بین المذاہب ہم آہنگی کے لئے اقوام متحدہ کا کردار

        Independance, freedom, peace and justice can be included in basic human needs. Need of these qualities and disliking of wars existed in human being since long. To accomplishes his task, UN was established in 1945.  It is working for peace, resolution of disputes, restoration of human rights, welfare of mankind, freedom, religious and interfaifaith harmony.                This world is a temporary abode where the humans are deemed as social beings. Allah Almighty has also endowed the humans with intellect and reason which has made conflict and difference of opinion inevitable among communities. Coupled with this are factors that can prove fatal and lead the humans astray. In such a critical scenario it is high time to work for global peace and harmony and to look for ways and means that ensure mutual understanding, tolerance, respect for humanity and above all respect for all religions.        This artical descides how much successfull UN was in achieving its goals and what are the future prospects

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.