubravko Ćulibrk, The Institute for Artificial Intelligence R&D of Serbia, Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia.
  • MS4: AI and Social Wellbeing
    Organizers: Ljubiša Bojić, The Institute for Artificial Intelligence R&D of Serbia, Novi Sad, Serbia; Milan Čabarkapa, Faculty of Engineering, University of Kragujevac, Serbia; Igor Pantić, Faculty of Medicine, University of Belgrade, Serbia.
  • MS5: Future Of Workforce
    Organizers: Jelena Ćulibrk, The Institute for Artificial Intelligence R&D of Serbia, Novi Sad, Serbia; Bojana Jokanović, The Faculty of Technical Sciences, University of Novi Sad, Serbia; Dunja Bošković, The Faculty of Technical Sciences, University of Novi Sad, Serbia.
  • MS6: Delivering on The Promise of AI to Improve Health Outcomes
    Organizers: Tijana Geroski, Faculty of Engineering, University of Kragujevac, Serbia; Nenad Filipović, Faculty of Engineering, University of Kragujevac, Serbia.
  • MS7: Heritage Mining: Theory and Examples
    Organizers: Veljko Milutinović, Guest Lecturer and Former Faculty, Purdue University, USA Adjunct Professor, University of Indiana in Bloomington, USA, Adjunct Professor, Technical University of Graz, Austria Visiting Professor, University of Kragujevac Visiting Professor, University of Belgrade Visiting Professor, University of Montenegro.

Plenary Speakers

  • Prof. Amir A. Amini – University of Louisville, Louisville, Kentucky, USA; Title: 4D Flow MRI: Efficient Acquisition and Deep Learning Strategies for Assessment of Hemodynamics.
  • Prof. Borko Furht – Florida Atlantic University, Boca Raton, Florida, USA; Title: Successful Engineering Education Requires Applied Industry Projects.
  • Prof. Themis Exarchos – Ionian University, Corfu, Greece; Title: Using Explainable AI (xAI) to predict the conversion from Mild Cognitive Impairment to Alzheimer’s Disease.
  • Prof. Emil Jovanov – University of Alabama at Huntsville, USA; Title: Integrating AI and IoT for Personalized Healthcare.
  • Prof. Dubravko Ćulibrk – University of Novi Sad, Novi Sad, Serbia; Title: AI-disrupted Medicine and How to Apply it in Serbia.
  • Prof. Israel Koren – University of Massachusetts in Amherst, USA; Title: Protecting Vehicle Privacy against AI-Enhanced Attackers in Intelligent Transportation Systems.
  • Prof. Zoran Obradović – Temple University, Philadelphia, Pennsylvania, USA; Title: Characterizing Disruptive Events by Modeling Dynamics in Multiplex Networks.

Prof. Amir A. Amini

Professor and Endowed Chair in Bioimaging Director
Medical Imaging Laboratory
University of Louisville, Louisville, Kentucky, USA

Title: 4D Flow MRI: Efficient Acquisition and Deep Learning Strategies for Assessment of Hemodynamics


4D Flow MRI is a non-invasive method for imaging flow velocities, which provide time-resolved three directional blood flow velocities in a 3D volume. This provides an unprecedented opportunity for detailed mapping of hemodynamic biomarkers. However, the data collection is time consuming, and data usage is hampered by low resolution, noise, and artifacts. In this talk we will describe a unique in-vitro flow testbed to study stenotic flows which we have developed over the last few years which allows for validation of MR-based flow and velocity measurements. We will also describe k-space spiral read-out sequences and deep learning-based image reconstruction which lead to efficient collection of data. Finally, convolutional neural networks will be described for assessment of hemodynamics including intravascular pressures.


