A Review Analysis on Anomaly Detection Using Data Mining Techniques in Social Networking

Survey on Anomaly Detection using Data Mining Techniques

In the present world huge amounts of data are stored and transferred from one location to another. The data when transferred or stored is primed exposed to attack. Although various techniques or applications are available to protect data, loopholes exist. Thus to analyze data and to determine various kind of attack data mining techniques have emerged to make it less vulnerable. Anomaly detectio...

Sentiment Analysis of Social Networking Data Using Categorized Dictionary

Sentiment analysis is the process of analyzing a person’s perception or belief about a particular subject matter. However, finding correct opinion or interest from multi-facet sentiment data is a tedious task. In this paper, a method to improve the sentiment accuracy by utilizing the concept of categorized dictionary for sentiment classification and analysis is proposed.  A categorized dictiona...

Anomaly Detection from Log Files Using Data Mining Techniques

Log files are created by devices or systems in order to provide information about processes or actions that were performed. Detailed inspection of security logs can reveal potential security breaches and it can show us system weaknesses. In our work we propose a novel anomaly-based detection approach based on data mining techniques for log analysis. Our approach uses Apache Hadoop technique to ...

Anomaly Detection in Online Social Networks: Using Data- Mining Techniques and Fuzzy Logic

Automated detection of coronavirus disease (COVID-19) by using data-mining techniques: a brief report

Background: The clinical field has vast sick data that has not been analyzed. Discovering a way to analyze this raw data and turn it into an information treasure can save many lives. Using data mining methods is an efficient way to analyze this large amount of raw data. It can predict the future with accurate knowledge of the past, providing new insights into disease diagnosis and prevention. S...