اسلامی نظام اقتصاد کی اخلاقیات تجارت ومعیشت میں دینی و اخلاقی اقدار کی اہمیت کے حوالے سے علمی و تحقیقی جائزہ

Islam is a holistic and comprehensive system of life because the Creator of universe has given the instructions that are eternal and are perfect guide for humanity in all ages and circumstances. Islamic teachings are consistent with the comprehensive nature. Those instructions that human being has given for all walks of life in that the elements of morality is distinct and dominant and prohibited every act that is harmful to others. This fact is quite clear that the moral aspect of Islam is so important to consider the ethics of Islam has taken into consideration and there is no command to be out of the realm of ethics and particularly matters of life which is part of religion and are important aspect of society. Islamic teachings are so clear that there is no example of it in any economic system the world. Today we have different economic systems in the world which essentially teach us that the purpose of human life is to make money and collect goods even if affects the lives of others. But in Islamic economic world it is emphasized that this world is not the original house and satisfactory zone of human being and his purpose is not to collect wealth but the worship of Almighty. Human being is gifted fair system of order in every aspect of which there is clearly ethics thus human is strictly prohibited to collect money through unfair means and in cruel manners. In this article we will discuss the ethical consideration of Islamic economical system. So it is quite clear that the guarantor of peace, love and harmony in the world is Islamic system.

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.