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54. Al-Qamar/The Moon

54. Al-Qamar/The Moon

I/We begin by the Blessed Name of Allah

The Immensely Merciful to all, The Infinitely Compassionate to everyone.

54:01
a. The Last Hour has drawn near,
b. and the moon has been split open.

54:02
a. But whenever they see a Sign, they turn away, and mock:
b. Just the ‘same old deception, continuing!’

54:03
a. And they belie it and follow their whims.
b. But every matter will reach its proper end - so they will know the truth.

54:04
a. Certainly enough of the narratives of the fate of the former disbelieving nations would have
already come in which there is deterrence,

54:05
a. narratives full of far reaching wisdom,
b. yet the warnings do not benefit them.

54:06
a. So turn away from them.
b. The Time is going to come when the Caller will call all people to a horrible event;

54:07
a. with their eyes humbled, they will emerge out of their graves as if they were swarms of locust, looking confused and bewildered,

54:08
a. scrambling in a stampede and rushing towards the Caller.
b. The disbelievers will say remorsefully:
c. ‘This is such a difficult time!’

54:09
a. Before them the People of Noah too had belied OUR Messages and Messenger,
b. and they belied OUR servant Noah, and alleged:

c. ‘He is insane!’
d. And he was rebuked and prevented from advocacy.

54:10
a. So he appealed to his Rabb - The Lord in utter helplessness:
b. ‘I have certainly been overpowered.
c. So help’ me!

اخفائےمعانئ قرآن اوراشتراکِ لفظی جديد

Last revelation namely al- Quran has addressed the human beings in an eloquent way using all types of expressions and diction. The divine method of articulation for holy commandments is miraculous and opts all appealing techniques of communications that also includes use of homographs and metaphors. The word that are spelled the same but have different meaning are called Homographs. The reciter andreader of the Quran faces some difficulty in deciding the meaning of a Homographs used in the Quran that leads to difference of opinions. In the books of Quranic Sciences this term is called Mushtarak al –Lafzi. The article has been aimed to elaborate what is Mushtarak al –Lafzi and what are the impact of vagueness originated from these words of the Quran on Quranic exegesis. Some examples have been produced fromthe books of Quranic Studies regarding its influence on exegetical literature.

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.
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