ABSTRACT
A viral infection which is named as Coronavirus disease 2019 (COVID-19) is triggered by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). To date, almost two million cases and over 100,000 deaths from the disease caused by this virus were reported worldwide. The environmental and meteorological factors are claimed to stimulate the spread of the virus in which the transmissibility in terms of climatic fluctuations increases exponentially with high humidity and low temperature. In an attempt to understand this epidemic, there is a need to investigate the factors that could impact the spread and death of COVID-19. We, therefore, proposed to investigate global geographical climate impacts on the COVID-19 spread and death in Asia and America. The Artificial Neural Network (ANN) is a network that seeks to replicate neuronal functionality in the human brain. It is the preferred instrument for several predictive applications of data mining, due to its strength, versatility, and simplicity. A dataset of COVID-19 cases and deaths revealed from 49 states in America and 41 countries in Asia during April 2020 were tested. Nine covariates were used in the networks which are Cases, Death, High Temperature, Low Temperature, Average Temperature, Population, and Percentage of Cases over Population, Percentage of Death over Population, and Total Cases. Based on the analysis conducted, the global geographic climate is observed to have the least impacts on the COVID-19 spread and death in Asia and America particularly. However, different results could be reflected by different datasets used in the future. © 2021 Shafaf Ibrahim et al.
ABSTRACT
Fitness and keeping a balanced lifestyle are an important aspect in our daily lives. Being healthy will allow us to conduct our daily activities in a more productive manner. However, it is sidelined by the fact the people now a days are mostly busy with their work and other things, added the fact of the lack of motivation to conduct the physical activities, lead to people not having a balanced life. For that reason, the aim of the developed application is to provide users with an immersive and interactive jogging application which will help in motivating the user into wanting to conduct fitness activities. The interactivity is provided by implementing Geofencing technique, which is by generating a parameter around certain coordinates which when entered by a device will create certain events such as sending notification or directing to a different page. While the immersiveness aspect is implemented using Augmented Reality, which is where a 3D model is anchored into a certain plane which can be seen by using the mobile phones rear camera. The Rapid Development Life Cycle (RDLC) have been chosen as the methodology for the development of the application because it allows developer to iteratively change the system requirements during the development and presentation phase. Functionality testing have been conducted to the developed application. © 2020, World Academy of Research in Science and Engineering. All rights reserved.
ABSTRACT
As the world’s coronavirus disease 2019 (COVID-19) case total and death toll continue to climb, an increasing data collection and analysis are providing insights into the pandemic. Although outbreaks continue to develop rapidly, and researchers' understanding of the virus is increasing, a consensus is emerging on certain main aspects of the spread, symptoms, and deadliness of the virus. Enormous global data distribution on COVID-19 is made available online with a combination of global climate data, which creates an opening for further analysis to be conducted. To date, the global climate change has been studied widely, particularly regarding its influences on the distribution of species. This reflects the need for an analysis that is best suited to big data analysis which offers high performance and efficiency in understanding this pandemic issue. The state-of-art in data mining and statistics areas show that the adaptation of these methods could be the most suitable candidate for this purpose. We, therefore, proposed to investigate the influences of the global geographical climate towards the COVID-19 spread and death using a technique of Artificial Neural Network (ANN). It is believed that the proposed study could introduce a new suggestive strategy in improving the precaution measures, enhancing the new normal living activities, and to increase the performance scalability of big data processing comprehensively. © 2020, World Academy of Research in Science and Engineering. All rights reserved.