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检索条件"机构=Computer and programming engineering"

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Benefiting From Power of The Crowd: Contribution of Crowds to the Idea Evaluation Phase

Benefiting From Power of The Crowd: Contribution of Crowds t...

引用

engineering, Technology and Innovation (ICE/ITMC), IEEE International Conference on & Management of Technology (IAMOT) Joint Conference, International Association For

作者： Ahmet Çubukcu Özlem Akarçay Tuba Ulusoy Department of Computer Forensics Engineering Necmettin Erbakan University Konya Turkey Department of Computer Programming Program KTO Karatay University Konya Turkey Department of Industrial Engineering Necmettin Erbakan University Konya Turkey

ISBN: (纸本)9781665488181

The Covid-19 pandemic has put the world in a serious crisis by damaging the functioning of many systems (i.e.,health, economy, trade). Online communities have also tried to find solutions to minimize the negative effects caused by the Covid-19 pandemic. One of these communities conducted a program in Turkey to minimize the risky effects brought by the COVID-19 pandemic. The program took place over a three-month period (April 2020 to June 2020) during the open innovation platform powered by innoCentrum. In this process, an accelerator program was carried out to gather, evaluate, and implement innovative ideas. During the evaluation phase of an idea, the assessments of other entrepreneurs who submitted ideas were also taken into consideration by a rating system. The aim of this study is to reveal whether there is a relationship between the contribution levels of crowds (entrepreneurs) and the best ideas selected during the evaluation phase. While the result of the study shows that there is a significant relationship between the average scores given by the crowds with the scores given by the mentors who mainly evaluated the ideas, there is no significant relationship between the number of crowds' comments and the number of crowds' vote with the scores given by the mentors.

关键词： COVID-19 Technological innovation Pandemics

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Models and algorithms for determining the probability of occurrence of a critical combination of events for three-element minimum sections in intelligent decision support systems

Models and algorithms for determining the probability of occ...

引用

Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Oleksii Kolomiitsev Iryna Zakharchenko Viacheslav Tretiak Serhii Osiievskyi Alina Rybalchenko Hennadii Heiko Computer Engineering and Programming Department National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine Department of mathematics and software of ACS Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine

A method for determining the probability of occurrence of a critical combination of events for the three-element minimum cross-sections of attributes of the characteristics of the software quality model of intelligent...

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Sequential Series-Based Prediction Model in Adaptive Cloud Resource Allocation for Data Processing and Security 13

Sequential Series-Based Prediction Model in Adaptive Cloud R...

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13th International Conference on Dependable Systems, Services and Technologies, DESSERT 2023

作者： Petrovska, Inna Kuchuk, Heorhii Kuchuk, Nina Mozhaiev, Oleksandr Pochebut, Maxim Onishchenko, Yurii National Technical University Kharkiv Polytechnic Institute Kharkiv Ukraine National Technical University Kharkiv Polytechnic Institute Department of Computer Engineering and Programming Kharkiv Ukraine Kharkiv National University of Internal Affairs Department of Cyber Security and DATA Technologies Kharkiv Ukraine SET University Academic Board Kiev Ukraine Kharkiv National University of Internal Affairs Department № 4 Kharkiv Ukraine

ISBN: (纸本)9798350396119

A developed adaptive forecasting model for cloud resource allocation is presented. It employs principal component analysis on a sequence of virtual machine (VM) requests. Requests are processed to detect anomalies, and adaptive predictions are computed using EEMD-ARIMA or EEMD-RT-ARIMA methods. The choice between EEMD-ARIMA and EEMD-RT-ARIMA methods is determined by comparing the execution time values Ri (sequential series test) with the threshold value Rt d. If Ri > Rtd, EEMD-ARIMA is used;if Ri ≤ Rt d, EEMD-RT-ARIMA is applied. This adaptive approach enables the selection of a prediction method based on data characteristics and resource demands. To optimize the selection of the Rt d threshold, the impact on accuracy and time costs is examined. A quartile method is utilized to detect dynamic spikes, and cubic spline interpolation is employed to smooth data. EEMDRT-ARIMA-based forecasting enhances accuracy through preprocessing of dynamic spikes and adaptive method selection. Calculations of time costs indicate that this method reduces forecasting time by 1.5 times by extracting core component sequences. © 2023 IEEE.

关键词： Virtual machine

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Optimizing machine learning algorithms to enhance blood donation for liver patients: a case study

Optimizing machine learning algorithms to enhance blood dona...

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7th IET Smart Cities Symposium (SCS 2023)

作者： Maad M. Mijwil El-Sayed M. El-kenawy Indu Bala Mohammad Aljanabi Mostafa Abotaleb Abdelhameed Ibrahim Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood.

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A method for targeting unmanned aerial vehicles for a distributed intelligent control system for a group of dissimilar unmanned aerial vehicles 4

A method for targeting unmanned aerial vehicles for a distri...

