علم قراءت اور محدث تابعينِ قراء كرام: قرآن، حدیث اور تاریخی تناظر میں تجزیاتی

The Holy Quran is one of the most vibrant fields of knowledge in Islamic Sharī’ah because it is the primary source of Islamic Law and teachings. It has many sub-fields and branches of knowledge. One of the most significant fields of the Holy Quran’s knowledge is ‘Ilm al-Qira’at (Science of Qurānic Variants). It means the recitation of Quran in various styles, accents, methodology and approaches. According to deeper Islamic perception of Knowledge the Holy Quran was reveled in seven Ahruf (accents, meanings, phrases and styles). This Knowledge ultimately deals with these styles and methodologies. The wise and faithful companions of the Holy Prophet (pbuh) get more and more expertise under the divine leadership of the Kind Prophet (pbuh). After it the nearest era of the Holy Prophet (pbuh) is the era of Tābī’n (Successors of the Companions of the Holy Prophet). They worked hard and get more height and boom in different domains of knowledge of Islamic Sharī’ah. They served every field of Islamic sciences and knowledge but emphases on one of them and became (leader) Imam of it.

A Smart Methodology for Handling Missing Data in Time Series Datasets

Missing information or missing data occurs when no data value is stored for any variable in the dataset. It is usually occurred and can have a substantial effect on the decisions. These days the treatment of the missing data is a real issue. In data quality, a severe threat is missing data which have a direct impact on the certainty of what is being presented to the end user. Imputation of missing values from predictive strategies created the idea that the influences accredited have some association with actual data. If these values did not have an association with actual data, it could produce uncertain results. There are different factual strategies of machine learning techniques like neural systems; genetic programming, and data mining strategies, which are utilized to impute such missing perceptions. In data analytics, predictive methodologies are exceptionally attractive because it can prompt better results. If there are missing data values in datasets certain machine algorithms cannot be applied to predict and forecast data. We can use different time series datasets of different organizations listed in the Pakistan Stock Exchange (PSX). The missing values can be discovered in these selected datasets. We have developed algorithms that can fill the missing dates in the datasets because some of the forecasting algorithms did not initially predict with missing values. The datasets with none missing values can be used for the training of some machine learning algorithms. The results based on different datasets of PSX can be used to forecast the next day value of the stock.