scholarly journals Detecting Covid-19 Chaos Driven Phishing/Malicious URL Attacks by a Fuzzy Logic and Data Mining based Intelligence System

2021 ◽  
Author(s):  
Syed Rameem Zahra ◽  
Mohammad Ahsan Chishti ◽  
Asif Iqbal Baba ◽  
Fan Wu
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Intelligence System

Comparison of Linguistic Summaries and Fuzzy Functional Dependencies Related to Data Mining

2014 ◽  
pp. 174-203 ◽  
Author(s):  
Miroslav Hudec ◽  
Miljan Vučetić ◽  
Mirko Vujošević
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Relational Databases ◽  
Missing Values ◽  
Expert Knowledge ◽  
Real Data ◽  
Research Area ◽  
Functional Dependencies ◽  
Useful Knowledge ◽  
Important Research Area

Data mining methods based on fuzzy logic have been developed recently and have become an increasingly important research area. In this chapter, the authors examine possibilities for discovering potentially useful knowledge from relational database by integrating fuzzy functional dependencies and linguistic summaries. Both methods use fuzzy logic tools for data analysis, acquiring, and representation of expert knowledge. Fuzzy functional dependencies could detect whether dependency between two examined attributes in the whole database exists. If dependency exists only between parts of examined attributes' domains, fuzzy functional dependencies cannot detect its characters. Linguistic summaries are a convenient method for revealing this kind of dependency. Using fuzzy functional dependencies and linguistic summaries in a complementary way could mine valuable information from relational databases. Mining intensities of dependencies between database attributes could support decision making, reduce the number of attributes in databases, and estimate missing values. The proposed approach is evaluated with case studies using real data from the official statistics. Strengths and weaknesses of the described methods are discussed. At the end of the chapter, topics for further research activities are outlined.

Decision-Making Methods in MIS Outsourcing

2011 ◽  
pp. 134-152
Author(s):  
Jeanette Nasem Morgan
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Fuzzy Logic ◽  
Case Studies ◽  
Cost Benefit ◽  
Information Services ◽  
Data Warehouses ◽  
Government Decision ◽  
Trade Offs ◽  
Decision Making Processes

This chapter commences with a discussion of corporate and government decision-making processes and the management sciences that support development of decisions. Special decision-making considerations, trade-offs analyses, and cost-benefit studies all figure into decisions that result in outsourcing. Technologies that support different methods of decision-making include data warehouses and data mining, rules-based logic, heuristical processes, fuzzy logic, and expert-based reasoning are presented. The chapter presents case studies and current and evolving technologies. The following sections will address the decision-making methods that are used in considering, executing and monitoring outsourced MIS projects or in service lines related to provision of information services in the organization.

Big data based research on the management system framework of ideological and political education in colleges and universities

2020 ◽  
pp. 1-10
Author(s):  
Yuejun Xia
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Big Data ◽  
Ideological And Political Education ◽  
Political Education ◽  
Representative Point ◽  
Point Sets ◽  
Mining Technology ◽  
Education Management ◽  
Intelligence System

Artificial intelligence model combined with data mining technology can mine useful data from college ideological and political education management, and conduct process evaluation and teaching management. Therefore, based on the superiority of data mining technology and artificial intelligence system, this paper improves the traditional algorithm and constructs a university ideological and political education management model based on big data artificial intelligence. Moreover, this study uses a local sensitive hash function to generate representative point sets and uses the generated representative point sets for clustering operations. In order to verify the performance of the algorithm model, a control experiment is designed to compare the algorithm of this paper with traditional data mining methods. It can be seen from the research results that the algorithm model constructed in this paper has good performance and can be applied to practice.

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