فقہ اسلامی میں قسامت کا تصور

Islam lays great emphasis on security and the sanctity of human life. The holy Quran terms killing of an innocent person as killing of the whole humanity. It prohibits unjust killing of human being in unequivocal terms. The holy Qur’an and Sunnah terms killing of an innocent person as one of the greatest sins. An eternal torment is the destiny of a killer who takes life of a person unjustly. However, it is also a bitter fact that hardly   a crime free society could be found   anywhere in the world. Peace prevails only in those societies where culprits are brought to justice. This is why Islamic penal code has prescribed punishments for all kinds of crimes. It has prescribed punishment of Qisâs in case of intentional murder and Diyat (blood money in case of killing of a person by mistake, it is also due in case if remission is made by the heirs in intentional murder case). To prove the crime of murder, testimony of two reliable witnesses or confession of the killer is required before the court. However, if a corpse is found in a place where killer is unknown and witnesses are unavailable,    then Islam enjoins the process of Qasâmah to safeguard rights of the heirs of the deceased. Qasâmah is a process of taking oath by fifty persons selected by the heirs of the slain. In this article the concept of Qasâmah has been elaborated. It  has three parts , in the  first part conditions for the validity of  Qasâmah has been elaborated, while in the second part its process has been discussed with elaborate opinions  of jurists  regarding taking of  oath, as some of them opine that  the  heirs of the slain  have to take oath, mentioning name of the killer,   while others say  oath will be taken by the defendants that they  didn’t kill him, Both these opinions  have been discussed by producing arguments of  the both sides. While in the third part the issue of Qisâs and Diyat has been discussed as according to some jurists the Qasâmah entails Qisâs while other say that it entails Diyat only; arguments of both sides have been discussed in detail.

Boosting Based Multiclass Ensembles and Their Applications in Machine Learning

Boosting is a generic statistical process for generating accurate classifier ensembles from only a moderately accurate learning algorithm. AdaBoost (Adaptive Boosting) is a machine learning algorithm that iteratively fits a number of classifiers on the training data and forms a linear combination of these classifiers to form a final ensemble. This dissertation presents our three major contributions to boosting based ensemble learning literature which includes two multi-class ensemble learning algorithms, a novel way to incorporate domain knowledge into a variety of boosting algorithms and an application of boosting in a connectionist framework to learn a feed-forward artificial neural network. To learn a multi-class classifier a new multi-class boosting algorithm, called M-Boost, has been proposed that introduces novel classifier selection and classifier combining rules. M-Boost uses a simple partitioning algorithm (i.e., decision stumps) as base classifier to handle a multi-class problem without breaking it into multiple binary problems. It uses a global optimality measures for selecting a weak classifier as compared to standard AdaBoost variants that use a localized greedy approach. It also uses a confidence based reweighing strategy for training examples as opposed to standard exponential multiplicative factor. Finally, M-Boost outputs a probability distribution over classes rather than a binary classification decision. The algorithm has been tested for eleven datasets from UCI repository and has consistently performed much better for 9 out of 11 datasets in terms of classification accuracy. Another multi-class ensemble learning algorithm, CBC: Cascaded Boosted Classifiers, is also presented that creates a multiclass ensemble by learning a cascade of boosted classifiers. It does not require explicit encoding of the given multiclass problem, rather it learns a multi-split decision tree and implicitly learns the encoding as well. In our recursive approach, an optimal partition of all classes is selected from the set of all possible partitions and training examples are relabeled. The reduced multiclass learning problem is then learned by using a multiclass learner. This procedure is recursively applied for each partition in order to learn a complete cascade. For experiments we have chosen M-Boost as the multi-class ensemble learning algorithm. The proposed algorithm was tested for network intrusion detection dataset (NIDD) adopted from the KDD Cup 99 (KDDâ˘A ´ Z99) prepared and managed by MIT Lincoln Labs as part of the 1998 DARPA Intrusion Detection Evaluation Program. To incorporate domain knowledge into boosting an entirely new strategy for incorporating prior into any boosting algorithm has also been devised. The idea behind incorporating prior into boosting in our approach is to modify the weight distribution over training examples using the prior during each iteration. This modification affects the selection of base classifier included in the ensemble and hence incorporate prior in boosting. Experimental results show that the proposed method improves the convergence rate, improves accuracy and compensate for lack of training data. A novel weight adaptation method in a connectionist framework that uses AdaBoost to minimize an exponential cost function instead of the mean square error minimization is also presented in this dissertation. This change was introduced to achieve better classification accuracy as the exponential loss function minimized by AdaBoost is more suitable for learning a classifier. Our main contribution in this regard is the introduction of a new representation of decision stumps that when used as base learner in AdaBoost becomes equivalent to a perceptron. This boosting based method for learning a perceptron is called BOOSTRON. The BOOSTRON algorithm has also been extended and generalized to learn a multi-layered perceptron. This generalization uses an iterative strategy along with the BOOSTRON algorithm to learn weights of hidden layer neurons and output neurons by reducing these problems into problems of learning a single layer perceptron.