Fire detection using deep learning methods

Fire detection is an important task in the field of safety and emergency prevention. In recent years, deep learning methods have shown high efficiency in solving various computer vision problems, including detecting objects in images. In this paper, monitoring wildfires was considered, which allows you to quickly respond to them and prevent their spread using deep learning methods. For the experiment, images from the satellite and images from the FireWatch sensor were taken as initial data. In this work, the deep learning algorithms you only look once (YOLO), convolutional neural network (CNN), and fast recurrent neural network (FastRNN) were considered, which makes it possible to determine the accuracy of a natural fire. As a result of the experiments, an automated fire recognition algorithm using YOLOv4 deep learning methods was created. It is expected that the results of the study will show that deep learning methods can be successfully applied to detect fire in images. This may lead to the development of automated monitoring systems capable of quickly and reliably detecting fire situations, which will help improve safety and reduce the risk of fires

Control of Telecommunication Network Parameters under Conditions of Uncertainty of the Impact of Destabilizing Factors

The classification of the natural and anthropogenic destabilizing factors of a telecommunications network as a complex system is presented herein. This research shows that to evaluate the parameters of a telecommunications network in the presence of destabilizing factors, it is necessary to modify classical linear methods to reduce their sensitivity to the incompleteness of a priori information. Using generalized linear models of multiple regression, a combined method was developed for assessing and predicting the survivability of a telecommunications network under conditions of uncertainty regarding the influence of destabilizing factors. The method consists of accumulating current information about the parameters and state of the network, the statistical analysis and processing of information, and the extraction of sufficient sample statistics. The basis of the developed method was balancing multiple correlation–regression analysis with the number of regression equations and the observed results. Various methods of estimating the mathematical expectation and correlation matrix of the observed results under the conditions of random loss of part of the observed data (for example, removing incomplete sample elements, substituting the average, pairwise crossing out, and substituting the regression) were analyzed. It was established that a shift in the obtained estimates takes place under the conditions of a priori uncertainty of the statistics of the observed data. Given these circumstances, recommendations are given for the correct removal of sample elements and variables with missing values. It is shown that with significant unsteadiness of the parameters and state of the network under study and a noticeable imbalance in the number of regression equations and observed results, it is advisable to use stepwise regression methods

Research Distributed Attacks in Computer Networks

This paper deals with the questions of computer network, attacks, threats, network attacks, "Denial of Service", DoS -attacks, DDoS -attacks, DRDoS -attacks, mathematical model. The article presents an approach to detection of the distributed network attacks to refusal in service, the offered method increases efficiency of use of the calculated resource of a computer network at the big distributed network attacks to "Denial of Service". The paper proposes a mathematical model of compromised node and the number of all possible routes that can have an admission to access points, have done a comparative characteristics of attacks DoS / DDoS / DRDoS in computer network

Effective detection of breast pathology using machine learning methods

This work is devoted to the research and development of methods for effectively identifying breast pathologies using modern machine learning technologies, such as you only look once (YOLOv8) and faster region-based convolutional neural network (R-CNN). The paper presents an analysis of existing approaches to the diagnosis of breast diseases and an assessment of their effectiveness. YOLOv8 and Faster R-CNN architectures are then applied to create pathology detection models in mammography images. The work analyzed and classified identified breast pathologies at six levels, taking into account different degrees of severity and characteristics of the diseases. This approach allows for more accurate determination of disease progression and provides additional data for more individualized treatment planning. Classification results at various levels can improve the quality of medical decisions and provide more accurate information to doctors, which in turn improves the overall efficiency of diagnosis and treatment of breast diseases. Experimental results demonstrate high accuracy and speed of image processing, providing fast and reliable detection of potential breast pathologies. The data obtained confirm the effectiveness of the use of machine learning algorithms in the field of medical diagnostics, providing prospects for the further development of automated systems for detecting breast diseases in order to improve early diagnosis and treatment efficiency