ABSTRACT
Catering to the widespread COVID-19 pandemic, the authors aim to develop a system based on machine learning combined with the knowledge of medical science. Considering the prevailing situation, it becomes necessary to diagnose the COVID-19 at initial stages. The idea behind the described designed model is to identify the spread of infection in patients as fast as possible. The paper sketches two different approaches: K-fold cross-validation and deep network designer which are based on deep learning technology for the prediction of COVID-19 in the initial stages by using the chest X-rays. The performance evaluation of the cross-fold validation process is compared with the designed application in the deep network designer to find an effective and efficient methodology for classification which attained better accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
In the present world, online news platform greatly influences our society and culture both in positive and negative ways. Being dependent on social media there is widespread fake news with misleading information leading to the chances where the reputation of the company is threatened. The influence of media has led to heights of depression and mental health issues as they don’t find the real cause of it. This makes it an important issue that needs to be explored, analyzed and resolved to maintain peace and harmony in the world. Herein, this kind of pandemic as well where everything is unpredictable there are many cases where false news are been circulated and due to which the fear and panic have increased in the people. To resolve the issue, the chapter elaborates on developing a system using Machine Learning and Natural Language processing that uses RNN and its techniques like LSTM and Bi-LSTM for the detection of misleading information. The implementation is done for general fake news and purely Covid-19 fake news. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.