Prof. Amir Amini is Endowed Chair in Bioimaging and Professor of Electrical and Computer Engineering at the University of Louisville. His prior faculty appointments were at Yale and Washington University in St. Louis. He has had leadership roles in organization of numerous conferences in medical imaging and image analysis as scientific program committee member, scientific program chair, as well as conference chair. He was symposium co-chair of SPIE Medical Imaging in 2007 and the IEEE International Symposium in Biomedical Imaging in 2018. He currently serves as Associate Editor for IEEE Transactions on Medical Imaging, IEEE Trans. On Biomedical Engineering, IEEE Reviews in Biomedical Engineering, and Computerized Medical Imaging and Graphics. Dr. Amini served as Vice President for Publications for the IEEE Engineering in Medicine and Biology Society in 2020-21. Under funding from the NIH, private foundations, and industry, his laboratory conducts research in development and application of MRI methods for motion and flow measurement and development of biomedical image processing and analysis methods based on Deep Learning to cardiovascular imaging, computer aided diagnosis, and radiation therapy of lung cancer. He received the University of Massachusetts at Amherst College of Engineering Distinguished Alumni Award in 2020. He was elected a Fellow of the IEEE in 2007, Fellow of the American Institute for Medical and Biological Engineering in 2017, and Fellow of SPIE, the International Society of Optics and Photonics, in 2018.

Prof. Borko Furht

Professor of Computer Science and Engineering
Director of the NSF Industry/University Cooperative Research Center
Florida Atlantic University, Boca Raton, Florida, USA

Title: Successful Engineering Education Requires Applied Industry Projects


In this talk we present non-traditional, radical university arrangements that we implemented in the College of Engineering and Computer Science at FAU to create an Entrepreneurial University. To produce successful engineers our thesis is that they should be involved in the applied industry projects. This “re-conceptualization” involves non-traditional, often radical university arrangements. We will present our entrepreneurial, research, and innovation strategy The backbone of our new concept is the NSF-sponsored Industry/University Cooperative Research Center for Advanced Knowledge Enablement with 45 industry members, a more than 50 applied research projects. The university has created a Research Park with more than 20 high-tech companies and an incubator with more than 30 start-up companies. We also present several successful projects by our faculty and students that resulted in creating start-up companies, patent inventions, and successful products and services.


Prof. Borko Furht is a professor in the Department of Electrical & Computer Engineering and Computer Science (CEECS) at Florida Atlantic University (FAU) in Boca Raton, Florida. He is also Director of the NSF-sponsored Industry/University Cooperative Research Center on Advanced Knowledge Enablement at FAU. From 2010 to 2013, he served as Chair of the CEECS Department, and from 2002-2009 as Chair of Computer Science and Engineering Department at FAU. From 2006-2008, he was also Senior Assistant Vice President for Engineering and Technology at FAU. Before joining FAU, he was a vice president of research and a senior director of development at Modcomp (Ft. Lauderdale), a computer company of Daimler Benz, Germany, a professor at University of Miami in Coral Gables, Florida, and a senior researcher in the Institute Boris Kidric-Vinca, Yugoslavia. Professor Furht received Ph.D. degree in electrical and computer engineering from the University of Belgrade. His current research is in multimedia systems, multimedia big data, video coding and compression, 3D video and image systems, wireless multimedia, cloud computing, and social networks. He has been presently Principal Investigator and Co-PI of several multiyear, multimillion dollar projects. He received total funding of $15 million from government agencies including NSF, NIH, Department of Navy, DoD, and NASA, and private industries including IBM, Google, Apple, LexisNexis, Motorola, Emerson, and others. He is the author of numerous books and articles in the areas of multimedia, computer architecture, real-time computing, and operating systems. He is a founder and editor-in-chief of the Journal of Multimedia Tools and Applications (Springer), and he recently co-founded Journal of Big Data (Springer). He has received several technical and publishing awards, and has consulted for many high-tech companies including IBM, Hewlett-Packard, Xerox, General Electric, JPL, NASA, Honeywell, and RCA. He was named as 2013 Researcher of the Year at FAU. He has also served as a consultant to various colleges and universities. He has given many invited talks, keynote lectures, seminars, and tutorials. He served as Chairman and Director on the Board of Directors of several high-tech companies and as an expert witness for Cisco, Qualcomm, Adobe, and Bell Canada. He is presently Special Advisor for Technology and Innovations for the United Nations Global Millennium Development Foundation.

