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              <text>AI Based Non-invasive Glucose Detection Using Urine</text>
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              <text>Artificial intelligence; Benedicts reagent; Diabetes mellitus; Non-invasive</text>
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              <text>This proposed device uses urine to predict the glucose level present in the patient using non-invasive technique with a high level of accuracy for detection of diabetes. The paper presents a urine glucose level diagnosing and prediction using a computer-based polarimeter held in a portable device, to provide a fast and accurate on-field result. The instrument consists of an LCD screen, optical sensor, Benedicts reagent, a detachable tank, and an embedded system-on-chip (SoC).  Springer Nature Singapore Pte Ltd 2020.</text>
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              <text>Dhrupad U.; Vignesh N.H.; Murthy H.; Mukherjee C.; Unal A.</text>
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              <text>Lecture Notes in Networks and Systems, Vol-93, pp. 513-519.</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-981-15-0630-7_51" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-15-0630-7_51&lt;/a&gt;
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              <text>ISSN: 23673370</text>
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              <text>Dhrupad U., Faculty of Engineering, CHRIST (Deemed to be University), Bangalore, India; Vignesh N.H., Faculty of Engineering, CHRIST (Deemed to be University), Bangalore, India; Murthy H., Faculty of Engineering, CHRIST (Deemed to be University), Bangalore, India; Mukherjee C., Faculty of Engineering, CHRIST (Deemed to be University), Bangalore, India; Unal A., Digital Monozukuri, Palo Alto, CA, United States</text>
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