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IEEE International Conference on Data Stream Mining & Processing (DSMP)

作者： Romanna Malets Heorgiy Shynkarenko Igor Malets Petro Vahin Department of programming Ivan Franko National University of Lviv Lviv Ukraine Department of Information System Opole University of Technology Opole Poland Lviv Ukraine Department of Project Management Information Technologies and Telecommunications Lviv State University of Life Safety Lviv Ukraine Department of Information System Ivan Franko National University of Lviv Lviv Ukraine

ISBN: (纸本)9781538628751

The paper presents the construction and analysis of vibration problem of thermoviscoelastic shells under the influence of non-stationary heat and under forced loads. The studied model was based on application of simplest finite element semidiscretization to mixed variational problem of dynamical thermoviscoelasticity. The problem in addition to the mutual influence of temperature field and stress field is also taken into account the viscoelastic properties of the material thin wall body. For assumptions quite suitable for applications we prove the well-posedness forthis model of time harmonic vibrations.

关键词： Heating systems Vibrations Harmonic analysis Mathematical model Tensile stress Load modeling Method of moments

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Virtual Keyboard and Fingers

Virtual Keyboard and Fingers

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International Conference on Information Science and Communications Technologies (ICISCT)

作者： Bakhrom Kabulov Nadira Tashpulatova Marguba Akbarova Department of Applied Mathematics Joint-stock Company "Uzneftegazinformatika" Tashkent Uzbekistan Department of Information science and Computer Graphic Tashkent University of Information Technologies Tashkent Uzbekistan Department of System and applied programming Tashkent University of Information Technologies Tashkent Uzbekistan

ISBN: (数字)9781728125640

ISBN: (纸本)9781728125657

The problem of data entry in mobile devices is considered. The shortcomings of virtual projected keyboards are analyzed. The new input technology called VKF (Virtual Keyboard and Fingers) is offered. It is shown that VKF is more convenient not only in comparison with projected virtual keyboards, but also with data entry methods including use of on-screen keyboards.

关键词： Keyboards Virtual reality Human computer interaction Mobile handsets

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AI-based Power Transformer Condition Assessment

AI-based Power Transformer Condition Assessment

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International Conference on Information Science and Communications Technologies (ICISCT)

作者： Mekhriddin Rakhimov Dias Abdurakhmanov Department of Artificial Intelligence Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi Tashkent Uzbekistan Department of System and Application Programming Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi Tashkent Uzbekistan

Diagnostics of electrical equipment today is one of the main “devices” for assessing its technical condition and allows you to predict its service life (residual resource), which is an a priori task for such strategically important facilities as power transformer. Over the past decade, the understanding of the economic feasibility of technical diagnostics and condition assessment of electrical equipment has increased for many reasons. Therefore, determining which transformers require the most attention can be an extremely difficult task. Condition assessment and data analysis based on artificial intelligence is of great importance for improving the completeness, efficiency and accuracy of the state assessment. Artificial intelligence technologies, such as machine learning, are among the most effective that can be used to solve the problems of assessing the condition of power transformers. K-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve a binary classification. This paper presents the selection of important technical indicators of the transformer and the implementation of the KNN algorithm that aims to improve the accuracy of the binary classification.

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The prediction of students' academic performances with a classification model built using data mining techniques

The prediction of students' academic performances with a cla...

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6th Smart Cities Symposium (SCS 2022)

作者： H. Alkattan M. Abotaleb A. Ali Subhi O. A. Adelaja A. Kadi H. K. Ibrahim Al-Mahdawi Department of System Programming South Ural State University Chelyabinsk Russia University of Diyala Diyala Iraq Information Communication and Technology Department Lagos State University Lagos Nigeria Department of Food and Biotechnology South Ural State University Chelyabinsk Russia

In the purview of most educational sectors today, numerous reviews regarding data mining have been the primary focus, with goals of discovering vast knowledge patterns for students' data. This paper focuses on building model that predicted the performance of computer science students in some courses they were examined through classification technique. The model built for the purpose of classification via decision tree classifier which was adopted in WEKA and KNIME analytics platform. The workflow model basically connected nodes to generate the decision trees while J4.8 Decision Tree Classifier programmable outputs were generated for the courses to obtain the confusion matrix and other instances. Bar Charts were plotted to display the students' performance in the courses. The mean absolute errors, RMS error and relative absolute error all read 0% while the kappa value obtained from the analysis ranged between 0.991 – 1.00 which perfectly agrees for most statistical values.

