Clustering of COVID-19 Multi-Time Series-Based K-Means and PCA With Forecasting

The COVID-19 pandemic is one of the current universal threats to humanity. The entire world is cooperating persistently to find some ways to decrease its effect. The time series is one of the basic criteria that play a fundamental part in developing an accurate prediction model for future estimations regarding the expansion of this virus with its infective nature. The authors discuss in this paper the goals of the study, problems, definitions, and previous studies. Also they deal with the theoretical aspect of multi-time series clusters using both the K-means and the time series cluster. In the end, they apply the topics, and ARIMA is used to introduce a prototype to give specific predictions about the impact of the COVID-19 pandemic from 90 to 140 days. The modeling and prediction process is done using the available data set from the Saudi Ministry of Health for Riyadh, Jeddah, Makkah, and Dammam during the previous four months, and the model is evaluated using the Python program. Based on this proposed method, the authors address the conclusions.

A Survey of COVID-19 Detection From Chest X-Rays Using Deep Learning Methods

The coronavirus (COVID-19) outbreak has opened an alarming situation for the whole world and has been marked as one of the most severe and acute medical conditions in the last hundred years. Various medical imaging modalities including computer tomography (CT) and chest x-rays are employed for diagnosis. This paper presents an overview of the recently developed COVID-19 detection systems from chest x-ray images using deep learning approaches. This review explores and analyses the data sets, feature engineering techniques, image pre-processing methods, and experimental results of various works carried out in the literature. It also highlights the transfer learning techniques and different performance metrics used by researchers in this field. This information is helpful to point out the future research direction in the domain of automatic diagnosis of COVID-19 using deep learning techniques.

Antecedents of Organizational Agility During Business Uncertainty in Noninformation Technology Sectors

The prolonged COVID-19 pandemic, economic stress, and geopolitical tensions have caused market disruptions and other forces that have likely increased organizational agility. This article focuses on the antecedents of organizational agility under such business uncertainty in the noninformation technology (IT) sectors. The research model stems from the uncertainty reduction theory and the following three frameworks: (1) dynamic capabilities;(2) decision making;and (3) business intelligence and analytics (BI&A) competitive advantage maturity model. It considers intelligence (risk and opportunity) and aligned decision making as agility predictors. It lists employee capability and IT flexibility as antecedents of intelligence, aligned decision making, and organizational agility. The results indicate that employee capability affects agility through the mediating variables of intelligence and aligned decision making. IT flexibility impacts agility only through intelligence. Both intelligence and aligned decision making have significant direct effects on agility.

Physically Distancing Humans With An App for That: Physically Distancing Mangement Technologies

As COVID-19 continues to create havoc in everyday lives, the need to limit the spread of the virus remains a challenge, even with advances in medical knowledge and patient care, and the promise of a vaccine. Furthermore, COVID-19 is one in a recent series of airborne diseases, and probably not the last one, given the ongoing encroachment of humans into animal habitat. This paper proposes that a key challenge related to virus containment is physical containment. That is, maintaining safe distance from individuals who might have been exposed to the virus. A physical distancing app is proposed, and the challenges associated with its development and use identified.