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14th International Conference on Advanced computer information Technologies, ACIT 2024

作者： Vasylkiv, Nadia Dubchak, Lesia Karpinski, Mikolaj Turchenko, Iryna Flud, Liubomyr Nadvynychna, Tetiana West Ukrainian National University Department of Information Computer Systems and Control Ternopil Ukraine West Ukrainian National University Department of Computer Engineering Ternopil Ukraine University of the National Education Commission Department of Cyber Security Krakow Poland Ukrainian National Forestry University Department of Information Systems and Computer Modeling Lviv Ukraine West Ukrainian National University Subdepartment of Psychology and Social Work Ternopil Ukraine

ISBN: (纸本)9798350350036

A fuzzy system for setting the priority of IT project work has been developed, in which a preliminary analysis of the metrics of readiness, permission and complexity of tasks is carried out, which makes it possible to determine the priority of the project work. Based on this, the business analyst can effectively make decisions about the workload of the project team. The results of the work can be successfully applied to the effective management of the implementation of IT projects and the proactive management of project teams. © 2024 IEEE.

关键词： Project management

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Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

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computer systems Science & Engineering 2023年第7期46卷 883-896页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Tatiana Makarovskikh Amal H.Alharbi Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of System Programming South Ural State University454080ChelyabinskRussia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Several instances of pneumonia with no clear etiology were recorded in Wuhan,China,on December 31,*** world health organization(WHO)called it COVID-19 that stands for“Coronavirus Disease 2019,”which is the second version of the previously known severe acute respiratory syndrome(SARS)Coronavirus and identified in short as(SARSCoV-2).There have been regular restrictions to avoid the infection spread in all countries,including Saudi *** prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus ***:Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory(LSTM).The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius(BER)***:To evaluate the effectiveness of the proposed methodology,a dataset is collected based on the recorded cases in Saudi Arabia between March 7^(th),2020 and July 13^(th),*** addition,six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed *** achieved results show that the proposed approach could reduce the mean square error(MSE),mean absolute error(MAE),and R^(2)by 5.92%,3.66%,and 39.44%,respectively,when compared with the six base *** the other hand,a statistical analysis is performed to measure the significance of the proposed ***:The achieved results confirm the effectiveness,superiority,and significance of the proposed approach in predicting the infection cases of COVID-19.

关键词： COVID-19 prediction meta-heuristic optimization LSTM Al-Biruni earth radius algorithm

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Simulator of Microcontroller's Power Consumption 12

Simulator of Microcontroller's Power Consumption

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12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing systems: Technology and Applications, IDAACS 2023

作者： Kochan, Orest Osolinskyi, Oleksandr Sachenko, Anatoliy Kochan, Volodymyr Romanets, Ihor Lviv Polytechnic National University Lviv Ukraine West Ukrainian National University Department for Information Computer Systems and Control Ternopil46020 Ukraine

ISBN: (纸本)9798350358056

This paper proposed a simulator of power consumption for pulse consumers (in particular, microcontrollers, henceforth MC). It simulates the character of their power consumption. Due to the function of self-correction of error of circuit components, the simulator provides high accuracy. At the same time, the simulator provides a simulation of the MC operation at frequencies that considerably exceed the frequency of the MC clock generator. This eliminates frequency errors when calibrating the measuring channels of devices and systems designed to measure the MC's power consumption. Due to the calibration, these measurement channels can provide traceability of measurements when studying the MC's power consumption when executing instructions, commands, programs, and their fragments. The simulator can be a reference for creating the system of metrological service of the specified above measurement channels. © 2023 IEEE.

关键词： MCU power consumption pulse consumer simulator

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Prediction of the Magnitude of Lightning Impulse Interference in an Electronic Device Using an Artificial Neural Network

Prediction of the Magnitude of Lightning Impulse Interferenc...

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2024 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2024

作者： Gizatullin, Zinnur Mubarakov, Ramis Kazan National Research Technical University Named After A.N. Tupolev-KAI Department For Automated Systems For Information Processing And Control Department For Computer-Aided Design Systems Kazan Russia Kazan National Research Technical University Named After A.N. Tupolev-KAI Department For Computer-Aided Design Systems Kazan Russia

ISBN: (纸本)9798350395013

Reliable operation of modern electronic devices is the basis for their effective use. Electromagnetic interference, such as lightning, creates the risk of temporary or permanent disruption of the functioning of device elements. Therefore, it is necessary to take into account the possible consequences of such interference on the basis of prediction even at the design stage of electronic devices. The paper a technique for predicting the magnitude of lightning impulse interference in electronic devices using an artificial neural network is proposed. The advantage of this approach over simulation modeling is that rather complex three-dimensional models and long computation times are not needed. A stand was developed and obtained by measuring the amount of lightning interference in an electronic device. The features of the task of training an artificial neural network based on experimental data are considered. The architecture and parameters of the artificial neural network have been selected. An artificial neural network was trained based on experimental data. The root mean square error between the predicted data and the average impulse interference voltage values on the training data does not exceed acceptable values. The results of predicting the magnitude of lightning interference in an electronic device are presented. If we consider further prospects for using an artificial neural network to predict electromagnetic interference, we can note the relevance of the problem of the impact of electrostatic discharge, intentional short pulse and etc. © 2024 IEEE.

