检索结果-内蒙古大学图书馆

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

作者： Khidirova, Charos Sadikova, Shakhnoza Mukhsinov, Shamil Nashvandova, Gulrukhsor Mirzaeva, Sarvinoz Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of System and Applied Programming Tashkent Uzbekistan Tashkent State Technical University Department of Information Processing and Control Systems Tashkent Uzbekistan

ISBN: (纸本)9781665432580

Machine learning (ML) methods are the main tool of artificial intelligence, the use of which makes it possible to automate the processing and analysis of big data, to reveal hidden or non-obvious patterns on this basis, and to extract new knowledge. The review presents an analysis of scientific literature on the use of ML methods for diagnosing and predicting the clinical course of coronary heart disease. Provides information on reference databases, the use of which allows you to develop models and validate them (European ST-T Database, Cleveland Heart Disease database, Multi-Ethnic Study of Atherosclerosis, etc.). The advantages and disadvantages of individual ML methods (logistic regression, support vector machines, decision trees, naive Bayesian classifier, k-nearest neighbors) for the development of diagnostic and predictive algorithms are shown. The most promising ML methods include deep learning, which is implemented using multilayer artificial neural networks. It is assumed that the improvement of models based on ML methods and their introduction into clinical practice will help support medical decision-making, improve the effectiveness of treatment and optimize health care costs. © 2021 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Learning extended finite state machines

引用

12th International Conference on Software Engineering and Formal Methods, SEFM 2014

作者： Cassel, Sofia Howar, Falk Jonsson, Bengt Steffen, Bernhard Dept. of Information Technology Uppsala University Sweden Carnegie Mellon University Moffet CA United States Department of Programming Systems Technical University Dortmund Germany

ISBN: (纸本)9783319104300

We present an active learning algorithm for inferring extended finite state machines (EFSM)s, combining data flow and control behavior. Key to our learning technique is a novel learning model based on so-called tree queries. The learning algorithm uses the tree queries to infer symbolic data constraints on parameters, e.g., sequence numbers, time stamps, identifiers, or even simple arithmetic. We describe sufficient conditions for the properties that the symbolic constraints provided by a tree query in general must have to be usable in our learning model. We have evaluated our algorithm in a black-box scenario, where tree queries are realized through (black-box) testing. Our case studies include connection establishment in TCP and a priority queue from the Java Class Library. © 2014 Springer International Publishing.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

The methods for Contour Analysis of Images 14

The methods for Contour Analysis of Images

引用

14th IEEE International Scientific and Technical Conference on Computer Sciences and information Technologies, CSIT 2019

作者： Shamuratov, Oleksii Ryshkovets, Yuriy Shakhovska, Nataliya Kohut, Ihor Lviv Polytechnic National University Department of Artificial Intelligence Systems Lviv Ukraine Lviv Polytechnic National University Information Systems and Network Department Lviv Ukraine Lviv Polytechnic National University Department of Computational Mathematics and Programming Lviv Ukraine

This paper reflects an analysis of contour analysis methods. Their efficiency and computational costs are analysed. The Laplace operator is highlighted. This method is faster and has a lower computational cost. At the... 详细信息

ISBN: (纸本)9781728108063

关键词： Computational efficiency

来源：评论

学校读者我要写书评

暂无评论

METHOD OF DETECTION OF INTRUSIONS BASED ON STACKING META-ALGORITHM 5

METHOD OF DETECTION OF INTRUSIONS BASED ON STACKING META-ALG...

引用

5th IEEE KhPI Week on Advanced Technology, KhPIWeek 2024

作者： Gavrylenko, Svitlana Zozulia, Vladislav Khatsko, Nataliia Department of Computer Engineering and Programming National Technical University "Kharkiv Polytechnic Institute" Kharkiv Ukraine Department Information Systems and Technologies National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine

ISBN: (纸本)9798350384499

An analysis of modern computer network intrusion detection systems was carried out. The application of machine and deep learning methods for classification problems has been investigated. The UNSW-NB15 dataset, developed at the Australian Cyber Security Center's (ACCS) Cyber Range Laboratory, contains data on normal network operations and synthetic intrusions. Data pre-processing was performed, including class balancing using the SMOTEENN method and selection of informative features using the Recursive Feature Elimination method. The possibility of using the stacking meta-algorithm to detect intrusions into computer networks has been investigated. A new algorithm for generating packets of raw data is proposed, which generates two sets of training data: one for training basic models, the other for a meta-model. A study of the effectiveness of using Random Forest, ANN, K Nearest Neighbor methods and Support Vector Machine and Random Forest as a decision-making meta-model was conducted. The use of the stacking meta-algorithm with the proposed algorithm for forming packets of output data, as well as basic models and a meta-model, led to a significant improvement in the quality of the model. It was found that, on average, recall and f1 score increased by 55.6% and 37.4%, respectively, compared to raw data and other models. ©2024 IEEE.

