Traditional statistical tools and qualitative techniques were employed in the literature to discover and forecast charac teristics/factors that impact student retention the most. Modeling the links between these early available indicators and a student's future status of engineering persistence can be very useful in improving student retention in engineering. For some years, machine learning approaches have been used in education to predict retention and discover factors impacting retention rates, with better outcomes since 2010. This study focuses on different machine learning techniques used in literature for improving students’ retention; we have identified various factors that might affect the students’ retention and employed SVM and Neural Networks for predicting students’ retention rates.
Security has always been a significant concern since the dawn of human civilization. That is why we build houses to keep ourselves and our belongings safe. And we do not hesitate to spend a lot on front-door locks and install CCTV cameras to monitor security threats. This paper presents an innovative automatic Front Door Security (FDS) algorithm that uses Human Activity Recognition (HAR) to detect four different security threats at the front door from a real-time video feed with 73.18% accuracy. The activities are recognized using an innovative combination of GoogleNet-BiLSTM hybrid network. This network receives the video feed from the CCTV camera and classifies the activities. The proposed algorithm uses this classification to alert any attempts to break the door by kicking, punching, or hitting. Furthermore, the proposed FDS algorithm is effective in detecting gun violence at the front door, which further strengthens security. This Human Activity Recognition (HAR)-based novel FDS algorithm demonstrates the potential of ensuring better safety with 71.49% precision, 68.2% recall, and an F1-score of 0.65.
Traditional statistical tools and qualitative techniques were employed in the literature to discover and forecast charac teristics/factors that impact student retention the most. Modeling the links between these early available indicators and a student's future status of engineering persistence can be very useful in improving student retention in engineering. For some years, machine learning approaches have been used in education to predict retention and discover factors impacting retention rates, with better outcomes since 2010. This study focuses on different machine learning techniques used in literature for improving students’ retention; we have identified various factors that might affect the students’ retention and employed SVM and Neural Networks for predicting students’ retention rates.
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