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This project is designing of Automated Health Monitoring System Using IoT. Internet of Things (IoT) describes the network of physical objects, “things” that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet. By this system, people can monitor their day-to-day activities of health constraints and take necessary action in timely events. Further, the vital parameters of body should monitor always such as body temperature, room temperature, pulse counting, and heartbeat every day. As compared to existing one, the proposed design added with GSM and sound sensor. GSM sends the information to the family members with family doctor and notifies the consultant doctor for the medication in the individual risk factor. Any user can use this design. It is not limited to only patients and also no need to rush to hospital at regular intervals, at every time for the check-up, and do check-up safely at home. If any risk factor arises, one can visit the hospital.

XGBoost algorithm and Lasso regression and compare r-square, Mean Square Error (MSE), Root MSE, and RMSLE values. The algorithm should be efficient enough to produce the exact fare amount of the trip before the trip starts. The sample size for implementing this work was N=10 for each of the groups considered. It was iterated 20 times for efficient and accurate prediction of cab price prediction with G power in 80% and threshold 0.05%, CI 95% mean and standard deviation. The sample size calculation was done with clincle. The pretest analysis was kept at 80%. The sample size calculation was done using clincalc. The statistical analysis shows that the significance value for calculating r-squared and MSE was 0.63 and 0.581(p>0.05), respectively. The XGBoost algorithm gives a slightly better accuracy rate with a mean r-squared percentage of 72.62%, and the Lasso regression algorithm has a mean r-square of 70.47%. Through this, the prediction is made for the online booking of cabs or taxis, and the Xgboost algorithm gives a slightly better r-squared value and MSE values than the Lasso regression algorithm.

The most affected disease in recent years is Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) that is notable as COVID-19. It has been started as a disease in one place and arisen as a pandemic throughout the world. A serious health problem is developed in the lungs due to the effect of this coronavirus. Sometimes it may result in death as a consequence of extensive alveolar damage and progressive respiratory failure. Hence, early detection and appropriate diagnosis of corona virus in patient’s body is very essential to save the lives of affected patients This work evolves a Silicon (Si) based label-free electrical device i.e. the reduced graphene oxide field-effect transistor (rGO FET) for SARS-CoV-2 detection. Firstly rGO FET functionalized with SARS-CoV-2 monoclonal antibodies (mAbs). Then the rGO FET characteristic response is observed to detect the antibody-antigen reaction of SARS-CoV-2 with different molar ranges. The developed GFET shows better performance towards the drain current and limit-of-detection (LoD) up to 2E-18 M. Therefore, we believe that an intense response was observed than the earlier developed devices and signifies impressive capability for subsequent implementation in point-of-care (PoC) diagnostic tests.

In every service based or product based company customer services is considered to be an important sector to maintain customer relationship. This sector also consumes a lot of resources from the company both labor wise and money wise. In this sector the usage of resources are high due to the demand in the sector, A good company is defined how good is their customer service, Today most of the companies lack a good customer interaction, Hence to ease this process of customer services in this paper we propose to use A.I chatbot in the customer service sector. The result will be faster and more optimal customer service solutions.