Deep Learning For Improved Heat Index Using Iot-Based Data

Et. al. Dr. C. Jayaprakash

Türk bilgisayar ve matematik eğitimi dergisi · 10 May 2021

In one sentence. Deep learning (LSTM-style) predicts heat index from IoT temperature sensor time series and is compared with generalized linear regression; the study is low-rigour.

Abstract

In this era, global warming is the chief cause of climate pollutant produced by CO2 and other chemical emissions. This set of components generates a high temperature and drastically changes the climate. Minimizing carbon-emitting activities can help lower the global warming. as a result of developed technology, almost all the products have carbon dioxide as a major by-product With regard to global warming, a prediction is necessary to avoid catastrophe. Several researchers were examining the projected temperature range based on the yield of crop yields, one of which found an increase. However, algorithms were challenged by a room monitoring or crop yield like application. So, instead, a deep learning is suggested to achieve the task of predicting the heat index in this context. There's a database in the Climate Prediction Center in which the temperature data was collected from the Kaggle dataset. it includes temperature sensor-based time series data, in the Internet of Things Incomplete data is predicted with the long-term memory and deep learning model The effectiveness of the neural network is assessed through accuracy, sensitivity, and specificity, and it is compared to a generalized linear regression. All MATLAB R2020b software is running under Windows 10 in parallel.

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