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Deaf and Mute people cannot communicate efficiently to express their feelings to ordinary people. The common method these people use for communication is the sign language. But these sign languages are not very familiar to ordinary people. Therefore, effective communication between deaf and mute people and ordinary people is seriously affected. This paper presents the development of an Android mobile application to translate sign language into speech-language for ordinary people, and speech into text for deaf and mute people using Convolution Neural Network (CNN). The study focuses on vision-based Sign Language Recognition (SLR) and Automatic Speech Recognition (ASR) mobile application. The main challenging tasks were audio classification and image classification. Therefore, CNN was used to train audio clips and images. Mel-frequency Cepstral Coefficient (MFCC) approach was used for ASR. The mobile application was developed by Python programming and Android Studio. After developing the application, testing was done for letters A and C, and these letters were identified with 95% accuracy.

COVID-19 was announced as a global pandemic by the World Health Organization (WHO) in March 2020. With more than 31.3 million confirmed cases and over 965 thousand deaths recorded as of September 2020, it has inflicted catastrophic damage worldwide. The aim of this study is to develop an algorithm based on artificial intelligence (AI) and image processing techniques to identify COVID-19 patients with the aid of CT chest scan images. This study used a CT scan image dataset that is publically available for the researchers at Kaggle. We randomly extracted 27% of positive CT (pCT) images and 11% of negative CT (nCT) images from the original dataset. In the testing process, 120 of the test subjects in both nCT and pCT were used to validate the algorithm. Based on the experimental findings, the proposed COVID-19 detection algorithm shows promising results for the identification of COVID-19 patients with 90.83% accuracy at an average precision of 0.905.

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