Sparse Representation Using Deep Learning to Classify Multi-Class Complex Data

Fard, Seyed Mehdi Hazrati; Hashemi, Sattar

doi:10.1007/s40998-018-0154-5

Cited by 7 publications

(4 citation statements)

References 35 publications

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“…While in traditional ML methods, it was prevalent to extract some features from images and then proceed with other ML methods. For example, Fard et al [45] employed an autoencoder to reach the best feature space for discriminating each user in the system and authenticate them based on their locally linear reconstruction error. Since their proposed method www.videleaf.com is very low cost, it can be a good choice to be used in telehealth authentication systems where we need to authenticate users in real-time.…”

Section: Model Selection and Trainingmentioning

confidence: 99%

Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial

Hazratifard,

Gebali,

Mamun

2023

Prime Archives in Sensors

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Section: Model Selection and Trainingmentioning

confidence: 99%

Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial

Hazratifard,

Gebali,

Mamun

2023

Prime Archives in Sensors

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show abstract

“…While in traditional ML methods, it was prevalent to extract some features from images and then proceed with other ML methods. For example, Fard et al [ 45 ] employed an autoencoder to reach the best feature space for discriminating each user in the system and authenticate them based on their locally linear reconstruction error. Since their proposed method is very low cost, it can be a good choice to be used in telehealth authentication systems where we need to authenticate users in real-time.…”

Section: Machine Learning Life Cycle For Authenticationmentioning

confidence: 99%

Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial

Fard

Gebali

Mamun

2022

Sensors

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Telehealth systems have evolved into more prevalent services that can serve people in remote locations and at their homes via smart devices and 5G systems. Protecting the privacy and security of users is crucial in such online systems. Although there are many protocols to provide security through strong authentication systems, sophisticated IoT attacks are becoming more prevalent. Using machine learning to handle biometric information or physical layer features is key to addressing authentication problems for human and IoT devices, respectively. This tutorial discusses machine learning applications to propose robust authentication protocols. Since machine learning methods are trained based on hidden concepts in biometric and physical layer data, these dynamic authentication models can be more reliable than traditional methods. The main advantage of these methods is that the behavioral traits of humans and devices are tough to counterfeit. Furthermore, machine learning facilitates continuous and context-aware authentication.

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“…Developing a verification model for user identification using ML and DLLs offers many advantages, such as being human-independent, cost-effective, reliable, faster, and more precise [ 15 ]. Numerous ML models have been developed for biometric-based authentication systems, with DNNs being widely applied in this area recently [ 16 ]. DNNs are preferred because they can extract the best feature set from raw data during training without requiring extra effort.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Siamese Network (ESN) Using ECG Signals for Human Authentication in Smart Healthcare System

Fard

Agrawal

Gebali

et al. 2023

Sensors

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Advancements in digital communications that permit remote patient visits and condition monitoring can be attributed to a revolution in digital healthcare systems. Continuous authentication based on contextual information offers a number of advantages over traditional authentication, including the ability to estimate the likelihood that the users are who they claim to be on an ongoing basis over the course of an entire session, making it a much more effective security measure for proactively regulating authorized access to sensitive data. Current authentication models that rely on machine learning have their shortcomings, such as the difficulty in enrolling new users to the system or model training sensitivity to imbalanced datasets. To address these issues, we propose using ECG signals, which are easily accessible in digital healthcare systems, for authentication through an Ensemble Siamese Network (ESN) that can handle small changes in ECG signals. Adding preprocessing for feature extraction to this model can result in superior results. We trained this model on ECG-ID and PTB benchmark datasets, achieving 93.6% and 96.8% accuracy and 1.76% and 1.69% equal error rates, respectively. The combination of data availability, simplicity, and robustness makes it an ideal choice for smart healthcare and telehealth.

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Sparse Representation Using Deep Learning to Classify Multi-Class Complex Data

Cited by 7 publications

References 35 publications

Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial

Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial

Using Machine Learning for Dynamic Authentication in Telehealth: A Tutorial

Ensemble Siamese Network (ESN) Using ECG Signals for Human Authentication in Smart Healthcare System

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