Prof. Themis Exarchos

Associate Professor of Data Modeling and Decision Support Systems in the Department of Informatics, Ionian University, Corfu, Greece and the Director of the Technology Transfer Office of Ionian University

Title: Using Explainable AI (xAI) to predict the conversion from Mild Cognitive Impairment to Alzheimer’s Disease


Mild Cognitive Impairment (MCI) is a cognitive state frequently observed in older adults, characterized by significant alterations in memory, thinking, and reasoning abilities that extend beyond typical cognitive decline. It is worth noting that around 10%-15% of individuals with MCI are projected to develop Alzheimer’s disease, effectively positioning MCI as an early stage of Alzheimer’s. We present a novel approach involving the utilization of eXtreme Gradient Boosting to predict the onset of Alzheimer’s disease during the MCI stage. The methodology entails utilizing data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Through the analysis of longitudinal data spanning from the baseline visit to the 12-month follow-up, a predictive model is constructed. The proposed model calculates, over a 36-month period, the likelihood of progression from MCI to Alzheimer’s disease, achieving an accuracy rate of 85%. To further enhance the precision of the model, the study implements feature selection using the recursive feature elimination technique. Additionally, Shapley method is employed to provide insights into the model’s decision-making process, thereby augmenting the transparency and interpretability of the predictions.


Prof. Themis Exarchos holds an Engineering Diploma, from the Dept. of Computer Engineering and Informatics of University of Patras (2003) and a PhD in Medical Informatics from the Medical School of the University of Ioannina (2009). He has more than 200 publications in journals, conference proceedings and book chapters. He has worked in many research and development projects, funded by EU and other bodies. He is an Associate Professor of Data Modeling and Decision Support Systems in the Dept. of Informatics, Ionian University, Corfu, Greece and the Director of the Technology Transfer Office of Ionian University. He is also visiting Professor in the Faculty of Engineering, University of Kragujevac.

Prof. Emil Jovanov

Professor in the Electrical and Computer Engineering Department at the University of Alabama in Huntsville, USA

Title: Integrating AI and IoT for Personalized Healthcare


Traditional healthcare is going through a massive change from reactive, disease focused, to predictive, preventive, personalized, and participatory (P4) healthcare. Massive deployment of IoT devices, both wearable and embedded in our environment, facilitates collection of continuous streams of health-related big data. This data, when processed by AI algorithms, converts big data into smart data and enables the prediction of health trends, early detection of potential health issues, and actionable insights to both users and healthcare providers. This paper outlines current opportunities and challenges, and addresses the technical, ethical, and privacy challenges. As examples, we present health monitoring using standard video cameras, WiFi signal based deep learning network for human activity recognition during activities of daily living, and embedded on-sensor AI models for detection of abnormal heart activity.


Prof. Emil Jovanov is Professor in the Electrical and Computer Engineering Department at the University of Alabama in Huntsville. He received his Dipl. Ing. and M.S. degree in Electrical Engineering, and PhD in Computer Engineering from the University of Belgrade. He is recognized as the originator of the concept of wireless body area networks for health monitoring and he is one of the leaders in the field of wearable health monitoring. His research interests include Wearable health monitoring, IoT (Internet of Things), wireless and sensor networks, ubiquitous and mobile computing, and biomedical signal processing. Dr. Emil Jovanov is promoted to IEEE Fellow for his contributions in the development of wearable health monitoring systems, and serves as a member of IEEE EMBS Technical Committee on Wearable Biomedical Sensors and Systems, IEEE Open Access Journal of Engineering in Medicine and Biology, IEEE Journal of Biomedical and Health Informatics, and IEEE Transactions on Biomedical Circuits and Systems, and Editorial Board member of Applied Psychophysiology and Biofeedback. He published more than 230 papers, 18 book chapters, and 13 U.S. patents. He received 2014 Innovator of the Year Award.

