نوجوانوں میں تعلیم و تربیت کے فقدان کا تدارک اور سدباب: سیرت مبارکہ کی روشنی میں

Youth is no doubt the asset of Muslim world. If they are utilized in charitable activities; in the protection of respect and honor; and developmental works, they can prove to be beneficial and a blessing. While if they are taken by destructive elements in their hands they will become source of destruction and harm. By providing proper Islamic education, our future will be in safe hands and will breath in an air of respect among other nations. In young age, deterioration in manners and moral corruption are alarming and cause mischief in society. Therefore, the interest taken in the problems of youth will result in a progressive society and ummah. Islam has given a special place to youth and has declared them future architect and leaders of humanity. The holy prophet S.A.W has given much importance to the youth and focused on them during his life time.  In Islamic history, Muslim youth has represented the Muslim world in golden words and has spread the message of Islam’s moral height and greatness to the coming generations and nations. Due to the importance of youth problem of the youth in current time regarding lack of education and training are particularly discussed in this article. The research article begins with the detailed study of the research done in the area of education and socialization of youth. Moreover, the importance of education and socialization has been discussed in the light of the teachings of Quran and Hadith. After this, decadence in education, factors responsible for the decadence in education and its worse consequences have been thoroughly discussed in the light of the teachings of Islam.  As there are several reasons responsible for the downfall of education like: parents, teachers, education system, society, state, preachers, political parties and media, therefore, responsibility of the remedy of this lack of proper education and socialization also depend on all these stake holders. In the end of the paper, recommendations are given for the cure of decadence in education.

Recognizing Human Actions in Realistic and Complex Scenarios Using Bag of Expression Boe Model

Human action recognition (HAR) has emerged as a core research domain for video understanding and analysis, thus attracting many researchers. Although signi cant results have been achieved in simple scenarios, HAR is still a challenging task due to issues associated with view independence, occlusion and inter-class variation observed in realistic scenarios. In previous research e orts, the classical Bag of Words (BoW) approach, along with its variations, has been widely used. In this dissertation, we propose a novel feature representation approach for action representation in complex and realistic scenarios. We also present an approach to handle the inter and intraclass variation challenge present in human action recognition. The primary focus of this research is to enhance the existing strengths of the BoW approach like view independence, scale invariance and occlusion handling. The proposed Bag of Expressions (BoE) includes an independent pair of neighbors for building expressions; therefore it is tolerant to occlusion and capable of handling view independence up to some extent in realistic scenarios. We apply a class-speci c visual words extraction approach for establishing a relationship between these extracted visual words in both space and time dimensions. To improve classical BoW, we propose a Dynamic Spatio-Temporal Bag of Expressions (D-STBoE) model for human action recognition without compromising the strengths of the classical bag of visual words approach. Expressions are formed based on the density of a spatiotemporal cube of a visual word. To handle inter-class variation, we use class-speci c visual word representation for visual expressions generation. The formation of visual expressions is based on the density of spatiotemporal cube built around each visual word, as constructing neighborhoods with axed number of neighbors would include non-relevant information hence making a visual expression less discriminative in scenarios with occlusion and changing viewpoints. Thus, the proposed approach makes our model more robust to occlusion and changing viewpoint challenges present in realistic scenarios. Comprehensive experiments on publicly available datasets show that the proposed approach outperforms existing state-of-the-art human action recognition approaches.