Conclusion and future direction in data mining and machine learning

Data becomes a new currency for the world. Due to COVID-19, a significantly fewer number of flights are running, and hence the scientists cannot forecast the weather accurately. The data capturing also goes low because of this smaller number of flights. Data mining techniques play a vital role in collecting data for prediction and forecasting using different machine learning techniques. Recommender systems are available at all emerging places like agriculture, admission, matchmaking, traveling, share market, housing loan, parenting, nutrition, and consultation. Cybersecurity and forensics are also very challenging domains to fight with cybercrimes. Only data can save an entity from cyber-attacks. This chapter concludes with the future direction in data mining and machine learning techniques dealing with some related issues. © 2022 Scrivener Publishing LLC. All rights reserved.

A Selection-Based Framework for Building and Validating Regression Model for COVID-19 Information Management

The world is facing pandemic situation, i.e., COVID-19, all the researchers and scientist are working hard to overcome this situation. Being human it is everyone’s duty to take care of family and the society. In this case study, an attempt has been made to find the relation between various variables by dividing them into the independent and dependent variables. A dataset is selected for analysis purpose which consists of variables like location (countries across the globe, date, new cases, new deaths, total deaths, smoking habits washing habits, diabetic prevalence, etc. Approach is to identify the impact of independent variable on the dependent variable by applying the regression modeling. Hence, proposed case study is based on selection-based framework for validating the regression modeling for COVID-19 data analysis. Regression modeling is applied, and few representations are shown to understand the current pandemic situation across the world. In the end, using regression modeling interceptor and coefficient values for different approaches (using different variables) is computed. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Statistical analysis on the COVID-19 infection spread in United State of America: A Prophet Forecasting Model

In the current scenario, the pandemic COVID-19 spread globally starting from the end of 2019, in Wuhan, a city of China. As per the current data taken up to 26th of May 2020, globally there are a huge number of people are affected (Approximately 3 billions) by the pandemic. Though the entire data varies depending upon the several parameters like, population size, congestion of area, climate condition, awareness of peoples etc. we have only analyzes on the data of the country USA. The entire data is partitioned into various categories such as: infected rate, mortality rate. A statistical analysis is prepared to analyze or predict the future strategies of the infected rate as well as the removal (Death/cured) rate. The growth of both the infected and the removed can be predicted with the same observed data taken on daily basis from 15th February 2020. We retrieved these data from an authenticate source provided by Worldometer (http://www.worldometers.info). However, Prophet Forecasting Model (PMF) is used to simulate and discussed for the prediction of the mortality rate, active rate due to pandemic COVID-19. The proposed method is also tested for accuracy of model via cross validation method. © 2021 IEEE.

Role of Fog-Assisted Internet of Things-Enabled System for Managing the Impact of COVID-19

The effect of pandemic COVID-19 outbreak has become a big matter of concern in the world. Healthcare industry demands for new technologies to fight against the pandemic. Fog-assisted IoT-enabled technology (Fog-IoT) is the alternative to cloud technology, which has potential strength to fulfill the requirements of patients as well as healthcare organization. In this paper, we explore and review the fog computing technology to mitigate the impact of COVID-19. Fog computing technology provides resources to IoT at proximity of network. This integrated technology is useful for dynamic monitoring of patients and provides rapid diagnosis to high risk patients. In healthcare industry, the delay sensitive patient information should be accessed in a fraction of seconds. So, fog computing could be a better solution for providing response intensive IoT application for medical emergencies. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.