Comparative Analysis of Classifiers for Prediction of Epileptic Seizures

Epilepsy is a neurological disease in which people suffer from seizure attack and lose the normal function of brain. Almost 50 million people have epilepsy in the world due to which it has become the most common neurological disease. Early prediction of epilepsy helps patients to avoid epilepsy and live normal life. Many studies have been conducted for the early prediction of epilepsy. However, selection of the most appropriate classifier has always been a question that needs to be resolved. In this study, we are using six classifiers of machine learning which are KNN, Naïve Bayes, Linear Classification Model, Discriminant Analysis Model, Support Vector Machine and Decision Tree, to find the best classifier for the prediction of epileptic seizures, in term of accuracy. Dataset from “Kaggle” was used. Preprocessing and cross-validation of the data was carried out for training and testing of classifiers. The results depict that Naive Bayes classifier has a better average accuracy of 95.739% as compared to other classifiers. The future work of this study is to implement the suggested model in real time, so that the workload of medical members could be reduced.

A Study of Effectiveness of Quality Assurance Measures Applied at University Level in Punjab.

The present research entitled ―A Study of the Effectiveness of Quality Assurance Measures Applied at University Level in Punjab‖ was a survey study. The main objectives of the study were to review, differentiate, evaluate, and give recommendation regarding the enhancement of quality assurance measures applied at university levels in Pakistan. 77 academic administrators (out of 100), 194 university faculty (out of 200), and 1000 students were taken and selected randomly from 20 universities (out of 44) in the Punjab as a sample of the study. The data was collected through three questionnaires developed by the researcher using five point Likert type rating scales. The instrument was distributed personally to 100 academic administrators, 200 university faculty and 1000 students. The data was analyzed and the results were interpreted in terms of percentage ii mean score value and median. A majority of respondents such as academic administrators, university faculty and students agreed that academic programs are evaluated according to HEC prescribed criteria, there is need to provide training in management and leadership to administrators, university faculty perform academic duties relevant to their abilities, and students like prevalent evaluation system of universities. It was recommended that the quality assurance measures may be given proper weightage practically at the time of faculty promotion and recruitment without political interference;