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Promising Developments in Cyber Physical System Tools for Background Monitoring of Reserve Landscape 11

Promising Developments in Cyber Physical System Tools for Ba...

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11th International Conference on Advanced computer information technologies, ACIT 2021

作者： Nykolaychuk, Yaroslav Grynchychyn, Taras Pitukh, Ihor Petrashchuk, Yaroslav Nykolaychuk, Lyubov Hryha, Volodymyr West Ukrainian National University Department of Specialized Computer Systems Ternopil Ukraine Nadvirna Vocational College by National Transport University Department of Programming and Information Technologies Nadvirna Ukraine Gorgany Nature Reserve Nadvirna Ukraine Ivano-Frankivsk National Technical University of Oil and Gas Department of Social Communication and Law Ivano-Frankivsk Ukraine Vasyl Stefanyk Precarpathian National University Department of Computer Engineering and Electronics Ivano-Frankivsk Ukraine

Experience in designing and building cyber physical interactive distributed monitoring systems for industrial facilities and reserve landscapes is analyzed. Advantages of the existing interactive and dialogue computerized systems, replicated by many companies, are systematized and their functional limitations are determined. The legal and system requirements for the implementation of this class of information system components, which can be applied in strict Nature Reserve, are clarified. It is proposed to expand the functionality and increase the reliability of the ring-star system for background monitoring of nature reserves based on binary-manipulated optical signals. A sensor system for measuring the height of snow cover along the monitoring route is developed using a relay sensor network and quadcopters. © 2021 IEEE.

关键词： Cyber Physical System

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Structure and Functioning of information Systems of Background Monitoring of Landscape Elements of Gorgany Nature Reserve 10

Structure and Functioning of Information Systems of Backgrou...

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10th International Conference on Advanced computer information technologies, ACIT 2020

作者： Nykolaychuk, Yaroslav Petrashchuk, Yaroslav Slobodian, Olena Pitukh, Ihor Grynchyshyn, Taras Nykolaychuk, Lyubov Hryha, Volodymyr Ternopil National Economic University Department of Specialized Computer Systems Ternopil Ukraine Gorgany Nature Reserve Nadvirna Ukraine Nadvirna College by National Transport University Department of Programming and Information Technologies Nadvirna Ukraine Ivano-Frankivsk National Technical University of Oil and Gas Department of Social Communication and Law Ivano-Frankivsk Ukraine Vasyl Stefanyk Precarpathian National University Department of Computer Engineering and Electronics Ivano-Frankivsk Ukraine

This paper discusses the structural organization of the background monitoring system (BMS) of the transboundary UNESCO World Heritage Site 'Beech forests of the Carpathians and ancient beech forests of Germany' using actual data and information from Gorgany Nature Reserve in Ivano-Frankivsk, Ukraine. The features of the structure and functions of background monitoring of the protected natural resources, forests and wildlife of Gorgany Nature Reserve is based on modern methods of collecting and processing of digital data, implementation of the principles of Internet of Things and cloud technologies, combined with GPS-systems, sensor networks with the use of optical and fiber-optic telecommunication channels. The information technology of the background monitoring system is based on figurative cluster models of monitoring objects according to theoretical principles of statistical, correlatory, spectral, cluster, logic-statistical and entropy analysis. © 2020 IEEE.

关键词： Sensor networks

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Analysis of Hardware and Software Tools for Implementation of Cognitive Radio Networks

Analysis of Hardware and Software Tools for Implementation o...

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International Conference on information Science and Communications technologies (ICISCT)

作者： Ernazar Reypnazarov Halimjon Khujamatov Doston Khasanov Zamira Allamuratova Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Dean of the Computer Enjineering Faculty Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Data Communication Networks and Systems Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Information-Computer Technologies and Programming Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan

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, were studied according to their main characteristics, among which the widely used ones were comparatively analyzed. As a result of the analysis, the HackRF One device was selected as the most preferred option for cognitive network research, and its working principles, structure, and characteristics were revealed in detail. It also provides information about the GNU Radio software environment and its capabilities, which are used to operate the HackRF One device.

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Method of Detection of Intrusions Based on Stacking Meta-Algorithm

Method of Detection of Intrusions Based on Stacking Meta-Alg...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Svitlana Gavrylenko Vladislav Zozulia Nataliia Khatsko Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine Department Information Systems and Technologies National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine

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.

关键词： Training Support vector machines Computational modeling Stacking Training data Intrusion detection Predictive models Nearest neighbor methods Data models Random forests

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Artificial Intelligence in Medicine for Chronic Disease Classification Using Machine Learning

Artificial Intelligence in Medicine for Chronic Disease Clas...