Prof. Dubravko Ćulibrk

Director of The Institute for Artificial Intelligence Research and Development of Serbia
Professor of Information Systems, Engineering Department of Industrial Engineering and Management, University of Novi Sad, Serbia 
University Ambassador and Certified Instructor, Nvidia Deep Learning Institute

Title: AI-disrupted Medicine and How to Apply it in Serbia


In recent years, the integration of Artificial Intelligence (AI) in the field of medicine has brought about a significant transformation in healthcare delivery. This talk will delve into the disruptive potential of AI in revolutionizing various aspects of medicine, from diagnosis and treatment to personalized medicine and patient care. By leveraging machine learning algorithms, big data analytics, and advanced technologies, AI is reshaping the landscape of healthcare by enabling faster and more accurate diagnoses, optimizing treatment plans, and improving patient outcomes.
The Institute for Artificial Intelligence Research and Development of Serbia is implementing several projects in this domain and finds itself at a forefront of the effort to apply AI to transform heltcare in Serbia to the benefit of all its citizens.
This talk will explore the ethical considerations, challenges, and opportunities that arise with the widespread adoption of AI in medicine, but will also present practical limitations that need to be overcome to achieve the optimal effect of this transofmation within the Serbian ecosystem.
Join our speaker as we navigate through the exciting realm of AI-disrupted medicine and envision a future where technology plays a pivotal role in enhancing healthcare for the people of Serbia and beyond.


Prof. Dubravko Ćulibrk is a Full Professor of Information Systems Engineering at the Department of Industrial Engineering and Management of the Faculty of Technical Sciences, University of Novi Sad, Serbia and the Director of The Institute for Artificial Intelligence Research and Development of Serbia. Since his PhD days spent at the Florida Atlantic University (2003-2006), USA he has been conducting research in the domain of biologically-inspired machine learning, with a particular focus on computer vision and multimedia understanding.
He spent two years (2013-2015) as a postdoc researcher at the University of Trento, Italy with the Multimedia and Human Understanding Group, working with prof. Nicu Sebe. On his more recent sabbatical (2018-2019) he succumbed to his entrepreneurial side and manned the post of Senior Research Scientist at Tandemlaunch, a unique deep-tech startup foundry in Montreal. He is an NVIDIA Deep Learning Institute University Ambassador and a member of Serbian Entrepreneurs. His current research interests include: Neural Networks and Deep Learning, Computer Vision, Machine Learning and Data Science, Multimedia, Visual Attention, Image/Video Processing and Remote Sensing.

Prof. Israel Koren

Department of Electrical and Computer Engineering, University of Massachusetts in Amherst, USA

Title: Protecting Vehicle Privacy against AI-Enhanced Attackers in Intelligent Transportation Systems


Connected and autonomous vehicles in Intelligent Transportation Systems (ITS) rely on vehicle-to-vehicle (V2V) communication to maintain safe inter-vehicle spacing. However, such communications, which frequently update the vehicle’s position, threaten privacy by allowing an eavesdropper to potentially track vehicles through their journey.
Mix-zones, which are zones of radio silence where vehicles can change their pseudonyms and thus attempt to throw off potential trackers, have been proposed to deal with this problem. Mix-zones are most effective when placed in high traffic intersections but may cause traffic throughput reduction. We present a mix-zone placement algorithm that allows controlling the tradeoff between vehicle anonymity and a reduced traffic throughput. We then present a new traffic management algorithm within mix-zones that enhances vehicles’ privacy. We analyze the effectiveness of the proposed algorithm against a sophisticated attacker, that unlike prior publications, is making use of a powerful supervised machine learning algorithm. Finally, to deal with traffic patterns changes (with time-of-day, day-of-week, and season) we present an approach to place mix-zones dynamically to match the prevailing traffic pattern. Extensive simulations, based on New York City and Boston traffic, are presented to validate the advantages of our algorithms.


Prof. Israel Koren is a Professor Emeritus of Electrical and Computer Engineering at the University of Massachusetts, Amherst and a fellow of the IEEE. He has been a consultant to companies like IBM, Analog Devices, Intel, AMD and National Semiconductors. His research interests include Fault-Tolerant systems, cyber-physical systems, secure cryptographic devices, computer architecture and computer arithmetic. He publishes extensively and has over 300 publications in refereed journals and conferences. He is the author of the textbook “Computer Arithmetic Algorithms,” 2nd Edition, A.K. Peters, Ltd., 2002, and a co-author of the textbook “Fault Tolerant Systems,” 2nd Edition, Morgan-Kaufman, 2020.

