استدراكات الحافظ ابن حجر في فتح الباري على الإمام الكرماني في الكواكب الدراري في الحكم على المتون

A collection of Aḥādīth of Prophet Muḥammad (May peace and blessing be upon him) compiled by Imām Bukhārī. The Muslim scholars of past and present time gave great importance to this book by making their efforts to extract the treasures hidden in this book and to present the different approaches and benefits of this book. “Al Kawākib al-Durārī” by Imām Al kirmānī is an old explanation of Ṣaḥīḥ Bukhārī. He was among those scholars who were expert in many fields at a time like knowledge about ╓adīth, its narrators, Commentary, Qirā’t, Qur‘ānic Sciences, Islamic Jurisprudence, Arabic language, Faith, Medicine, History, Geography, Astronomy etc. ╓afiz Ibn ╓ajar who also had the specialization in science of hadith and knowledge about biographies of narrators. During studying “Fatḥ al Bārī” I found that ╓afiz Ibn ╓ajar criticized on the commentary of Al kirmānī at many times in relating different sayings and signals. In this article I studied these comments of ╓afiz Ibn ╓ajar on Imam Alkirmani a critical comparison. After research I have found that ╓afiz Ibn ╓ajar has consulted “Al Kawākib al- Durārī” and quoted Imām Al kirmānī’s commentary and added it. This article approves that judgments of ╓afiz Ibn ╓ajar on conversion and transformation of text and on distorted, additional and incomplete words in the text are more authentic than Imām Al kirmānī.

Robust Algorithm for Genome Sequence Short Read Error Correction Using Hadoop-Mapreduce

Biological sequences consist of A C G and T in a DNA structure and contain vital information of living organisms. The development of computing technologies, especially NGS technologies have increased genomic data at a rapid rate. The increase in genomic data presents significant research challenges in bioinformatics, such as sequence alignment, short-reads error correction, phylogenetic inference, etc. Next-generation high-throughput sequencing technologies have opened new and thought-provoking research opportunities. In particular, Next-generation sequencers produce a massive amount of short-reads data in a single run. However, these large amounts of short-reads data produced are highly susceptible to errors, as compared to shotgun sequencing. Therefore, there is a peremptory demand to design fast and more accurate statistical and computational tools to analyze these data. This research presents a novel and robust algorithm called HaShRECA for genome sequence short reads error correction. The developed algorithm is based on a probabilistic model that analyzes the potential errors in reads and utilizes the Hadoop-MapReduce framework to speed up the computation processes. Experimental results show that HaShRECA is more accurate, as well as time and space efficient as compared to previous algorithms.