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

作者： Kuchkorov, Temurbek Khamzaev, Jamshid Allamuratova, Zamira Ochilov, Temur Tashkent University of Information Technologies Nmaed after Muhammad Al-Khwarizmi Department of Computer Systems Tashkent Uzbekistan Tashkent University of Information Technologies Nmaed after Muhammad Al-Khwarizmi Joint Faculty TUIT-BSUIR Department of Information-computer Technologies and Programming Tashkent Uzbekistan

ISBN: (纸本)9781665432580

Automatic road sign recognition is one of the most important steps to help a driver prevent accidents. In this paper, the deep convolutional neural network (Deep CNN) was used for the autonomous traffic and road signs detection and recognition system. The proposed system works in real-time recognition of the image of road signs. The contribution of this article is that it consists of a newly developed datasets for 42 different road signs using combination of existing datasets and collected local road signs. The images were taken from different angles and included other parameters and conditions. More than 30K images were collected to create the dataset, which is named Uzbekistan traffic and road signs (UZ-TRS-2021). The CNN architecture was used with various parameters to achieve the best recognition speed. Experimental results show that the proposed CNN architecture achieved 98% accuracy. © 2021 IEEE.

关键词： Convolutional neural networks

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Analyzing a Two-Staged Multi-objective Transportation Problem Under Quantity Dependent Credit Period Policy Using q-fuzzy Number

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International Journal of Applied and Computational Mathematics 2020年第5期6卷 1-33页

作者： Bera, Raj Kumar Mondal, Shyamal Kumar Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University Midnapore 721102 W.B. India

Recently, the inclusion of a credit period policy becomes a beneficial strategy to improve a transportation business from the point of views of source and destination. In this regard, this study addresses a two-staged multi-objective transportation problem to transport the products from a production house to some retailers through some distributors by minimizing total transportation cost of retailers and by maximizing total profit of distributors simultaneously. Here, distributors offer a quantity dependent credit period policy to the retailers to induce them to procure the items from that distributors. Under this policy, those retailers who collect the materials more than a threshold value, would get this facility. Ideally, to assign a value of the credit period, there is no specific rule. It is totally the own strategy of the distributors with uncertainty. So, it takes different amounts at different times depending on the market situations. In that sense, it has been considered as q-fuzzy number, which is a combination of linear and non-linear memberships. To simplify the model, some arithmetic operations on q-fuzzy number and its defuzzification procedure have been introduced. Finally, two examples have been considered to examine the feasibility for both the cases (crisp and fuzzy) of the model and a non-elitist multi-objective genetic algorithm has been used to find the optimal solution. © 2020, Springer Nature India Private Limited.

关键词： Multi-objective transportation problem Non-elitist multi-objective genetic algorithm q-fuzzy number Quantity dependent credit period

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Data poisoning: issues, challenges, and needs

Data poisoning: issues, challenges, and needs

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7th IET Smart Cities Symposium (SCS 2023)

作者： Mohammad Aljanabi Alaa Hamza Omran Maad M Mijwil Mostafa Abotaleb El-Sayed M. El-kenawy Sahar Yousif Mohammed Abdelhameed Ibrahim 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

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.

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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...

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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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Sports programming as a Tool for Regional Development of the Digital Industry and Higher Education

Sports Programming as a Tool for Regional Development of the...

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Technology Enhanced Learning in Higher Education (TELE), International Conference on

作者： Alexander Galkin Artem Miroshnikov Daniil Sapronov Elena Skarzhinskaya Faculty of Automation and Computer Science Lipetsk State Technical University Lipetsk Russia Department of Applied Mathematics Lipetsk State Technical University Lipetsk Russia Lipetsk branch of the Russian Federation of Sports Programming Lipetsk Russia Department of Cybersport Moscow Financial and Industrial University “Synergy” Moscow Russia

ISBN: (数字)9798350353532

ISBN: (纸本)9798350353549

Abstract-The article presents the history of the development of sports programming as a sport in Russia. The main stages of the formation of the legislative basis for this sport are given. The rationale and description of the process of training athletes in higher education institutions are given - possible traj ectories in the disciplines of “algorithmic programming” and “product programming”, the influence of the development of sports programming on improving the quality of IT education. As an example, the organization of training students in the discipline of “algorithmic programming” at the Lipetsk State Technical University is given. The activities of the Lipetsk regional branch of the All- Russian Sports Federation to popularize this sport including among students are also described.