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Sinkhorn Transformations for Single-Query Postprocessing in Text-Video Retrieval

arXiv

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arXiv 2023年

作者： Yakovlev, Konstantin Polyakov, Gregory Alimova, Ilseyar Podolskiy, Alexander Bout, Andrey Nikolenko, Sergey Piontkovskaya, Irina Huawei Noah’s Ark Lab Moscow Russia Ivannikov Institute for System Programming of the RAS Moscow Russia St. Petersburg Department the Steklov Institute of Mathematics St. Petersburg Russia

A recent trend in multimodal retrieval is related to postprocessing test set results via the dual-softmax loss (DSL). While this approach can bring significant improvements, it usually presumes that an entire matrix of test samples is available as DSL input. This work introduces a new postprocessing approach based on Sinkhorn transformations that outperforms DSL. Further, we propose a new postprocessing setting that does not require access to multiple test queries. We show that our approach can significantly improve the results of state of the art models such as CLIP4Clip, BLIP, X-CLIP, and DRL, thus achieving a new state-of-the-art on several standard text-video retrieval datasets both with access to the entire test set and in the single-query setting. Copyright © 2023, The Authors. All rights reserved.

关键词： Information retrieval

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AbotalebNet: A Novel Deep Learning Architecture for Time Series Forecasting with Convolutional and LSTM Layers Enhanced by Multi-Head Attention 2

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

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Abotaleb, Mostafa Makarovskikh, Tatiana Mijwil, Maad M. Bala, Indu Hiran, Kamal Kant Doshi, Ruchi South Ural State University Dep. of System Programming Chelyabinsk454080 Russia Baghdad College of Economic Sciences University Computer Techniques Engineering Department Baghdad Iraq Lovely Professional University Electrical and Electronics Engineering Punjab India Sir Padampat Singhania University Dep. of Computer Science Udaipur India Universidad Azteca Dep. of Computer Science and Engineering Chalco Mexico

ISBN: (纸本)9798350384277

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. © 2024 IEEE.

关键词： Convolutional neural networks

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Study of Digitization of Russian Higher Education as Basis for Smart Education

Study of Digitization of Russian Higher Education as Basis f...

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IEEE Conference on Quality Management, Transport and Information Security, Information Technologies (IT&MQ&IS)

作者： Natalia V. Dneprovskaya Tuyana A. Bayaskalanova Vitaly L. Ruposov Inessa V. Shevtsova Department of Information System Management and Programming Plekhanov Russian University of Economics Moscow Russia Management Department Irkutsk National Research Technical University Irkutsk Russia School of Public Administration Lomonosov Moscow State University Moscow Russia

ISBN: (纸本)9781538667583

The research objective is to assess the status of digitalization of Russian higher education as a basis for the development of smart education. To solve the tasks of public administration of education, various state information systems are being created. 80% of Russian universities use different information systems in the administrative activities of universities. The assessments of the use of ICT in the educational process point to a great potential that has remained unoccupied in the last few years, less than 30% of Russian students are trained using e-learning and distance education technologies. The use of educational technologies and techniques using ICT is fragmentary. The initiative to develop educational tools and techniques on the basis of ICT, as a rule, belongs to nongovernmental organizations that highly appreciate not only the effectiveness of the use of these technologies in the educational process, but also the potential of the market of educational services provided online. The study shows that the Russian education system has created the necessary backlog for the creation of an ICT infrastructure, regulatory and legal support, which should be used to develop smart education.

关键词： Electronic learning Information and communication technology Economics Task analysis Information systems Training

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Reliable system Design with a High Degree of Diagnostic Procedures for Embedded systems

Reliable System Design with a High Degree of Diagnostic Proc...