Prof. Zoran Obradović

Laura H. Carnell Professor of Data Analytics
Data Analytics and Biomedical Informatics Center, Computer and Information Sciences Department, Statistics Department, Temple University

Title: Characterizing Disruptive Events by Modeling Dynamics in Multiplex Networks


This presentation will delve into effective machine learning-based approaches for identifying, categorizing, and forecasting disruptive weather events, even when data is limited and labels are imprecise. We’ll showcase some of our latest techniques, which address this challenge by leveraging multiplex evolving networks to jointly analyze structured and unstructured data sources. Our findings demonstrate that by utilizing deep learning and transfer learning techniques, the accuracy and efficacy of diagnostics and risk monitoring for weather events can be greatly enhanced. Specifically, integrating information from weather, geophysical, and social media sources of varying quality and resolutions can yield significant improvements in predicting and managing weather-related disruptive events.


Prof. Zoran Obradović is a Distinguished Professor and a Center director at Temple University, an Academician at the Academia Europaea (the Academy of Europe) and a Foreign Academician at the Serbian Academy of Sciences and Arts. He mentored about 50 postdoctoral fellows and Ph.D. students, many of whom have independent research careers at academic institutions (e.g. Northeastern Univ., Ohio State Univ,) and industrial research labs (e.g. Amazon, eBay, Facebook, Hitachi Big Data, IBM T.J.Watson, Microsoft, Yahoo Labs, Uber, Verizon Big Data, Spotify). Zoran is the editor-in-chief at the Big Data journal and the steering committee chair for the SIAM Data Mining conference. He is also an editorial board member at 13 journals and was the general chair, program chair, or track chair for 11 international conferences. His research interests include data science and complex networks in decision support systems addressing challenges related to big, heterogeneous, spatial and temporal data analytics motivated by applications in healthcare management, power systems, earth and social sciences. His studies were funded by AFRL, DARPA, DOE, KAUST, NIH, NSF, ONR, PA Department of Health, US Army ERDC, US Army Research Labs, and industry. He published about 450 articles and is cited more than 33,000 times (H-index 68). For more details see


  • Nenad Filipović, University of Kragujevac

Local Organization:

  • Tijana Geroski, University of Kragujevac
  • Smiljana Tomašević, University of Kragujevac
  • Aleksandra Vulović, University of Kragujevac
  • Ognjen Pavić, University of Kragujevac
  • Lazar Dašić, University of Kragujevac
  • Đorđe Dimitrijević, University of Kragujevac
  • Đorđe Ilić, University of Kragujevac
  • Jelena Živković, University of Kragujevac
  • Milica Kaplarević, University of Kragujevac
  • Milena Đorđević, University of Kragujevac
  • Marija Gačić, University of Kragujevac
  • Neda Vidanović Miletić, University of Kragujevac
  • Miloš Kojić, Serbian Academy of Sciences and Arts
  • Miloš Đuran, Serbian Academy of Sciences and Arts
  • Vesna Ranković, University of Kragujevac
  • Vladimir Ranković, University of Kragujevac

Organizing Committee​:

