PANACEA: An Automated Misinformation Detection System on COVID-19

In this demo, we introduce a web-based misinformation detection system PANACEA on COVID-19 related claims, which has two modules, fact-checking and rumour detection. Our fact-checking module, which is supported by novel natural language inference methods with a self-attention network, outperforms state-of-the-art approaches. It is also able to give automated veracity assessment and ranked supporting evidence with the stance towards the claim to be checked. In addition, PANACEA adapts the bi-directional graph convolutional networks model, which is able to detect rumours based on comment networks of related tweets, instead of relying on the knowledge base. This rumour detection module assists by warning the users in the early stages when a knowledge base may not be available. © 2023 Association for Computational Linguistics.

Multi-Attentional U-Net for Medical Image Segmentation

Deep learning (DL) approaches for image segmentation have been gaining state-of-the-art performance in recent years. Particularly, in deep learning, U-Net model has been successfully used in the field of image segmentation. However, traditional U-Net methods extract features, aggregate remote information, and reconstruct images by stacking convolution, pooling, and up sampling blocks. The traditional approach is very inefficient due of the stacked local operators. In this paper, we propose the multi-attentional U-Net that is equipped with non-local blocks based self-attention, channel-attention, and spatial-attention for image segmentation. These blocks can be inserted into U-Net to flexibly aggregate information on the plane and spatial scales. We perform and evaluate the multi-attentional U-Net model on three benchmark data sets, which are COVID-19 segmentation, skin cancer segmentation, thyroid nodules segmentation. Results show that our proposed models achieve better performances with faster computation and fewer parameters. The multi-attention U-Net can improve the medical image segmentation results. © 2022 IEEE.

CCTwins: A Weakly-Supervised Transformer-based Crowd Counting Method with Adaptive Scene Consistency Attention

Recently, crowd counting has attracted significant attention, particularly in the context of the COVID-19 pandemic, due to its ability to automatically provide accurate crowd numbers in images. To address the challenges of location-level labeling, several transformer-based crowd counting methods have been proposed with only count-level supervision. However, these methods directly use the transformer as an encoder without considering the uneven crowd distribution. To address this issue, we propose CCTwins, a novel transformer-based crowd counting method with only count-level supervision. Specifically, we introduce an adaptive scene consistency attention mechanism to enhance the transformer-based model Twins-SVT-L for feature extraction in crowded scenes. Additionally, we design a multi-level weakly-supervised loss function that generates estimated crowd numbers in a coarse-to-fine manner, making it more appropriate for weakly-supervised settings. Moreover, intermediate features supervised by count-level labels are utilized to fuse multi-scale features. Experimental results on four public datasets demonstrate that our proposed method outperforms the state-of-the-art weakly-supervised methods, achieving up to a 16.6% improvement in MAE and up to a 13.8% improvement in RMSE across all evaluation settings. Moreover, the proposed CCTwins obtains competitive counting performance, even when compared to the state-of-the-art fully-supervised methods. IEEE

Identify Type of Lung Infection from Lung Patients X-RAY Image LIVERAGING Computer Vision

This research proposes a computer vision-based solutions to identify whether a patient is covid19/normal/Pneumonia infected with comparable or better state-of-The-Art accuracy. Proposed solution is based on deep learning technique CNN (Convolutional Neural networks) with multiple approaches to cover all open issues. First approach is based on CNN models based on pre-Trained models;second approach is to create CNN model from scratch. Experimentation and evaluation of multiple approaches helps in covering all open points and gaps left unattended in related work performed to solve this problem. Based on the experimentation results of both the approaches and study of related work done by other researchers, Both the approaches are equally effective can be recommended for multi-class classification of lung disease. © 2023 IEEE.

