A Smart Sensing Technologies-Based Intelligent Healthcare System for Diabetes Patients.

An Artificial Intelligence (AI)-enabled human-centered smart healthcare monitoring system can be useful in life saving, specifically for diabetes patients. Diabetes and heart patients need real-time and remote monitoring and recommendation-based medical assistance. Such human-centered smart healthcare systems can not only provide continuous medical assistance to diabetes patients but can also reduce overall medical expenses. In the last decade, machine learning has been successfully implemented to design more accurate and precise medical applications. In this paper, a smart sensing technologies-based architecture is proposed that uses AI and the Internet of Things (IoT) for continuous monitoring and health assistance for diabetes patients. The designed system senses various health parameters, such as blood pressure, blood oxygen, blood glucose (non-invasively), body temperature, and pulse rate, using a wrist band. We also designed a non-invasive blood sugar sensor using a near-infrared (NIR) sensor. The proposed system can predict the patient's health condition, which is evaluated by a set of machine learning algorithms with the support of a fuzzy logic decision-making system. The designed system was validated on a large data set of 50 diabetes patients. The results of the simulation manifest that the random forest classifier gives the highest accuracy in comparison to other machine learning algorithms. The system predicts the patient's condition accurately and sends it to the doctor's portal.

Adsorption of CO over the Heusler alloy CrCoIrGa(001) surface: first-principles insights.

In this study, the adsorption of CO molecule over (001) surface of the Heusler alloy CrCoIrGa, has been investigated using DFT+U calculations. It is demonstrated that, after relaxation, the (001) surface retains the bulk atomic positions, exhibiting no apparent surface reconstruction. Owing to the emergence of unsaturated bonds at the surface, the surface layer atoms are found to carry more spin-polarization (SP) and atomic moments than that of inner layer atoms. The ground state total SP (magnetic moment) is found to be 27% (42.256 µ B). To explore the CO adsorption over the surface, five different adsorption configurations (sites) are considered and the strength of CO to surface interaction is estimated from the computed density of states (DOS), adsorption energy (E a), change in magnetic moment (ΔM), vertical height between molecule and surface (h), charge transfer (ΔQ), and charge density difference (CDD) plots. For all configurations, the E a lies in the range of -2.15 to -2.34 eV, with CO molecule adsorbed on the top of Ir atom as the most favorable adsorption configuration. The observed E a, ΔQ, h, and ΔM values, collectively predict that the (001) surface has strong interaction (chemisorption) with CO gas molecule, thus, might be useful in gas sensing applications.