الروايات التاريخية الأندلسية بين الفن والواقع دراسة مقارنة بين الأدبين العربي والأردي

Comparative study is one of the most important aspects of literary criticism in literature. It helps us to discover what differentiates the work of different writers in different literatures. It clarifies many important aspects which may have been left unnoticed while studying independently. It opens many vistas of literary research. In the present dissertation an effort has been made to compare Jurj฀ Zayd฀n, M฀r฀f al Arnaw฀t, Abdul Hal฀m Sharar and An฀yatull฀h Al-Tamash as historical novelists because most of these writers considered as pioneer of historical novels, while everyone is at top list in novel writing in respective country. The present work on a comparative study of historical novelists of Arabic and Urdu literature (about Spanish Era) may, perhaps, be the first research work in Arabic on four writers belonging to four different climates, cultures and origins by any University in Pakistan In this research work you will find out, the similarities and differences in the narrative techniques of writers in their novels and how these writers creates a conformity between romance and real history through literary innovation. In what way they represent the distinctive individualities and civilizations of a particular era. What aspects of theme, characterization and various narrative techniques they use to make the historical novel an attractive and coherent representation of the social and political life. There is a certain connection and dependence on history but using imagination how they contributes to color the descriptive details with romantic flavor. You will find out whether these writers work subordinated to history or distorting history. History tells us what really happens and fiction relates what can happen. In the historical novel the writer tries to create a coalition between history and fiction. In this research you will find that coalition.

Classification and Associative Classification Rule Discovery Using Ant Colony Optimization

The primary goal of this research is to investigate the suitability of ant colony optimization, a swarm intelligence based meta-heuristic developed by mimicking some aspects of the food foraging behavior of ants, for building accurate and comprehensible classifiers which can be learned in reasonable time even for large datasets. Towards this end, a novel classification rule discovery algorithm called AntMiner-C and its variants are proposed. Various aspects and parameters of the proposed algorithms are investigated by experimentation on a number of benchmark datasets. Experimental results indicate that the proposed approach builds more accurate models when compared with commonly used classification algorithms. It is also computationally less expensive than previously available ant colony algorithm based classification rules discovery algorithms. A hybrid classifier using ant colony optimization is also proposed that combines association rules mining and supervised classification. Experiments show that the proposed algorithm has the ability to discover high quality rules. Furthermore, it has the advantage that association rules of each class can be mined in parallel if distributed processing is used. Experimental results demonstrate that the proposed hybrid classifier achieves higher accuracy rates when compared with other commonly used classification algorithms. A feature subset selection algorithm is also proposed which is based on ant colony optimization and decision trees. Experiments show that better accuracy is achieved if the subset of features selected by the proposed approach is used instead of full feature set and number of rules is also decreased substantially.