Smart Attendance Tracking System Using Various Machine Learning Algorithms

The major goal of this study is to predict the effectiveness of maintaining day-to-day activities of attendant data by using a unique Support Vector Machines (SVM) method to monitor the faces. Materials and Methods: Novel SVM algorithm with sample size = 42 and Convolutional Neural Networks (CNN) with sample size = 42 were validated many times to forecast efficiency percentage. Novel SVM algorithm makes use of face vectors to recognize countenance (face) data. Results and Discussions: The recognition of countenance data by monitoring faces performs more efficiently using SVM (Support Vector Machine) algorithm with mean efficiency 92.65% and comparing Convolutional Neural Networks with mean efficiency 79.02%. The Support Vector Machine algorithm performed significantly better than the Convolutional Neural Networks algorithm is 0.001 (p<0.005). Conclusion: Novel SVM algorithm performs more efficiently than the CNN algorithm to predict correct attendance tracking using countenance images.