اسلامی اور مغربی تناظر میں آزادی اظہار رائے کا تقابلی جائزہ

Freedom of Expression: A Comparative Study from Islamic & Western Perspective Living on the earth is all about freedom and as we know that the first man on the earth Adam was sent on the earth with the freedom to worship only to Allah. This freedom is a blessing of Allah and the value of it would only be judged in the era of slavery. It is an ultimate principle in Islam that the total submission is to be Allah alone. Thus, a man would never be a slave of any other person and whenever they become a slave to any other authority they are treated inhumanly. Allah (SW) made this glorious world for human beings and granted them the status of Ashraf ul Makhlūq. Human has such value which can be testified by this reason that everything is tightened to them and the world is only created for them. But human can only be benefited from the world till they are better for them and others, living friendly and create atmosphere of peace. So, till the end they will be benefited and blessed. They will also be in peace and freedom if they take care and have the respect for humanity. As man is a social animal and doesn’t live without his community. Sometimes due to controversy in thinking and other routes fall in conflicts, for which a proper law is administered. The differences in views are not forbidden. It is important, not to cross limits and to create peaceful atmosphere in order to rest humanity in peace and make the world peaceful. This article deals with the comparison of Islamic freedom of expression and the modern Western criterion of freedom of speech. The study concludes that the Islamic and western thought on freedom is different from each other.  In this study comparative and critical approach is adopted.

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.