This paper explains in detail several methods for utilization of class based n-gram language models for automatic speech recognition, within the Kaldi speech recognition framework. It reexamines an existing implementa...
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In this paper are presented the description of the developed and implemented system for tests control and the analysis of the results received from the testing of students. The database and the developed forms are rev...
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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...
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 purpose of researching the method of energy determination in spectrum sensing in cognitive radio networks. Also, the main methods of single-band spectrum sensing in cognitive radio networks were initially studied in the article, and the main spectrum detection algorithms were comparatively analyzed.
Neural networks are used in many tasks today. One of them is the images processing. Autoencoder is very popular neural networks for such problems. Denoising autoencoder is an important autoencoder because some tasks w...
ISBN:
(数字)9781728199573
ISBN:
(纸本)9781728199580
Neural networks are used in many tasks today. One of them is the images processing. Autoencoder is very popular neural networks for such problems. Denoising autoencoder is an important autoencoder because some tasks we need a preprocessed image to get less noisy result. This research describes ways to analyze noisy images produced by a physically-based render engine and how to reduce that noise. The results showed that the algorithms are logarithmic.
The increasing prevalence of botnet attacks in IoT networks has led to the development of deep learning techniques for their detection. However, conventional centralized deep learning models pose challenges in simulta...
The increasing prevalence of botnet attacks in IoT networks has led to the development of deep learning techniques for their detection. However, conventional centralized deep learning models pose challenges in simultaneously ensuring user data privacy and detecting botnet attacks. To address this issue, this study evaluates the efficacy of Federated Learning (FL) in detecting IoT malware traffic while preserving user privacy. The study employs N-BaIoT, a dataset of real-world IoT network traffic infected by malware, and compares the effectiveness of FL models using Convolutional Neural Network, Long Short-Term Memory, and Gated Recurrent Unit models with a centralized approach. The results indicate that FL can achieve high performance in detecting abnormal traffic in IoT networks, with the CNN model yielding the best results among the three models evaluated. The study recommends the use of FL for IoT malware traffic detection due to its ability to preserve data privacy.
Collaborative work, with the need to keep HTML/XML code up-to-date, is now becoming vital particularly in the Web Development field. In order to fully support collaborative work and resolve related problems the need h...
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The article raises the issue of organizing the signal processing process in existing classes of digital systems and analogue-to-digital converters. The authors indicate a significant problem of the synthesis of contro...
The article raises the issue of organizing the signal processing process in existing classes of digital systems and analogue-to-digital converters. The authors indicate a significant problem of the synthesis of controlled action (and its adequate perception by the control system through the "sensory level of perception", in particular, the aspectology of redundancy in computing power and the problem of choice and delegation in stream processing of signals).A solution for an information system for collecting and managing data based on peripheral interrogation of sensors is generalized and formalized. For the first time authors are developing a new class of hardware devices that combine the principles of organization of neural pathogens (perceptrons) that work within the activation function and trend line (neural memory), and quantum logic in a Boolean basis. The functional testing of the developed device is presented, the simplicity and reliability of the hardware implementation is indicated. While formulating research methods, the goal is to develop a reliable integrative system for information support of the hardware circuit system. A structural block of controlled addiction has been implemented, taking into account quantum caching that is taken as a mean of ensuring the unity and consistency of data transmission from the measuring environment in case of increasing its structural component. Options for the practical implementation of the information and analytical structure of the hardware perceptron in the industrial Internet of Things are considered in the conclusion.
The algebraic structures have many applications in coding theory, cryptography, and security networks. In this paper, the notion of hybrid subalgebras of BCH-algebras is introduced and related properties are investiga...
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Currently, there is a need in Ukraine for the design and development of multidisciplinary decision support systems (DSSs) for the medical and other related spheres - for example, the medical law sector, in particular,...
ISBN:
(数字)9781728199573
ISBN:
(纸本)9781728199580
Currently, there is a need in Ukraine for the design and development of multidisciplinary decision support systems (DSSs) for the medical and other related spheres - for example, the medical law sector, in particular, the DSS for the surrogacy's legal regulation field. This DSS will be able to protect doctors from potentially wrong decisions by taking into account all available information when making the decisions. For design such DSS, it's necessary to conduct the modelling of the decision-making process on surrogacy's legal conducting, that is the goal of this research. In this paper, there are the theoretical foundations of using the ontologies for the decision-making process on surrogacy's legal conducting, a model of base ontology with the necessary requirements and recommendations for conducting the surrogate motherhood, and ontology-based model of the decision-making process on surrogacy's legal conducting. The conducted modelling is a theoretical basis for the development of methods and design of DSS in the field of legal regulation of surrogate motherhood.
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