Towards Building a Facial Identification System Using Quantum Machine Learning Techniques

Easom-McCaldin, Philip; Bouridane, Ahmed; Belatreche, Ammar; Jiang, Richard

doi:10.12720/jait.13.2.198-202

Cited by 6 publications

(1 citation statement)

References 17 publications

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“…Recently, QML became a popular AI technique for medical diagnosis based on clinical data [14] [15]. A wide variety of applications used data-driven approaches with reasonable results [16], i.e, Image-based diagnosis [17]- [19] and sentiments analysis [20] are just some examples. In such an era, the current paper presents the two efficient QML algorithms for COVID-19 patients classification.…”

Section: Introductionmentioning

confidence: 99%

Severity Classification of COVID-19 Patients Data using Quantum Machine Learning Approaches

Ullah

Maheshwari

Gloyna

et al. 2022

2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)

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Artificial intelligence (AI) is assisting in several aspects of the COVID-19 pandemic, including medical diagnosis and therapy, drug development, molecular research and epidemiology. The involvement of AI in healthcare can help doctors to detect symptoms more quickly. In such era, we use the Quantum Machine Learning (QML) approaches to address clinical applications of machine learning (ML) technology, such as electronic healthcare data and clinical features. This paper presents the two QML algorithms, i.e, Enhanced Quantum Support Vector Machine (E-QSVM) and Quantum Random Forest (QRF) applied to COVID-19 and influenza datasets, which were collected from different private hospitals. The experimental results show that the proposed models outperform in terms of accuracy by achieving the highest accuracy of 78% for the E-QSVM model and 75% for the QRF model respectively. The competency of the models is obtained by comparing them with classical models and recently published quantum models.

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