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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Parkhomenko, Danylo Voronin, Viktor Rysovanyi, Oleksandr Startsev, Volodymyr Burtsev, Valery Panchenko, Volodymyr Scientific and Organizational Department of Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine Computer Engineering and Programming Department National Technical University "Kharkiv Polytechnic Institute" Kharkiv Ukraine Department of the Faculty of Anti-aircraft Missile Forces Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine

The application of a distributed intelligent control system for a group of unmanned aerial vehicles is substantiated, a method for coordinating their interaction to maximize the target indicator is proposed and substa... 详细信息

ISBN: (纸本)9798350395532

关键词： Unmanned aerial vehicles (UAV)

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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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Modeling of DC Traction Motor In Ansys RMXPRT

Modeling of DC Traction Motor In Ansys RMXPRT

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Vitalii Herasymenko Vladyslav Pliuhin Valentyn Noskov Oleksandr Zakovorotnyi Mykola Mezentsev Tetyana Orlova Department of Electric Transport O. M. Beketov National University of Urban Economy in Kharkiv Kharkiv Ukraine Department of the Systems of Electricity Supply and Electricity Consumption of Cities O. M. BeketovNational University of Urban Economy in Kharkiv Kharkiv Ukraine Department of Computer Engineering and Programming National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine Computer Engineering and Programming Department National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine

DC traction motors are widely used in all branches of urban economy. They began to be used in industry about a hundred years ago with the advent of the first calculation methods. When designing a DC motor, it is important to consider it comprehensively, together with the electric drive system. The article discusses the application of a more correct approach to modeling a series-excited DC motor using the ANSYS software package, in particular, the ANSYS RMxprt / Maxwell/ Simplorer bundle. The main stages of initial data input are considered and the main calculation results are shown. Using ANSYS Maxwell allows you to quickly and automatically build a typical machine geometry using RMxprt. When solving non-standard structures, it is possible to use 3D design with step-by-step elements. The analysis of the obtained graphic dependences of electric power parameters makes it possible to judge the efficiency of the electric motor and its operational properties in idling and under load. The simulation results show excellent agreement with the data obtained experimentally.

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The Method of Dynamic Routing in Self-Healing Networks

The Method of Dynamic Routing in Self-Healing Networks

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Shyman Anna Nina Kuchuk Andriy Kovalenko Svitlana Kashkevich Stanislav Partyka Oleksandr Yankovskyi Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine Department of Electronic Computers Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of Computerized Management Systems National Aviation University Kharkiv Ukraine

ISBN: (数字)9798350384499

ISBN: (纸本)9798350384505

The report proposes a dynamic routing method for Self-healing Networks (ShN). The method takes into account the specific features of ShN. During routing, the flow of service information is reduced. The search for the optimal route is carried out using a convolutional neural network. The method significantly outperforms existing methods when some network nodes fail.

关键词： Receivers Performance gain Routing Routing protocols Maintenance Convolutional neural networks Standards

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Sensing metric-based hybrid underlay/overlay transmission framework for CR-IoT networks for smart city applications

Sensing metric-based hybrid underlay/overlay transmission fr...

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7th IET Smart Cities Symposium (SCS 2023)

作者： Indu Bala Maad M. Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Abdelhameed Ibrahim School of Electrical and Electronics Engineering Lovely Professional University Punjab India Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

With the increasing number of IoT devices, there is a growing need for bandwidth to support their communication. Unfortunately, there is a shortage of available bandwidth due to preallocated bands for various services. To address this issue, Cognitive Internet of Things (CR-IoT) enables devices to optimize their efficiency and enhance their communication capabilities by intelligently accessing available bandwidth. This is achieved through the use of soft sensing metrics, where devices continuously monitor the RF environment and transmit data opportunistically in overlay mode if a free channel is detected, or in underlay mode if not. In this paper, a soft sensing metric based hybrid transmission framework is proposed for CR-IoT devices to meet the data rate requirement for the smart city applications. The efficacy of this approach is demonstrated through simulation results.

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Research of Intelligent Data Analysis Methods for Identification of computer System State 30

Research of Intelligent Data Analysis Methods for Identifica...

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30th International Scientific Symposium Metrology and Metrology Assurance, MMA 2020

作者： Gavrylenko, Svetlana Chelak, Viktor Hornostal, Oleksii National Technical University Kharkiv Polytechnic Institute' Department of Computer Engineering and Programming Kharkiv Ukraine

ISBN: (纸本)9781728197197

Classification is one of the methods of the intelligent data analysis (data mining), which allows to study the existing data set and create a model for each class based on its data features. To increase the accuracy of classification, ensemble methods are used, including some of the most effective ensemble methods based on boosting and bagging. This study presents the results of a comparative analysis of machine learning methods for classifying the state of a computer system. The ensemble methods based on boosting and bagging were modified using a special procedure for selecting the main classification parameters: The number of trees (the number of learners), the number of branches (the number of splits or leaves nodes), and the learning speed (the learning rate). Modified methods were tested with the following initial data of the computer system functioning: loading of the central processor and memory, the amount of incoming and outgoing traffic (network utilization), the number of read / write operations to disk, the statistical data of the system events analysis (the number of operations with the system registry, file system, number of processes, etc.). It was found that the classification accuracy with the test sample was more than 99%. © 2020 IEEE.

关键词： bagging decision trees ensemble classification methods boosting

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