关键词： Network intrusion

来源：评论

学校读者我要写书评

暂无评论

LearnLib: A framework for extrapolating behavioral models

引用

International Journal on Software Tools for Technology Transfer 2009年第5期11卷 393-407页

作者： Raffelt, Harald Steffen, Bernhard Berg, Therese Margaria, Tiziana Department of Programming Systems TU Dortmund Otto-Hahn-Str. 14 44227 Dortmund Germany Department of Information Technology Uppsala University 751 05 Uppsala Sweden Department of Services and Software Engineering Universität Potsdam August-Bebel-Str. 89 14482 Potsdam Germany

In this paper, we present the LearnLib, a library of tools for automata learning, which is explicitly designed for the systematic experimental analysis of the profile of available learning algorithms and corresponding optimizations. Its modular structure allows users to configure their own tailored learning scenarios, which exploit specific properties of their envisioned applications. As has been shown earlier, exploiting application-specific structural features enables optimizations that may lead to performance gains of several orders of magnitude, a necessary precondition to make automata learning applicable to realistic scenarios. © Springer-Verlag 2009.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

A declarative pipeline language for complex data analysis

A declarative pipeline language for complex data analysis

引用

22nd International Symposium on Logic-Based Program Synthesis and Transformation, LOPSTR 2012

作者： Christiansen, Henning Theil Have, Christian Torp Lassen, Ole Petit, Matthieu Research Group PLIS: Programming Logic and Intelligent Systems Department of Communication Business and Information Technologies Roskilde University P.O. Box 260 Roskilde 4000 Denmark

ISBN: (纸本)9783642381966

We introduce BANpipe - a logic-based scripting language designed to model complex compositions of time consuming analyses. Its declarative semantics is described together with alternative operational semantics facilitating goal directed execution, parallel execution, change propagation and type checking. A portable implementation is provided, which supports expressing complex pipelines that may integrate different Prolog systems and provide automatic management of files. © 2013 Springer-Verlag.

关键词： Pipelines

来源：评论

学校读者我要写书评

暂无评论

METHODS FOR IMPROVING THE QUALITY OF CLASSIFICATION ON IMBALANCED DATA 4

METHODS FOR IMPROVING THE QUALITY OF CLASSIFICATION ON IMBAL...

引用

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

作者： Gavrylenko, Svitlana Vladislav, Zozulia Khatsko, Nataliia Department of Computer Engineering and Programming National Technical University "Kharkiv Polytechnic Institute" Kharkiv Ukraine Department of Information Systems and Technologies National Technical University «Kharkiv Polytechnic Institute» Kharkiv Ukraine

ISBN: (纸本)9798350395532

The subject of the study is methods of balancing raw data. The purpose of the article is to improve the quality of intrusion detection in computer networks by using class balancing methods. Task: to investigate methods of balancing classes and to develop a classification method on imbalanced data to increase the level of network security. The methods used are: methods of artificial intelligence, machine learning. The following results were obtained: Class balancing methods based on Undersampling, Oversampling and their combinations were studied. The following methods were chosen for further research: SMOTEENN, SVMSMOTE, BorderlineSMOTE, ADASYN, SMOTE, KMeansSMOTE. The UNSW-NB 15 set was used as the source data, which contains information about the normal functioning of the network and during intrusions. A decision tree based on the CART (Classification And Regression Tree) algorithm was used as the basic classifier. According to the research results, it was found that the use of the SMOTEENN method provides an opportunity to improve the quality of detection of intrusions in the functioning of the network. Conclusions. The scientific novelty of the obtained results lies in the complex use of data balancing methods and the method of data classification based on decision trees to detect intrusions into computer networks, which made it possible to reduce the number of Type II errors. © 2023 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Intelligent system of a smart house 3

Intelligent system of a smart house

引用

3rd International Conference on Advanced information and Communications Technologies, AICT 2019

作者： Lytvyn, Vasyl Vysotska, Victoria Mykhailyshyn, Vladyslav Peleshchak, Ivan Peleshchak, Roman Kohut, Ihor Information Systems and Networks Department Lviv Polytechnic National University Lviv Ukraine Department of Physics Pedagogical University Drohobych Ukraine Department of Computational Mathematics and Programming Lviv Polytechnic National University Lviv Ukraine

ISBN: (纸本)9781728123998

In the work intelligent system of a smart house is developed, which is designed to create from any house, office, or building a smart room. The best solutions and tools were chosen to develop the system, which allowed us to create a fast, optimized, reliable and easy-To-use system with a comfortable mobile and web-based user interface. The optimal parameters for deployment of the system (both hardware and software) were investigated and determined, and its operation in the planned mode, without any interruptions of the system operation. Upon completion of the design and development of the system, an analysis of the product was conducted, based on a control example, which made it possible to verify that the system was operating in accordance with the planned requirements, and provides access to all of the intended functionality. © 2019 IEEE.

关键词： Intelligent systems

来源：评论

学校读者我要写书评

暂无评论

Traffic and road sign recognition using deep convolutional neural network

Traffic and road sign recognition using deep convolutional n...

引用

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

来源：评论

学校读者我要写书评

暂无评论

LearnLib: A library for automata learning and experimentation

LearnLib: A library for automata learning and experimentatio...

引用

FMICS'05 - 10th International Workshop on Formal Methods for Industrial Critical systems

作者： Raffelt, Harald Steffen, Bernhard Berg, Therese University of Dortmund Chair of Programming Systems Baroper Str. 301 44227 Dortmund Germany Department of Information Technology Uppsala University S-751 05 Uppsala Sweden

ISBN: (纸本)1595931481

In this paper we present the LearnLib, a library for automata learning and experimentation. Its modular structure allows users to configure their tailored learning scenarios, which exploit specific properties of the envisioned applications. As has been shown earlier, exploiting application-specific structural features enables optimizations that may lead to performance gains of several orders of magnitude, a necessary precondition to make automata learning applicable to realistic scenarios. Copyright 2005 ACM.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：