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Forecasting and modeling on average rainwater and vapor pressure in Chelyabinsk Russia using deep Learning models 6

Forecasting and modeling on average rainwater and vapor pres...

引用

6th Smart Cities Symposium, SCS 2022

作者： Abotaleb, Mostafa Makarovskikh, Tatiana Subhi, Alhumaima Ali Alkattan, Hussein Adebayo, Adelaja Oluwaseun Department of System Programming South Ural State University Chelyabinsk454080 Russia Electronic and Computer Center University of Diyala BaqubahMJJ2+R9G Iraq Information Communication and Technology Department Lagos State University Lagos102101 Nigeria

ISBN: (纸本)9781839538544

Machine learning technique is full-fledged as a boosting sector to develop modeling and forecasting of complex time series observations in the present environment. This study made an attempt to inspect the future performance of rainfall data and vapor in Chelyabinsk by using a machine learning technique. The data series is divided into a training set (60%) and a test set (40%) for model developing a validation purpose. We further developed deep learning models such as, LSTM, BILSTM, GRU and compared on the basis of ME, RMSE, MAE, MPE, MAPE, ACF1 on the training data set. For testing data set, we compared these deep learning models based on RMSE. LSTM model acts as a superior machine learning model over BILSTM and GRU in this data series. Forecasting performance of these three models significantly at par. This finding may be significant to build a strong literature of the Chelyabinsk weather's forecast, which can be helpful for policy makers and researchers. Also, we strongly believe that, this work could be used as a literature adaptation of machine learning technique for complex time series over statistical models. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Statistical tests

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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...

引用

2022 International Conference on information Science and communications Technologies, ICISCT 2022

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

ISBN: (纸本)9781665472296

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. © 2022 IEEE.

关键词： Digital signal processing

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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...

引用

2022 International Conference on information Science and communications Technologies, ICISCT 2022

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

ISBN: (纸本)9781665472296

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. © 2022 IEEE.

关键词： Cognitive radio

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The prediction of students' Academic performances with a classification Model Built using Data Mining Techniques 6

The prediction of students' Academic performances with a cla...

引用

6th Smart Cities Symposium, SCS 2022

作者： Alkattan, Hussein Abotaleb, Mostafa Subhi, Alhumaima Ali Adelaja, Oluwaseun A. Kadi, Ammar Ibrahim Al-Mahdawi, Hassan K. Department of System Programming South Ural State University Chelyabinsk454080 Russia Electronic and Computer Center University of Diyala BaqubahMJJ2+R9G Iraq Information Communication and Technology Department Lagos State University Lagos102101 Nigeria Department of Food and Biotechnology South Ural State University Chelyabinsk454080 Russia

ISBN: (纸本)9781839538544

In the purview of most educational sectors today, numerous reviews regarding data mining have been the primary focus, with goals of discovering vast knowledge patterns for students' data. This paper focuses on building model that predicted the performance of computer science students in some courses they were examined through classification technique. The model built for the purpose of classification via decision tree classifier which was adopted in WEKA and KNIME analytics platform. The workflow model basically connected nodes to generate the decision trees while J4.8 Decision Tree Classifier programmable outputs were generated for the courses to obtain the confusion matrix and other instances. Bar Charts were plotted to display the students' performance in the courses. The mean absolute errors, RMS error and relative absolute error all read 0% while the kappa value obtained from the analysis ranged between 0.991 - 1.00 which perfectly agrees for most statistical values. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Decision trees

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Forecasting and modeling on average rainwater and vapor pressure in Chelyabinsk Russia using deep learning models

Forecasting and modeling on average rainwater and vapor pres...

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6th Smart Cities Symposium (SCS 2022)

作者： M. Abotaleb T. Makarovskikh A. Ali Subhi H. Alkattan A.O. Adebayo Department of System Programming South Ural State University Chelyabinsk Russia University of Diyala Diyala Iraq Information Communication and Technology Department Lagos State University Lagos Nigeria

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A Horizontal Federated Learning Approach to IoT Malware Traffic Detection: An Empirical Evaluation with N-BaIoT Dataset 26

A Horizontal Federated Learning Approach to IoT Malware Traf...

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26th International Conference on Advanced communications Technology, ICACT 2024

作者： Do, Phuc Hao Le, Tran Duc Vishnevsky, Vladimir Berezkin, Aleksandr Kirichek, Ruslan The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint-Petersburg Russia Danang Architecture University Da Nang Viet Nam University of Telecommunications Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State Russia University of Science and Technology The University of Danang Da Nang Viet Nam The University of Danang University of Science and Technology Information Technology Faculty Danang Viet Nam V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia Institute of Control Sciences of Russian Academy of Sciences Russia Bonch Bruevich Saint Petersburg State University of Telecommunications Department of Programming Engineering and Computer Science Russia

ISBN: (纸本)9791188428120

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. © 2024 Global IT Research Institute - GIRI.

关键词： Malware

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The prediction of students' academic performances with a classification model built using data mining techniques

The prediction of students' academic performances with a cla...

引用

6th Smart Cities Symposium (SCS 2022)

作者： H. Alkattan M. Abotaleb A. Ali Subhi O. A. Adelaja A. Kadi H. K. Ibrahim Al-Mahdawi Department of System Programming South Ural State University Chelyabinsk Russia University of Diyala Diyala Iraq Information Communication and Technology Department Lagos State University Lagos Nigeria Department of Food and Biotechnology South Ural State University Chelyabinsk Russia

In the purview of most educational sectors today, numerous reviews regarding data mining have been the primary focus, with goals of discovering vast knowledge patterns for students' data. This paper focuses on building model that predicted the performance of computer science students in some courses they were examined through classification technique. The model built for the purpose of classification via decision tree classifier which was adopted in WEKA and KNIME analytics platform. The workflow model basically connected nodes to generate the decision trees while J4.8 Decision Tree Classifier programmable outputs were generated for the courses to obtain the confusion matrix and other instances. Bar Charts were plotted to display the students' performance in the courses. The mean absolute errors, RMS error and relative absolute error all read 0% while the kappa value obtained from the analysis ranged between 0.991 – 1.00 which perfectly agrees for most statistical values.

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Digital education in the coronavirus era 1

Digital education in the coronavirus era

引用

1st International Scientific Conference on Advances in Science, Engineering and Digital Education, ASEDU 2020

作者： Dzhangarov, A.I. Hanmurzaev, H.E. Potapova, N.V. Department of Programming and Information and Communication Chechen State University Sheripova Street 32 Groznyy364093 Russia Department of Information Educational Technologies Kuban State University Stavropol'skaya street 149 Krasnodar350001 Russia

following work outlines a study of the transformation of the educational process during the coronavirus pandemic. The object of the study is the Moscow region, which in the study is represented by seven universities. The subject of the research is the reports of students of these universities, which contain an opinion about the quality of distance education and the degree of satisfaction with it. Messages were collected and classified using machine learning methods-they were grouped according to various topics, from organizational issues to questions of personal well-being. Based on the results of the research carried out, a general assessment of the digital educational process by students is given. © Published under licence by IOP Publishing Ltd.

关键词： Coronavirus

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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

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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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