Insight about Detection, Prediction and Weather Impact of Coronavirus (Covid-19) using Neural Network

The world is facing a tough situation due to the catastrophic pandemic caused by novel coronavirus (COVID-19). The number people affected by this virus are increasing exponentially day by day and the number has already crossed 6.4 million. As no vaccine has been discovered yet, the early detection of patients and isolation is the only and most effective way to reduce the spread of the virus. Detecting infected persons from chest X-Ray by using Deep Neural Networks, can be applied as a time and laborsaving solution. In this study, we tried to detect Covid-19 by classification of Covid-19, pneumonia and normal chest X-Rays. We used five different Convolutional Pre-Trained Neural Network models (VGG16, VGG19, Xception, InceptionV3 and Resnet50) and compared their performance. VGG16 and VGG19 shows precise performance in classification. Both models can classify between three kinds of X-Rays with an accuracy over 92%. Another part of our study was to find the impact of weather factors (temperature, humidity, sun hour and wind speed) on this pandemic using Decision Tree Regressor. We found that temperature, humidity and sun-hour jointly hold 85.88% impact on escalation of Covid-19 and 91.89% impact on death due to Covid-19 where humidity has 8.09% impact on death. We also tried to predict the death of an individual based on age, gender, country, and location due to COVID-19 using the LogisticRegression, which can predict death of an individual with a model accuracy of 94.40%.

Prediction of Solar Radiation Using Artificial Neural Network

Most solar applications and systems can be reliably used to generate electricity and power in many homes and offices. Recently, there is an increase in many solar required systems that can be found not only in electricity generation but other applications such as solar distillation, water heating, heating of buildings, meteorology and producing solar conversion energy. Prediction of solar radiation is very significant in order to accomplish the previously mentioned objectives. In this paper, the main target is to present an algorithm that can be used to predict an hourly activity of solar radiation. Using a dataset that consists of temperature of air, time, humidity, wind speed, atmospheric pressure, direction of wind and solar radiation data, an Artificial Neural Network (ANN) model is constructed to effectively forecast solar radiation using the available weather forecast data. Two models are created to efficiently create a system capable of interpreting patterns through supervised learning data and predict the correct amount of radiation present in the atmosphere. The results of the two statistical indicators: Mean Absolute Error (MAE) and Mean Squared Error (MSE) are performed and compared with observed and predicted data. These two models were able to generate efficient predictions with sufficient performance accuracy.