جو نظر نظر میں سما رہا ترا کون تھا
پسِ آئنہ جو کھڑا رہا ترا کون تھا
وہ جو جانتا تھا تمھارے خواب و خیال سب
جو حقیقتوں میں بسا رہا ترا کون تھا
یہ جہانِ وسوسہ ساز سارا گمان ہے
وہ جہاں نما جو بنا رہا ترا کون تھا
جسے حسرتیں تھیں اکیلے جینے کا روگ تھا
جو اداسیوں سے بجھا رہا ترا کون تھا
جو فضاؔ کو دیکھنے کے لیے بڑی دیر تک
سرِ بزمِ ناز کھڑا رہا ترا کون تھا
Abstract By preserving and narrating hadīth, a chain of narrators was started to maintain its authenticity. When the experts of hadīth realized that some unreliable transmitters might try to fabricate Hadīth, this work started more systematically. Even the chain gradually attained such importance that every Muhaddīth was concerned much about it. In the second century of Hijra, when the teaching and learning of hadīth became the standard of honor and great respect, some people devoted their lives to this work. They travelled to many countries of world and obtained the knowledge of Hadīth from prominent scholars of their time. Experts of Asmā-ul-Rijāl awarded them the certificate of holding the highest position of trust and credibility. The chains having such trustworthy transmitters are considered to be of higher rank than others. Among such traditions there is also one "Mālik-an-Nāfi'-an-Ibn-e-Umar". Due to the reliability of its narrators, Imām Bukhāri and many other Muhaddithīn considered it as "golden chain". When some of the Orientalists started raising objections to the Prophetic Hadīth, they criticized the narrators of the Hadīth as well. Especially the narrators who were declared trustworthy and reliable by Muslim scholars. For this, they especially criticized Abu Hūraira from among the companions and Imām Zuhri among the Successors. The chain of hadīth, (Mālik an-Nāfi' an-Ibn e Umar) “golden chain” was also seriously criticized by Joseph Schacht and Juynboll etc. In this article, a comparative study of the viewpoints of the Muhaddithīn and the Orientalists regarding the chain “Mālik an-Nāfi' an-Ibn eUmar” is presented.
Volumetric increase in data along with the curse of dimensionality has diverted the recent trends of computer science. Processing such a massive amount of data is a computationally expensive job. Feature selection is the process of selecting subset of data from the entire dataset that contains most of the information. The selected subset is called Reduct. Feature selection has materialized the idea of jumbling with attributes. Subset of attributes is favored which bounces the same information as the wide-ranging set of variables. Various dynamic reduct finding algorithms have been proposed. Dynamic reducts is an extension to the idea of reduct extraction based on rough set. Sub-tables are randomly drawn from the original decision table and reducts are extracted from these sub-tables. These reducts are considered to be the stable reducts for complete dataset. However, all the existing dynamic reduct finding algorithms are computationally too expensive to be used for datasets beyond smaller size. In this research, a novel dynamic reduct finding technique based on rough set theory is proposed, where dynamic reducts and relative dependency are the two key notions. Reducts are selected, optimized and further generalized through strenuous Parallel Feature Sampling (PFS) algorithm. In-depth analysis is performed using various benchmark datasets to justify the proposed approach. Results have shown that the proposed algorithm outperforms the existing state of the art approaches in terms of both efficiency and effectiveness.