Chronic diabetes among adults is a public health concern and clinicians are trying to implement new strategies to effectively manage the disease. Traditionally, healthcare professionals are used to monitor and track the lab reports of patients. After that, they used to provide respective medicines and lifestyle plans to manage the chronic disease. The lifestyle of the patients and access to safe and secure food products is also responsible for developing chronic diseases. Thus, the Internet of Things (IoT) has taken an utmost interest in managing diabetes. This research is going to analyze the accuracy of IoT in assisting chronic diabetes management and determining food safety. To accomplish the research objectives, the researchers performed a linear regression analysis to understand whether IoT devices and Artificial Intelligence (AI) assist in assessing food safety and diabetes management. The independent variables selected were lab test values, treatment records, epoch size of AI, and image resolution of the training dataset. Dependent variables were the accuracy of IoT. Here, the accuracy of IoT and AI has been determined. Moreover, the accuracy of clinicians in diabetes management has been observed. It has been found that clinicians have high variance in accuracy (max 99%) whereas machines have limited variance in accuracy (max. 98%). Secondary research identified that clinicians need to be involved along with IoT devices for better management of this chronic disease and help patients by providing the safest food options.

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In a comb resonator the spring mass and dynamic mass is the parameter for generating resonant frequency and the resonance frequency of above 15 KHz is the main challenge for applications like accelerometer, oscillator and gyros. The electrostatic force helps in the generation of resonant frequency and created between the two electrodes for actuation and sensing making it two port resonator. This paper presents deals with design of computing spring constant for support is 0.355N/m and we obtained the effective mass of the structure as 24.1 nanogram. Also the resonant frequency as 19.29 KHz. Also checked the effect of capacitance for the input. Also we considered the inclination of comb fingers and its relative effects on the design.

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