Generated rules for AIDS and e-learning classifier using rough set approach

The emergence and growth of internet usage has accumulated an extensive amount of data. These data contain a wealth of undiscovered valuable information and problems of incomplete data set may lead to observation error. This research explored a technique to analyze data that transforms meaningless data to meaningful information. The work focused on Rough Set (RS) to deal with incomplete data and rules derivation. Rules with high and low left-hand-side (LHS) support value generated by RS were used as query statements to form a cluster of data. The model was tested on AIDS blog data set consisting of 146 bloggers and E-Learning@UTM (EL) log data set comprising 23105 URLs. 5-fold and 10-fold cross validation were used to split the data. Naïve algorithm and Boolean algorithm as discretization techniques and Johnson’s algorithm (Johnson) and Genetic algorithm (GA) as reduction techniques were employed to compare the results. 5-fold cross validation tended to suit AIDS data well while 10-fold cross validation was the best for EL data set. Johnson and GA yielded the same number of rules for both data sets. These findings are significant as evidence in terms of accuracy that was achieved using the proposed mode

Generated rules for AIDS and e-learning classifier using rough set approach

The emergence and growth of internet usage has accumulated an extensive amount of data. These data contain a wealth of undiscovered valuable information and problems of incomplete data set may lead to observation error. This research explored a technique to analyze data that transforms meaningless data to meaningful information. The work focused on Rough Set (RS) to deal with incomplete data and rules derivation. Rules with high and low left-hand-side (LHS) support value generated by RS were used as query statements to form a cluster of data. The model was tested on AIDS blog data set consisting of 146 bloggers and E-Learning@UTM (EL) log data set comprising 23105 URLs. 5-fold and 10-fold cross validation were used to split the data. Naïve algorithm and Boolean algorithm as discretization techniques and Johnson’s algorithm (Johnson) and Genetic algorithm (GA) as reduction techniques were employed to compare the results. 5-fold cross validation tended to suit AIDS data well while 10-fold cross validation was the best for EL data set. Johnson and GA yielded the same number of rules for both data sets. These findings are significant as evidence in terms of accuracy that was achieved using the proposed mode

Web pre-fetching schemes using Machine Learning for Mobile Cloud Computing

Pre-fetching is one of the technologies used in reducing latency on network traffic on the Internet. We propose this technology to utilise Mobile Cloud Computing (MCC) environment to handle latency issues in context of data management. However, overaggressive use of the pre-fetching technique causes overhead and slows down the system performance since pre-fetching the wrong objects data wastes the storage capacity of a mobile device. Many studies have been using Machine Learning (ML) to solve such issues. However, in MCC environment, the pre-fetching using ML is not widely used. Therefore, this research aims to implement ML techniques to classify the web objects that require decision rules. These decision rules are generated using few ML algorithms such as J48, Random Tree (RT), Naive Bayes (NB) and Rough Set (RS).These rules represent the characteristics of the input data accordingly. The experimental results reveal that J48 performs well in classifying the web objects for all three different datasets with testing accuracy of 95.49%, 98.28% and 97.9% for the UTM blog data, IRCache, and Proxy Cloud Computing (CC) datasets respectively. It shows that J48 algorithm is capable to handle better cloud data management with good recommendation to users with or without the cloud storage