عقیدہ تناسخ اور عہد الست میں فرق کے حوالے سے امام رازی کے موقف کا جائزہ

Reincarnation is a basic Hindu belief according to which the soul of a person is recreated for second time in different shapes according to their different actions. It is known as the belief of Samsara or reincarnation in Hinduism. If the person who passes away is good, his soul is transferred into a beautiful and nice body like that of birds etc. But if he is an evil person, his soul is transferred into ugly insects and animals etc. According to this belief, the difference between two human beings is due to the difference in their previous action or “karma” that he has committed in his previous birth. Human actions cannot be fruitful in this world and this is why a second birth is needed. This belief is wrong from Shariah perspective and it contradicts the basic Islamic belief of resurrection. Reincarnation assumes that there is no specific day on which actions will be rewarded; rather it is Auagun or Juni Cycle through which a human being deserves positive or negative reward. Imam Razi has refuted this belief through both logical and textual evidences. He has also replied the objections raised against the covenant of “alast”. (الست) According to Shariah, there is a second world beyond this physical for reward or punishment of deeds which is known as the Day of Judgment Doomsday.  On this day, the Scale will be set and human actions will be weighed. Consequently, he will deserve either Paradise or hell. Paradise is an abode of perpetual rest and satisfaction whereas hell is a place of humiliation and degradation.  

Machine Learning Based Approach for Facial Expression Classification

Facial expressions deliver intensive information about human emotions and the most valuable way of social collaborations, despite difference in ethnicity, culture, and geography. These differences addresses the three main problems, which are; facial appearance variation, facial structure variation, and inter-expression resemblance. Due to these problems the existing facial expression recognition techniques are very inconsistent. This study presents several computational algorithms to handle these problems in order to get high expression recognition accuracy. We proposed a novel ensemble classifier for cross-cultural facial expression recognition. The proposed ensemble classifier consists of three stages; base-level, meta-level and predictor, where binary neural network adopted as base-level classifier, neural network ensemble (NNE) collections as meta-level classifier and naive Bayes (NB) with Bernoulli distribution as predictor. The NB classifier takes the binary output of NNE collections and classifies the sample image as one of the possible facial expressions. The Viola-Jones algorithm is used to detect the face and expression concentration region. The acted still images of three databases JAFFE, TFEID, and RadBoud originate from four different cultures are combined to form multi-culture facial expression dataset. Three different feature extraction techniques LBP, ULBP and PCA are applied for facial feature representation. Further, boosted NNE collections are developed to enhance the facial expression recognition accuracy. The proposed boosting technique combines multiple NNEs which are complement to each other. The combination of boosted NNE collections with HOG-PCA feature vector perform significantly better than NNE collections. Later on the multi-culture dataset is extended by adding more cultural diversity from KDEF and CK+ databases, which is used to train the SVM based ensemble collections. The introduction of SVM ensemble collections at meta-level provides strong generalization ability to learn the vast variety of cultural variations in expression representation. Moreover, sensitivity analysis and inter-expression resemblance analysis are performed to quantify the level of complexity in cross-cultural facial expression recognition. It shows that expressions of happiness, surprise and anger are easy to recognize as compare to expressions of sadness and fear. It proves that these expressions are innate and universal across all cultures with minor variations. The experimental results demonstrate that proposed cross-cultural facial expression recognition techniques perform significantly better than state of the art techniques.