Streaming Big Data with Open-source: A Comparative Study and Architectural Recommendations

This study focusses on providing state-of-the-art infrastructure for data pipelines in e-Commerce sector, especially for online stores. With people going digital and also latest impact of Covid-19, daily e-Commerce companies are dealing with large amount of data (terabytes to petabytes). With growing Internet of Things, systems of computing devices which are interrelated. The inter-relation may be between mechanical and digital machines, objects or people. The interrelated objects will be provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. Growth of big data poses several challenges and opportunities in every field of its usage. Realtime analysis of data and its inference gives a competitive edge over its partners in every business field especially in e-commerce. Recent advances in technology and tools have exposed new opportunities to get actionable insights from historical data like market data, customer demographics, along with real-time data. Advancement in distributed streaming technology makes it important to investigate existing streaming data pipeline capabilities in eCommerce sector with a focus on online stores. This study analyzes the published research works on streaming data pipelines in e-commerce sector also to facilitate e-commerce's variety of data streaming applications requirement. A state-of-the-art lambda architecture for streaming is proposed completely based on open-source technologies. Challenge in proprietary owned streaming platforms are vendor lock-in, limited ability to customize, cost, limited innovation & support. Proposed reference architecture will address many streaming use cases compared to its competitors, it has support of large open-source community in providing the inter-operability between streaming & related technologies like connectors, apart from providing better performance apart from other open-source based product advantages. © 2023 IEEE.

Early Prediction of COVID-19 Infection with IoT and Machine Learning

The deadly virus COVID-19 has heavily impacted all countries and brought a dramatic loss of human life. It is an unprecedented scenario and poses an extreme challenge to the healthcare sector. The disruption to society and the economy is devastating, causing millions of people to live in poverty. Most citizens live in exceptional hardship and are exposed to the contagious virus while being vulnerable due to the inaccessibility of quality healthcare services. This study introduces ubiquitous computing as a state-of-The-Art method to mitigate the spread of COVID-19 and spare more ICU beds for those truly needed. Ubiquitous computing offers a great solution with the concept of being accessible anywhere and anytime. As COVID-19 is highly complicated and unpredictable, people infected with COVID-19 may be unaware and still live on with their life. This resulted in the spread of COVID-19 being uncontrollable. Therefore, it is essential to identify the COVID-19 infection early, not only because of the mitigation of spread but also for optimal treatment. This way, the concept of wearable sensors to collect health information and use it as an input to feed into machine learning to determine COVID-19 infection or COVID-19 status monitoring is introduced in this study. © 2023 IEEE.

Distance is no longer a barrier to healthcare services: current state and future trends of telehealth research

PurposeTaking a business lens of telehealth, this article aims to review and provide a state-of-the-art overview of telehealth research.Design/methodology/approachThis research conducts a systematic literature review using the scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR) protocol and a collection of bibliometric analytical techniques (i.e. performance analysis, keyword co-occurrence, keyword clustering and content analysis).FindingsUsing performance analysis, this article unpacks the publication trend and the top contributing journals, authors, institutions and regions of telehealth research. Using keyword co-occurrence and keyword clustering, this article reveals 10 major themes underpinning the intellectual structure of telehealth research: design and development of personal health record systems, health information technology (HIT) for public health management, perceived service quality among mobile health (m-health) users, paradoxes of virtual care versus in-person visits, Internet of things (IoT) in healthcare, guidelines for e-health practices and services, telemonitoring of life-threatening diseases, change management strategy for telehealth adoption, knowledge management of innovations in telehealth and technology management of telemedicine services. The article proposes directions for future research that can enrich our understanding of telehealth services.Originality/valueThis article offers a seminal state-of-the-art overview of the performance and intellectual structure of telehealth research from a business perspective.

Evaluation of Early Diagnosis of COVID-19 Algorithms

The coronavirus disease 2019 (COVID-19) has been stated as a global pandemic, and the BA.4 and BA.5 variants are anticipated to drive the next wave of COVID-19 infection. Early diagnosis of this infection reduces its viral excretion. In this paper, after a large study of existing algorithms for pre-symptomatic COVID-19 detection in the state-of-the-art, we discovered a notable flaw in most models related to the choice of the evaluation function, such that, all the tested algorithms perform worse (from the evaluation function perspective) than an algorithm that generates alarms randomly from a binomial distribution. Therefore, we propose a simple and less biased evaluation function to better compare the quality of different algorithms. Comprehensive experimental evaluations of the state-of-the-art algorithms over the real-world dataset published by Nature Medicine journal contains 84 COVID-19 patients and 2,000 healthy participants show the effectiveness and the relevance of our evaluation method. Moreover, the proposed framework is released as an open-source library. © 2022 IEEE.

