سیرت نگاری کی مشرقی و مغربی روایت: منتخب مصنفین کا تقابلی جائزہ

In the past, mode of Sirah writing was historical and descriptive but during the 19th century, this trend has been changed to the empirical research methodology. Due to religious devotion, unauthentic narrations were not judged and had been quoted constantly in Sirah literature on which orientalists laid the foundation of their objections. In response, various books are produced introducing argumentative, logical and scholarly way of writing to defend “Sirah of Prophet (PBUH)”. Question is what are the motives and causes which have changed classical trend of Sirah writing and what are its consequences? In this paper, a comparative analysis will be taken of books of Eastern Muslim scholars; Rehmatullah Keranwi, Syed Ahmad Khan, Karam Shah Alazhari, Shibli Noumani, Sana ullah Amertasri as well as Western Muslim scholars Mohammad Hussain Haykel, Hameed ullah, and Mohammad Asad. This paper will elaborate the difference between both of their research methodology and its effect in the field of Sirah writing and will be helpful for researchers.  

A Framework to Predict the Student S Performance in Programing Courses

Academic grades prediction is considered as one of the hot research areas since last decade, which comes under the domain of educational data mining. It has been observed that in undergraduate computer science programs, programming courses are considered challenging. This results in higher tendency of earning lower grades, failures or drop-outs than other computer science subjects. An early prediction of the students who have high probability of failure (known as at-risk students) will enable the instructors to intervene and provide extra guidance to learners. An accurate prediction of student?s grades can directly influence the overall quality of any degree program and the retention rate of the institution. This research presents a machine learning based classification model for undergraduate students grades prediction, enrolled in any programming course(s) in traditional education system. The proposed model is built after careful collection and pre-processing of data, appropriate feature selection, and model evaluation based on four metrics namely accuracy, precision, recall and F1-score. Six widely used supervised machine learning techniques including Random Forest, Artificial Neural Network, K-Nearest Neighbors, Na?ve Bayes, Ordinal Regression, and Support Vector Machine are used after tuning and optimization. The data used for this research is collected from a private sector university in Lahore. The collected data covers two major domains: student?s academic record and demographic data. The results show that Support Vector Machine and K-Nearest Neighbors give highest scores (ranging from 81% to 94%) for all the evaluation metrics and for all the seven programming courses considered for this study.