Deep Neural Network

This article discusses a device focused on images that enables users to recognise and detect many face-related features using the webcam. In this article, we are performing comprehensive and systemic studies to check the efficacy of these classic representation learning structures on class-imbalanced outcomes. We also show that deeper discrimination can be learned by creating a deep network that retains inter-cluster differences both and within groups. MobileNet, which provides both offline and real-time precision and speed to provide fast and consistent stable results, is the recently suggested Convolutional Neural network (CNN) model. The recently proposed Convolutional Neural Network (CNN) model is MobileNet, which has both offline and real-time accuracy and speed to provide fast and predictable real-time results. This also solved a problem related to the face that occurs in the identification and recognition of the face. This paper presents the different methods and models used by numerous researchers in literature to solve the issue of faces. They get a better result in using the highest number of layers. It is also noted that the combination of a machine learning approach with multiple image-based dataset increases the efficiency of the classifier to predict knowledge related to face detection and recognition

According to numerous studies, the Qur’anic surahs exhibit internal structure and organization, with each surah serving a distinct purpose. Although each surah focuses on a specific theme and the Qur’an identifies 114 broad themes, the arrangement of the surahs and the remarkable similarity between adjacent surahs (neighbors) underscores the chain-link and deliberate positioning of the surahs within the Qur’an. To investigate this phenomenon, a multifaceted and compound model was developed, comprising two main parts: embedding and autoencoding. The first part was carried out by preparing the words and roots of the Qur’anic text using the BERT model for meaning-topic representation. In the second part, the data was clustered in a soft labeling mode by the autoencoder. Analysis of the distribution of surahs within clusters revealed that neighboring surahs exhibited an average similarity of 80, while surahs with greater distance showed an average similarity of 20. The findings support the placement of similar surahs in close proximity,  substantiating the organized sequence of Qur’anic surahs. To conclude, the results provide compelling evidence for the structured arrangement of Qur’anic surahs.