Moral Reforms in a Community: Modus Operandi in the Light of Sīrah

It’s always the people who define the status of a society. If the people of a society are civilized, the society will be called an ideal society. Moral reforms and behavioural therapy of individuals of the society are basic factors to establish an ideal society. Such an ideal society was established by Prophet Muḥammad (P.B.U.H) in Madina where he laid the foundations of brotherhood and equality among citizens of that state. He showed tolerance towards people belonging to different religions by protecting all their human rights and by providing indiscriminate and quick justice. He not only made the social institutions strong but also developed a history of human civilization. We simply need to work on our moral values and behaviours. We should study the daily life of Muḥammad (P.B.U.H) and mould our lives according to that. Dire need of the modern world is the rectification of human behavior in the light of the ideal life of Muḥammad (P.B.U.H). It will give an opportunity to breathe the same spirit into the contemporary social attitudes so that our society may become peaceful.

Feature Selection Using Rough Set Based Heuristic Dependency Calculation

The amount of data to be processed is significantly increasing day by day. The increase in data size is not only due to more number of records but also due to substantial number of attributes added to space. The phenomenon is leading to the dilemma called curse of dimensionality i.e. datasets with exponential number of attributes. The ideal approach is to reduce the number of dimensions such that resulted reduced set contains the same information as present in the entire set of attributes. There are various approaches to perform this task of dimensionality reduction. Recently, rough set-based approaches, which use attribute dependency to carry out feature selection, have been prominent. However, this dependency measure requires the calculation of the positive region, which is a computationally expensive task. In this research, we have proposed a new concept called the “Dependency Classes”, which calculates the attribute dependency without using the positive region. Dependency classes define the change in attribute dependency as we move from one record to another. By avoiding the positive region, they can be an ideal replacement for the conventional dependency measure in feature selection algorithms, especially for large datasets. A comparison framework was devised to measure the efficiency and effectiveness of the proposed measure. Experiments on various publically available datasets show that the proposed approaches provide significant computational performance with same accuracy as provided by conventional approach. We have also recommended seven feature selection algorithms using this measure. The experimental results have shown that algorithms using the classes were more effective than their counterparts using the positive region-based approach in terms of accuracy, execution time and required runtime memory.