سیرت طیبہ ﷺ کی روشنی میں تصور فلاح و بہبود

Islam is the religion of welfare. It emphasizes on accomplishing the rights of human beings concurrently the rights of Allah. Similarly, the government and the member of society jolted regarding this collectively. And everyone is stimulated concerning his responsibilities and adjudges the followers just like one body. Islamic community is just like a cooperative society. It is a duty of everybody to assist the deprived not only financially but also ethically. Until, to meet affably is declared charity. The leader of Islamic state is the responsible for the welfare of everybody. Rather he is bound to feed every being. That is why Haḍrat ʻUmar said that he is liable in the Day of Judgment if a dog is died due to starvation in his regime. In the research paper, the concept of welfare has been manifested in the light of Sīrat-al-Nabī. The method adopted for the research is qualitative as well as descriptive. The Holy prophet urged the believers of Islam on mutual assistance in adverse times. In disaster situations, the responsibilities of every Muslim increase and he should devote his intention tooth, nail and body to rescue.

A Comparativestudy of State-Of-The-Artmachine Learning Classificationmethods

In this era of information and technology data mining has gained much fame. Millions of versatile data records in various forms such as text, digits and images are going to store in databases and online data repositories. Machine learning techniques are playing vital role in analyzing such bulk of data in better way. Health department is considered as one of the most significant domain of generating huge collection of data associated to patient?s care, diagnostics, analysis and recommendations in various contexts based on disease and medical situations. The analysis of health care data can be very helpful for diagnosis of patients and decision making. A number of comparative researches in machine learning techniques have been performed in the literature on health data; however most of these approaches have been limited to a single dataset analysis, focused on a small number of parameters evaluation such as accuracy measurement and lack of graphical representation of statistical performance metrics. There is need to use more parameters and multiple data sets in order to evaluate machine learning algorithms for their maximum performance. The purpose of this research work was to propose and conduct empirical analysis of multiple machine learning classifiers through accuracy, precision, sensitivity, specificity and F-measure parameters to measure their maximum performance on health data. In this regard Diabetes, Kidney, Liver, Lungs and Heart datasets have been analyzed using Na?ve Bayes, LMT, SMO, JRip and J48 Decision Tree classifiers. It has been concluded from analysis that J48 classifier has shown optimal functionality on health datasets having large number of attributes. It has shown high accuracy and F-measure value on CKD (Chronic Kidney Dataset) dataset that is the highest ratio among other classifiers. While in case of small datasets (Lung cancer) Na?ve Bayes and SMO has beaten other classifiers. In graphical representation ROC curve has proved that Na?ve Bayes classifiers presented maximum performance. Precision-Recall curve proved that J48 has beaten other classifiers. Graphical representation of the results of different statistical performance metrics of machine learning Algorithms have also been provided.