عہد نبوی میں حفاظت قرآن سے متعلق مستشرقین کے اعتراضات کا علمی جائزہ

Islam is the complete code of life. The Prophet (PBUH) and his companions made every effort to safeguard it. They handed it over in fully preserved form without any flaw to their true followers. However, adversaries of Islam have been trying to find faults with safety of Quran and the life of Prophet (PBUH). Orientalists are among them. Orientalists are those non-Muslim scholars, who do their research about Muslim’s beliefs, culture and values. Their purpose is to double edge. First to create doubts in the minds of Muslims regarding their religion. Secondly to marsh hated in the hearts and minds of non-believers. This is why these prejudiced scholars criticize Quran and the personal life of Prophet (PBUH). In the following discussion we have analyzed these objections in detail regarding the safeguard of the Holy Quran during the time of Muhammad (PBUH).

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.