A systematic literature review on supply chain resilience in SMEs: learnings from COVID-19 pandemic

PurposeThe study aims to review state-of-art literature on supply chain resilience in SMEs in the context of the coronavirus (COVID-19) pandemic and provides a comprehensive view of insights gained, gaps identified and suggests potential areas of future research.Design/methodology/approachUsing a thorough search strategy, 46 articles were found relevant for this study. Each of these articles was further reviewed, classified and analysed to highlight the development of literature in this field and identify the significant focal area of research in this domain.FindingsThe classification of studies indicates a growing number of articles in the last two years with a significant focus on multiple industries and survey-based research design. The study's findings suggest that literature on supply chain resilience in SMEs falls into four categories: supply chain resilience principle, impact of COVID-19 pandemic on SMEs, strategies for developing supply chain resilience and role of Industry 4.0 technologies in supply chain resilience. We also identified knowledge gaps and suggested directions for future research to catalyse studies at the interface of supply chain resilience, SMEs and COVID-19 pandemic.Research limitations/implicationsThe generalisability of this study can be limited to a specific population of online databases and selected time periods chosen for a particular period.Originality/valueThe study provides a structured literature review on studies published between 2012 and 2022 for the use of academicians and practitioners. Findings will be of great value for SMEs to improve their resilience during the uncertain business environment.

Trends of respiratory virus detection in point-of-care testing: A review.

In resource-limited conditions such as the COVID-19 pandemic, on-site detection of diseases using the Point-of-care testing (POCT) technique is becoming a key factor in overcoming crises and saving lives. For practical POCT in the field, affordable, sensitive, and rapid medical testing should be performed on simple and portable platforms, instead of laboratory facilities. In this review, we introduce recent approaches to the detection of respiratory virus targets, analysis trends, and prospects. Respiratory viruses occur everywhere and are one of the most common and widely spreading infectious diseases in the human global society. Seasonal influenza, avian influenza, coronavirus, and COVID-19 are examples of such diseases. On-site detection and POCT for respiratory viruses are state-of-the-art technologies in this field and are commercially valuable global healthcare topics. Cutting-edge POCT techniques have focused on the detection of respiratory viruses for early diagnosis, prevention, and monitoring to protect against the spread of COVID-19. In particular, we highlight the application of sensing techniques to each platform to reveal the challenges of the development stage. Recent POCT approaches have been summarized in terms of principle, sensitivity, analysis time, and convenience for field applications. Based on the analysis of current states, we also suggest the remaining challenges and prospects for the use of the POCT technique for respiratory virus detection to improve our protection ability and prevent the next pandemic.

A review on tele-manipulators for remote diagnostic procedures and surgery

With modern medicine and healthcare services improving in leaps and bounds, the integration of telemedicine has helped in expanding these specialised healthcare services to remote locations. Healthcare telerobotic systems form a component of telemedicine, which allows medical intervention from a distance. It has been nearly 40 years since a robotic technology, PUMA 560, was introduced to perform a stereotaxic biopsy in the brain. The use of telemanipulators for remote surgical procedures began around 1995, with the Aesop, the Zeus, and the da Vinci robotic surgery systems. Since then, the utilisation of robots has steadily increased in diverse healthcare disciplines, from clinical diagnosis to telesurgery. The telemanipulator system functions in a master–slave protocol mode, with the doctor operating the master system, aided by audio-visual and haptic feedback. Based on the control commands from the master, the slave system, a remote manipulator, interacts directly with the patient. It eliminates the requirement for the doctor to be physically present in the spatial vicinity of the patient by virtually bringing expert-guided medical services to them. Post the Covid-19 pandemic, an exponential surge in the utilisation of telerobotic systems has been observed. This study aims to present an organised review of the state-of-the-art telemanipulators used for remote diagnostic procedures and surgeries, highlighting their challenges and scope for future research and development.