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International Conference on Application of information and Communication technologies (AICT)

作者： Mekhriddin Rakhimov Ravshanjon Akhmadjonov Shahzod Javliev Department of Artificial Intelligence Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi Tashkent Uzbekistan Department of System and Application Programming Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi Tashkent Uzbekistan Department of Computer System Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9781665451635

Artificial intelligence (AI) systems in medicine are one of the most important modern trends in global healthcare. Artificial intelligence technologies are fundamentally changing the global healthcare system, making it possible to radically rebuild the system of medical diagnostics while reducing healthcare costs. AI is actively used in research to develop methods for diagnosing coronary heart disease (CHD). There are different types of CHD. Before treating a disease, it is necessary to determine which class of diseases it belongs to. Based on the feature space of the disease, it is possible to classify the type of CHD. Machine learning algorithms can solve this problem. The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve classification problems. The dataset is the more important part of the supervised machine learning algorithm for training. Gathering data is the most important step in solving any supervised machine learning problem. But choosing more important part from the collected data is one of the tasks to be solved. The main purpose of this study is to select more useful parametric attributes from the dataset to obtain a high F1-score of CHD classification.

关键词： Heart Training Machine learning algorithms Machine learning Market research Classification algorithms information and communication technology

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Research of Energy Detection Method in Spectrum Sensing in Cognitive Radio Networks

Research of Energy Detection Method in Spectrum Sensing in C...

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International Conference on information Science and Communications technologies (ICISCT)

作者： Ernazar Reypnazarov Halimjon Khujamatov Doston Khasanov Zamira Allamuratova Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Computer Enjineering Faculty Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of Data Communication Networks and Systems Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Information-Computer Technologies and Programming Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan

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.

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Methods for Improving the Quality of Classification on Imbalanced Data

Methods for Improving the Quality of Classification on Imbal...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Svitlana Gavrylenko Zozulia Vladislav Nataliia Khatsko Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine Department of Information Systems and Technologies National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine

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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Analysis of Twitter hashtags: Fuzzy clustering approach

Analysis of Twitter hashtags: Fuzzy clustering approach

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Conference of the North American Fuzzy information Processing Society - NAFIPS

作者： Lotfi A. Zadeh Ali M. Abbasov Shahnaz N. Shahbazova Department of Electrical Engineering and Computer Sciences University of California Berkeley CA Minister of Communications High Technologies of the Republic of Azerbaijan Azerbaijan AZ Department of Information Technology & Programming Azerbaijan Technical University Baku Azerbaijan

The overwhelming acceptance of social media creates opportunities to learn about facts and events that are echoed in posts, tweets and messages uploaded by users. Analysis of these data can lead to interesting observations and conclusions. Of special importance are aspects related to temporal and dynamic nature of these findings. This paper presents a simple fuzzy-based approach of pre-processing and analyzing Twitter hashtags. The obtained fuzzy clusters are further examined in order to gain insight related to temporal trends and patterns of hashtags' popularity.

关键词： Twitter Tagging Pragmatics Data visualization Diamonds Media

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Optics in computer Architectures: Basis of WDM, technologies and Potentials

Optics in Computer Architectures: Basis of WDM, Technologies...

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ICONS International Conference on Systems

作者： Y. Wang T. Kusserrow S. Irmer A. Hasse M. Bartels N. Dharmarasu H. Hillmer J. Borcsok O. Mikami Institute of Nanostructure Technologies and Analytics (INA) University of Kassel Kassel Germany Department of Computer Architecture and System Programming University of Kassel Germany Department of Information Telecommunication and Electronics Tokai University Hiratsuka-shi Japan

We present novel concepts, technologies and potentials of optical data communication, especially for future computer architectures. The WDM (wavelength division multiplexing) and the optical wiring technologies are explained in detail. Experimental results of tunable optical filters, which play a major role in WDM systems, are reported. New concepts for novel optical interconnectors and novel miniaturized lasers for direct generation of green light are proposed as examples for possible next generation components to improve the performance of optical communication based computer architecture.

关键词： Optical computing computer architecture Wavelength division multiplexing Optical filters Optical interconnections Data communication Wiring Tunable circuits and devices Optical devices Optical fiber communication

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Advanced computer-Aided Optimization of Quartz Resonator-Based Sensors

Advanced Computer-Aided Optimization of Quartz Resonator-Bas...

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International Conference on Perspective technologies and Methods in MEMS Design (MEMSTECH)

作者： Volodymyr Savchenko Oksana Mnushka Oleg Sokulskyi Computer Engineering and Programming Department National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine Information Systems and Technologies Department National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine

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.

关键词： Micromechanical devices Electrodes Accuracy Resonant frequency Linear programming Mathematical models Sensors

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