فتنہ انکار حدیث کا ایک جائزہ

Human rights are an important issue of every century. The importance of this issue has increased immensely in this age of information and technology where every person is well aware of his rights, among which is social justice. Islam has designed rules and regulations for social justice. It has provided a well-balanced system for it, has made people aware of their social rights, and encouraged them to get those rights. The life and teachings of Prophet Muhammad S.A.W are true reflections of a social justice system that is needed in this age. The Prophet’s last sermon delivered at the occasion of Hajjatul Wida is considered to be the first detailed charter of human rights. He mentioned in that sermon the value of life, property and the esteem of a human being. Islam urges Muslims to deal with women, the oppressed, orphans, those in debt, laborers, neighbors and non-Muslims with sympathy and equality. This article attempts to present the true teachings of Islam regarding social justice, in light of the Quran and the Prophet’s life.

A Framework for Software Defect Prediction Using Ensemble Learning

The development of high-quality software at lower cost has always been the main concern of the developers as well as of the users. Eliminating the defects in software at the initial development stage can increase quality and reduce the overall cost. Testing only those modules which are likely to be defective are helpful for development team to manage and use resources effectively. Many machine learning-based frameworks have been proposed for the prediction of software defects in initial development stage however accuracy evaluation of proposed techniques on benchmark datasets was lacked. In this research, we proposed a framework for the prediction of software defects using ensemble learning and feature selection techniques by using WEKA. The accuracy of the proposed model has been evaluated by using publicly available cleaned NASA datasets. Moreover, the results have been compared with the widely used advanced classification techniques. The Proposed framework consists of five stages. First stage is dealing with the extraction of relevant dataset. Second stage is dealing with variants of base classifiers and selection. The base classifiers include: ?Decision Tree (DT), K-nearest neighbor (kNN), Naive Bayes (NB), Random forest (RF) and Support Vector Machine (SVM)?. Pre-processing and feature selection have been done in third stage. In fourth stage, we used stacking technique to create an ensemble of the classifier-variants, which have performed well in third stage. Fifth stage deals with the results and performance evaluation by using different measures including: ?Precision, Recall, F-measure, Accuracy, MCC and ROC?.