IDENTIFICATION OF CRITICAL INFRASTRUCTURE USING DATA MINING TECHNIQUES

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Su-Ling Fan ◽  
Chang-Saar Chai ◽  
Kumar Vikram
Keyword(s):  
Data Mining ◽  
Continuous Flow ◽  
Critical Infrastructure ◽  
Economic Factors ◽  
Data Mining Techniques ◽  
The Social ◽  
Using Data ◽  
Disaster Protection ◽  
The Relationship

Critical Infrastructure (CI) is a term used to describe important national assets for producing or distributing a continuous flow of essential goods or services. They are marked by immense complexity, characterized predominantly by strong intra and interdependencies as well as hierarchies. These interconnections take many forms, including flows of information, shared security, physical flows of commodities, and others. Previous research has illustrated the relationship between the physical impacts of natural disasters and the social and economic factors on CI. Some research emphasized more the role of CI interdependencies and their importance and influence over the functioning of industries while others have looked the impacts due to disruption of CI after disasters. Nowadays comprehensive identification of all interdependency relationships of CI remains a challenge. As the complexity and interconnectedness of a country's CI evolve, threats and vulnerabilities increase. Thus, investigating how a set of CI interacts and identification of criticality of CI becomes an important topic. This research has made utilization of data mining techniques and proposes a method to identify the criticality of Critical Infrastructure so that to develop better disaster protection and prevention management.

scholarly journals Mining on Social Media Data: To Determine the Personality of Unrevealed Person

2019 ◽  
Vol 8 (4) ◽  
pp. 8574-8577
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Information Mining ◽  
Social Media Data ◽  
Data Mining Techniques ◽  
Network Discovery ◽  
The Social ◽  
Mining Methods ◽  
Using Data ◽  
Media Data

The unavoidable utilization of online networking like Facebook is giving exceptional measures of social information. Information mining methods have been broadly used to separate learning from such information. The character of the person is predicted whether he is good or not by using data mining techniques from user self-made data. Mining methods are being broadly using to separate learning from such information, main examples for them are network discovery and slant investigation. Notwithstanding, there is still a lot of room to investigate as far as the occasion information (i.e., occasions with timestamps, for example, posting an inquiry, altering an article in Wikipedia, and remarking on a tweet. These occasions react users' personal conduct standards and working forms in the social media websites.

Promoting Persistence: The Role of Academic Advisors

2020 ◽  
pp. 152102512092480
Author(s):  
Megan M. Tippetts ◽  
Andrea Thomas Brandley ◽  
Jolyn Metro ◽  
Meredith King ◽  
Christopher Ogren ◽  
...  
Keyword(s):  
Academic Performance ◽  
Public University ◽  
Colleges And Universities ◽  
Economic Factors ◽  
Academic Advisors ◽  
Using Data ◽  
The Relationship

Retention and persistence to graduation have been concerns for colleges and universities across the country. Research has pointed to sociodemographic and economic factors that affect persistence. Our analyses isolate the relationship between advising appointments and the likelihood of persistence controlling for the possibility of an endogenous relationship and while controlling for sociodemographic and academic performance covariates. Using data from one college in a large, public university, we found that students enrolled in Spring 2018 who met with an advisor one or more times in January through August 2018 were 9% more likely to persist and enroll in Fall 2018 than otherwise similar students who did not visit an advisor at all during that period.

scholarly journals Application of Data Mining in the Analysis of Martial Arts Athlete Competition Skills and Tactics

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Lingrong Tao
Keyword(s):  
Data Mining ◽  
Martial Arts ◽  
Statistical Data ◽  
Practical Significance ◽  
Data Mining Techniques ◽  
Develop Technology ◽  
Competitive Success ◽  
Daily Training ◽  
Using Data ◽  
The Relationship

In martial arts, data mining technologies are used to describe and analyze the moves of athletes and changes in the process and sequences. Martial arts is a process in which athletes use all kinds of strengths and actions to make offensive and defensive changes according to the tactics of opponents. One such martial arts is Wushu arts as it has a long history in reference to Chinese martial arts. During the Wushu competition, Wushu athletes show their adaptability and technical level in complex, random, and nonlinear competitive abilities, organized and systematic skills, tactics, and position movement. Using data mining techniques, in-depth mining a particular type of martial arts competition technology and tactics behind statistical data, and using the data to find the law of change to solve some problems, for martial arts athletes in daily training to develop technology and tactics and improve competition results, is the practical significance of data mining in martial arts athletes competition. This research explored the relationship between goal-oriented and mental intensity and their effect on competitive success outcomes.