关键词： Training Industries Organizations History programming profession Sports

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Analysing the Impact of COVID-19 outbreak and Economic Policy Uncertainty on Stock Markets in Major Affected Economies 6

Analysing the Impact of COVID-19 outbreak and Economic Polic...

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6th Smart Cities Symposium, SCS 2022

作者： Matuka, Adelajda Asafo, Shuffield Seyram Eweke, Gamaliel O. Mishra, Pradeep Ray, Soumik Abotaleb, Mostafa Makarovskikh, Tatiana Alhussan, Amel Ali Abdelhamid, Abdelaziz A. El-Kenawy, El-Sayed M. Dutta, P.K. Chowdhury, Subrata University of Macerata Department of Economics and Law Italy Niger Delta University Department of Banking & Finance Nigeria J.N.K.V.V. College of Agriculture M.P. Powarkheda India Centurion University of Technology and Management Odisha Paralakhemundi India Department of System Programming South Ural State University Chelyabinsk454080 Russia Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia Department of Computer Science Faculty of Computer and Information Sciences Ain Shams University Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and Technolog Egypt School of Engineering and Technology Amity University West Bengal Kolkata India AP Chittoor India

ISBN: (纸本)9781839538544

The covid-19 pandemic and Economic Policy Uncertainty resulting from the shutdown of production, withdrawal of investments, enforcement of lockdowns and quarantines globally, have been directly affecting stock markets worldwide. This study is thus an attempt to analyse the impact of the COVID-19 pandemic on stock market behaviour in major affected economies. Moreover, the time frame was extended by using current data which investigate the impact of the virus during the boom and the blast phase in the country's most hit by the pandemic crisis such as China, Italy, UK and US. The frequency of the data is daily, and it dates from 3 January 2020 up to 10 February 2021. The considered time framework will give a deep insight into how stock markets behave in the case of an exogenous shock. The Dickey-Fuller Augmented Unit Root Test indicates that all the variables are stationary at first difference, which is one of the main conditions to have robust result, and the ARCH-LM test for the heteroskedasticity of the residuals, which show that all the probability values are significant, rejecting the null hypothesis of no ARCH effect. Based on the results of GARCH (1,1), we conclude that the change in stock markets volatility is positive and significant in China, Italy, UK and USA. This suggests that the impacts of COVID-19 outbreak and economic policy uncertainty on Stock Markets are a significant and Homogeneous across the studied countries, and the shock on FTSE Italia All Share has the longest time to vanish which makes it the riskiest to invest during this period, while the shock on SP500 USA has the shortest time to disappear meaning that it is safest Stock Market in this study. These findings have significant implications for policymakers, institutional and individual investors and Financial Markets analysts. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Investments

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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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Evaluating carbon cap and trade policy effects on a multi-period bi-objective closed-loop supply chain in retail management under mixed uncertainty: Towards greener horizons

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Expert Systems with Applications 2024年 250卷

作者： Bhunia, Satyajit Das, Soumen Kumar Jablonsky, Josef Roy, Sankar Kumar Department of Pure and Applied Science Midnapore City College Kuturia West Bengal Bhadutala721129 India Department of Industrial Management National Taiwan University of Science and Technology Taipei Taiwan Department of Econometrics Faculty of Informatics and Statistics Prague University of Economics and Business Praha 3 Prague Czech Republic Department of Applied Mathematics with Oceanology and Computer Programming Vidyasagar University West Bengal Midnapore721102 India

