The Preservation and Authenticity of Ḥadīth As a Source of Sīrah: A Critique of Robert Spencer’S Views on Historicity of Muḥammad

The account of the deeds and sayings of the prophet (ﷺ) has been carefully preserved since the dawn of Islam. This is what actually led Ernest Renan to believe that “[Islam] was born in the full light of history”, and that the life of Muḥammad (ﷺ) can be known as explicitly as that of any sixteenth century reformer. Notwithstanding, some contemporary writers doubt the historicity of Muḥammad (ﷺ) ; Robert Spencer is one of them. He is an American author. He is quite famous for his criticism of Islam and the prophet of Islam. He has published twelve books so far. He is a member of Melkite Greek Catholic Church. He contends that Muḥammad’s (ﷺ) historical value is in no way greater and more reliable than mythological figures or characters such as Robin Hood and Macbeth. He maintains that the very idea of subjecting the traditionally accepted account of the life of Muḥammad (ﷺ) and the origins of Islam will be regarded by many Muslims as an affront. To substantiate his argument, he rigorously criticizes the historical value of ḥadīth. He argues that aḥādīth were fabricated and compiled in the third century. He maintains that aḥādīth were concocted much later to be used as weapons in political strife and religious controversies. This paper is an academic attempt to prove that aḥādīth were painstakingly preserved and for that purpose various techniques were employed. Besides, it also sheds light on the reasons for the fabrication of aḥādīth and the ways and means utilized for sifting the genuine aḥādīth from forgeries. Findings of this article suggest that the minute details of the life of Muḥammad (ﷺ) have been scrupulously preserved; hence aḥādīth can be used as a reliable source of Sīrah.

Lung Cancer Classification With Discriminant Features of Mutated Genes Using Machine Learning

Machine learning based mathematical and statistical models are employed for the development of improved classification systems. These decision based systems have the capability of automatically learning from complex sequential data. In this work, machine learning models are developed for the classification of lung cancer. The early classification of lung cancer is critical for successful cancer treatment. Genes and proteins are important in the normal functioning of the human body. The abnormal processes due to somatic mutations transform normal cells into cancer cells. The somatic mutations in genes are ultimately reflected in gene expression and proteins amino acid sequences. Influential information is extracted during the statistical analysis of gene expression and proteins amino acid sequences data. This information is transformed into discriminant feature spaces using physiochemical properties. The machine learning capability is exploited effectively using discriminant information of mutated genes in proteomic and genomic data.This study aims to develop artificial intelligent lung cancer classification systems. The development was carried out in three main phases. In the first phase, lung cancer classification system using protein amino acid sequences is developed by employing various individual learning algorithms. In the second phase, lung cancer classification system using protein amino acid sequences is developed by employing multi-gene genetic programming. This approach exploits evolutionary learning capability by optimally combining the selected discriminant features with primitive functions. The third phase is focussed on the development of improved lung cancer classification system using influential features of gene expression with the imbalanced dataset by employing rotation forest. In the thesis work, extensive experiments are conducted to evaluate the performance of various lung cancer classification systems. The proposed systems have obtained excellent accuracy values in the range of 95%99%. The comparative analysis highlights that proposed lung cancer classification systems are better than previous approaches. It is expected that research outcome would impact in the fields of diagnosis, prevention, and effective treatment of lung cancer.