Technology-Enhanced Learning Environments and Adaptive Learning Systems - Development of Functionality Taxonomies

Especially against the background of the current coronavirus crisis, technology-enhanced learning environments (TELEs) increasingly characterize teaching at universities. For the successful use and integration of TELEs, it is important to understand the functionalities of the technologies used. Based on the state of the art and following [1], we develop two taxonomies. The first taxonomy depicts eleven functionalities with different dimensions relevant for successfully designing TELEs. Sound knowledge of the functionalities supports research on adaptive learning within TELEs and the implementation of student-centered learning opportunities, which is structured in a second functionality taxonomy for adaptive learning systems (ALSs). We contribute to current research on TELEs and ALSs by providing a structured overview of functionalities and suggestions for further research with our research opportunities. © 2022 IEEE Computer Society. All rights reserved.

Customer Sentiments Toward Saudi Banks During the Covid-19 Pandemic

In view of the recent interest of Saudi banks in customers' opinions through social media, our research aims to capture the sentiments of bank users on Twitter. Thus, we collected and manually annotated more than 12, 000 Saudi dialect tweets, and then we conducted experiments on machine learning models including: Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (RL) as well as state-of-the-art language models (i.e. MarBERT) to provide baselines. Results show that the accuracy in SVM, LR, RF, and MarBERT achieved 82.4%, 82%, 81%, and 82.1% respectively. Our models code and dataset will be made publicly available on GitHub. © ICNLSP 2022.All rights reserved

Game-Based Approaches to Enhancing Public Understanding of Science: A Descriptive Literature Review

With prominent looming global issues such as climate change and COVID-19, public understanding of science (PUS) is increasingly perceived to be vital for humanity to address and adapt to global wicked challenges. Compared to conventional approaches that struggle with public engagement, games can potentially remedy this by proactively engaging players towards more fruitful performance in and outside games. While the employment of game-based approaches in pedagogy in general is not a new development, gamifying PUS has only recently grown to relative prominence for its superiority in engaging the public with active science-derived interpretation, deliberation, and consequent action. To understand the state-of-the-art of this field, we conduct a systematic descriptive literature review of the extant corpus. We reviewed 29 papers and investigated their types of interventions, contexts, populations, and outcomes. The results overall indicate diverse yet imbalanced research focuses thus far, for which we discuss implications for future research. © 2023 IEEE Computer Society. All rights reserved.

Generating Masked Facial Datasets Using Dlib-Machine Learning Library

In 2020, the COVID-19 pandemic spread globally, leading to countries imposing health restrictions on people, including wearing masks, to prevent the spread of the disease. Wearing a mask significantly decreases distinguishing ability due to its concealment of the main facial features. After the outbreak of the pandemic, the existing datasets became unsuitable because they did not contain images of people wearing masks. To address the shortage of large-scale masked faces datasets, a developed method was proposed to generate artificial masks and place them on the faces in the unmasked faces dataset to generate the masked faces dataset. Following the proposed method, masked faces are generated in two steps. First, the face is detected in the unmasked image, and then the detected face image is aligned. The second step is to overlay the mask on the cropped face images using the dlib-ml library. Depending on the proposed method, two datasets of masked faces called masked-dataset-1 and masked-dataset-2 were created. Promising results were obtained when they were evaluated using the Labeled Faces in the Wild (LFW) dataset, and two of the state-of-the-art facial recognition systems for evaluation are FaceNet and ArcFace, where the accuracy of using the two systems was 96.1 and 97, respectively with masked-dataset-1 and 87.6 and 88.9, respectively with masked-dataset-2. © 2022 IEEE.

Applications of Mobile Payment Services in Financial Strategies: State-of-the-Art, Taxonomy and Upcoming Directions with a Focus on Pandemic Crisis

Amid the pandemic infection, people are bound to use contactless mobile payment (M-Payment) services. M-Payment is a payment method using an application in a mobile device, such as a mobile phone, and gadget. Owing to the convenience, reliability and contact-free feature of M-Payment, it has been diffusely adopted to reduce the direct and indirect contacts in transactions, allowing social distancing to be maintained and facilitating the stabilization of the social economy. Consequently, it has become one of the day's most important topics. Therefore, the purpose of this study is to provide a systematic literature review (SLR) on the applications of M-payment services in financial strategies, focusing on the pandemic crisis. 19 papers were collected and divided into three groups for further analysis. The results showed that M-Payments applications in financial strategies during the pandemic crisis could help reduce the spread of infection risks by hastening the transition to touchless habits. © 2023 World Scientific Publishing Company.

