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

作者： Ganiev, Abdukhalil A. Kerimov, Kamil F. Azizova, Zarina I. Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Information Security Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of System and Application Programming Tashkent Uzbekistan

ISBN: (纸本)9781665432580

This paper presents the process of data anonymisation and de-identification, the use of de-identification techniques as a means of data protection, and analyses de-identification and their relevance to the tasks they perform. There is also understanding re-identification risks and reasons to attempt a re-identification attacks. In addition, it also focuses on the actuality of solving the problem of data anonymisation and the problems of its implementation. © 2021 IEEE.

关键词： Data privacy

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AI-based Power Transformer Condition Assessment

AI-based Power Transformer Condition Assessment

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2022 International Conference on Information Science and Communications Technologies, ICISCT 2022

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

ISBN: (纸本)9781665472296

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. © 2022 IEEE.

关键词： Power transformers

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Artificial Intelligence in Medicine for Chronic Disease Classification Using Machine Learning 16

Artificial Intelligence in Medicine for Chronic Disease Clas...

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16th IEEE International Conference on application of Information and Communication Technologies, AICT 2022

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

ISBN: (纸本)9781665451628

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. © 2022 IEEE.

关键词： Diseases

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Formalization of data derivative structures

Formalization of data derivative structures

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2020 International Conference on Information Science and Communications Technologies, ICISCT 2020

作者： Vosilovich Kabulov, Rustam Bakhtiyorovich Ruzibaev, Ortik Temur Ataxanovich, Kuchkorov Saidqulovich Muminov, Sanjar Tashkent University of Information Technologies Named after Muhammad Al-Khwarizimi Department of Software Engineering Amir Temur str. 108 Tashkent100200 Uzbekistan Department of System and Application Programming of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Amir Temur str. 108 Tashkent100200 Uzbekistan

ISBN: (纸本)9781728199696

The paper considers the topical issue of formal description of data structures. Data structures are widely used in modern object-oriented programming to solve various practical problems. The paper introduces functions for performing operations with such data structures as stack, queue, and priority queue. The conditions that these functions must satisfy are introduced as axioms. To describe the axioms, an approach based on an abstract data type, algebraic and programmatic approaches were used. © 2020 IEEE.

关键词： Object oriented programming

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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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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

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

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OREGON'S GET TOUGH SENTENCING REFORM: A LESSON IN JUSTICE system ADAPTATION

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Criminology & Public Policy 2006年第1期5卷

作者： NANCY MERRITT TERRY FAIN SUSAN TURNER Extensive experience conducting justice system policy studies at the state and local level. As a Policy Analyst with the RAND Corporation and government agencies she designed and directed studies focused on the development implementation and evaluation of sentencing policy as well as juvenile and adult correctional programs. Her current work focuses on the implementation and impact of mandatory sentencing laws cross-jurisdictional variations in their application and the effects of prosecutorial discretion. Additional areas of interest include the development implementation and evaluation of juvenile justice programming. Ms. Merritt received her M.S. from the Pennsylvania State University University Park and is a Ph.D. candidate with the Rutgers University School of Criminal Justice. M.A. M.S. is a Senior project associate in the Behavioral Sciences Department at RAND Corporation. He has extensive research experience in criminal justice and substance abuse. He is expert in computer statistical techniques as well as in managing large datasets. He has conducted analysis for many RAND projects and is author or co-author of numerous publications both from RAND and in peer-reviewed journals. His research interests include alternatives to incarceration substance abuse and treatment racial disparities in prison admissions and research collaborations with state and local justice agencies. Professor in the Department of Criminology Law and Society at the University of California's Irvine campus. She received her Ph.D. in Social Psychology from the University of North Carolina at Chapel Hill. She has led a variety of research projects including studies on racial disparity field experiments of private sector alternatives for serious juvenile offenders work release day fines and a 14–site evaluation of intensive supervision probation. Dr. Turner's areas of expertise include the design and implementation of randomized field experiments and research collaborations with state and local justice agencies. He

Research Summary: Oregon's Measure 11, a mandatory minimum sentencing policy passed in 1994, had fewer negative system impacts than had been anticipated by many state and local criminal justice administrators, due largely to the fact that prosecutors exercised the discretion provided them under the law to selectively prosecute cases. Consequently, fewer Measure 11-eligible cases were sentenced under the relevant statues than before passage of the measure, and more were sentenced to lesser related offenses. At the same time, incarceration rates and sentence lengths increased for both Measure 11 and lesser related offenses. Trial rates increased for two years after Measure 11 took effect before reverting to previous levels. Policy Implications: The “unintended consequences” that Measure 11 produced should not have been unexpected. Our research indicates that the entire system will quickly adapt to mitigate the more draconian outcomes predicted by those who assume a simplistic implementation, which underscores the importance of understanding system dynamics and inter-relationships before implementing reform, as well as the pitfalls of designing legislation for either symbolic appeal or formal logic rather than for actual effect.

关键词： Sentencing Reform Mandatory Minimums Prosecutorial Discretion “Get Tough” Sentencing

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