Data Mining for Predicting and Finding Factors of Bankruptcy

The financial crisis, since the pandemic outbreak due to COVID-19, the dissemination and invasive systemic risk in the global financial environment have drawn the attention to organizations' solvency monitoring methods. Inevitably, in this paper we have looked at the both bankruptcy prediction and the factors that lead to bankruptcy. The dataset for this study was acquired from Kaggle, which was based on the Taiwan Economic Journal, from 1999 to 2009. The corporate statutes of the Taiwan Stock Exchange were utilized to determine a company's bankruptcy. It was a highly imbalanced dataset having 220 Non-bankrupt and 6,599 bankrupt data. We have used Random Forest, Support Vector Machine, Artificial Neural Network, XGBoost, and LightGBM classifiers regarding bankruptcy prediction. On the other hand, to find the factors that lead to bankruptcy, we did an empiric analysis for which we calculated fourteen statistical values of both bankrupted and non-bankrupted features and saw their cosine similarities. These factors will help any financial company to plan its financial ratios for preventing bankruptcy. Here we got the best performance from the Artificial Neural Network with 98.64% accuracy. And we found four factors that were responsible for the bankruptcy in our dataset. Here, the factors determining bankruptcy are crucial because by finding these factors and the likelihood of bankruptcy, companies can take the necessary steps to plan their financial ratios and ensure the solvency of their businesses. © 2022 IEEE.

A Cost-Effective Solution for Real Time Remote Monitoring of Vital Signs in Patients

The outbreak of COVID-19 hit the world with an incomparable magnitude and introduced new challenges in the diagnosis and treatment of patients. Personal interactions have suddenly become dangerous which can be reduced by the use of digital technology in healthcare. Towards this, we have developed a low-cost remote vital sign monitoring system (VSM) that can be used at hospitals as well as at home for continuous and long-term monitoring of different clinical status, and provide extended support to the vulnerable patients. The proposed VSM has been designed with four layers: sensing layer, data processing layer, networking layer, and applications layer. It comes with three units: a wrist unit, a bedside monitor and a web-based graphical user interface (GUI) accessible by the nurse, physician or attendants remotely from anywhere. The effectiveness of measurement, transmission, and remote monitoring has been demonstrated by experiments. The system is designed with open source and low-cost hardware devices to ensure that it can be afforded and implemented in low resource settings of the developing countries. The proposed system can provide an effective way of delivering care to more patients while protecting everyone involved from infection.

Ambient Assisted Living for Elderly Care and Monitoring in CQVID-19 Pandemic

In late 2019, a novel Coronavirus broke out from China, which has dispersed all over the globe and has taken away countless lives. Despite the fact that every person is at risk of getting infected with the virus, older people are more likely to fall victim to the virus due to their declining immune systems. Although there has been significant development of vaccines, it is seen that the mutation of the COVID-19 has made it tough to control with the medication available. Due to an uncountable number of Coronavirus strains, many countries are now facing several waves of the pandemic. Assisted living technologies are evolving with time to give people a better life. This technology can be used for older people in Coronavirus pandemic situations as most of the older people have physical and cognitive impairments. In this paper, we have proposed an Internet of Things(loT)-architectured system incorporated with Artificial intelligence and deep learning that can help diagnose COVID-19 in older people. The proposed architecture will collect all the data from different medical loT sensors and relay them to the cloud, where the system will process and help us monitor the health of older people. This information could be seen from a dedicated dashboard where the user would be able to get diagnosis status of COVID-19 by our system. In order to be prepared for any future pandemic, this type of system will be beneficial. © 2021 IEEE

Estimating Parameters of Two-Level Individual-Level Models of the COVID-19 Epidemic Using Ensemble Learning Classifiers

The ongoing COVID-19 pandemic has led to a serious health crisis, and information obtained from disease transmission models fitted to observed data is needed to inform containment strategies. As the transmission of virus varies from city to city in different countries, we use a two-level individual-level model to analyze the spatiotemporal SARS-CoV-2 spread. However, inference procedures such as Bayesian Markov chain Monte Carlo, which is commonly used to estimate parameters of ILMs, are computationally expensive. In this study, we use trained ensemble learning classifiers to estimate the parameters of two-level ILMs and show that the fitted ILMs can successfully capture the virus transmission among Wuhan and 16 other cities in Hubei province, China. © Copyright © 2021 Liu, Deardon, Fu, Ferdous, Ware and Cheng.