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Dinesh Kumar

Royal Melbourne Institute of Technology (RMIT University) - School of Engineering

Bundoora, Victoria

Australia

SCHOLARLY PAPERS

3

DOWNLOADS

225

TOTAL CITATIONS

3

Scholarly Papers (3)

1.

On the Inter-Dataset Generalisation of Machine Learning Approaches to Parkinson’s Disease Detection From Voice

Forthcoming, International Journal of Medical Informatics, https://doi.org/10.1016/j.ijmedinf.2023.105237
Number of pages: 21 Posted: 27 Jan 2023 Last Revised: 19 Jan 2024
Technical University of Košice, Technical University of Košice, Royal Melbourne Institute of Technology (RMIT University), Royal Melbourne Institute of Technology (RMIT University) and Royal Melbourne Institute of Technology (RMIT University) - School of Engineering
Downloads 160 (474,889)
Citation 3

Abstract:

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Parkinson's disease, computerized voice analysis, convolutional neural network, feature extraction, cross-dataset generalization, acoustic analysis

2.

Which Generative Adversarial Network Yields High-Quality

Number of pages: 15 Posted: 16 Apr 2022
University of São Paulo (USP), University of São Paulo (USP), University of São Paulo (USP), University of São Paulo (USP), University of Melbourne, University of Wolverhampton and Royal Melbourne Institute of Technology (RMIT University) - School of Engineering
Downloads 50 (1,086,013)

Abstract:

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Age-Related Macular Degeneration, Data Augmentation, Generative Adversarial Networks

3.

Integrating Stakeholder Perspectives in the Co-Design of Medical Device Software to monitor Parkinson’s Disease Symptoms- A Case-study

Number of pages: 14 Posted: 10 Feb 2026
affiliation not provided to SSRN, affiliation not provided to SSRN, Royal Melbourne Institute of Technology (RMIT University), Royal Melbourne Institute of Technology (RMIT University), Macquarie University and Royal Melbourne Institute of Technology (RMIT University) - School of Engineering
Downloads 15 (1,534,404)

Abstract:

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Co-design, Software as a Medical Device (SaMD), Stakeholder engagement, Parkinson's disease, digital health