Autonomous Intelligent Systems: Agents and Data Mining

High failure rates are a worrying and relevant problem in Brazilian universities. From a data set of student transcripts, we performed a study case for both general and Computer Science contexts, in which Data Mining Techniques were used to find patterns concerning failures. The knowledge acquired can be used for better educational administration and also build intelligent systems to support students’ decision making.

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Application of Knowledge and Data Mining to Build Intelligent Systems

Nanoelectronics, Circuits and Communication Systems - Lecture Notes in Electrical Engineering ◽

10.1007/978-981-15-7486-3_5 ◽

2020 ◽

pp. 33-44

Author(s):

R. Kavitha

Keyword(s):

Data Mining ◽

Intelligent Systems

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Context-Sensitive Attribute Evaluation

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch052 ◽

2011 ◽

pp. 328-332

Author(s):

Marko Robnik-Šikonja

Keyword(s):

Data Mining ◽

Intelligent Systems ◽

Regression Tree ◽

Feature Weighting ◽

Feature Subset Selection ◽

Context Sensitive ◽

Relief Algorithm ◽

Sensitive Attribute ◽

Attribute Evaluation ◽

Tree Building

The research in machine learning, data mining, and statistics has provided a number of methods that estimate the usefulness of an attribute (feature) for prediction of the target variable. The estimates of attributes’ utility are subsequently used in various important tasks, e.g., feature subset selection, feature weighting, feature ranking, feature construction, data transformation, decision and regression tree building, data discretization, visualization, and comprehension. These tasks frequently occur in data mining, robotics, and in the construction of intelligent systems in general. A majority of attribute evaluation measures used are myopic in a sense that they estimate the quality of one feature independently of the context of other features. In problems which possibly involve much feature interactions these measures are not appropriate. The measures which are historically based on the Relief algorithm (Kira & Rendell, 1992) take context into account through distance between the instances and are efficient in problems with strong dependencies between attributes.

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Discrete and Fuzzy Models of Time Series in the Tasks of Forecasting and Diagnostics

Axioms ◽

10.3390/axioms9020049 ◽

2020 ◽

Vol 9 (2) ◽

pp. 49

Author(s):

Anton Romanov ◽

Valeria Voronina ◽

Gleb Guskov ◽

Irina Moshkina ◽

Nadezhda Yarushkina

Keyword(s):

Data Mining ◽

Time Series ◽

Knowledge Base ◽

Intelligent Systems ◽

Time Series Data ◽

Series Data ◽

Quality Of Data ◽

Dynamic Data ◽

Production Program ◽

Time Series Data Mining

The development of the economy and the transition to industry 4.0 creates new challenges for artificial intelligence methods. Such challenges include the processing of large volumes of data, the analysis of various dynamic indicators, the discovery of complex dependencies in the accumulated data, and the forecasting of the state of processes. The main point of this study is the development of a set of analytical and prognostic methods. The methods described in this article based on fuzzy logic, statistic, and time series data mining, because data extracted from dynamic systems are initially incomplete and have a high degree of uncertainty. The ultimate goal of the study is to improve the quality of data analysis in industrial and economic systems. The advantages of the proposed methods are flexibility and orientation to the high interpretability of dynamic data. The high level of the interpretability and interoperability of dynamic data is achieved due to a combination of time series data mining and knowledge base engineering methods. The merging of a set of rules extracted from the time series and knowledge base rules allow for making a forecast in case of insufficiency of the length and nature of the time series. The proposed methods are also based on the summarization of the results of processes modeling for diagnosing technical systems, forecasting of the economic condition of enterprises, and approaches to the technological preparation of production in a multi-productive production program with the application of type 2 fuzzy sets for time series modeling. Intelligent systems based on the proposed methods demonstrate an increase in the quality and stability of their functioning. This article contains a set of experiments to approve this statement.

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CONSTRUCTION OF SYSTEMS OF DETECTION OF INVASIONS INTO THE INFORMATI TON AND TELECOMMUNICATIONS NETWORK ON THE BASIS OF METHODS OF INTELLECTUAL DISTRIBUTION OF DATA

Collection of scientific works of the Military Institute of Kyiv National Taras Shevchenko University ◽

10.17721/2519-481x/2020/68-09 ◽

2020 ◽

pp. 80-89

Author(s):

S. Toliupa ◽

O. Pliushch ◽

I. Parhomenko

Keyword(s):

Data Mining ◽

Experimental Research ◽

Intelligent Systems ◽

Detection System ◽

Attack Detection ◽

Information Network ◽

Distributed Information ◽

Network Attack ◽

Detection Model ◽

Detection Systems

The article proposes a combinatorial construction of a network attack detection system based on selected methods of data mining and conducts experimental research that confirms the effectiveness of the created detection model to protect the distributed information network. Experiments with a software prototype showed the high quality of detection of network attacks and proved the correctness of the choice of methods of data mining and the applicability of the developed techniques. The state of security of information and telecommunication systems against cyberattacks is analyzed, which allowed to draw conclusions that to ensure the security of cyberspace it is necessary to implement a set of systems and protection mechanisms, namely systems: delimitation of user access; firewall; cryptographic protection of information; virtual private networks; anti-virus protection of ITS elements; detection and prevention of intrusions; authentication, authorization and audit; data loss prevention; security and event management; security management. An analysis of publications of domestic and foreign experts, which summarizes: experience in building attack detection systems, their disadvantages and advantages; of attack and intrusion detection systems based on the use of intelligent systems. Based on the results of the review, proposals were formed on: construction of network attack detection systems on the basis of selected methods of data mining and experimental research, which confirms the effectiveness of the created detection model for the protection of the distributed information network.

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