Design and Optimization of Low Power Intelligent System for Wearable Devices

Design and Optimization of Low Power Intelligent System for Wearable Devices K. Laleeth Keerthi Vels Institute of Science, Technology & Advanced Studies, Chennai, 600117, India S. Sridevi Vels Institute of Science, Technology & Advanced Studies, Chennai, 600117, India Kalaivani Kathirvelu Department of Computer Science and Engineering Vels Institute of Science, Technology and Advanced Studies (VISTAS), Pallavaram, Chennai, India 03 01 2025 261 269 1555-130X(20250301)20:3L.261;1- 10.1166/jno.2025.3719 https://www.ingentaconnect.com/content/10.1166/jno.2025.3719 https://www.ingentaconnect.com/content/asp/jno/2025/00000020/00000003/art00004 https://www.ingentaconnect.com/content/asp/jno/2025/00000020/00000003/art00004 Journal of Ayurveda and Integrative Medicine Rao 9 3 pp.225 2018 10.1016/j.jaim.2017.10.002 Ayurveda and the science of aging. International Journal of Research in Ayurveda and Pharmacy Chinthala 10 3 pp.18 2019 10.7897/2277-4343.100355 Ancient archives of deha-prakriti (human body constitutional traits) in ayurvedic literature: A critical review. Journal of Ayurveda and Integrative Medicine Bhalerao 7 1 pp.69 2016 10.1016/j.jaim.2015.08.002 Prakriti-based research: Good reporting practices. International Journal of Health Science and Research Verma 8 1 pp.219 2018 Possible measures to assess functional states of tridosha: A critical. International Journal of Ayurveda and Pharma Research Prasuna 2 1 pp.124 2014 Comparative study of personality with ayurvedic prakriti. International Journal of Ayurveda and Pharma Research Wani 5 9 pp.86 2017 Prakriti analysis and its clinical significance. International Journal of Information Technology Niranjan 13 pp.1667 2021 10.1007/s41870-021-00690-2 Machine learning based analysis of pulse rate using panchamahabhutas and ayurveda. Journal of Ayurveda and Integrative Medicine Dunlap 8 4 pp.257 2017 10.1016/j.jaim.2017.04.011 Reliability of self-reported constitutional questionnaires in ayurveda diagnosis. An International Quarterly Journal of Research in Ayurveda Shilpa 32 3 p. 308 2011 Development and standardization of mysore tridosha scale. Computers in Biology and Medicine Almansour 109 pp.101 2019 10.1016/j.compbiomed.2019.04.017 Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study. GIScience & Remote Sensing Omrani 54 3 pp.283 2017 10.1080/15481603.2016.1265706 Integrating the multi-label land-use concept and cellular automata with the artificial neural networkbased land transformation model: An integrated ml-ca-ltm modeling framework. Neurocomputing Jiang 275 pp.1132 2018 10.1016/j.neucom.2017.09.056 Deep neural networks with elastic rectified linear units for object recognition. Computers in Biology and Medicine Acharya 100 1 pp.270 2018 10.1016/j.compbiomed.2017.09.017 Deep convolutional neural network for the automated detection and diagnosis of seizure using eeg signals. Computers in Biology and Medicine Deepak 111 pp.1 2019 10.1016/j.compbiomed.2019.103345 Brain tumor classification using deep cnn features via transfer learning. Journal of Computer and Communications Karabulut 2 9 pp.32 2014 10.4236/jcc.2014.29005 Analysis of car-diotocogram data for fetal distress determination by decision tree based adaptive boosting approach. Computers & Chemical Engineering Dev 128 pp.392 2019 10.1016/j.compchemeng.2019.06.001 Formation lithology classification using scalable gradient boosted decision trees. International Journal of Engineering and Advanced Technology Murty 8 6 pp.2249 2019 10.35940/ijeat.F8684.088619 Accurate liver disease prediction with extreme gradient boosting. Advanced Engineering Informatics Chi 41 6 p.100922 2019 Xgboost application on bridge management systems for proactive damage estimation. International Research Journal of Engineering and Technology Narang 5 3 pp.3704 2018 Ayurveda based disease diagnosis using machine learning. International Journal of Computer Trends and Technology Batvia 52 2 pp.68 2017 10.14445/22312803/IJCTT-V53P114 A survey on ayurvedic medicine classification using tensor flow.