عبد الحق کی اقبال شناسی

Abdul Haq, India’s most popular researcher and literary figure, got retirement from the University of Delhi as dean of the Department of Urdu. For the very first time in India, he selected Iqbaliat as his doctorate topic. His books on Iqbaliat highlighted various unexplored topics and personalities. He presented and preached Iqbal’s thoughts and quotations exactly the way Iqbal wanted them to be presented. His articles on Iqbal always received great appreciation from all the literary circles and conferences in which he presented them. As a profound lover and having an understanding of Iqbal’s poetry, he gave new dimensions to his poetry, previously unknown to the world. Furthermore, as a researcher, he discussed numerous scholars who claimed to have an understanding of Iqbal’s poetry and analyzed their work critically. The current study is an acknowledgement of Professor Abdul Haq’s endeavors in the field of education, literature and his services for Iqbaliat. Moreover, the present study encompasses his written publications on the topic of Iqbaliat.

A Novel Feature Selection Technique Using Rough Set Theory

This is era of information. It is common to find the datasets with hundreds and thousands of features used by real world applications. Feature selection is a process to select subsets or features which are more informative. Feature selection technique is used to remove irrelevant and redundant features without losing much of the information. Recently Rough Set Theory (RST) becomes a dominant tool for FS. It is a theory which provides both data structures and methods to perform data analysis. Rough set theory has offered new ideas and trends for the features selection and deal with inconsistent information. Reduction of attribute is an important issue in rough set theory. Many feature selection techniques have been presented in literature using RST. However, majority of these techniques do not ensure optimal feature subsets and suffer serious performance bottlenecks especially in case of large datasets. In this thesis, we modified genetic algorithm to find subset of features within minimum execution time. In its conventional form, genetic algorithm is heuristic based approach; however, using genetic algorithm does not ensure the optimal feature sub selection. In this thesis, we have modified the algorithm such that the resulted feature subsets are not only the optimal but the resulting performance is also improved. The proposed approach was examined with other state of the art FS approaches various publically available datasets at UCI. Results show that efficiency and effectiveness of the proposed approach are better.