In response to the growing imperative of addressing environmental concerns and aligning with governmental regulations in supply chain management, this study navigates the optimization landscape of closed-loop supply chains through the lens of carbon cap and trade policy. The optimization model aims to account for the impact of consumer demands, distribution networks, and retail operations on the overall closed-loop supply chain dynamics. The innovative approach introduces an optimization model designed to grapple with the challenges posed by imprecise information within an intuitionistic type-2 fuzzy framework. The bi-objective mixed-integer model integrates pivotal objectives, including carbon emissions reduction, total cost minimization, and trade policy compliance. For a nuanced comprehension of the proposed model, we present three numerical examples, offering clarity on its complexities. Following this, a sensitivity analysis visually depicts the impact of fluctuations in demand and capacity on the defined objective functions. Furthermore, these conclusions unveil promising avenues for future research and exploration within the ambit of this study. © 2024 Elsevier Ltd

关键词： Multiobjective optimization

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AbotalebNet: A Novel Deep Learning Architecture for Time Series Forecasting with Convolutional and LSTM Layers Enhanced by Multi-Head Attention

AbotalebNet: A Novel Deep Learning Architecture for Time Ser...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Mostafa Abotaleb Tatiana Makarovskikh Maad M. Mijwil Indu Bala Kamal Kant Hiran Ruchi Doshi Dep. of System Programming South Ural State University Chelyabinsk Russia Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Electrical and Electronics Engineering Lovely Professional University Punjab India Dep. of Computer Science Sir Padampat Singhania University Udaipur India Dep. of Computer Science and Engineering Universidad Azteca Chalco Mexico

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This paper introduces AbotalebNet, a novel deep learning architecture optimized for time series forecasting, with a particular focus on the complexities of COVID-19 data. AbotalebNet’s architecture is mathematically represented as:\begin{equation*}\hat Y = {\text{Attention}}(\operatorname{LSTM} ({\text{CNN}}(X)))\tag{1}\end{equation*}Here, X represents the input time series data, and Ŷ is the forecasted output. AbotalebNet integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the sequential data processing power of Long Short-Term Memory (LSTM) networks, further enhanced by a Multi-Head Attention *** address overfitting, a key challenge in deep learning, the model incorporates regularization strategies, dropout mechanisms, and batch normalization:\begin{equation*}\mathop {\min }\limits_\Theta ( Loss (X,\hat Y;\Theta ) + \lambda R(\Theta ))\tag{2}\end{equation*}In this equation, Θ denotes the model parameters, R(Θ) represents regularization terms, and λ is the regularization coefficient. These additions aid in preventing the model from overfitting to the training data, ensuring robust performance on unseen *** evaluation on COVID-19 time series data demonstrates AbotalebNet’s enhanced predictive accuracy over traditional models, solidifying its potential for advanced non-linear time series analysis.

关键词： COVID-19 Analytical models Solid modeling Time series analysis Training data Predictive models Data models

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Modelling the Process of Determining the Tourist Demand Based on Fishbein's Multi-Attribute Model 15

Modelling the Process of Determining the Tourist Demand Base...

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15th IEEE International Scientific and Technical Conference on computer Sciences and Information Technologies, CSIT 2020

作者： Hovorushchenko, Tetiana Glukhov, Vladyslav Hovorushchenko, Olha Computer Engineering and System Programming Department Khmelnytskyi Ukraine Khmelnytskyi Lyceum No. 17 Khmelnytskyi Ukraine

ISBN: (纸本)9781728174433

The purpose of this paper is automating the analysis of tourist demand by developing a web-service for surveying tourists (both real and potential) and for presenting the survey results in the form of determining the tourist demand. The model of the process of determining the tourist demand based on Fishbein's multi-attribute model has been developed. The most important attributes, by which tourists evaluate their travels, and the relative importance of such attributes are identified. Rules for translating the thoughts and ratings of the user into an acceptable for the developed model 7-point bipolar scale have been developed. The web-service, which provides automating the calculation of tourist attraction of different countries, has been developed. © 2020 IEEE.

关键词： Web services

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