ABSTRACT
In this paper, we are trying to explore how blockchain technology can assist in the field of medical research. Here, we propose the use of this technology to store the records and observations after the research on various pathogens conducted by doctors and scientists. By this method we can create a data pool that is trustworthy, on which the researchers on this field can depend upon for the future studies of these pathogens and the diseases that they cause. This can also accelerate the process of developing a vaccine due to the abundance of accurate information from all over the world. The Coronavirus scenario showed us that our healthcare system could be made much better and we need to keep improving our technologies and methods to effectively identify, analyze and take necessary actions with life threatening pathogens in an early stage. By integrating the technique of blockchain to the framework of our healthcare system we can make the process of transferring information much more secure and efficient. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
India is one of the best countries which follows the conventional education system and forces the student to learn the subjects by attending the classes directly. The normal education system in the country has changed at the starting of February 2020, when the government confirmed the first case of coronavirus infection in India. The schools were suspended suddenly and the situation continued for a few months. But the government found a solution that the students who are not permitted to go to class can pick an online training framework. This research is mainly for comparing the pre and post covid education system and evaluation of the student performance using machine learning techniques such as Artificial Neural Network (ANN), Logistic Regression, and Naïve Bayes. The prediction algorithms are focus on the attributes such as understandability of subject topics, internal assessment, level of concentration, language proficiency, and percentage of marks in the main exam during regular class and online class. The prediction model compares the attributes and predicts either the regular class or the online class increases the student’s performance. The dataset for research is collected mainly from graduation and post-graduation students through Google form due to the pandemic. ANN is the model that gave a higher accuracy rate of 0.95. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Coronavirus is a profoundly irresistible infection and has ruinous impacts far and wide. This virus affects both the economic and health sectors of all nations. It also freezes the day-to-day life in the world. In this paper, predictive and classification models for COVID-19 in Indonesia using machine learning, deep learning, and genetic algorithm techniques are developed. In predictive models, we used machine learning algorithms like Logistic Regression, Support Vector Machine, and Recurrent Neural Network. Among the machine learning algorithms, feature scaling with Logistic Regression (94.75%) performed well. Decision Tree Classifier and Genetic algorithms are used to classify the clusters like green, yellow, orange, and red conforming to the new death of each province in which Genetic Algorithm gave better accuracy (93%). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.