Understanding the dynamics of tidal rivers and estuaries is critical for reliable water management. Recently, the use of Machine Learning (ML) has increased in favor of hydrologic and hydraulic models. The advantages of ML over physically based models are most evident in modeling complex and nonlinear hydrologic processes and inverse problems. This study provides a […]
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Building Individual Player Performance Profiles According to Pre-Game Expectations and Goal Difference in Soccer
Soccer player performance is influenced by multiple unpredictable factors. During a game, score changes and pre-game expectations affect the effort exerted by players. This study used GPS wearable sensors to track players’ energy expenditure in 5-min intervals, alongside recording the goal timings and the win and lose probabilities from betting sites. A mathematical model was […]
Aiming at Well-Being with Brain Implants: Any Risk of Implanting Unprecedented Vulnerabilities?
Many experimental brain-computer interfaces (BCIs) are currently being medically tested in paralyzed patients. While the new generations of implantable BCIs move rapidly ahead at trying to increase the patients’ well-being, ethical concerns about their potential effects on patients’ psychological dimensions (e.g. sense of agency and control) are growing. An important ethical concern to explore is […]
Development of computational algorithms based on digital signal processing and artificial intelligence methods with application in modern transport systems
The project is directed toward developing computational algorithms based on advanced digital signal processing techniques and artificial intelligence methods. The research will particularly focus on the computer-aided analysis of non-stationary signals using two-dimensional quadratic time-frequency distributions, where the improvement in time-frequency resolution and robustness to noise will be thoroughly studied. Moreover, adaptive algorithms for non-stationary […]
DSC Europe: Navigating Soccer Injury Prediction and More!
Arian Skoki, a Teaching Assistant at the University of Rijeka, enthusiastically reflects on the remarkable experience at the Data Science Conference Europe in Belgrade! 🚀 Immersed in a dynamic community of passionate and knowledgeable individuals, the event provided an excellent platform for Arian to connect with industry peers, fostering a valuable exchange of ideas and […]
INFCON23
INFCON23 – Scientific gathering of computer science PhD students, November 11 and 12, 2023! 🚀 An incredible showcase of 16+ top PhD researchers from the AIRI Center and invited lectures by Mirjana Kljajić Borštnar, FOV Maribor and Antonija Mandić, University North and presentations of activities at the UNIRI Doctoral School and the Prometej Fund by […]
Retweet Prediction Based on Heterogeneous Data Sources: The Combination of Text and Multilayer Network Features
Retweet prediction is an important task in the context of various problems, such as information spreading analysis, automatic fake news detection, social media monitoring, etc. In this study, we explore retweet prediction based on heterogeneous data sources. In order to classify a tweet according to the number of retweets, we combine features extracted from the […]
Research presentation in Nitra, Slovakia
On the 11th of October, 2023, prof. Lerga presented research results and activities of the AIRI Center in Nitra, Slovakia, at the Constantine the Philosopher University.
Is enhancement with brain–computer interfaces ethical? Evidence in favor of symbiotic augmentation
This book chapter clarifies the unique status of BCI enhancement as symbiotic augmentation based on three distinct characteristics, which we derive from evidence pertaining to empirical cases of implanted patients…This chapter clarifies the concept of symbiotic augmentation and shows how the symbiotic ethical framework aids the users to coherently integrate the changes to personal self-understanding, […]
Method for Automatic Estimation of Instantaneous Frequency and Group Delay in Time–Frequency Distributions with Application in EEG Seizure Signals Analysis
Instantaneous frequency (IF) is commonly used in the analysis of electroencephalogram (EEG) signals to detect oscillatory-type seizures. However, IF cannot be used to analyze seizures that appear as spikes. In this paper, we present a novel method for the automatic estimation of IF and group delay (GD) in order to detect seizures with both spike […]
