How COVID-19 is Accelerating the Digital Revolution: Challenges and Opportunities

This book explores how digital technologies have proved to be a useful and necessary tool to help ensure that local and regional governments on the frontline of the emergency can continue to provide essential public services during the COVID-19 crisis. Indeed, as the demand for digital technologies grows, local and regional governments are increasingly committed to improving the lives of their citizens under the principles of privacy, freedom of expression and democracy. The Digital Revolution began between the late 1950s and 1970s and represents the evolution of technology from the mechanical and analog to the digital. The advent of digital technology has also changed how humans communicate today using computers, smartphones and the internet. Further, the digital revolution has made a tremendous wealth of information accessible to virtually everyone. In turn, the book focuses on key challenges for local and regional governments concerning digital technologies during this crisis, e.g. the balance between privacy and security, the digital divide, and accessibility. Privacy is a challenge in the mitigation of COVID-19, as governments rely on digital technologies like contact-tracking apps and big data to help trace peoples patterns and movements. While these methods are controversial and may infringe on rights to privacy, they also appear to be effective measures for rapidly controlling and limiting the spread of the virus. Next, the book discusses the 10 technology trends that can help build a resilient society, as well as their effects on how we do business, how we work, how we produce goods, how we learn, how we seek medical services and how we entertain ourselves. Lastly, the book addresses a range of diversified technologies, e.g. Online Shopping and Robot Deliveries, Digital and Contactless Payments, Remote Work, Distance Learning, Telehealth, Online Entertainment, Supply Chain 4.0, 3D Printing, Robotics and Drones, 5G, and Information and Communications Technology (ICT). © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Medical image-based detection of COVID-19 using Deep Convolution Neural Networks.

The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures.

A WATERMARK-BASED SECURE MODEL FOR DATA SECURITY AGAINST SECURITY ATTACKS FOR MACHINE LEARNING ALGORITHMS

Machine Learning (ML) has been known as one of the most widely used by the decision-based application. Most of the security sensitive applications have been using DL for the improvement and betterment of outcomes while solving real-life applications. Poisoning and evasions attacks are the common examples of security attacks where the attacker deliberately inject malicious injections into the dataset to get the information of model settings and dataset. Hence, in this paper we have proposed a watermark-based secure model for ensuring data security and robustness against poisoning and evasion attacks before training and testing the DL algorithms. Our proposed model has been developed on ML algorithms e.g., eXtreme Gradient Boosting (XGBOOST) and Random Forest to ensure the data security against most common security attacks. We have evaluated proposed watermark based secure model using benchmark mechanism to show that the by introducing secure model, the performance has not been disturbed. We have computed prediction of daily cases on COVID-19 dataset and achieved similar results. Finally, our proposed model can detect significant attack detection rate even for large numbers of attacks (poisoning and evasion attacks). It is believed that our proposed model can also be implemented in other learning environment to mitigate the security issues and improve security applications. © School of Engineering, Taylor's University.

The role of the IoT and digital twin in the healthcare digitalization process: IoT and digital twin in the healthcare digitalization process

The ability of IoT technology to simplify the adoption of artificial intelligence is precious to consumer product companies. The robustness of consumer companies' IoT initiatives will determine whether they benefit from the rise of IoT. A well-thought-out IoT strategy and execution will improve supply chain efficiency and align products with modern, post-COVID consumer behaviour. It must be noted that the network is not only restricted to computers but also has a web of devices of various sizes and kinds, including medical instruments and industrial systems. Expert analysts put forward the inherent capabilities of IoT devices to not only communicate and exchange information but also create a starting point for new, fresh revenue sources, ignite the business foundation and business models and enhance the techniques of services that propel numerous industries and sectors. © 2023, IGI Global. All rights reserved.