Reduce Cheating in e-Exams Using Machine Learning: State of the Art

Cheating on online exams becomes a black spot in distance learning environments. On the one hand, it threatens the credibility of these exams by violating the principle of equality and success on merit. On the other hand, it also has negative repercussions on the reputation of the institutions. Without a doubt, in the Covid-19 health crisis and following the recommendations of the World Health Organization to respect social distancing, the majority of establishments have adopted the distance learning system, including online exams. However, the difficulty of monitoring learner activity in remote settings characterizes this type of assessment by inequity. In practice, each establishment has relied on a monitoring solution adapted according to certain criteria in order to guarantee a fair passage of the exams and to control them well. AI-assisted proctoring tools add a layer of protection to online exams. In this article we will discuss and compare the different uses of Artificial Intelligence tools to reduce cheating in online exams, based on the use of Machine Learning techniques. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification

With the devastating outbreak of COVID-19, vaccines are one of the crucial lines of defense against mass infection in this global pandemic. Given the protection they provide, vaccines are becoming mandatory in certain social and professional settings. This paper presents a classification model for detecting COVID-19 vaccination related search queries, a machine learning model that is used to generate search insights for COVID-19 vaccinations. The proposed method combines and leverages advancements from modern state-of-the-art (SOTA) natural language understanding (NLU) techniques such as pretrained Transformers with traditional dense features. We propose a novel approach of considering dense features as memory tokens that the model can attend to. We show that this new modeling approach enables a significant improvement to the Vaccine Search Insights (VSI) task, improving a strong well-established gradient-boosting baseline by relative +15% improvement in F1 score and +14% in precision. © 2022 Association for Computational Linguistics.

Automatic Detection of COVID-19 Using Ensemble Transfer Learning Based on Lung CT Scans

In order to curb the rapid spread of COVID-19, early and accurate detection is required. Computer Tomography (CT) scans of the lungs can be utilized for accurate COVID-19 detection because these medical images highlight COVID-19 infection with high sensitivity. Transfer learning was implemented on six state-of-the-art Convolutional Neural Networks (CNNs). From these six CNNs, the three with the highest accuracies (based on empirical experiments) were selected and used as base learners to produce hard voting and soft voting ensemble classifiers. These three CNNs were identified as Vgg16, EfficientNetB0 and EfficientNetB5. This study concludes that the soft voting ensemble classifier, with base learners Vgg16 and EfficientNetB5, outperformed all other ensemble classifiers with different base learners and individual models that were investigated. The proposed classifier achieved a new state-of-the-art accuracy on the SARS-CoV-2 dataset. The accuracy obtained from this framework was 98.13%, the recall was 98.94%, the precision was 97.40%, the specificity was 97.30% and the F1 score was 98.16%. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Augmented Reality Implementation for Human Anatomy Learning in Medical Education: A Review

The COVID-19 pandemic has changed the style of teaching and learning, one of them in medical education. Face-to-face meetings during the COVID-19 pandemic can no longer be done freely. With existing limitations, students are required to learn to understand learning without face-to-face with educators, especially medical students. So, it is necessary touse learning media that can increase active learning and learning independently. Learning media such as augmented reality is one that needs to be studied to help students learn. This research aims to understand the state-of-the-art of augmented reality implementation for human anatomy learning in the medical education field from 2018 to 2022. This research reviews recent studies about augmented implementation for human anatomy learning in medical education. The results of this research explain that there are things that can still be a challenge for further research. Future research can explore research for general people who use augmented reality to educate about human anatomy and the impact of its use. Another suggestion is to develop by minimizing the cost as low as possible. © 2022 IEEE.