Rushit Dave
University of Wisconsin–Eau Claire
38 Papers
12 Citations
Rushit Dave is an academic researcher from University of Wisconsin–Eau Claire. The author has contributed to research in topics: Computer science & Biometrics. The author has an hindex of 6, co-authored 17 publications. Previous affiliations of Rushit Dave include North Carolina Agricultural and Technical State University.
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Papers
An Investigation of Biometric Authentication in the Healthcare Environment
Janelle Mason,Rushit Dave,Prosenjit Chatterjee,Ieschecia Graham-Allen,Albert Esterline,Kaushik Roy +5 more
- 01 Dec 2020
TL;DR: A new technique is proposed that fuses the use of periocular biometrics and the electronic master patient index in healthcare information systems to identify humans in the healthcare environment.
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User Authentication Schemes Using Machine Learning Methods—A Review
Nyle Siddiqui,Laura Pryor,Rushit Dave +2 more
- 01 Jan 2021
TL;DR: In this paper, the authors discuss the advantages and challenges that arise with the accuracy, usability, and overall security of machine learning methods in these authentication systems, including behavioral biometrics and physical layer authentication.
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Applications of Recurrent Neural Network for Biometric Authentication & Anomaly Detection
TL;DR: In this paper, the benefits and drawbacks of using recurrent neural networks (RNNs) for biometric authentication, expression recognition, anomaly detection, and applications to aircraft are discussed.
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Touch-Based Active Cloud Authentication Using Traditional Machine Learning and LSTM on a Distributed Tensorflow Framework
TL;DR: A distributed tensorflow framework for cloud authentication using touch biometric pattern is developed that helps alleviate the drawback of the computationally intensive recognition of the substantial amount of raw data from the user.
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An Analysis of IoT Cyber Security Driven by Machine Learning
Sam Strecker,Willem Van Haaften,Rushit Dave +2 more
- 01 Jan 2021
TL;DR: In this paper, the authors explored the use of machine learning in IoT security measures and found that the methods with the highest threat detection accuracy utilized the random forest and K-nearest neighbor algorithms and the most efficient methods utilized software-defined networks and the fog layer of networks.
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