RT Journal Article
A1 Ramalingaswamy Cheruku
A1 Damodar Reddy Edla
A1 Venkatanareshbabu Kuppili
A1 Ramesh Dharavath
A1 Nareshkumar Reddy Beechu

PB iet
T1 Automatic disease diagnosis using optimised weightless neural networks for low-power wearable devices
JN Healthcare Technology Letters
VO 4
IS 4
SP 122
OP 128
AB Low-power wearable devices for disease diagnosis are used at anytime and anywhere. These are non-invasive and pain-free for the better quality of life. However, these devices are resource constrained in terms of memory and processing capability. Memory constraint allows these devices to store a limited number of patterns and processing constraint provides delayed response. It is a challenging task to design a robust classification system under above constraints with high accuracy. In this Letter, to resolve this problem, a novel architecture for weightless neural networks (WNNs) has been proposed. It uses variable sized random access memories to optimise the memory usage and a modified binary TRIE data structure for reducing the test time. In addition, a bio-inspired-based genetic algorithm has been employed to improve the accuracy. The proposed architecture is experimented on various disease datasets using its software and hardware realisations. The experimental results prove that the proposed architecture achieves better performance in terms of accuracy, memory saving and test time as compared to standard WNNs. It also outperforms in terms of accuracy as compared to conventional neural network-based classifiers. The proposed architecture is a powerful part of most of the low-power wearable devices for the solution of memory, accuracy and time issues.
K1 bioinspired based genetic algorithm
K1 optimised weightless neural networks
K1 low power wearable devices
K1 noninvasive devices
K1 variable sized random access memories
K1 pain free devices
K1 automatic disease diagnosis
K1 modified binary TRIE data structure
K1 quality of life
K1 memory constraint
DO https://doi.org/10.1049/htl.2017.0003
UL https://digital-library.theiet.org/;jsessionid=8kiaom0tqo16i.x-iet-live-01content/journals/10.1049/htl.2017.0003
LA English
SN
YR 2017
OL EN