Al-Sukākī’s Classification of Metaphor and Qurānic Discourse

The present study is divided into two main sections; the first section will give a general overview about the figurative language and more focus on metaphor (istiᶜārah in Arabic) because the metaphor is considered as one of the most literary devices and the main category of the figurative language. So in this study has given various definitions of figurative language and metaphor according to Muslims and Non-Muslims linguists and along with this explained Al-sukākī’s classification of metaphor which is little close to Al-Jurjānī’s classification of metaphor and view respectably among Muslims and Non-Muslims linguists. The second section of this study deals with metaphors given in Holy Qur'ān, which are denoted according to Al-sukākī’s classification in this respect. In this reference the verses are presented with detailed tafsīrī literature so the reader could well comprehend the purposes and the classical aspect of metaphors in text and also could evaluate linguistic architecture of  Holy Qur'ān.

Ant Colony Optimization Based Emerging Pattern Discovery for Supervised Learning

With the growth of internet and advance in computing technologies, large volume of data is generated and processed on daily bases. The situation is very appealing for sophisticated tools and techniques to process and get the insight of such a large data ubiquitous in every domain. Classi - cation and pattern recognition is an important technique in data mining task tond unexplored knowledge within the data. Emerging Pattern (EP) based technique is used to discover an abrupt change in across the datasets. Emerging pattern based classi ers are very e cient innd- ing patterns inherently exist among the collection of large datasets in hand. Nevertheless, the discovery of emerging patterns is a challenging and non-trivial task due to the inherent complexity of datasets. More- over, in contemporary literature plethora of work is available tond a hidden emerging pattern. Among these techniques,tree-based approach is commonly utilized for the discovery of emerging pattern. However, a tree-based approach follows greedy search technique, su ers various limitations i.e. tree size grows with problem size up to the level where tree-based computation is not cost e ective. In this dissertation, a novel approach for discovering emerging patterns has been proposed. The pro- posed approach exploited Ant Colony Optimization (ACO) technique for the exploration of high quality emerging patterns in the classi cation problem. Furthermore, in contrast to the deterministic approach used in tree-based classi ers, the proposed probabilistic method provides com- petitive classi ers. The proposed approach is capable to e ciently avoid an exhaustive search of tree-based approach and obtain comparatively better accuracy to discover strong jumping emerging patterns. The pro- posed approach has been evaluated on various benchmark datasets for accuracy and robustness. Additionally, most of the classi ers are work with discrete data only and Discretization methods are used to change continuous data into discrete before input to a particular classi er. To evaluate the performance of proposed approach on di erent discretiza- tion methods several experiments are carried out to evaluate the robust- ness. Experimental results show that proposed approach provides better performance on di erent datasets in comparison with the state-of-the-art techniques.