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Dental disease is the first complaint of 10 major diseases in Indonesia. Almost the entire population of Indonesia has dental and oral health problems. Delay in early detection of dental and oral health problems can result in loss of productivity, longer duration of treatment, loss of teeth, higher costs, complications with other organs, which can lead to death due to cancer. We propose the invention concerns wireless oral image recording instruments and identification methods thereof. Several existing inventions do not yet have the following features including wireless connection, object detection, and prediction of oral and dental diseases that integrate the knowledge of clinical experts using the application. The purpose of this invention was to add to the features mentioned above by using Artificial Intelligence of Medical Things (AIoMT) concept, designing object detection based on deep learning (DL) algorithms, and integrating prediction systems high performance using collaborative datasets. This invention integrated intraoral image recording hardware in the Intraoral Camera Unit (ICU) with software in the Display Processing Unit (DPU) which produces visualization of predicted results, and a webserver unit as a data collection unit and image classifier according to the validated disease interpretation. The integration of these three units can then be used by medical scholars, researchers and medical practitioners to strengthen the reliability of the interpretation process, especially related to oral diseases using an integrated digital dental support system. [ABSTRACT FROM AUTHOR]

Problems in learning due to the Covid-19 pandemic have occurred in several activities e.g. teaching in Taman Pendidikan Al-Qur'an (TPA). In carrying out its activities, TPA relies heavily on the teacher to make a learning pattern on how to pronounce the Arabic alphabet of 28 letters adequately. It requires a unique approach due to the various types of sound pronunciation in reading the Qur'an. In view of health protocol rules by the World Health Organization, face-to-face meetings are replaced with online sessions, thereby affecting the learning quality. To solve this problem, a system has been developed which consists of a deep learning model. Voice data were collected for TPA students and pre-processed before the voice data were used to develop the deep learning model. Four techniques were tested to identify the best technique for pre-processing the voice data. The techniques were Spectrogram, Padding, Mel-Spectrogram, and Mel-Frequency Cepstral Coefficient techniques. The pre-processing process is to prepare the training and testing data for the deep learning stage. Test questions were later appeared with an application designed with Tkinter to lay out the exam questions. Once the user pronounced a letter, the voice is recorded and pre-processed to be predicted with a deep learning model. The quality and similarity of pronunciation of the letter is validated an Arabic speech grading algorithm. Results showed that the used of Padding technique in the pre-processing stage provides the best classification accuracy for the Arabic letter pronunciation. [ABSTRACT FROM AUTHOR]