This research introduces a novel approach, MBO-NB, that leverages Migrating Birds Optimization (MBO) coupled with Naive Bayes as an internal classifier to address feature selection challenges in text classification ha...
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An analysis of modern computer network intrusion detection systems was carried out. The application of machine and deep learning methods for classification problems has been investigated. The UNSW-NB15 dataset, develo...
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ISBN:
(数字)9798350384499
ISBN:
(纸本)9798350384505
An analysis of modern computer network intrusion detection systems was carried out. The application of machine and deep learning methods for classification problems has been investigated. The UNSW-NB15 dataset, developed at the Australian Cyber Security Center's (ACCS) Cyber Range Laboratory, contains data on normal network operations and synthetic intrusions. Data pre-processing was performed, including class balancing using the SMOTEENN method and selection of informative features using the Recursive Feature Elimination method. The possibility of using the stacking meta-algorithm to detect intrusions into computer networks has been investigated. A new algorithm for generating packets of raw data is proposed, which generates two sets of training data: one for training basic models, the other for a meta-model. A study of the effectiveness of using Random Forest, ANN, K Nearest Neighbor methods and Support Vector Machine and Random Forest as a decision-making meta-model was conducted. The use of the stacking meta-algorithm with the proposed algorithm for forming packets of output data, as well as basic models and a meta-model, led to a significant improvement in the quality of the model. It was found that, on average, recall and f1 score increased by 55.6% and 37.4%, respectively, compared to raw data and other models.
Brain tumors are regarded as one of the most lethal, devastating, and aggressive diseases, significantly reducing the life expectancy of affected individuals. For this reason, in pursuit of advancing brain tumor diagn...
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In this study, we present an improved approach to optimizing the parameters of sensor sensing elements using quartz resonators with an interelectrode gap. We combine graphical and analytical methods to solve the multi...
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ISBN:
(数字)9798350378627
ISBN:
(纸本)9798350378634
In this study, we present an improved approach to optimizing the parameters of sensor sensing elements using quartz resonators with an interelectrode gap. We combine graphical and analytical methods to solve the multi-criteria optimization problem. The work focuses on the modes of operation of a quartz resonator equipped with an interelectrode gap in sensors with a frequency output, determining a set of optimal parameters and representing them as surfaces in a particular optimization parameter space. The presented results reveal specific areas on the surface of the objective function where optimization of design and technological parameters is possible, taking into account technological influences and features of the sensor design, including the initial value of the interelectrode gap. By using an improved mathematical model for the oscillations of a quartz resonator with an electrode gap, this study achieves results with an accuracy of ($8 . . .11$)% higher than previous studies, demonstrating the effectiveness of the proposed method in improving the design and functionality of piezoelectric sensors for advanced applications.
This paper analyzes of the hardware and software tools for implementing a cognitive radio network. In particular, software-configurable radio modules, which are considered the main element of the cognitive network, we...
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This paper presents the threshold value determination model, its implementation algorithm, and the characteristics of the threshold value depending on the probability of false signal detection, developed for the purpo...
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Joint safety and security analysis of cyber-physical systems is a necessary step to correctly capture inter-dependencies between these properties. Attack-Fault Trees represent a combination of dynamic Fault Trees and ...
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Educational, online learning has become a common practice due to rapid digitalization and recent global events. This study focuses on developing a comprehensive online learning platform that addresses users' needs...
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ISBN:
(数字)9798331542634
ISBN:
(纸本)9798331542641
Educational, online learning has become a common practice due to rapid digitalization and recent global events. This study focuses on developing a comprehensive online learning platform that addresses users' needs by integrating advanced AI capabilities for personalized support and adaptive learning. The platform supports user registration and authentication, course and lesson management, and offers interactive features like AI-driven chat support. In this study, we show the importance of creating user-friendly online learning platforms with the implementation of modern AI trends. The development and testing of this platform is ongoing to determine the adaptation of the system to different learning styles.
Automatic road sign recognition is one of the most important steps to help a driver prevent accidents. In this paper, the deep convolutional neural network (Deep CNN) was used for the autonomous traffic and road signs...
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The subject of the study is methods of balancing raw data. The purpose of the article is to improve the quality of intrusion detection in computer networks by using class balancing methods. Task: to investigate method...
The subject of the study is methods of balancing raw data. The purpose of the article is to improve the quality of intrusion detection in computer networks by using class balancing methods. Task: to investigate methods of balancing classes and to develop a classification method on imbalanced data to increase the level of network security. The methods used are: methods of artificial intelligence, machine learning. The following results were obtained: Class balancing methods based on Undersampling, Oversampling and their combinations were studied. The following methods were chosen for further research: SMOTEENN, SVMSMOTE, BorderlineSMOTE, ADASYN, SMOTE, KMeansSMOTE. The UNSW-NB 15 set was used as the source data, which contains information about the normal functioning of the network and during intrusions. A decision tree based on the CART (Classification And Regression Tree) algorithm was used as the basic classifier. According to the research results, it was found that the use of the SMOTEENN method provides an opportunity to improve the quality of detection of intrusions in the functioning of the network. Conclusions. The scientific novelty of the obtained results lies in the complex use of data balancing methods and the method of data classification based on decision trees to detect intrusions into computer networks, which made it possible to reduce the number of Type II errors.
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