بچوں کے عمرانی مسائل: اسلامی تعلیمات اور بین الاقوامی قوانین کے تناظر میں ایک جائزہ

The way the religion of Islam has emphasized the human rights, might be any other religion has given importance to this very specific topic. Although in Islam, there has no any chapter making been made in Quran and hadith on the permanent bases on the topic of basic human rights, yet if some search is made to find out these Quran and Sunnah, these are also present in expended form in various places and along with these guided principles are also present there. In Quran there is admonition in many places for the protection of human soul, goods and honor as well. Whereas Hadith along with the other various teachings, specifically on the eve of the last sermon known as ( Khutba hajja tul wida) of the Rasool(PBUH) the prescribed charter of human rights is enough to highlight its importance, but no distinctive laws are cited with a special regarded to  children. Anyhow, in 18th century, issues relevant to children came into view those also became the cause of many movements and all the countries are formulated unanimous laws which are still being applied. In present era, the social issues of the children are becoming obnoxious severe. On the behalf of these issues their growth, training and many other facts of life are also attention catching. In this article, an analysis of Islamic and International laws with respect to children will be made and those causes will also be cited due to which social issues of children are continuously being generated.

Softwaredefect Prediction Model Using Multi-Layer Feed Forwardneural Networks

Software defect prediction techniques are being focused by many researchers due to its effectiveness for cost reduction in testing process. Most of the software defect dataset contains uncleaned, noisy, high dimensional and imbalance data. These problems reduce the prediction accuracy of a classifier. In this paper, a framework is proposed which combines approaches to deal with all these problems. This framework comprises of four stages 1) data preprocessing 2) feature selection (FS) 3) class balancing, and 4) classification through ensemble learning. Normalization is performed on cleaned datasets. Multilayer perceptron (MLP) is used as subset evaluator in FS process with six search methods Best first (BF), Greedy Stepwise (GS), Genetic Search (GA), Particle swam Optimization Search (PSO), Rank Search (RS) and Linear forward selection (LFS). Resample and SMOTE algorithm are used for class balancing. In classification stacking ensemble model is applied to build model on 80% of the input data. Here meta classifier is set to MLP and base classifiers include decision tree (J48), Random forest (RF), Support vector machine (SVM), K nearest neighbor (kNN) and Bayes Net (BN). Parameter tuning of meta and base classifier is also performed. Performance is evaluated on NASA MDP using precision, recall, F-measure, MCC, ROC and accuracy. Results have shown that the proposed method showed significant improvement is defect prediction compared to base classifiers.