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Classroom management relies heavily on the ability to keep track of student attendance. Attendance checks by calling names or handing out a sign-in sheet are time-consuming and vulnerable to fraud, especially the latter. Data science and image processing are the focus of this regarding counting the number of people present at a gathering for various objectives, such as determining a person's duty status, determining a person's physical presence in a classroom, determining a person's security clearance to enter a meeting hall, etc. It takes a lot of time and effort to maintain a database for future use when generating attendance records using the standard technique. With the help of the latest technology, attendance can be entered automatically. It is a frequently used face recognition technique that generates a binary code for each cell and compares it to the reference image. With the use of deep learning, this LBP method has been reworked in order to automate attendance generation.

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Internet of Medical Things (IOMT) is playing critical role in healthcare business to boost the accuracy, reliability and efficiency of electronic equipment. Researchers are working towards a computerized healthcare system by integrating the existing medical resources and healthcare services. As IOT converging different sectors but our emphasis is connected to research contribution of IOT in healthcare domain. This study covers the peoples contribution of IOT in healthcare sector, application and future problems of IOT in term of medical services in healthcare. We do expect that our study will be valuable for academics and practitioners in the area, allowing them to comprehend the tremendous potential of IoT in medical domain and identification of important problems in IOMT. This investigation would also allow the academics to comprehend applications of IOT in healthcare area. This input would allow the academics to comprehend the prior contribution of IOT in healthcare business.