Blood Glucose Prediction Using Machine Learning on Jetson Nanoplatform

Parab, Jivan S.; Sequeira, Martín E.; Lanjewar, Madhusudan G.; Pinto, Cristina M.; Naik, G.M.

doi:10.1002/9781119792642.ch39

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Penelitian yang dilakukan oleh (Guleken, Z. et al, 2022) untuk pengukuran level antibody pada penderita covid-19 menggunakan metode spektroskopi dan ML. Pemantauan glukosa dalam darah menggunakan NIR telah dilakukan oleh (Parab, J., Sequeira, M., Lanjewar, M., Pinto, C., & Naik, 2022) menggunakan algoritma Partial Least Square Regression (PLSR) dan Backpropagation Artificial Neural Network (BP-ANN).…”

Section: Pendahuluanunclassified

Mengatasi Ketimpangan Data Deep Neural Network dengan Pelipatan Fitur Data Klasifikasi Spektroskopi Darah

Hastomo

Karno²,

Sutarno³

et al. 2022

Pencerah

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Permasalahan utama dalam penelitian ini adalah ketimpangan data masukan menghasilkan dampak negatif yang signifikan terhadap hasil prediksi dari model Deep Neural Network (DNN). Kemampuan klasifikasi DNN sangat akurat hanya untuk dataset yang berimbang, namun DNN pada awalnya tidak di rancang untuk menangani ketimpangan data. Ketimpangan data merupakan hal yang sering dijumpai dalam dunia nyata, menjadikan ini sebagai tantangan besar dalam prediksi klasifikasi menggunakan model DNN. Penelitian ini berfokus untuk memprediksi tingkat kandungan kolesterol tinggi, kolesterol rendah dan hemoglobin, menggunakan data kasus di kompetisi Zindi Blood Spectroscopy Classification Challenge. Dengan melakukan analisa data, cleansing outlier, fine tunning, model neural network, jaringan pengelompokan data target dengan kategori sejenis, urutan pemrosesan, pemilihan nilai pelipatan (7 pelipatan) yang tepat terhadap data input train dan data test serta epoch 60, dapat meningkatkan hasil nilai score prediksi yang cukup tinggi sebesar 0.94594.

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Section: Pendahuluanunclassified

Mengatasi Ketimpangan Data Deep Neural Network dengan Pelipatan Fitur Data Klasifikasi Spektroskopi Darah

Hastomo

Karno²,

Sutarno³

et al. 2022

Pencerah

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“…The created deployed Heroku link was verified on a mobile phone and performed well. The benefit of this method is that the model is accessible anywhere on the mobile or embedded device [51,52]. There is no need for extra hardware or device required.…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone

Lanjewar

Shaikh

Parab

2022

Multimed Tools Appl

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COVID-19 has engulfed over 200 nations through human-to-human transmission, either directly or indirectly. Reverse Transcription-polymerase Chain Reaction (RT-PCR) has been endorsed as a standard COVID-19 diagnostic procedure but has caveats such as low sensitivity, the need for a skilled workforce, and is time-consuming. Coronaviruses show significant manifestation in Chest X-Ray (CX-Ray) images and, thus, can be a viable option for an alternate COVID-19 diagnostic strategy. An automatic COVID-19 detection system can be developed to detect the disease, thus reducing strain on the healthcare system. This paper discusses a real-time Convolutional Neural Network (CNN) based system for COVID-19 illness prediction from CX-Ray images on the cloud. The implemented CNN model displays exemplary results, with training accuracy being 99.94% and validation accuracy reaching 98.81%. The confusion matrix was utilized to assess the models’ outcome and achieved 99% precision, 98% recall, 99% F1 score, 100% training area under the curve (AUC) and 98.3% validation AUC. The same CX-Ray dataset was also employed to predict the COVID-19 disease with deep Convolution Neural Networks (DCNN), such as ResNet50, VGG19, InceptonV3, and Xception. The prediction outcome demonstrated that the present CNN was more capable than the DCNN models. The efficient CNN model was deployed to the Platform as a Service (PaaS) cloud.

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