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Algorithmic Support for Building a Distributed IoT System in a Cloud Service

Algorithmic Support for Building a Distributed IoT System in...

引用

Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Mykola Zamkovyi Svitlana Gavrylenko Kyrylo Khatsko Nataliia Khatsko dept. Information Systems and Technologies National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine

The Internet of Things (IoT) today already connects billions of different IoT devices with the Internet. With the advent of new services and services, new challenges arise: how to make a cloud service publicly available, and reliable, how and where to store data. The article considers an example of the implementation of an IoT application with cloud components, considers the implementation of connecting IoT components to a cloud provider, and presents the IoT architecture. The focus is on the architecture and implementation of data exchange protocols, the implementation of protocols? for data collection and management of remote system components Given the large amount of data transmitted by IoT devices to the cloud, data compression and interaction with cloud services is a very relevant topic. Therefore, the article also offers recommendations for managing the traffic of IoT devices. The purpose of this article is to provide a description and algorithm for connecting IoT devices to the cloud, to bring together disparate information, and make it open and understandable to beginners and academic researchers.

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Design of Intelligent Decision Support System With Ensembled Machine Learning to Predict Students' Performance

Design of Intelligent Decision Support System With Ensembled...

引用

2024 International Conference on Communication, computer Sciences and Engineering, IC3SE 2024

作者： Deepan, S. Rajendran, Dineshkumar Dhaliwal, Navdeep Praveena, K. Lourens, Melanie Anandhi, R.J. School Of Cs & Ai Sr University Telangana Warangal India School Of Computing And Innovative Technologies British University Computer Games Design And Programming Viet Nam Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Dundigal Hyderabad India Durban University Of Technology Faculty Of Management Sciences South Africa New Horizon College Of Engineering Department Of Information Science Engineering Bangalore India

ISBN: (纸本)9798350366846

Evaluating student performance is important for universities and institutions in the current student education landscape because it helps them create models that work better for students. The automation of various features related to fundamental student traits and behaviours that manage massive amounts of data efficiently processes these. To handle student records that included information about students' behaviour and how it related to their academic performance, the companies employed models of classification with mining concepts. Additionally, the quality of result classification can be substantially improved by using learning analytics and Educational Data Mining (EDM). The educational establishments are making an effort to lower the low student performance. To address this issue, numerous methods for assessing student performance have been devised, allowing the relevant faculties to intervene and enhance the final product. Three classes - Low Performance Student, Average Student, and Smart Student - were created using the K-Mean Clustering methodology for classifying student records. Features including grade point, number of deficits, student attendance, medium of education, and board of education are taken into account when classifying the data. In this case, the WEKA tool is also utilized for implementing the model and outcome assessments. © 2024 IEEE.

关键词： Decision support systems

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Special Processors Simulation for Forming and Digital Processing of Error-Correcting Codes in computer Systems

Special Processors Simulation for Forming and Digital Proces...

引用

International Conference on Modern Problems of Radio Engineering, Telecommunications and computer Science

作者： Taras Grynchyshyn Volodymyr Hryha Artur Voronych Mykhaylo Ilko Department of Programming and Information Technologies National Transport University Nadvirna UKRAINE Department of Computer Engineering and Electronics Vasyl Stefanyk Precarpathian National University Ivano-Frankivsk UKRAINE Department of Computer System and Networks Ivano-Frankivsk National Technical University of Oil and Gas Ivano-Frankivsk UKRAINE

ISBN: (数字)9781728155661

ISBN: (纸本)9781728155678

The methods of digital and pulse signal modulation in modern computer systems of data transmission are analysed. Process of forming non-redundant coding that use the errors detection and correction in the Galois basis are offered. Structures of special processors for forming signal correcting codes are built. Working algorithms for proposed coding methods are developed and their software implementation are offered. Circuit design of Galois code sequence generator is implemented. Work simulation and synthesis on FPGA of Xilinx are performed.

关键词： digital signal processing chips encoding error correction codes error detection codes field programmable gate arrays Galois fields logic design microprocessor chips Error correction codes Field programmable gate arrays digital signal processing chips error detection codes computer systems Galois fields Microprocessor chips Logic design coding Forming Galois sequence generator Digital signal processing

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Foundations of Intelligent Systems 1

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Troels Andreasen Henning Christiansen Juan-Carlos Cubero Zbigniew W. Raś

ISBN: (数字)9783319083261

ISBN: (纸本)9783319083254

This book constitutes the refereed proceedings of the 21st International Symposium on Methodologies for Intelligent Systems, ISMIS 2014, held in Roskilde, Denmark, in June 2014. The 61 revised full papers were carefully reviewed and selected from 111 submissions. The papers are organized in topical sections on complex networks and data stream mining; data mining methods; intelligent systems applications; knowledge representation in databases and systems; textual data analysis and mining; special session: challenges in text mining and semantic information retrieval; special session: warehousing and OLAPing complex, spatial and spatio-temporal data; ISMIS posters.

