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Your search keyword '"Dharani, N.P."' showing total 6 results
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6 results on '"Dharani, N.P."'

1. IoT sensor data retrieval and analysis through cloud environment for effective power management

Author

Srilakshmi Ch, Uma K, Rakesh Kumar Yadav, Daya Sagar K.V, Dharani N.P, and Priyanka Sharma

Subjects
Internet of things, Sensor, Power efficiency, Data management, Electric apparatus and materials. Electric circuits. Electric networks, TK452-454.4
Abstract

The Internet of Things (IoT) brings new products to everyone to improve daily life. Other newly created techniques, including Big Data, Cloud Services, and surveillance, can participate through these technological advances. In this study, the researchers look at the four systems mentioned above to identify their shared functions and integrate them to create useful potential applications. Despite the limitations of the smart city concept, researchers would seek innovative methods to collect and process sensor information within an IoT-enabled smart building. A cloud service would have been used as the foundation technology for the proposed schema management platform, collecting the information generated by each sensor in the smart factory. It was easy to manage and manipulate remotely using IoT technology and a mobile device with a network connection. Here, to enhance safety of the entire building by installing multiple IoT devices in a smart building. The proposed systems were designed in a simulated Cooja simulator environment. The proposed approaches to the collection and manipulation of sensor information in smart cities can help us create a green, resource-efficient project.

Published
2024
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6. Evaluation of performance of an LR and SVR models to predict COVID-19 pandemic

Author

Dharani, N.P., Bojja, Polaiah, and Raja Kumari, Pamula

Subjects
KAGGLE, Machine Learning, Corona virus, Support Vector Machine, Linear Regression, MAE, Article, RMSE
Abstract

Recently, in December 2019 the Coronavirus disease surprisingly influenced the lives of millions of people in the world with its swift spread. To support medical experts/doctors with the overpowering challenge of prediction of total cases in India, a machine-learning algorithm was developed. In this research article, the author describes the possibility of predicting the COVID-19 total, active cases, death and cured cases in India up to 25th June 2020 by applying linear regression and support vector machine. It is extremely tricky to manage the occurrence of corona virus since it is expanding exponentially day to day and is difficult to handle with a limited number of doctors and beds to treat the infected individuals with limited time. Hence, it is essential to develop a machine learning based computerized predicting model. The development effort in this article is based on publicly available data that is downloaded from KAGGLE to estimate the spread of the disease within a short period. We have calculated the RMSE, R2, MAE of LR and SVR models and concluded that the RMSE of linear regression is less than the SVR. Therefore, the LR will help doctors to forecast for the next few days.

Published
2021

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