COVID-19: Symptoms Clustering and Severity Classification Using Machine Learning Approach

COVID-19 is an extremely contagious illness that causes illnesses varying from either the common cold to more chronic illnesses or even death. The constant mutation of a new variant of COVID-19 makes it important to identify the symptom of COVID-19 in order to contain the infection. The use of clustering and classification in machine learning is in mainstream use in different aspects of research, especially in recent years to generate useful knowledge on COVID-19 outbreak. Many researchers have shared their COVID-19 data on public database and a lot of studies have been carried out. However, the merit of the dataset is unknown and analysis need to be carried by the researchers to check on its reliability. The dataset that is used in this work was sourced from the Kaggle website. The data was obtained through a survey collected from participants of various gender and age who had been to at least ten countries. There are four levels of severity based on the COVID-19 symptom, which was developed in accordance to World Health Organization (WHO) and the Indian Ministry of Health and Family Welfare recommendations.  This paper presented an inquiry on the dataset utilising supervised and unsupervised machine learning approaches in order to better comprehend the dataset. In this study, the analysis of the severity group based on the COVID-19 symptoms using supervised learning techniques employed a total of seven classifiers, namely the K-NN, Linear SVM, Naive Bayes, Decision Tree (J48), Ada Boost, Bagging, and Stacking. For the unsupervised learning techniques, the clustering algorithm utilized in this work are Simple K-Means and Expectation-Maximization. From the result obtained from both supervised and unsupervised learning techniques, we observed that the result analysis yielded relatively poor classification and clustering results. The findings for the dataset analysed in this study do not appear to be providing the correct result for the symptoms categorized against the severity level which raises concerns about the validity and reliability of the dataset

COVID-19: Symptoms Clustering and Severity Classification Using Machine Learning Approach

COVID-19 is an extremely contagious illness that causes illnesses varying from either the common cold to more chronic illnesses or even death. The constant mutation of a new variant of COVID-19 makes it important to identify the symptom of COVID-19 in order to contain the infection. The use of clustering and classification in machine learning is in mainstream use in different aspects of research, especially in recent years to generate useful knowledge on COVID-19 outbreak. Many researchers have shared their COVID-19 data on public database and a lot of studies have been carried out. However, the merit of the dataset is unknown and analysis need to be carried by the researchers to check on its reliability. The dataset that is used in this work was sourced from the Kaggle website. The data was obtained through a survey collected from participants of various gender and age who had been to at least ten countries. There are four levels of severity based on the COVID-19 symptom, which was developed in accordance to World Health Organization (WHO) and the Indian Ministry of Health and Family Welfare recommendations.  This paper presented an inquiry on the dataset utilising supervised and unsupervised machine learning approaches in order to better comprehend the dataset. In this study, the analysis of the severity group based on the COVID-19 symptoms using supervised learning techniques employed a total of seven classifiers, namely the K-NN, Linear SVM, Naive Bayes, Decision Tree (J48), Ada Boost, Bagging, and Stacking. For the unsupervised learning techniques, the clustering algorithm utilized in this work are Simple K-Means and Expectation-Maximization. From the result obtained from both supervised and unsupervised learning techniques, we observed that the result analysis yielded relatively poor classification and clustering results. The findings for the dataset analysed in this study do not appear to be providing the correct result for the symptoms categorized against the severity level which raises concerns about the validity and reliability of the dataset

Cost Minimization of Aircraft Critical Components for Planning and Maintenance Requirements

Spare parts management of the aircrafts is a part of maintenance planning that requires effective and efficient planning in order to reduce aircraft downtime in maintenance processes. Spare parts unavailability during maintenance is one of the factors that affects operational availability of the aircrafts fleet. This study focused on identifying the current problem affecting operational availability of the aircrafts fleet, developing an optimization model for cost minimization of spare parts and assessing the impact on spare parts availability using the cost minimization optimization model. Data analysis using machine learning techniques in WEKA was used to identify the current problem that is affecting aircrafts fleet operational availability. Then, Linear Integer Programming (LIP) and Goal Programming (GP) methods were used to develop the cost minimization optimization model by considering the current stock level of the critical components and the budget constrain. From this study, we discovered that spare parts unavailability during maintenance was the biggest contributing factor to the operational availability of the aircrafts fleet. Besides that, the cost minimization optimization model developed in the research had produced optimum level of inventory for the critical components with minimum cost. Spare parts availability for the aircrafts fleet had been improved with the application of the model which directly maximizes the operational availability of the aircraft fleet