Recognition of sleep patterns and posture has sparked interest in various clinical applications. Sleep postures can be monitored autonomously and constantly to provide useful information for decreasing health risks. Existing systems mostly use images to train the model to learn based on many sensors. For example, a camera, pressure sensor, and electrocardiogram. In this study, a model (named as SleepCon) was designed using deep learning, which will have the capability to train with any threshold image obtained from any sensor. This paper presented a system where data was obtained from a camera installed on the top of a mattress. The camera located the movement of the body posture on the mattress while the subject was lying down on the mattress. In doing so, CNN and other pre-processed steps took place to collect data and then analyze the data to recognize different sleep postures. This model was stored for use in real-time applications. The system can recognize the three major postures, i.e., left, right, and supine. A real-time application is also developed and operates the stored SleepCon model through an accompanying desktop application for detecting the posture live. The accuracy of classification was greater than 90%, while the actual application accuracy was 100% after carrying out the experiment on the SleepCon model. Doi: 10.28991/ESJ-2023-07-01-04 Full Text: PDF

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In this research, a single batch machine scheduling problem with sequence dependent setup time is studied with the aim of minimizing the completion time. This problem has been proven to be NP-hard and therefore, a tabu search algorithm is developed to solve the single batch machine scheduling problem. Furthermore, a genetic algorithm and several dispatching heuristics are also developed to study the capabilities of all the algorithms in providing the average completion time. The computational result reveals that the developed tabu search algorithm is capable of producing a better outcome compared to the other algorithms.