مشاركة المسلم في الأعمال السياسية بالبلاد غير الإسلامية وحكمها

In this paper, the question of the participation of the resident Muslim in the foreign countries in politics and its rulings and the statement of some issues that are presented to the Muslim, such as: to elect, run or participate in political parties and other issues related to this topic. And also, tried to study the views of the scholars, and presented them and their attitudes, evidence in them, compared with them. The modern world has witnessed phenomena, which is not hidden to every sane person, which was, the Muslim participation in the political activities in the non-Muslim countries. The importance of this research paper is clear because it reveals the meaning of the political concept in the Holy Quran and prophetic tradition, which made the researcher in the attempt to legalize the matter in this regard and to summarize the statements of ancient and contemporary scholars based on their arguments and evidences. The study concluded That the participation in politics activities non-Muslims societies does not fall within the scope of the doctrine originally, since no one doubts in the doctrine of Muslims that it is not permissible to resort to the tyrant and not to judge except with Allah almighty revealed rules and regulations, and when this doctrine disappears or enter doubt of any act of apostasy and disbelief, which is not satisfied by any sound Muslim at this very moment that falls into haram category. At the end conclusion is drawn from variant views of the scholars, and the main findings and recommendations have been given.

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.