关键词： Artificial Intelligence Data Mining and Knowledge Discovery Database Management information Storage and Retrieval information Systems Applications (incl. Internet) User Interfaces and Human computer Interaction

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Method for calculating of R-learning traffic peakedness

Method for calculating of R-learning traffic peakedness

引用

International Scientific-Practical Conference Problems of Infocommunications Science and Technology

作者： Nina Kuchuk Oleksandr Mozhaiev Mykhailo Mozhaiev Heorgii Kuchuk Department of Computer Science National University Kharkiv Ukraine Laboratory on Speech and Audio Computer Engineering and Telecommunication Kharkov SRI examinations named Dist. prof. N.S. Bokariusa Kharkiv Ukraine Department of Information Technologies Kharkiv National University of Internal Affairs Kharkiv Ukraine Department of Computer science and programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine

In this article e-leaming traffic which is served by system on the hyper convergent platform is considered. It was done the analysis of the known methods of determine the degree of pulsation of traffic. The method for calculating of e-learning traffic peakedness is proposed. Simulation and calculation results peakedness is analyzed.

关键词： Wireless networks Economic indicators System implementation

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Multiservice network security metric

Multiservice network security metric

引用

International Conference on Advanced information and Communication technologies (AICT)

作者： Oleksandr Mozhaev Heorgii Kuchuk Nina Kuchuk Mykhailo Mozhaev Mykhailo Lohvynenko Department of Information Technologies Kharkiv National University of Internal Affairs Kharkiv Ukraine Department of Computer science and programming “Kharkiv Polytechnic Institute” Kharkiv Ukraine Faculty of computer science V.N. Karazin Kharkiv National University Kharkiv Ukraine Laboratory on Speech and Audio Computer Engineering and Telecommunication Kharkiv SRI examinations Kharkiv Ukraine Sector on Computer Engineering and Telecommunication Examinations Kharkiv SRI examinations Kharkiv Ukraine

ISBN: (纸本)9781538606384

This report proposes a new approach to determination of security complex metric. Such approach allows to obtain a quantitative assess based on network security recorded monitoring. Assessment of separate network host, which could be accessed from other networks, is conducted. Proposed metric considers a possibility of attack propagation within the network.

关键词： Silicon Security Measurement Monitoring Probabilistic logic

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

Structure and Functioning of Information Systems of Backgrou...

引用

International Conference on Advanced computer information technologies (ACIT)

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

ISBN: (数字)9781728167602

ISBN: (纸本)9781728167619

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.

关键词： Monitoring Forestry Correlation Optical fiber sensors Optical fibers Biological system modeling

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Design of Intelligent Decision Support System With Ensembled Machine Learning to Predict Students' Performance

Design of Intelligent Decision Support System With Ensembled...

引用

Communication, computer Sciences and Engineering (IC3SE), International Conference on

作者： S Deepan Dineshkumar Rajendran Navdeep Dhaliwal K Praveena Melanie Lourens Anandhi R J School of CS & AI SR University Warangal Telangana India Computer Games Design and Programming School of Computing and Innovative Technologies British University Vietnam Vietnam Lovely Professional University Phagwara India Institute of Aeronautical Engineering Dundigal Hyderabad Deputy Dean Faculty of Management Sciences Durban University of Technology South Africa Department of Information Science Engineering New Horizon College of Engineering Bangalore

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Evaluating student performance is important for universities and institutions in the current student education landscape because it helps them create models that work better for students. The automation of various features related to fundamental student traits and behaviours that manage massive amounts of data efficiently processes these. To handle student records that included information about students' behaviour and how it related to their academic performance, the companies employed models of classification with mining concepts. Additionally, the quality of result classification can be substantially improved by using learning analytics and Educational Data Mining (EDM). The educational establishments are making an effort to lower the low student performance. To address this issue, numerous methods for assessing student performance have been devised, allowing the relevant faculties to intervene and enhance the final product. Three classes—Low Performance Student, Average Student, and Smart Student—were created using the K-Mean Clustering methodology for classifying student records. Features including grade point, number of deficits, student attendance, medium of education, and board of education are taken into account when classifying the data. In this case, the WEKA tool is also utilized for implementing the model and outcome assessments.

关键词： Deep learning Decision support systems Analytical models Data analysis Automation Education Companies

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