اہل بدعت سے تحدیث و سماع کا حکم: علامہ احمد شاکر کی آراء کا اختصاصی مطالعہ

Allama Ahmad Shakir was a great researcher and has a good command on religious literatures and studies. In his era, he took a great place among the scholars as a specialist in hadith, Islamic jurisprudence, Quranic interpretation, history & principals of aforementioned Subjecta. Now, he is recognized as an authority on principalities of sciences of hadith and jurisprudence. He discussed in his books regarding Sciences of hadith about narrators of hadith which are called ahl e bid‘at and the status of their Ahad?th. Because, there is a huge conflict between many principalities in perspective of accepting their Ahad?th or rejecting them. In this article, based an analytical study, some of his major and innovent concepts and justified principals about ahl e bid‘at are discussed which he had presented in his books or shows his research methodologies in different books with a special study on al-musnad by Imam Ahmad Bin Hanbal (r.a). The research shows his viewpoints regarding this kind of narrators that they are, with some conditions, acceptable and their Ahad?th are also should be narrated. Although, some ancient scholars do not allow with primarily conditions, which are described in this study along with their status & conditions.

Neural Networks Ensemble Evaluation of Aggregation Algorithms for Forecasting

The aim of the thesis is to examine and analyze different aggregation algorithms to the forecasts obtained from individual neural network (NN) models in an ensemble. In this study an ensemble of 100 NN models are constructed with a heterogeneous architecture. The outputs from the individual NN models were combined by four different aggregation algorithms in NNs ensemble. These algorithms include equal weights combination of Best NN models, combination of trimmed forecasts, combination through Variance-Covariance method and Bayesian Model Averaging. The aggregation algorithms were employed on the forecasts obtained from all individual NN models as well as on a number of the best forecasts obtained from the best NN models. The output of the aggregation algorithms of NNs ensemble were analyzed and compared with each other and with the individual NN models used in NNs ensemble. The results of the aggregation algorithms of NNs ensemble are also compared with the Simple Averaging method. The performances of these aggregation algorithms of NNs ensemble were evaluated with the mean absolute percentage error and symmetric mean absolute percentage error. In the empirical analysis, the methodologies developed were tested on the Universiti Teknologi PETRONAS load data set of five years from 2006 to 2010 for forecasting. It can be concluded from the results that the aggregation algorithms of NNs ensemble can improve the accuracy of forecast than the individual NN models with a test data set. Furthermore, in the comparison with the Simple Averaging method, the aggregation algorithms of NNs ensemble demonstrate slightly better performance than the Simple Averaging. It has also been observed during the empirical analysis that; reducing the size of ensemble increases the diversity and, hence, accuracy. Moreover, it has been concluded that more benefits can be achieved by the utilization of an advanced method for forecast combinations.