Search from the Journals, Articles, and Headings
Advanced Search (Beta)
Loading...
Loading...
Loading...
Loading...
Loading...
Loading...

حسرت ہی رہی مجھ پہ و ہ احسان کرے گا

حسرت ہی رہی مجھ پہ وہ احسان کرے گا
دیدار سے ہر درد کا درمان کرے گا

ہے عید کا دن آج وہ نکلے گا سنور کے
وہ عید کے دن حشر کا سامان کرے گا

بے چین مرے دل نہ رقیبوں سے ہوا کر
دشمن ہے تو ہر حال میں نقصان کرے گا

بیمارِ محبت ہے نکل جائے مطب سے
یہ اور مریضوں کو پریشان کرے گا

تائبؔ سے کبھی جان کو تو مانگ کے تو دیکھ
سو بار ترے نام پہ قربان کرے گا

Importance of Evidence of DNA in Perspective of Islamic Jurisprudence

DNA or Genetic fingerprinting technology is the topic of the day. It has revolutionized the forensic science. Islamic Jurisprudence has its own procedure and priorities of evidences, which mainly depend upon eyewitness, personal evidence and testimony. It was introduced in 1984. It is used in the identification of parentage, forensic sciences, treatment and diagnosis of diseases. The sequence of base pairs varies from person to person and the relativity of persons is identified by identifying the matching of base pairs. The Contemporary International Institutions of Collective Ijtih฀d have launched heavy discussions on this new evidence and reviewed relevant serious law making efforts based on it, which results in very valuable Fat฀w฀ and resolutions, regarding the use of DNA techniques, as evidence in criminal cases and its limitations and scope in Islamic Jurisprudence. This article discusses and concludes that the genetic fingerprinting technique should be used for the attestation of the cases related to it, along with the traditional way to acquire evidences, even though, it does not have self-sustaining priority, but depends upon other evidences for making a judicial verdict. Like other forensic evidences, it has also errors and intervening factors that limit its accuracy. Therefore, the decisions of crimes liable to ฀ud฀d, Qi฀฀฀ and Diyyat should not depend only upon DNA fingerprinting. Thus, we can say that in the absence of stipulated evidences, rebuking punishment may be sentenced on the basis the evidence of DNA.

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.
Asian Research Index Whatsapp Chanel
Asian Research Index Whatsapp Chanel

Join our Whatsapp Channel to get regular updates.