Survival Analysis Approach For Early Prediction Of Student Dropout

Retention of students at colleges and universities has long been a concern for educators for many decades. The consequences of student attrition are significant for both students, academic staffs and the overall institution. Thus, increasing student retention is a long term goal of any academic institution. The most vulnerable students at all institutions of higher education are the freshman students, who are at the highest risk of dropping out at the beginning of their study. Consequently, the early identification of at-risk students is a crucial task that needs to be addressed precisely. In this thesis, we develop a framework for early prediction of student success using survival analysis approach. We propose time-dependent Cox (TD-Cox), which is based on the Cox proportional hazard regression model and also captures time-varying factors to address the challenge of predicting dropout students as well as the semester that the dropout will occur, to enable proactive interventions. This is critical in student retention problem because not only correctly classifying whether student is going to dropout is important but also when this is going to happen is crucial to investigate. We evaluate our method on real student data collected at Wayne State University. The results show that the proposed Cox-based framework can predict the student dropout and the semester of dropout with high accuracy and precision compared to the other alternative state-of-the-art methods

PUF-BASED SOLUTIONS FOR SECURE COMMUNICATIONS IN ADVANCED METERING INFRASTRUCTURE (AMI)

Advanced Metering Infrastructure (AMI) provides two-way communications between the utility and the smart meters. Developing authenticated key exchange (AKE) and broadcast authentication (BA) protocols to provide the security of unicast and broadcast communications in AMI is an essential part of AMI design. The security of all existing cryptographic protocols are based on the assumption that secret information are stored in the non-volatile memory of each party. These information must be kept unknown to the adversary. Unfortunately, in an AMI network, the attackers can obtain some or all of the stored secret information from non-volatile memories by a great variety of inexpensive and fast side channel attacks. Especially, the smart meters which are located in physically insecure environments are more vulnerable to these attacks. Thus, all existing AKE and BA protocols are no longer secure against such attacks. In this paper, we investigate how to develop secure AKE and BA protocols with the presence of memory attack. As a solution, we propose to embed a Physical Unclonable Function (PUF) in each communicating party which generate the secret values as required without need to store them. By combining PUFs and two well-known and secure protocols, we propose a PUF-based Authenticated Key Exchange protocol (PUF-AKE) for unicast communications and a PUF-based Broadcast Authentication (PUF-BA) for broadcast communications. We show that our proposed protocols are memory leakage resilient. Also, we prove the security of them in a standard model. Performance analysis of both of the protocols show they are efficient for AMI applications. The proposed protocols can be easily implemented in AMI networks

Automated robot‐assisted surgical skill evaluation: Predictive analytics approach

BackgroundSurgical skill assessment has predominantly been a subjective task. Recently, technological advances such as robot‐assisted surgery have created great opportunities for objective surgical evaluation. In this paper, we introduce a predictive framework for objective skill assessment based on movement trajectory data. Our aim is to build a classification framework to automatically evaluate the performance of surgeons with different levels of expertise.MethodsEight global movement features are extracted from movement trajectory data captured by a da Vinci robot for surgeons with two levels of expertise – novice and expert. Three classification methods – k‐nearest neighbours, logistic regression and support vector machines – are applied.ResultsThe result shows that the proposed framework can classify surgeons’ expertise as novice or expert with an accuracy of 82.3% for knot tying and 89.9% for a suturing task.ConclusionThis study demonstrates and evaluates the ability of machine learning methods to automatically classify expert and novice surgeons using global movement features.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141457/1/rcs1850.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/141457/2/rcs1850_am.pd