Keeping track of employee attendance in academic settings can be a difficult task. It frequently wastes a significant percentage of the category’s productive time when done manually. In this study, the OpenCV open-source image processing library presents an effective Raspberry Pi-based methodology that reduces product cost and aids in connecting to heterogeneous devices for attendance. When teaching and testing and collecting employee photos and taking attendance, the system delivers a user-friendly interface that maximizes the user experience. Face detection and recognition are done with LBP histograms, and the database is updated with SQLite (a lightweight version of SQL for the Raspberry Pi) rather than MySQL.

Diabetes mellitus is a condition caused due to increase in blood glucose level. More than 90% of people are diagnosed with Type 2 diabetes disease,T2D is a fast-growing, chronic disease caused by the imbalance in insulin function. Diabetes is a now the leading cause of heart disease, stroke, blindness, non-traumatic limb amputations and end-stage renal failure. Early detection may take a step towards keeping diabetes patients healthy and it also reduces the risk of such serious complications. Nowadays, the application of Machine learning in the medical field is gradually increasing. This can aid in improving the classification system used for disease diagnosis, that assist medical experts in detecting the fatal diseases at an early stage. This paper presents a performance comparison of the machine learning algorithms in diabetes detection. Techniques like SVM, Random forest, Gradient Boosting, Navie Bayes, Logistic regressionand KNN are used in this work.

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