A web usage mining approach based on LCS algorithm in online predicting recommendation systems

The Internet is one of the fastest growing areas of intelligence gathering. During their navigation Web users leave many records of their activity. This huge amount of data can be a useful source of knowledge. Advanced mining processes are needed for this knowledge to be extracted, understood and used. Web Usage Mining (WUM) systems are specifically designed to carry out this task by analyzing the data representing usage data about a particular Web site. WUM can model user behavior and, therefore, to forecast their future movements. Online prediction is one Web Usage Mining application. However, the accuracy of the prediction and classification in the current architecture of predicting users' future requests systems can not still satisfy users especially in huge Web sites. To provide online prediction efficiently, we advance an architecture for online predicting in Web Usage Mining system and propose a novel approach based on LCS algorithm for classifying user navigation patterns for predicting users' future requests. The Excremental results show that the approach can improve accuracy of classification in the architecture

Analyzing the opinions and emotions of Internet customers using deep ensemble learning based on rbm

Background: The emotions and opinions of Internet users are critical, as they directly influence the provision of proper services. The aim of this study was analyzing the opinions and emotions of internet customers using deep ensemble learning based on rbm. Methods: Method of this study was based on the deep ensemble learning technique which uses a deep ensemble neural network based on Gaussian restricted Boltzmann machine and cost-sensitive tree the opinions and emotions of Internet customers were analyzed in terms of semantics and linguistics in virtual shops. To analyze behavior or emotions, existing algorithms were divided into groups of semantic approach, language approach and machine learning. The semantic, linguistic and group learning aspects (machine learning) were considered together. The opinions, feelings, and behaviors of Internet customers were analyzed. The proposed method was implemented in MATLAB software. To evaluate this method, conventional criteria that were /applied in data mining applications have been used including accuracy, recall, and F score. Results: Based on the experiments performed and by evaluating this method against individual and ensemble methods plus the approaches presented in data mining so far, it was revealed that the proposed model outperforms other methods regarding data mining assessment criteria. Conclusion: Based on social engineering, the suggested model is provided to forecast consumer behavior. In addition to analyzing customers' behavior which examined their emotions and feelings based on their opinions. The results of this study can be used by planners in the field of competitive internet markets

The efficacy of using data mining techniques in predicting academic performance of architecture students.

In recent years, there has been a tremendous increase in the number of applicants seeking placement in the undergraduate architecture programme. It is important to identify new intakes who possess the capability to succeed during the selection phase of admission at universities. Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during selection process. The present study investigates the efficacy of using data mining techniques to predict academic performance of architecture student based on information contained in prior academic achievement. The input variables, i.e. prior academic achievement, were extracted from students' academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data was divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models. The results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement are good predictors of academic performance of architecture students. Although the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students. The developed SVM model can be used a decision-making tool for selecting new intakes into the architecture program at Nigerian universities