Role of Data Mining and Knowledge Discovery in Managing Telecommunication Systems

2010 ◽  
pp. 14-29
Author(s):  
Ibrahiem Mahmoud Mohamed El Emary
Keyword(s):  
Data Mining ◽  
Quality Of Service ◽  
Expert Systems ◽  
Telecommunications Industry ◽  
Data Mining Techniques ◽  
Telecommunication Systems ◽  
The Status ◽  
Using Data

This chapter is interested in discussing how to use data mining techniques to assist in achieving an acceptable level of quality of service of telecommunication systems. The quality of service is defined as the metrics which are predicated by using the data mining techniques, decision tree, association rules and neural networks. Routing algorithms can use this metric for optimal path selection which in turn will affect positively on the system performance. Also, in this chapter management axis using data mining techniques were handled, i.e., check the status of the telecommunication networks, role of data mining in obtaining optimal configuration, how to use data mining technique to assure high level of security for the telecommunication. The popularity of data mining in the telecommunications industry can be viewed as an extension of the use of expert systems in the telecommunications industry. These systems were developed to address the complexity associated with maintaining a huge network infrastructure and the need to maximize network reliability while minimizing labor costs (Liebowitz, J. 1988). The problem with these expert systems is that they are expensive to develop because it is both difficult and time consuming to elicit the requisite domain knowledge from experts.

A Conceptual Framework for Social Network Data Security

2013 ◽  
pp. 58-86 ◽  
Author(s):  
Sanur Sharma ◽  
Vishal Bhatnagar
Keyword(s):  
Data Mining ◽  
Social Network ◽  
Conceptual Framework ◽  
Social Networking Sites ◽  
Community Analysis ◽  
Network Data ◽  
Data Mining Techniques ◽  
Social Network Data ◽  
The Social ◽  
Using Data

In recent times, there has been a tremendous increase in the number of social networking sites and their users. With the amount of information posted on the public forums, it becomes essential for the service providers to maintain the privacy of an individual. Anonymization as a technique to secure social network data has gained popularity, but there are challenges in implementing it effectively. In this chapter, the authors have presented a conceptual framework to secure the social network data effectively by using data mining techniques to perform in-depth social network analysis before carrying out the actual anonymization process. The authors’ framework in the first step defines the role of community analysis in social network and its various features and temporal metrics. In the next step, the authors propose the application of those data mining techniques that can deal with the dynamic nature of social network and discover important attributes of the social network. Finally, the authors map their security requirements and their findings of the network properties which provide an appropriate base for selection and application of the anonymization technique to protect privacy of social network data.

Role of Data Mining and Knowledge Discovery in Managing Telecommunication Systems

Data Mining ◽  
2013 ◽  
pp. 1591-1606
Author(s):  
Ibrahiem Mahmoud Mohamed El Emary
Keyword(s):  
Data Mining ◽  
Quality Of Service ◽  
Expert Systems ◽  
Telecommunications Industry ◽  
Data Mining Techniques ◽  
Telecommunication Systems ◽  
The Status ◽  
Using Data

This chapter is interested in discussing how to use data mining techniques to assist in achieving an acceptable level of quality of service of telecommunication systems. The quality of service is defined as the metrics which are predicated by using the data mining techniques, decision tree, association rules and neural networks. Routing algorithms can use this metric for optimal path selection which in turn will affect positively on the system performance. Also, in this chapter management axis using data mining techniques were handled, i.e., check the status of the telecommunication networks, role of data mining in obtaining optimal configuration, how to use data mining technique to assure high level of security for the telecommunication. The popularity of data mining in the telecommunications industry can be viewed as an extension of the use of expert systems in the telecommunications industry. These systems were developed to address the complexity associated with maintaining a huge network infrastructure and the need to maximize network reliability while minimizing labor costs (Liebowitz, J. 1988). The problem with these expert systems is that they are expensive to develop because it is both difficult and time consuming to elicit the requisite domain knowledge from experts.

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