In recent years, the role of pattern recognition in systems based on human computer interaction (HCI) has spread in terms of computer vision applications and machine learning, and one of the most important of these applications is to recognize the hand gestures used in dealing with deaf people, in particular to recognize the dashed letters in surahs of the Quran. In this paper, we suggest an Arabic Alphabet Sign Language Recognition System (AArSLRS) using the vision-based approach. The proposed system consists of four stages: the stage of data processing, preprocessing of data, feature extraction, and classification. The system deals with three types of datasets: data dealing with bare hands and a dark background, data dealing with bare hands, but with a light background, and data dealing with hands wearing dark colored gloves. AArSLRS begins with obtaining an image of the alphabet gestures, then revealing the hand from the image and isolating it from the background using one of the proposed methods, after which the hand features are extracted according to the selection method used to extract them. Regarding the classification process in this system, we have used supervised learning techniques for the classification of 28-letter Arabic alphabet using 9240 images. We focused on the classification for 14 alphabetic letters that represent the first Quran surahs in the Quranic sign language (QSL). AArSLRS achieved an accuracy of 99.5 for the K-Nearest Neighbor (KNN) classifier.

A voice controlled smart home automation system using artificial intelligent and internet of things

Muslims suffer from not knowing the validity of the Hadiths or verification of its degree, which represented the second source of legislation in Islam after the Quran. Although many sites allow users in general and Muslims, in particular, the possibility of verifying the authenticity of the hadith, but it is through an information system which is connected to the database of the Hadith. So, there is no intelligent system that can distinguish the hadith automatically, therefore, in this study, we propose a model that can recognize and categorize the hadith automatically and conclusion the essential features through Hadith classification into Sahih, Hasan, Da’if, and Maudu, based on machine learning techniques. This study is primarily concerned with classifying the hadith according to the memory and reliability of the Hadith’s narrators. This classification does not depend only on the text of the hadith, as in the rest of the other Arab documents, but depend on also the Sanad of Hadith. Therefore, this study was conducted on three methodologies; these methodologies help us to obtain an accuracy that is more reliable for hadith classification compared to previous researches in this area. In addition to building a model using the Decision tree technique based on the Sanad of hadith for helping us deciding to judge the validity of the hadith, the accuracy of this classifier reached up to 92.59%. Several Learning algorithms have been used in this study, but we reported the best three classifiers (LinearSVC, SGDClassifier, and Logistic regression), which achieved higher accuracy reached up to 93.69%, 93.51, and 92.27% respectively.

Arabic sign language (ArSL) is method of communication between deaf communities in Arab countries; therefore, the development of systemsthat can recognize the gestures provides a means for the Deaf to easily integrate into society. In this research we implemented a computational structurefor an intelligent interpreter that automatically recognizes the isolated dynamic gestures. The proposed system recognizes and translates gesturesperformed with one or both hands. It comprises five subsystems, building dataset, video processing, feature extraction, mapping between ArSL and Arabictext, and text generation. To apply the system, 100-signs of ArSL was used, which was applied on 1500 video files. It's were divided into five classes:alphabet, numbers, "prepositions, pronouns and question words", Arabic life expressions, and "nouns and verbs". The evaluation indicated that thesystem automatically recognizes and translates isolated dynamic ArSL gestures by highly accurate manner. The results showed that the system accuracy is 95.8%.

Deaf and dumb Muslims cannot reach advanced levels of education due to the impact of obstruction on their educational attainment. This leads to their inability to learn, recite, and understand the meanings and interpretations of the Holy Qur’an as easily as ordinary people, which also prevents them from applying Islamic rituals such as prayer that require learning and reading the Holy Qur’an. In this paper, we propose a new model for Qur’anic sign language recognition based on convolutional neural networks through data preparation, preprocessing, feature extraction, and classification stages. The proposed model is aimed at recognizing the movements of the Arabic sign language by recognizing the hand gestures that refer to the dashed Qur’anic letters in order to help the deaf and dumb learn their Islamic rituals. The experiments have been conducted on a part of a large Arabic sign language dataset called ArSL2018, which represents the 14 dashed letters in the Holy Qur’an, so that this part contains only 24,137 images. The experimental results demonstrate that the proposed model performs better than the other existing models.

Deaf and dumb Muslims face educational barriers. They can't read, recite, or comprehend the Holy Qur'an, hence they can't practise Islamic ceremonies. This study proposes a CNN-based Qur'anic sign language recognition methodology. First, photos are used to train for dynamic and static gesture recognition. Second, preparing images diversifies datasets. Finally, CNN-based deep learning models extract and classify features. To teach the deaf and dumb Islamic ceremonies, the programme recognises Arabic sign language hand motions referring to dashed Qur'anic letters. Only 24,137 photos of the Holy Qur'an's 14 dashed letters were used in the trials from ArSL2018, a huge Arabic sign language collection. SMOTE raises training and testing accuracy to 98.31% and 97.67%, respectively, whereas the proposed model reaches 98.05% and 97.13%. RMU obtains 98.66% and 97.52% training and testing accuracy, whereas RMO achieves 98.37% and 97.36%.