  • Martin Aleksandrov (TU Berlin, Germany)
  • Sandra Avila (University of Campinas (Unicamp), Brazil)
  • Christian Blum (Spanish National Research Council (CSIC), Spain)
  • Carlos Cardonha (University of Connecticut, United States)
  • Vinay Chaudhri (United States)
  • John Chinneck (Carleton University, Canada)
  • Andy Chun (City University of Hong Kong, Hong Kong)
  • Andre Augusto Cire (University of Toronto, Canada)
  • Bradley Clement (Jet Propulsion Laboratory, United States)
  • Dubravko Ćulibrk (University of Novi Sad, Serbia)
  • Veljko Milutinović (University of Kragujevac and University of Belgrade, Serbia)
  • Diane Cook (Washington State University, United States)
  • Gabriella Cortellessa (CNR-ISTC, National Research Council of Italy, Italy)
  • Lizhen Cui (Shandong University, China)
  • Akay Metin (University of Houston, USA)
  • Allen Robert (University of Southampton, UK)
  • Zoran Bosnić (University of Ljubljana, Slovenia)
  • Zlatan Car (Univeristy of Rijeka, Croatia)
  • Ciaccio Edward (Columbia University, USA)
  • Themis Exarchos (University of Ioannina, Greece)
  • Dimitrios Fotiadis (University of Ioannina, Greece)
  • Nikola Jorgovanović (University of Novi Sad, Serbia)
  • Zoran Marković (IIT, Serbia)
  • Michalopoulos George (University of Pittsburgh, USA)
  • Nikita Konstantina (National Technical University of Athens, Greece)
  • Zoran Obradović (Temple University, USA)
  • Ouzounis Christos (King’s College, UK)
  • Pattichis Constantinos (University of Cyprus, Cyprus)
  • Sheu Phillio (University of California, USA)
  • Stojanović Radovan (University of Montenegro, Montenegro)
  • Miroslav Trajanović (University of Niš, Serbia)
  • Tsiknakis Manolis (Hellenic Mediterranean University,  Greece)
  • Yang Guang-Zhong (Imperial College London, UK)
  • Zervakis Michalis (University of Crete, Greece)
  • Andre de Carvalho (University of São Paulo, Brazil)
  • Luca Di Gaspero (DPIA – University of Udine, Italy)
  • Matthew Gaston (Carnegie Mellon University, United States)
  • Carmen Gervet (Université de Montpellier, France)
  • Odd Erik Gundersen (Norwegian University of Science and Technology, Norway)
  • Koen Hindriks (Vrije Universiteit Amsterdam, Netherlands)
  • Neil Jacobstein (Singularity University, United States)
  • Binbin Jia (Southeast University, China)
  • Elias Khalil (Georgia Institute of Technology, United States)
  • Lars Kotthoff (University of Wyoming, United States)
  • Hoong Chuin Lau (Singapore Management University, Singapore)
  • Jimmy Lee (The Chinese University of Hong Kong, Hong Kong)
  • Lee Mccluskey (University of Huddersfield, United Kingdom)
  • Felipe Meneguzzi (Pontifical Catholic University of Rio Grande do Sul, Brazil)
  • Mitra Nasri (Delft University of Technology, Netherlands)
  • Barry O’Sullivan (University College Cork, Ireland)
  • Michael Orosz (University of Southern California Information Sciences Institute, United States)
  • Simon Parsons (University of Lincoln, United Kingdom)
  • Andrew Perrault (Harvard University, United States)
  • David Pynadath (University of Southern California, United States)
  • Claude-Guy Quimper (Laval University, Canada)
  • Howard Shrobe (Massachusetts Institute of Technology, United States)
  • Madhav Sigdel (University of Alabama in Huntsville, United States)
  • David Stracuzzi (Sandia National Laboratories, United States)
  • Dimitris Stripelis (University of Southern California, United States)
  • Nirmalya Thakur (University of Cincinnati, United States)
  • Kevin Tierney (Bielefeld University, Germany)
  • Michael Trick (Carnegie Mellon University, United States)
  • Pradeep Varakantham (Singapore Management University, Singapore)
  • Deng-Bao Wang (Southeast University, China)
  • Shinjae Yoo (Brookhaven National Laboratory, United States)
  • Yingqian Zhang (Eindhoven University of Technology, Netherlands)
  • Dubravko Ćulibrk (University of Novi Sad, Serbia)
  • Jovan Stojanović (Serbian AI Society, Serbia)
  • Stefan Badža (Serbian Government, Serbia)

AAI2024 Paper Publication

After the conference, invited papers presented at the conference will be published in the Applied Artificial Intelligence III- Learning and Analytics in Intelligent Systems, Springer. Only full papers (minimum 6 pages) will be accepted for publication. Deadline for submission of full papers is September 30th, 2024.