G-DCNN: GAN based Deep 2D-CNN for COVID-19 Classification

Recent progress in COVID-19 detection techniques involve deep learning models. The patient's image data like Chest X-Ray Images, CT-scan data help the physician for analyzing whether the patient is COVID-19 positive or negative. However, huge data size is essential for improving the classification accuracy of deep learning model. Data Augmentation (DA) is a promising solution to generate synthetic samples of data. Sampling is a traditional data augmentation technique to generate synthetic samples. Recently, Generative Adversarial Networks (GAN) have declared in generating high quality synthetic data from acutal small data to treat imbalance issue. This work proposed a method called GAN based Deep 2D-CNN (G-DCNN) for COVID-19 recognition. In this study, GAN has been used for synthesizing Chest X-Ray and CT-scan images followed by Deep 2D-CNN with the goal of detecting COVID-19. © 2022 IEEE.

Analyzing the Impact of Social Distancing on the Stoning Ritual of the Islamic Pilgrimage

The COVID-19 pandemic has resulted in a profound impact on large-scale gatherings throughout the world. Social distancing has become one of the most common measures to restrict the spread of the novel Coronavirus. Islamic pilgrimage attracts millions of pilgrims to Saudi Arabia annually. One of the mandatory rituals of pilgrimage is the symbolic stoning of the devil. Every pilgrim is required to perform this ritual within a specified time on three days of pilgrimage. This ritual is prone to congestion due to strict spatiotemporal requirements. We propose a pedestrian simulation model for implementing social distancing in the stoning ritual. An agent-based simulation is designed to analyze the impact of inter-queue and intra-queue spacing between adjacent pilgrims on the throughput and congestion during the stoning ritual. After analyzing several combinations of intra-queue and inter-queue spacings, we conclude that 25 queues with 1.5 meters of intra-queue spacing result in an optimal combination of throughput and congestion. The Ministry of Hajj in Saudi Arabia may benefit from these findings to manage and plan pilgrimage more effectively.

Prediction of suitable candidates for covid-19 vaccination

In the current times, COVID-19 has taken a handful of people’s lives. So, vaccination is crucial for everyone to avoid the spread of the disease. How-ever, not every vaccine will be perfect or will get success for everyone. In the pre-sent work, we have analyzed the data from the Vaccine Adverse Event Reporting System and understood that the vaccines given to the people might or might not work considering certain demographic factors like age, gender, and multiple other variables like the state of living, etc. This variable is considered because it explains the unmentioned variables like their food habits and living conditions. The target group for this work will be the healthcare workers, government bodies & medical research organizations. We analyze the data using machine learning techniques & algorithms and predict the working of COVID-19 vaccines on specific age groups developed by significant vaccine manufacturers, i.e., PFIZER \BIONTECH and MODERNA. Data visualization and analysis interpret the vaccine impact based on the above-said variables. It becomes clear that people belonging to a specific demographic factor can have an option to choose the vaccine accordingly based on the previous history of a particular manufacturer’s vaccine getting succeeded for that demographic factor. The various machine learning algorithms we have used are Logistic Regression, Adaboost, Decision Tree, and Random Forest. We have considered the DIED variable as the target variable as this results in a high life threat. On performance measure, perspective Adaboost is showing appreciable values. The prediction of the type of vaccine to be adminis-tered could be derived using this machine learning algorithm. The accuracy we achieved based on the experiment are as follows: Decision Tree Classifier with 97.3%, Logistic Regression with 97.31%, Random Forest with 97.8%, AdaBoost with 98.1%. © 2022, Tech Science Press. All rights reserved.

The Use of Emerging Technologies DIoT: Elderly Daily Living in Post-Epidemic Era

As the number of aging population increases, their Quality of Life (QoL) becomes a concern in the society. The elderly is not only vulnerable due to their chronic degeneration issues but may also be insensitive to technological innovation which could possibly improve their QoL in the Industrial Revolution 4.0. The outbreak of COVID-19 has significantly threatened their well-being in their living context and will consequentially change people's perspective towards normal lifestyle after this public health crisis. Recent studies have highlighted the usability of the drone technology in the automation of navigation, monitoring, and load carrying which can potentially facilitate various purposes of use in our daily life. This paper reviews recent academic works related to Drone-Based Internet of Things (DIoT) technology and extracts the advantages of DIoT applications, which have the potential to assist elderly's Activity of Daily Living (ADL) in post-epidemic time. Our results suggest that with the low energy consumption, the DIoT techniques are capable of reducing the body exposure under pandemic situation and satisfying the appreciation to normal and digital-connected life in the future. Nevertheless, the limited flying range and low technology penetration among elderly users significantly impede the implementation of DIoT application. Importantly, the DIoT technology upgrades manpower-based manual work. This paper updates to the knowledge of drone technology application in the context of elderly centre during post-pandemic.

