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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.

At present, people have a tendency to carry out higher education in a distance mode due to their busy lifestyles. However, open and distance learning (ODL) educational organizations encounter difficulties when delivering laboratory experiments. This paper presents the development of an online laboratory platform as a solution. It can be used to deliver laboratory experiments, using electronic components and instruments such as a signal generator and oscilloscope. Students are able to perform experimental tasks remotely utilizing real equipment and components. The system users can view laboratory environments via a camera which provides a sense of reality.The platform provides facilities to customize and rebuild the laboratory experiments according to the requirements of the organization. It can also be utilized as a useful educational tool to acquire pre-experience before entering the real laboratory. Thestatistical analysis shows no significant difference between the face-to-face laboratory (FFL) and online remote laboratory (ORL) experimental results within a 95% confidence level.The system can enhance the existing open and distance learning system by sharing the resources in a flexible manner.This system reduces the difficulties that distance learning students encounter when participating in FFL sessions. It also reduces the number of FFL sessions and is helpful to working students. One of the main objectives of ODL is to provide a learning environment for those who missed the opportunity for higher education for a variety of reasons. This system will help to achieve this objective.

This project is focused on analyzes squat performing video through image processing by considering key points taken from front and side view. There are conditions to check the performance of squat correct or not. If the Squat is correct, three angles are analyzed reference to five positions. The OpenCV is used to identify the five positions. Also, the Vector function is used to determine the reference angles by using the Cosine rule. These angles are calculated at the knee, waist, and ankle. All these angles are calculated on the free-weight back squat exercise. The ultimate purpose of this project is to minimize injuries that occur due to technical errors because most of the armature players get injured due to the difficulty in posing of correct technique. The mobile application is developed to identify user mistakes and can get instructions.

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