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7th IET Smart Cities Symposium, SCS 2023

作者： Bala, Indu Mijwil, Maad M. Abotaleb, Mostafa El-Kenawy, El-Sayed M. Ibrahim, Abdelhameed 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

ISBN: (纸本)9781839539831

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. © The Institution of engineering & Technology 2023.

关键词： Smart city

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Modeling of DC Traction Motor In Ansys RMXPRT 4

Modeling of DC Traction Motor In Ansys RMXPRT

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

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

ISBN: (纸本)9798350395532

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. © 2023 IEEE.

关键词： Efficiency

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The method of dynamic routing in Self-healing Networks 5

The method of dynamic routing in Self-healing Networks

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5th IEEE KhPI Week on Advanced Technology, KhPIWeek 2024

作者： Anna, Shyman Kuchuk, Nina Kovalenko, Andriy Kashkevich, Svitlana Partyka, Stanislav Yankovskyi, Oleksandr 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 Kyiv Ukraine

ISBN: (纸本)9798350384499

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. ©2024 IEEE.

关键词： Dynamic routing algorithms

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Data Poisoning: Issues, Challenges, and Needs 7

Data Poisoning: Issues, Challenges, and Needs

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7th IET Smart Cities Symposium, SCS 2023

作者： Aljanabi, Mohammad Omran, Alaa Hamza Mijwil, Maad M. Abotaleb, Mostafa El-Kenawy, El-Sayed M. Mohammed, Sahar Yousif Ibrahim, Abdelhameed Department of computer College of Education Aliarqai university Baghdad Iraq University of information technology and communications Baghdad Iraq 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 Science Department Computer Science &Information Technology College Anbar University Anbar Iraq Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Data poisoning attacks, where adversaries manipulate training data to degrade model performance, are an emerging threat as machine learning becomes widely deployed in sensitive applications. This paper provides a comprehensive overview of data poisoning including attack techniques, adversary incentives, impacts on security and reliability, detection methods, defenses, and key research gaps. We examine label flipping, instance injection, backdoors, and other attack categories that enable malicious outcomes ranging from IP theft to accidents in autonomous systems. Promising detection approaches include statistical tests, robust learning, and forensics. However, significant challenges remain in translating academic defenses like adversarial training and sanitization into practical tools ready for operational use. With safety and trustworthiness at stake, more research on benchmarking evaluations, adaptive attacks, fundamental tradeoffs, and real-world deployment of defenses is urgently needed. Understanding vulnerabilities and developing resilient machine learning pipelines will only grow in importance as data integrity is fundamental to developing safe artificial intelligence. © The Institution of engineering & Technology 2023.

关键词： Machine learning

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Modeling the Impact of External Factors on E-Commerce Consumer Behavior

Modeling the Impact of External Factors on E-Commerce Consum...

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International computer Sciences and Information Technologies (CSIT)

作者： Oleksandra Bulgakova Viacheslav Zosimov Automation Robotics and Computer Programming P.M Platonov Educational and Scientific Institute of Computer Engineering Odesa National University of Technology Odesa Ukraine

ISBN: (数字)9798331542634

ISBN: (纸本)9798331542641

This paper presents the impact of external factors on consumer purchasing behavior in e-commerce. It examines how variables such as time of day, weather conditions, economic fluctuations, social influences, cultural trends, seasonal events, and others can influence online purchasing decisions. Using data from an e-commerce website, a model was built using GIA GMDH to analyze the impact of these factors on consumer purchasing behavior. The study found significant correlations between seemingly minor external factors and consumer behavior, highlighting the importance of holistic analysis for companies to adapt their strategies and improve e-commerce performance.

关键词： Adaptation models Analytical models Consumer behavior Temperature Computational modeling Urban areas Electronic commerce Cultural differences Context modeling Meteorology

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Research and Development of Kubernetes Operator for Machine Learning Pipelines

Research and Development of Kubernetes Operator for Machine ...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Andrey Trapezin Ivan Filianin Faculty of Programming and Infocommunications ITMO University St. Petersburg Russia Faculty of Software Engineering and Computer Engineering ITMO University St. Petersburg Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

The current research work addresses the problem of automating the delivery of machine learning models from MLflow to Kubernetes infrastructure. To solve the mentioned problem, a Kubernetes operator has been developed to automate the delivery of machine learning models to production by integrating MLflow for model tracking and Seldon Core for model serving. The developed operator allows data scientists to deploy models while maintaining the familiar MLflow environment. The operator's automatic deployment triggers upon tagging models in MLflow, greatly simplifying engineers' tasks and minimizing the need for manual infrastructure configuration. By automating configuration tasks and optimizing deployment workflows, the solution achieves a 40-50% reduction in model time to deployment (TTD) metric compared to manual processes and decreases error rates from 15% to around 3%. The practical relevance of the work is that it simplifies collaboration between data and infrastructure teams by providing a unified deployment framework, resulting in faster, more reliable, and automated integration of machine learning models into an organisation's business processes.

