اسلام میں شعر و شاعری کا تصور

The position of poetry remained unchanged in Islam as it was before Islam, however with due some changes it was used as a weapon for the sake of Islam. This article will explain that how the poetry played a vital role in preaching of Islam. Islam absolutely encourages good wholesome poetry, which inspires one towards the fear of Allah, towards His awe and obedience, and towards anything that is good and made permissible by Allah and His Messenger (ﷺ). Following discussions are made in this article: Firstly Qur’anic views towards poetry; as the word poet came in Qur’an four times while the word poetry once. The total verses in which we see the word poetry are six. Secondly preaching of ethics through poetry; as we see that before Islam the Arab society was without any ethics, the Muslim poet called them for an exemplary life like of the Holy Prophet (ﷺ) Using of Qur’anic notion in poetry. Thirdly the Qur’anic notion was used largely in the beginning of Islam, especially by Ḥassān bin Thābit, ʻAbdullāh Bin Rawāḥah, Kaʻb Bin Zubayr and Nābighah Al Jaʻdī etc. Fourthly Answer to non-believers through poetry; as Ḥassān bin Thābit did through his poetry, and answer to the opposition, which impacts more sharp than sword and lastly using of Poetry during the war; it was considered as one of the biggest source for encouraging towards holly wars, the example of Haḍrat Khansā is most prominent. The research article basically focuses upon the importance of poetry in Islam, moreover how the weapon of poetry has been used by Islamic poets for defending Islam and how Islamic poetry vastly used for spreading of golden teachings of Islam.

Efficient Facial Expression Classification Using Machine Learning Techniques

The Non-verbal communication plays a pivotal role in daily life and contributes around 55% to 93 % in overall communications. Facial expression is a type of non-verbal communication and its contribution towards recognition is around 55%. It exhibits the physical intention, behavior, personality and mental state of a person. Facial expression analysis can be effectively used in video surveillance, emotion analysis, smart homes, gesture recognition, patient monitoring, treatment of depression and anxiety, lie detection, automated tutoring, psychiatry, paralinguistic communication, robotics, operator fatigue detection and computer games. Highly accurate solution is a major challenge in the development of Efficient FER system. Data collected using poor quality cameras and/or captured from distance suffer from low resolution problem. Region of interest is usually smaller than original image size and image collected in real world environment suffer from low resolution problem. It results in drastic decrease in classification accuracy of the facial expression recognition. Environmental and source light variations during image acquisition results in poor illumination which is also major cause of performance degradation. Furthermore, curse of dimensionality poses another challenge in the development of fast and accurate techniques. With the increasing demand of surveillance camera-based applications, the Very Low Resolution(VLR) problem happens in many FER application systems. Existing FER recognition algorithms are unable to give satisfactory performance on the VLR face image. In addition to VLR, Variable lighting conditions in uncontrolled environment is another factor which can cause unpredictable illumination affects that leads to poor FER performance. Furthermore, feature vector containing correlated and irrelevant information also causes performance degradation. In this dissertation problems mentioned above related to facial expression recognition have been addressed. A novel framework has been proposed to handle high and low resolution images with equal capability. Excitation component of Weber local descriptor (WLD) is employed to compute the salient features and DWT has been utilized for features extraction which resolves multi-resolution problem. Least number of features having high variances is used to perform classification. Experimental results have shown that this framework not only handle low resolution problem but also gives improved classification performance both in terms of complexity (i.e., number of features) and recognition accuracy as compare to existing techniques present in the literature using CK+, MMI and JAFFE data sets. Secondly, facial expression recognition being a multi-class classification problem is a challenging task and becomes more complicated in real world environment with data having variation in illumination conditions. In order to tackle this problem, illumination invariant technique has been developed based on HOG features. These HOG based illumination invariant features are further reduced using DCT. These highly significant features are passed to the classifiers for accurate facial expression recognition. Proposed frameworks can effectively handle illumination variance and very low resolution data during facial expression recognition. Detail experimentation have been conducted using well known standard datasets containing images with varying illumination, resolution, gender and ethnicity. Comparison of the system has been presented with other state-of-art techniques using CK+, MMI and Cross datasets. Comprehensive experimentation shows that the proposed technique produces significantly better results than existing state-of-the-art techniques present in the related work.