Object detection is one of the key software components in the next generation of autonomous cars. Classical computer vision and machine learning approaches for object detection usually suffer from the slow response time. Modern algorithms and architectures based on artificial neural networks, such as YOLO (You Only Look Once) algorithm, solve this problem without precision losses. In this paper we provide the demonstration of the usage of the newest YOLOv3 algorithm for the detection of traffic participants. We have trained the network for 5 object classes (car, truck, pedestrian, traffic signs, and lights) and have demonstrated the effectiveness of the approach in the variety of the driving conditions (bright and overcast sky, snow, fog, and night).

To prevent or mitigate the negative impact of fires, spatial prediction maps of wildfires are created to identify susceptible locations and key factors that influence the occurrence of fires. This study uses artificial intelligence models, specifically machine learning (XGBoost) and deep learning (Kolmogorov-Arnold networks—KANs, and deep neural network—DNN), with data obtained from multi-sensor satellite imagery (MODIS, VIIRS, Sentinel-2, Landsat 8/9) for spatial modeling wildfires in Serbia (88,361 km²). Based on geographic information systems (GIS) and 199,598 wildfire samples, 16 quantitative variables (geomorphological, climatological, hydrological, vegetational, and anthropogenic) are presented, together with 3 synthesis maps and an integrated susceptibility map of the 3 applied models. The results show a varying percentage of Serbia’s very high vulnerability to wildfires (XGBoost = 11.5%; KAN = 14.8%; DNN = 15.2%; Ensemble = 12.7%). Among the applied models, the DNN achieved the highest predictive performance (Accuracy = 83.4%, ROC-AUC = 92.3%), followed by XGBoost and KANs, both of which also demonstrated strong predictive accuracy (ROC-AUC > 90%). These results confirm the robustness of deep and machine learning approaches for wildfire susceptibility mapping in Serbia. SHAP analysis determined that the most influential factors are elevation, air temperature, and humidity regime (precipitation, aridity, and series of consecutive dry/wet days).

The ability of perception and understanding all static and dynamic objects around vehicle in various driving and environmental conditions represent one of the main requirements for autonomous vehicles and most of Advanced Driving Assistance Systems (ADAS). Current promise to deliver safe ADAS in modern cars could be achieved by convolutional neural network (CNN). In this paper we present a software based on YOLO that is extended with a CNN for traffic sign recognition. Since real time detection is required for safe driving, YOLO network used in this paper is pre trained for detection and classification of only five objects which are separated in categories such as cars, trucks, pedestrians, traffic signs, and traffic lights. Detected traffic signs are further passed to CNN which can classify them in one of 75 categories. We demonstrate the high level of classification confidence by accurately recognition more than 99.2% of examined signs in quite diverse conditions.

Digital platforms now act as the primary environments for public discourse, where recommender systems shape visibility, emotion, and interpretation. This study introduces the Recommender Systems LLMs Playground (RecSysLLMsP), a simulation framework designed to examine how algorithmic personalization interacts with language generation to influence engagement and polarization. The research provides a reproducible and transparent environment for testing algorithmic effects on collective reasoning, which is an issue central to democratic communication. The study employs a one‑hundred‑agent simulation grounded in psychometric and demographic data from Serbian social media users. Agents interact through five stages of progressively personalized content feeds mediated by LLM‑generated posts. Quantitative metrics such as engagement intensity, network modularity, sentiment variance and qualitative linguistic validation are used to assess behavioral and structural change. Results reveal that moderate personalization maximizes engagement, while full personalization reduces diversity and amplifies both structural and affective polarization (Q = 0.22 → 0.68). LLM‑based agents successfully reproduce realistic patterns of emotional contagion and ideological clustering. The implications extend to computational social science and policy. Simulation‑based experimentation can inform ethical recommender design and algorithmic governance. Limitations concern the absence of genuine human cognition. Thus, findings indicate systemic tendencies rather than behavioral prediction. Future research should integrate real‑world datasets, multilingual testing, and policy‑driven intervention modeling to further calibrate this digital “laboratory” for exploring AI‑mediated communication.