Most developing countries suffer from inadequate health care facilities and a lack of medical practitioners as most of them emigrate to developed countries. The outbreak of the COVID-19 pandemic has left these countries more vulnerable to facing the worse outcome of the pandemic. This necessitates the need for a system that continuously monitors patient status and detects how their physiological variables will change over time. As a result, it will reduce the rate of mortality and mitigate the need for medical practitioners to monitor patients continuously. In this work, we show how an autoencoder and extreme gradient boosting can be merged to forecast physiological variables of a patient and detect anomalies and their level of divergence. An accurate detection of current and future anomalies will enable remedial action to be taken by medical practitioners at the right time and possibly save lives.

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Different reinforcement learning (RL) methods exist to address the problem of combining multiple different learners to generate a superior learner. These existing methods usually assume that each learner uses the same algorithm and/or state representation. We propose an ensemble learner that combines a set of base learners and leverages the strengths of the different base learners online. We demonstrate the proposed ensemble learner’s ability to combine the strengths of multiple base learners and adapt to changes in base learner performance on various domains, including the Atari Breakout domain.

This work presents a vision-based quality assurance system that does assembly line monitoring. The system is developed using machine learning hand tracking and object detection methods to monitor the worker’s hand movement while evaluating the correctness of the assembly. Feedback about the order of the steps the worker has taken is continuously shown to the user. This work has the potential to reduce the amount of manual work required for quality assurance in assembly line.