关键词： Neural networks

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Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

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SightAid: empowering the visually impaired in the Kingdom of Saudi Arabia (KSA) with deep learning-based intelligent wearable vision system

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Neural Computing and Applications 2024年第19期36卷 11075-11095页

作者： Talaat, Fatma M. Farsi, Mohammed Badawy, Mahmoud Elhosseini, Mostafa Machine Learning Department Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & amp Engineering New Mansoura University Gamasa35712 Egypt College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah46537 Saudi Arabia

In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environmental interaction, and overall life quality. Motivated by the desire to empower visually impaired individuals, who face navigational limitations, difficulties in object recognition, and inadequate assistance from traditional technologies, we propose SightAid. This innovative wearable vision system utilizes a deep learning-based framework, addressing the gaps left by current assistive solutions. Traditional methods, such as canes and GPS devices, often fail to meet the nuanced and dynamic needs of the visually impaired, especially in accurately identifying objects, understanding complex environments, and providing essential real-time feedback for independent navigation. SightAid comprises a seven-phase framework involving data collection, preprocessing, and training of a sophisticated deep neural network with multiple convolutional and fully connected layers. This system is integrated into smart glasses with augmented reality displays, enabling real-time object detection and recognition. Interaction with users is facilitated through audio or haptic feedback, informing them about the location and type of objects detected. A continuous learning mechanism, incorporating user feedback and new data, ensures the system's ongoing refinement and adaptability. For performance assessment, we utilized the MNIST dataset, and an Indoor Objects Detection dataset tailored for the visually impaired, featuring images of everyday objects crucial for safe indoor navigation. SightAid demonstrates remarkable performance with accuracy up to 0.9874, recall values between 0.98 and 0.99, F1-scores ranging from 0.98 to 0.99, and AUC-ROC values reaching as high as 0.9999. These metrics significantly surpass those of traditional methods, highlighting SightAid's potential to substan

关键词： Object recognition

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Management of Scientific Agricultural Digital Ecosystems Based on Ecosystem Classification 16

Management of Scientific Agricultural Digital Ecosystems Bas...

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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Kulba, Vladimir Medennikov, Viktor V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences Laboratory of Modular Data Processing and Control Systems Moscow Russia Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Information and Computing Systems Department Moscow Russia

ISBN: (纸本)9798350337907

The paper considers a mathematical model for managing a scientific digital ecosystem exemplified by the agriculture case;the model enables assessing the impact science exerts on the regions' socioeconomic position in order to build an adequate response to the advanced economies' regime of severe sanctions against Russia. For these purposes, a systemic analysis of the subject identification and classification of ecosystems was conducted to address the critical issue of determining the basic concepts and terms of the country's digitalization. © 2023 IEEE.

关键词： Agriculture

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

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computers, Materials & Continua 2022年第10期73卷 865-881页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Abdelhameed Ibrahim Said H.Abd Elkhalik Shady Y.El-Mashad Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

The design of an antenna requires a careful selection of its parameters to retain the desired ***,this task is time-consuming when the traditional approaches are employed,which represents a significant *** the other hand,machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended *** this paper,we propose a novel approach for accurately predicting the bandwidth of metamaterial *** proposed approach is based on employing the recently emerged guided whale optimization algorithm using adaptive particle swarm optimization to optimize the parameters of the long-short-term memory(LSTM)deep *** optimized network is used to retrieve the metamaterial bandwidth given a set of *** addition,the superiority of the proposed approach is examined in terms of a comparison with the traditional multilayer perceptron(ML),Knearest neighbors(K-NN),and the basic LSTM in terms of several evaluation criteria such as root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE).Experimental results show that the proposed approach could achieve RMSE of(0.003018),MAE of(0.001871),and MBE of(0.000205).These values are better than those of the other competing models.

关键词： Metamaterial antenna long short term memory(LSTM) guided whale optimization algorithm(Guided WOA) adaptive dynamic particle swarm algorithm(AD-PSO)

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Human-Machine Interaction in E-commerce: a Multi-Faceted Examination of CAPTCHA Effect

Human-Machine Interaction in E-commerce: a Multi-Faceted Exa...

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2024 Conference of Young Researchers in Electrical and Electronic Engineering, ElCon 2024

作者： Okoli, Kingsley Joseph, Innocent Chijioke, Famous Bekeneva, Yana Chijioke, Amadi Kodondo, Ibrahim Saint Petersburg Electrotechnical University 'LETI' Department of Computer Science and Engineering Saint Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' Department of Control in Technical Systems Saint Petersburg Russia Saint Petersburg Electrotechnical University 'LETI' Department of Information Systems and Technologies Saint Petersburg Russia

ISBN: (纸本)9798350360646

The growing prevalence of automated threats in e-commerce necessitates effective security measures, exemplified by CAPTCHAs (Completely Automated Public Turing test to tell computers and Humans Apart). However, the use of CAPTCHAs introduces challenges related to user perception, engagement, and frustration. This study aims to comprehensively analyze the impact of CAPTCHAs on ecommerce transactions, including their influence on user behavior, security effectiveness, and the complexity of their implementation. Through a structured questionnaire-based approach, we gathered insights from users, assessing their experiences and perceptions of CAPTCHAs during online shopping. Our analysis involved quantitative data, utilizing statistical software, to provide a holistic understanding of these security measures in the e-commerce domain. Users support the effort to make CAPTCHAs engaging, and many find gamified elements positive. A substantial number express high frustration with multiple CAPTCHAs, affecting their willingness to return to e-commerce platforms. This study outlines the importance of optimizing CAPTCHAs to balance security and user experience in e-commerce. © 2024 IEEE.

关键词： CAPTCHA E-commerce Gamified Security User-Centric

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