An effective online collaborative training in developing listening comprehension skills

The COVID-19 outbreak severely affected formal face-to-face classroom teaching and learning. ICT-based online education and training can be a useful measure during the pandemic. In the Pakistani educational context, the use of ICT-based online training is generally sporadic and often unavailable, especially for developing English-language instructors' listening comprehension skills. The major factors affecting availability include insufficient IT resources and infrastructure, a lack of proper online training for speech and listening, instructors with inadequate academic backgrounds, and an unfavorable environment for ICT-based training for listening comprehension. This study evaluated the effectiveness of ICT-based training for developing secondary-level English-language instructors' listening comprehension skills. To this end, collaborative online training was undertaken using random sampling. Specifically, 60 private-school instructors in Chakwal District, Pakistan, were randomly selected to receive online-listening training sessions using English dialogs. The experimental group achieved significant scores in the posttest analysis. Specifically, there were substantial improvements in the participants' listening skills via online training. Given the unavailability of face-to-face learning during COVID-19, this study recommends using ICT-based online training to enhance listening comprehension skills. Education policymakers should revise curricula based on online teaching methods and modules. © 2021 CRL Publishing. All rights reserved.

Extended forgery detection framework for covid-19 medical data using convolutional neural network

Medical data tampering has become one of the main challenges in the field of secure-aware medical data processing. Forgery of normal patients' medical data to present them as COVID-19 patients is an illegitimate action that has been carried out in different ways recently. Therefore, the integrity of these data can be questionable. Forgery detection is a method of detecting an anomaly in manipulated forged data. An appropriate number of features are needed to identify an anomaly as either forged or non-forged data in order to find distortion or tampering in the original data. Convolutional neural networks (CNNs) have contributed a major breakthrough in this type of detection. There has been much interest from both the clinicians and the AI community in the possibility of widespread usage of artificial neural networks for quick diagnosis using medical data for early COVID-19 patient screening. The purpose of this paper is to detect forgery in COVID-19 medical data by using CNN in the error level analysis (ELA) by verifying the noise pattern in the data. The proposed improved ELA method is evaluated using a type of data splicing forgery and sigmoid and ReLU phenomenon schemes. The proposed method is verified by manipulating COVID-19 data using different types of forgeries and then applying the proposed CNN model to the data to detect the data tampering. The results show that the accuracy of the proposed CNN model on the test COVID-19 data is approximately 92%. © 2021 Tech Science Press. All rights reserved.

COVID-19 Impact on Educational System Globally

Education plays a very important role in the development of the society. A good educational system produces good citizens. The nations which compromise their education live far behind in the race of development. The advancement of ICT has brought drastic positive changes in the educational system and made the knowledge available and accessible from everywhere. The E-Learning system is also prevailing in most modern societies, and a lot of people who are not able to attend regular classes are getting benefit from it. However, it cannot be the supplement of traditional face-to-face learning mechanisms. The blend of the traditional educational system with E-Learning is very fruitful, and a lot of developed societies are getting its benefit. Recently, the rapid growth of COVID-19 all over the world has put the traditional education system to halt. The policy of lockdown and isolation has put billions of children away from school. Most governments have officially declared school closure to prevent the widespread of COVID-19. In such a situation, online learning is the only solution to run the vehicle of the educational system. However, the current E-Learning infrastructure was not expecting this rapid paradigm shift from the traditional educational system toward E-Learning. Therefore, a lot of issues are reported daily such as Internet connectivity, security breaches, conducting the assessment, etc. In this paper, we will provide the impact of COVID-19 on the educational system worldwide and the strategies that might be useful to fill the gap created by school closure during COVID-19. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.