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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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Design and Implementation of an EMG-based System for Monitoring and Help Request

Design and Implementation of an EMG-based System for Monitor...

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2022 International Conference on Smart Systems and Power Management, IC2SPM 2022

作者： Zeine, Carla Daou, Roy Abi Zeid Telawi, Samer Boercsoek, Josef Hayek, Ali Haidar, Gaby Abou University of Kassel Faculty of Electrical Engineering /Computer Science Department of Computer Architecture and System Programming Kassel Germany Lebanese German University Faculty of Public Health Jounieh Department of Computer Architecture and System Programming Biomedical Technologies Department Jounieh Lebanon Université la Sagesse Faculty of Engineering Furn El Chebbak Mechatronics Department Beirut Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Marker Allee 76-78 Hamm59063 Germany American University of Science and Technology Faculty of Engineering Zahle Lebanon

ISBN: (数字)9781665452915

ISBN: (纸本)9781665452915

Integrating electrophysiological signals in larger systems is getting wider due to the fact that such modalities facilitate the daily tasks of users. However, the high cost of monitoring systems (e.g., oscilloscopes) limits the testing and analysis of the acquired signal from such noisy systems (i. e., electrophysiological devices). The proposed system is a part of a larger proj ect that deals with the identification of sign language movements through the use of Muscle-computer Interfaces. On one hand, the user is able to display the signal acquired by the electrodes on an LCD screen. On the other hand, the system helps a mute patient to issue a special message to his surrounding environment that does not know the sign language to ask for help. The second feature is not unique, as it can be replaced by any other application based on the user's needs. The system was tested on several persons, and the results are promising. © 2022 IEEE.

关键词： Liquid crystal displays

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On One Method of Constructing a Decision Rule in Pattern Recognition

On One Method of Constructing a Decision Rule in Pattern Rec...

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2021 International Conference on Information Science and Communications technologies, ICISCT 2021

作者： Kasimova, Shoista Kasimov, Saidaminxon Tashkent University of Information Technologies after Name Mukhammad Al-Khwarizmi Information and Computer Technologies and Programming Department Tashkent Uzbekistan Tashkent University of Information Technologies after Name Mukhammad Al-Khwarizmi Information Security Department Tashkent Uzbekistan

ISBN: (纸本)9781665432580

The general problem of pattern recognition is to establish the theory and principles of building systems that share complex situations and events into classes. In this case, usually the number of possible situations in the classes is great and they are almost impossible to be remembered. The method of estimates' calculating contributes to a variety of scoring functions' building, which in turn, allows us to formulate and solve a wide class of multivariate data processing. In recent years more and more widely distributed methods of pattern recognition, in which the primary step is to calculate the balance of information and consideration of evidence. Based on the latest evidence being implemented, all further procedures until the distinction of classes or assign any object to one of the classes. © 2021 IEEE.

关键词： Pattern recognition

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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...

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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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Finite groups in which generalized normality is a transitive relation

arXiv

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arXiv 2023年

作者： Safonova, Inna N. Skiba, Alexander N. Department of Applied Mathematics and Computer Science Belarusian State University Minsk220030 Belarus Department of Mathematics and Technologies of Programming Francisk Skorina Gomel State University Gomel246019 Belarus

In this paper, we discuss some well-known results and some open problems of the theory of σ-properties of a group related to the study of generalized *** Codes 20D10, 20D15, 20D30 Copyright © 2023, The Authors. ...

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Estimation of Energy Performance Classes of Concrete Structures with Artificial Intelligence Models 3

Estimation of Energy Performance Classes of Concrete Structu...

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3rd International Conference on High Technology for Sustainable Development, HiTech 2020

作者： Can, Hamit Minkovska, Daniela Stoyanova, Lyudmila Technical University of Sofia Department of Programming and Computer Technologies Sofia Bulgaria

ISBN: (纸本)9781728186511

The construction sector is one of the sectors that make energy efficiency a must. In almost every field related to the construction sector, there is a need for studies in which high performance can be achieved by including artificial intelligence in energy. In this study, energy class models, which are an area of artificial intelligence, which show the efficiency level of energy systems that determine the energy performance of buildings, are examined. © 2020 IEEE.

