Correction to: A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects.

[This corrects the article DOI: 10.1007/s40747-022-00767-w.].

A survey on COVID-19 impact in the healthcare domain: worldwide market implementation, applications, security and privacy issues, challenges and future prospects.

Extensive research has been conducted on healthcare technology and service advancements during the last decade. The Internet of Medical Things (IoMT) has demonstrated the ability to connect various medical apparatus, sensors, and healthcare specialists to ensure the best medical treatment in a distant location. Patient safety has improved, healthcare prices have decreased dramatically, healthcare services have become more approachable, and the operational efficiency of the healthcare industry has increased. This research paper offers a recent review of current and future healthcare applications, security, market trends, and IoMT-based technology implementation. This research paper analyses the advancement of IoMT implementation in addressing various healthcare concerns from the perspectives of enabling technologies, healthcare applications, and services. The potential obstacles and issues of the IoMT system are also discussed. Finally, the survey includes a comprehensive overview of different disciplines of IoMT to empower future researchers who are eager to work on and make advances in the field to obtain a better understanding of the domain.

An Intelligent Sensor Based Decision Support System for Diagnosing Pulmonary Ailment through Standardized Chest X-ray Scans.

Academics and the health community are paying much attention to developing smart remote patient monitoring, sensors, and healthcare technology. For the analysis of medical scans, various studies integrate sophisticated deep learning strategies. A smart monitoring system is needed as a proactive diagnostic solution that may be employed in an epidemiological scenario such as COVID-19. Consequently, this work offers an intelligent medicare system that is an IoT-empowered, deep learning-based decision support system (DSS) for the automated detection and categorization of infectious diseases (COVID-19 and pneumothorax). The proposed DSS system was evaluated using three independent standard-based chest X-ray scans. The suggested DSS predictor has been used to identify and classify areas on whole X-ray scans with abnormalities thought to be attributable to COVID-19, reaching an identification and classification accuracy rate of 89.58% for normal images and 89.13% for COVID-19 and pneumothorax. With the suggested DSS system, a judgment depending on individual chest X-ray scans may be made in approximately 0.01 s. As a result, the DSS system described in this study can forecast at a pace of 95 frames per second (FPS) for both models, which is near to real-time.

Classification of Non-Functional Requirements From IoT Oriented Healthcare Requirement Document.

Internet of Things (IoT) involves a set of devices that aids in achieving a smart environment. Healthcare systems, which are IoT-oriented, provide monitoring services of patients' data and help take immediate steps in an emergency. Currently, machine learning-based techniques are adopted to ensure security and other non-functional requirements in smart health care systems. However, no attention is given to classifying the non-functional requirements from requirement documents. The manual process of classifying the non-functional requirements from documents is erroneous and laborious. Missing non-functional requirements in the Requirement Engineering (RE) phase results in IoT oriented healthcare system with compromised security and performance. In this research, an experiment is performed where non-functional requirements are classified from the IoT-oriented healthcare system's requirement document. The machine learning algorithms considered for classification are Logistic Regression (LR), Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), K-Nearest Neighbors (KNN), ensemble, Random Forest (RF), and hybrid KNN rule-based machine learning (ML) algorithms. The results show that our novel hybrid KNN rule-based machine learning algorithm outperforms others by showing an average classification accuracy of 75.9% in classifying non-functional requirements from IoT-oriented healthcare requirement documents. This research is not only novel in its concept of using a machine learning approach for classification of non-functional requirements from IoT-oriented healthcare system requirement documents, but it also proposes a novel hybrid KNN-rule based machine learning algorithm for classification with better accuracy. A new dataset is also created for classification purposes, comprising requirements related to IoT-oriented healthcare systems. However, since this dataset is small and consists of only 104 requirements, this might affect the generalizability of the results of this research.

Randomly initialized convolutional neural network for the recognition of COVID‐19 using X‐ray images

By the start of 2020, the novel coronavirus (COVID‐19) had been declared a worldwide pandemic, and because of its infectiousness and severity, several strands of research have focused on combatting its ongoing spread. One potential solution to detecting COVID‐19 rapidly and effectively is by analyzing chest X‐ray images using Deep Learning (DL) models. Convolutional Neural Networks (CNNs) have been presented as particularly efficient techniques for early diagnosis, but most still include limitations. In this study, we propose a novel randomly initialized CNN (RND‐CNN) architecture for the recognition of COVID‐19. This network consists of a set of differently‐sized hidden layers all created from scratch. The performance of this RND‐CNN is evaluated using two public datasets: the COVIDx and the enhanced COVID‐19 datasets. Each of these datasets consists of medical images (X‐rays) in one of three different classes: chests with COVID‐19, with pneumonia, or in a normal state. The proposed RND‐CNN model yields encouraging results for its accuracy in detecting COVID‐19 results, achieving 94% accuracy for the COVIDx dataset and 99% accuracy on the enhanced COVID‐19 dataset.

Noninvasive Detection of Respiratory Disorder Due to COVID-19 at the Early Stages in Saudi Arabia

The Kingdom of Saudi Arabia has suffered from COVID-19 disease as part of the global pandemic due to severe acute respiratory syndrome coronavirus 2. The economy of Saudi Arabia also suffered a heavy impact. Several measures were taken to help mitigate its impact and stimulate the economy. In this context, we present a safe and secure WiFi-sensing-based COVID-19 monitoring system exploiting commercially available low-cost wireless devices that can be deployed in different indoor settings within Saudi Arabia. We extracted different activities of daily living and respiratory rates from ubiquitous WiFi signals in terms of channel state information (CSI) and secured them from unauthorized access through permutation and diffusion with multiple substitution boxes using chaos theory. The experiments were performed on healthy participants. We used the variances of the amplitude information of the CSI data and evaluated their security using several security parameters such as the correlation coefficient, mean-squared error (MSE), peak-signal-to-noise ratio (PSNR), entropy, number of pixel change rate (NPCR), and unified average change intensity (UACI). These security metrics, for example, lower correlation and higher entropy, indicate stronger security of the proposed encryption method. Moreover, the NPCR and UACI values were higher than 99% and 30, respectively, which also confirmed the security strength of the encrypted information.

A Novel Hybrid Deep Learning Model for Detecting COVID-19-Related Rumors on Social Media Based on LSTM and Concatenated Parallel CNNs

Spreading rumors in social media is considered under cybercrimes that affect people, societies, and governments. For instance, some criminals create rumors and send them on the internet, then other people help them to spread it. Spreading rumors can be an example of cyber abuse, where rumors or lies about the victim are posted on the internet to send threatening messages or to share the victim’s personal information. During pandemics, a large amount of rumors spreads on social media very fast, which have dramatic effects on people’s health. Detecting these rumors manually by the authorities is very difficult in these open platforms. Therefore, several researchers conducted studies on utilizing intelligent methods for detecting such rumors. The detection methods can be classified mainly into machine learning-based and deep learning-based methods. The deep learning methods have comparative advantages against machine learning ones as they do not require preprocessing and feature engineering processes and their performance showed superior enhancements in many fields. Therefore, this paper aims to propose a Novel Hybrid Deep Learning Model for Detecting COVID-19-related Rumors on Social Media (LSTM–PCNN). The proposed model is based on a Long Short-Term Memory (LSTM) and Concatenated Parallel Convolutional Neural Networks (PCNN). The experiments were conducted on an ArCOV-19 dataset that included 3157 tweets;1480 of them were rumors (46.87%) and 1677 tweets were non-rumors (53.12%). The findings of the proposed model showed a superior performance compared to other methods in terms of accuracy, recall, precision, and F-score.