ماحولیاتی آلودگی اور تعلیمات نبویﷺ

For the guidance of all human being and for resolving the problems Allah has told in Qur’an.  The environmental pollution is a major issue of our life, Allah has also fully guided for this regard too. There is mentioned in The Holy Quran about that. There are seven types of pollution are: Water pollution, Air pollution, Soil pollution, Thermal pollution, Radioactive pollution, Noise pollution, Light pollution. Environmental pollution has existed for centuries but only started to be significant in dub trial resolution. Pollution occurs when the natural environmental cannot destroy an element without creating harm or damage to itself. The elements involved are not produced by nature and the destroying process can vary from a few days to thousands of years. Though the first we should clean our self then our society will be cleaned and will not remain any kind of pollution. In this regard the Holly Quran is also telling us about the purification. There are two types of purification internal external. Internal purification to purity the soul form the effects of sins and act of disobedience though repenting sincerely form all sins and act of disobedience Purification of the heart from the fifth polytheism. External purification by removing of filth is by using pure water of the water for the removal of the for the worshiper's garment body and from the place of prayer. We must thin for this serious issue and have to reform our society from this important issue. In fort, we get rid from those absolutely in the right direction.

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.