关键词： Energy efficiency

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Determining the Qualities of Tests Designed for Students' Knowledge Assessment

Determining the Qualities of Tests Designed for Students' Kn...

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International Conference on High Technology for Sustainable Development (HiTech)

作者： Daniela Minkovska Elena Antonova Kiril Koparanov Krasin Georgiev Pamela Minkovska Department of Programming and Computer Technologies Technical University of Sofia Sofia Bulgaria Department of Computer Science Technical University of Sofia Sofia Bulgaria Department of Air Transport Technical University of Sofia Sofia Bulgaria Professional High School of Telecommunications Sofia Bulgaria

ISBN: (数字)9798350358100

ISBN: (纸本)9798350358117

As educators aim to evaluate students' knowledge, the design and quality of their assessment instruments play a vital role. Focusing on test difficulty, as well as comparing real and set results to determine knowledge levels ensure that tests adequately challenge students without overwhelming them. There are various methods incorporating these concepts, such as item response theory and classical test theory. Recognizing the patterns of student performance, thus highlighting areas requiring further attention, can foster targeted interventions to enhance learning outcomes.

关键词： Target recognition Instruments Focusing Pattern recognition Sustainable development

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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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Design and Implementation of an Instrumented walking cane for Detection of Freezing of Gait 3

Design and Implementation of an Instrumented walking cane fo...

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3rd IEEE International Multidisciplinary Conference on Engineering Technology, IMCET 2021

作者： Haidar Ahmad, Joelle El-Asmar, Alain Abi Zeid Daou, Roy Hayek, Ali Boercsoek, Josef Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon MART Learning Education and Research Center Chananir Lebanon University of Kassel Faculty of Electrical Engineering/Computer Science Department of Computer Architecture and System Programming Germany

ISBN: (纸本)9781665405874

Independent mobility is an important feature of an individual's normal life and with the elderly population continually growing, assistive devices such as walkers and canes provide help and guidance to users. Freezing of Gait (FoG) is an episodic phenomenon that is a common symptom of an age-related disease known as Parkinson Disease (PD). This paper is headed toward implementing a detection, diagnosis and correction system for FoG episodes. The proposed cane will generate visual, auditory and tactile cues to help patients overcome this problem, therefore reducing fall risks. In addition, a vibrating calf cuff will be attached to the calf for muscle stimulation. A compatible android phone application is designed to display heart rate measurements of the patient as well as his exact location in case of fall detection. The system is considered to be an electrically safe, portable, comfortable and a low-cost device. The performance of this system is analyzed thoroughly and the accuracy of all methodologies is considered carefully and evaluated. The results have shown no faulty errors and good accuracy and detection percentages. © 2021 IEEE.

关键词： Diagnosis

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Spectral Analysis of Neural Network Long-Term Predictions

Spectral Analysis of Neural Network Long-Term Predictions

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2021 International Conference on High Technology for Sustainable Development, HiTech 2021

作者： Koparanov, Kiril Trifonov, Rumen Minkovska, Daniela Georgiev, Krasin Technical University of Sofia Department of Programming and Computer Technologies Sofia Bulgaria Technical University of Sofia Information Technology in Industry Sofia Bulgaria Technical University of Sofia Department of Aeronautics Sofia Bulgaria

ISBN: (纸本)9781665448734

Long-term predictions of time series is a task with multiple applications. Exploiting neural networks for such problem has not been studied enough for financial purposes. An empirical study of specific cases with real financial multivariate time series and data augmentation was performed. The results are compared for different neural network models and two datasets using standard and customized spectral evaluation metrics. © 2021 IEEE.

关键词： Spectrum analysis

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