关键词： Production systems Error analysis Machine learning Manuals Tagging Data models Reliability Research and development Load modeling Resilience

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Fingerprint Liveness Detection Using Deep Learning 9

Fingerprint Liveness Detection Using Deep Learning

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9th International Conference on Future Internet of Things and Cloud, FiCloud 2022

作者： Ozkiper, Zeynep Inel Turgut, Zeynep Atmaca, Tulin Aydin, Muhammed Ali Halic University Computer Programming Istanbul Turkey Istanbul Medeniyet University Computer Engineering Istanbul Turkey Institut Polytechnique de Paris Samovar Lab./IMT/Telecom SudParis Evry France Istanbul University Computer Engineering Cerrahpasa Istanbul Turkey

ISBN: (数字)9781665493505

ISBN: (纸本)9781665493505

It has great importance to provide the highest accuracy from fingerprint identification and verification systems, which have a large number of biometric features. Fingerprint recognition systems are more widely utilized than other biometric feature recognition systems. For this reason, a fingerprint recognition system must be fast and reliable to realize the separation of the fake and live fingerprints and provide high accuracy. In this study fingerprint liveness detection system is presented using LivDet2015 dataset. SVM (Support Vector Machine), CNN (Convolutional Neural Network), CNN+SVM methods are used for classification and their performances are compared. Especially the classifying performance of CNN method is analyzed. Before the classification process is performed with SVM, edge enrichment, transformation and feature extraction steps are applied on images as preprocessing steps. The highest accuracy rate is obtained by using CNN-deep learning classifier. © 2022 IEEE.

关键词： Support vector machines

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Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

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computers, Materials & Continua 2023年第2期74卷 2677-2693页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Manal Abdullah Alohali Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology 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 Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

关键词： Feature selection grey wolf optimization dipper throated optimization intrusion detection internet-of-things(IoT)

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A Zero-Watermarking Algorithm for Use in RGB and Monochrome Images

A Zero-Watermarking Algorithm for Use in RGB and Monochrome ...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Vadym Poddubnyi Roman Gvozdov Oleksandr Sievierinov Vladyslav Sukhoteplyi Serhii Bulba Dmytro Lysytsia Department of of information technology security Kharkiv National University of Radio Electronic Kharkiv Ukraine Department of Computer Engineering and Programming Ivan Kozhedub Kharkiv National Air Force University Kharkiv Ukraine Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine

ISBN: (数字)9798350384499

ISBN: (纸本)9798350384505

The article proposes a method of zero-watermarking for colored RGB bitmap images. This algorithm can be used to protect property rights to multimedia resources in which it is impossible to use ordinary watermarks due to the impossibility of changing the image (such as medical data where high accuracy is required and the appearance of artifacts is unacceptable) or due to the presence of a higher distortion of the container. This algorithm is based on DWT, SVD and operations in the field by modulo 256. The software implementation of the algorithm was developed (using the Accord and *** libraries to implement DWT, SVD). The software implementation of the algorithm was tested. This algorithm has advantages in the form of normalization of the average error, but it needs further research to improve its performance.

关键词： Software algorithms Authentication Public key Watermarking Distortion Software Libraries Discrete wavelet transforms Faces Testing

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Study of Methods for Improving the Meta-Algorithm of the Bagging Classifier

Study of Methods for Improving the Meta-Algorithm of the Bag...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Svitlana Gavrylenko Oleksii Hornostal Department of Computer Engineering and Programming National Technical University “Kharkiv Polytechnic Institute” Kharkiv Ukraine

In this paper, we explored approaches that improve the performance of ensemble bagging classifiers for identifying the state of a computer system. The following algorithms are considered: Ensemble pruning, Advanced Voting Algorithms, Dynamic Voting Strategies, Confidence Calibration, Adaptation through Meta-Features and Meta-Learning, which allow improving the voting procedure of the bagging meta-algorithm. Artificial data was generated as initial data, which complicates the classification task and contains an increased amount of noise. In the Google Collab environment, software models of algorithms have been developed, and their quality has been assessed. It was found that the use of the Ensemble pruning and Adaptation through Meta-Features algorithm is the most qualitative. In addition, the Ensemble pruning algorithm reduces the number of basic ensemble classifiers, and, as a result, increases the efficiency of computer system identification. Based on the results of the study, a method for identifying a computer system was proposed through the integrated use of a bagging classifier and an optimization procedure based on the Ensemble pruning algorithm.

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