This article addresses the problem of identifying unique objects in aerial images of urban areas on the Earth’s surface, which can serve as stable landmarks for UAV navigation without GPS signals. The main contribution lies in proposing an approach to transforming the image into an object-oriented vector representation (embedding) that retains structural information about those objects. The proposed approach automatically identifies the most distinctive objects, which can serve as navigation landmarks. The study focuses on urban and suburban landscapes, where buildings are chosen as landmarks and YOLOv11 is used as the deep learning model. By employing dimensionality reduction methods, in particular PCA and t-SNE, it is demonstrated that in the proposed embedding space, buildings with atypical structural or visual characteristics differ significantly from other buildings and are easily classified as outliers, making them natural landmarks for navigation. Experimental results confirm the effectiveness and potential of the proposed approach for ensuring stable UAV navigation in scenarios where GPS may be inaccessible—the accuracy of identifying buildings designated as landmarks is twice that of ordinary buildings (Recall@1 = 0.51 vs. 0.28).
Intelligent systems for optimized object placement in medical and biological applications leverage artificial intelligence advanced data fusion techniques to enhance precision, efficiency, and patient outcomes. These systems tackle a range of issues, including the positioning of surgical tools, deployment of sensors, and analysis of diagnostic images. Advanced mathematical modeling has become essential in healthcare and biological research, driving innovative solutions for treatment planning and spatial arrangements. This paper introduces an intelligent system aimed at optimizing the placement of geometric objects in medical and biological contexts. We employ a universal mathematical model that functions as an intelligent agent, utilizing parameters to adapt to different scenarios and optimize outcomes. We develop mathematical models and advanced algorithms to ensure precise placement, achieving the desired therapeutic or research outcomes while minimizing adverse effects. The mathematical model is formulated as a knapsack problem and expressed as Mixed Binary Non-Linear Programming (MBNLP). Problems related to optimized object placement can be addressed by selecting different model parameters. Several implementations demonstrate this approach, including Gamma Knife radiosurgery, laser coagulation, brachytherapy, and chromosome territory modeling. These systems tackle a range of issues, including the positioning of surgical tools, deployment of sensors, and analysis of diagnostic images. Advanced mathematical modeling has become essential in healthcare and biological research, driving innovative solutions for treatment planning and spatial arrangements. This paper introduces a smart system aimed at optimizing the placement of geometric objects in medical and biological contexts.
This paper focuses on the development of an intelligent driver monitoring system based on adaptive deep learning models to enhance road safety. The research explores advanced deep learning techniques, particularly convolutional neural networks and their modifications, such as ResNet50 and MobileNetV2. Special attention is given to the stages of data preprocessing, augmentation, training and testing dataset formation, as well as model training and fine-tuning. A conceptual framework and architecture for an intelligent driver monitoring system have been developed, incorporating two modules based on different deep learning models. An experimental study was conducted to compare the performance of various convolutional neural network (CNN) architectures, including classical CNN, ResNet50, MobileNetV2, EfficientNetB0, and VGG16, in detecting driver fatigue and drowsiness. Signs of overfitting were identified in the ResNet50 and MobileNetV2 models when applied to the selected datasets, highlighting the need for further hyperparameter optimization. The developed testing scripts enable real-time analysis of behavioral indicators of drowsiness and driver distraction. The proposed system is designed for non-invasive and high-precision real-time monitoring of driver conditions, including fatigue, drowsiness, and distraction detection. The findings confirm the effectiveness of adaptive deep learning models for driver state monitoring. The developed system demonstrates the capability to detect signs of fatigue, drowsiness, and distraction, which may help reduce the likelihood of road accidents. Experimental results indicate that the choice of an optimal neural network architecture depends on the specific task requirements and the available computational resources.
In the paper, a proposed approach for improving efficiency of web propaganda patterns detection by transformer neural networks is presented. Approach consists of sequential use of three developed methods: method for dataset balancing, method for fine-tuning individual binary neural network models and method for detecting web propaganda patterns. Compared to existing analogues, the use of proposed approach allowed achieving an efficiency increase of 0.1 by F1 metric when detecting propaganda patterns in web texts using transformer neural networks due to dataset balancing optimization. Analyzing the impact of parameter that determines proportion of texts without web propaganda patterns allows assessing how the models ability to distinguish propaganda patterns from neutral texts and texts with other propaganda patterns. This allows finding the optimal ratio of dataset classes to increase the overall effectiveness for detecting web propaganda patterns. Conducted research has established that the highest results were achieved when forming the training dataset with a percentage of texts without patterns of 30% using the RoBERTa neural network, and was achieved 0.725 by F1 metric. Proposed approach ensures the determination of the optimal ratio between text sets with propaganda patterns and neutral text set, which improving the generalization ability of models and reduce their bias.
This article presents an approach to automated penetration testing using a distributed multi-agent system (MAS) based on the JADE platform. The proposed architecture includes four specialized agents that collaborate to dynamically bypass Web Application Firewalls (WAFs) and Intrusion Prevention Systems (IPS). The system enables the real-time generation of polymorphic payloads, reinforcement learning-based adaptation of attack strategies, and distributed deployment to reduce detection risks. Experimental validation confirms that this multi-agent approach enhances the efficiency of vulnerability detection through parallel execution, intelligent payload mutation, and reduced reliance on manual testing.