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IET Irish Signals and systems Conference

作者： Michael H. Schwarz Josef Boercsoek Department of Safety Computer Technology University of Kassel Department of Computer Architecture and System Programming University of Kassel

ISBN: (纸本)9781629935881

Maintenance starts with reliable diagnostics. programming Logic Controllers (PLCs) are often equipped with a high degree of diagnostic procedures in order to ensure that the processing unit is functioning correctly. It is vital to verify that the system with its programme is still within a 'healthy' state, otherwise a safety function is called and the system is brought into a safe state, or if possible, defect and malfunctioning components are exchanged during operation and the process can continue without shutting down the system. However, when it comes to smaller devices such as intelligent sensors, embedded controller devices with the functionality of an e.g. PID (Proportional-Integral-Derivative), predictive controller, filter or analytical algorithm, which is embedded into a FPGA or micro-controller then diagnostics and verification methods are often not considered in the way they should be. For example, if an intelligent sensor system is not able to diagnose that the sensor-head is malfunctioning, but the sensor-head still provides some data, then the smart algorithm bases its calculation on wrong data, which can cause a dangerous situation. This paper investigates and shows recent results to combine diagnostic methods for small scale devices. Several safety-related structures are considered with a high degree of diagnostic coverage. The paper presents relevant procedures and structures to increase the reliability of small devices without utilising a full scale microcontroller system.

关键词： Reliability Safety structure Maintenance Diagnosis

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Artificial Intelligence in Medicine for Chronic Disease Classification Using Machine Learning

Artificial Intelligence in Medicine for Chronic Disease Clas...

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International Conference on Application of Information and Communication Technologies (AICT)

作者： Mekhriddin Rakhimov Ravshanjon Akhmadjonov Shahzod Javliev Department of Artificial Intelligence Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi Tashkent Uzbekistan Department of System and Application Programming Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi Tashkent Uzbekistan Department of Computer System Tashkent University of Information Technologies Named After Muhammad Al-Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9781665451635

Artificial intelligence (AI) systems in medicine are one of the most important modern trends in global healthcare. Artificial intelligence technologies are fundamentally changing the global healthcare system, making it possible to radically rebuild the system of medical diagnostics while reducing healthcare costs. AI is actively used in research to develop methods for diagnosing coronary heart disease (CHD). There are different types of CHD. Before treating a disease, it is necessary to determine which class of diseases it belongs to. Based on the feature space of the disease, it is possible to classify the type of CHD. Machine learning algorithms can solve this problem. The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve classification problems. The dataset is the more important part of the supervised machine learning algorithm for training. Gathering data is the most important step in solving any supervised machine learning problem. But choosing more important part from the collected data is one of the tasks to be solved. The main purpose of this study is to select more useful parametric attributes from the dataset to obtain a high F1-score of CHD classification.

关键词： Heart Training Machine learning algorithms Machine learning Market research Classification algorithms Information and communication technology

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VR as a method and educational tool in the field of higher education of the Republic of Uzbekistan

VR as a method and educational tool in the field of higher e...

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International Conference on Information Science and Communications Technologies (ICISCT)

作者： Azizbek Khaitov Vasilakhan Gazieva N.P. Isxakova N.X. Latipova Marisheva Larisa Department of Hardware and software provision of management systems in telecommunications Tashkent University of Information Technologies Uzbekistan Tashkent Department of Foreign languages Tashkent University of Information Technologies Uzbekistan Tashkent Department of System and Applied Programming Tashkent University of Information Technologies Uzbekistan Tashkent

ISBN: (数字)9781728125640

ISBN: (纸本)9781728125657

The paper presents the development trends of the modern higher education system of the Republic of Uzbekistan, the concept of the use of virtual reality in the education system, the comparative characteristics of the classical higher education system with the education system using virtual reality, which leads to greater efficiency at low cost. The development of a virtual laboratory, made at the Tashkent University of Information Technologies, on the Unity platform using the example of Arduino Uno, is given.

关键词： Virtual reality Information technology Art Training programming profession

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