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.

Gender Pay Gap in Sports on a Fan-Request Celebrity Video Site

The internet is often thought of as a democratizer, enabling equality in aspects such as pay, as well as a tool introducing novel communication and monetization opportunities. In this study we examine athletes on Cameo, a website that enables bi-directional fan-celebrity interactions, questioning whether the well-documented gender pay gaps in sports persist in this digital setting. Traditional studies into gender pay gaps in sports are mostly in a centralized setting where an organization decides the pay for the players, while Cameo facilitates grass-roots fan engagement where fans pay for video messages from their preferred athletes. The results showed that even on such a platform gender pay gaps persist, both in terms of cost-per-message, and in the number of requests, proxied by number of ratings. For instance, we find that female athletes have a median pay of 30$ per-video, while the same statistic is 40$ for men. The results also contribute to the study of parasocial relationships and personalized fan engagements over a distance. Something that has become more relevant during the ongoing COVID-19 pandemic, where in-person fan engagement has often been limited. © 2023 Owner/Author.

Forecasting COVID-19 Pandemic using Prophet, LSTM, hybrid GRU-LSTM, CNN-LSTM, Bi-LSTM and Stacked-LSTM for India

The COVID-19 Pandemic has been around for four years and remains a health concern for everyone. Although things are somewhat returning to normal, increased incidence of COVID-19 cases in some regions of the world (such as China, Japan, France, South Korea, etc.) has bred worry and anxiety in world, including India. The scientific community, which includes governmental organizations and healthcare facilities, was eager to learn how the COVID-19 Pandemic would develop. The current work makes an attempt to address this question by employing cutting-edge machine learning and Deep Learning algorithms to anticipate the daily incidence of COVID-19 for India over the course of the next six months. For the purpose famous timeseries algorithms were implemented including LSTM, Bi-Directional LSTM and Stacked LSTM and Prophet. Owing to success of hybrid algorithms in specific problem domains- the present study also focuses on such algorithms like GRU-LSTM, CNN-LSTM and LSTM with Attention. All these models have been trained on timeseries dataset of COVID-19 for India and performance metrics are recorded. Of all the models, the simplistic algorithms have performed better than complex and hybrid ones. Owing to this best result was obtained with Prophet, Bidirectional LSTM and Vanilla LSTM. The forecast reveals flat nature of COVID-19 case load for India in future six months. . © 2023 IEEE.

Stacked Bi-directional Long Short Term Memory model for Multi-Class Arabic Sentiment Analysis on Covid-19

The world has been affected by the Covid-19 epidemic during the last three years. During that period, most people tended to use social networks, where by searching for topics related to Covid-19, information could be provided to manage decisions by organizations or governments about public health. With the importance of the Arabic language, despite the lack of research targeting it, using Arabic language as a source of data and analyzing it due to the large number of users on social networks gives an impetus to understand people's feelings about the Covid-19 pandemic. One of the challenges facing sentiment analysis in Arabic is the use of dialects. The most common and existing methods used have been quite ineffective as they are oblivious to contextual information and cannot handle long-distance word dependencies. The Iraqi Arabic dialect is one of the Arabic dialects that still suffers from a lack of research in sentiment analysis. In this study, the official page of the Iraqi Ministry of Health on Facebook was used to collect and analysis comments. Word2vec model is incorporated to extract words semantic characteristics. To capture contextual features, Stacked Bi-directional Long Short Term Memory model (Stacked Bi-LSTM) utilizes sequential word vectors derived from the Continuous Bag of Words model. When compared to most common and existing approaches, the proposed method performed well. © 2022 IEEE.

Pre-pandemic autonomic function as a predictor of the COVID clinical course in young adults.

Long coronavirus disease (COVID) is emerging as a common clinical entity in the current era. Autonomic dysfunction is one of the frequently reported post-COVID complications. We hypothesize a bi-directional relationship between the autonomic function and the COVID course. This postulation has been inadequately addressed in the literature. A retrospective cohort (pre and post-comparison) study was conducted on 30 young adults whose pre-COVID autonomic function test results were available. They were divided into case and control groups based on whether they tested reverse transcription polymerase chain reaction positive for COVID-19. Autonomic function tests were performed in both the case and control groups. COVID infection in healthy young adults shifts the sympatho-vagal balance from the pre-disease state. Postural orthostatic tachycardia syndrome was present in 35% of the COVID-affected group. COVID course parameters were found to be associated with parasympathetic reactivity and the baroreflex function. Baseline autonomic function (parasympathetic reactivity represented by Δ heart rate changes during deep breathing and 30:15 ratio during lying-to-standing test) was also associated with the COVID course, the post-COVID symptoms and the post-COVID autonomic function profile. Additionally, multiple regression analysis found that the baseline parasympathetic reactivity was a very important determinant of the clinical course of COVID, the post-COVID symptoms and the post-COVID autonomic profile. Sympatho-vagal balance shifts to parasympathetic withdrawal with sympathetic predominance due to COVID infection in healthy young adults. There is a bi-directional relationship between the autonomic function and the COVID course.

Scenarios for digital twin deployment in healthcare facilities management

Purpose: This paper aims to focus on identifying key health-care issues amenable to digital twin (DT) approach. It starts with a description of the concept and enabling technologies of a DT and then discusses potential applications of DT solutions in healthcare facilities management (FM) using four different scenarios. The scenario planning focused on monitoring and controlling the heating, ventilation, and air-conditioning system in real-time;monitoring indoor air quality (IAQ) to monitor the performance of medical equipment;monitoring and tracking pulsed light for SARS-Cov-2;and monitoring the performance of medical equipment affected by radio frequency interference (RFI). Design/methodology/approach: The importance of a healthcare facility, its systems and equipment necessitates an effective FM practice. However, the FM practices adopted have several areas for improvement, including the lack of effective real-time updates on performance status, asset tracking, bi-directional coordination of changes in the physical facilities and the computational resources that support and monitor them. Consequently, there is a need for more intelligent and holistic FM systems. We propose a DT which possesses the key features, such as real-time updates and bi-directional coordination, which can address the shortcomings in healthcare FM. DT represents a virtual model of a physical component and replicates the physical data and behavior in all instances. The replication is attained using sensors to obtain data from the physical component and replicating the physical component's behavior through data analysis and simulation. This paper focused on identifying key healthcare issues amenable to DT approach. It starts with a description of the concept and enabling technologies of a DT and then discusses potential applications of DT solutions in healthcare FM using four different scenarios. Findings: The scenarios were validated by industry experts and concluded that the scenarios offer significant potential benefits for the deployment of DT in healthcare FM such as monitoring facilities' performance in real-time and improving visualization by integrating the 3D model. Research limitations/implications: In addition to inadequate literature addressing healthcare FM, the study was also limited to one of the healthcare facilities of a large public university, and the scope of the study was limited to IAQ including pressure, relative humidity, carbon dioxide and temperature. Additionally, the study showed the potential benefits of DT application in healthcare FM using various scenarios that DT experts validated. Practical implications: The study shows the practical implication using the various validated scenarios and identified enabling technologies. The combination and implementation of those mentioned above would create a system that can effectively help manage facilities and improve facilities' performances. Social implications: The only identifiable social solution is that the proposed system in this study can manually be overridden to prevent absolute autonomous control of the smart system in cases when needed. Originality/value: To the best of the authors' knowledge, this is the only study that has addressed healthcare FM using the DT approach. This research is an excerpt from an ongoing dissertation. © 2023, Emerald Publishing Limited.

An Semantic Similarity Matching Method for Chinese Medical Question Text

For the purpose of capturing the semantic information accurately and clarifying the user's questioning intention, this paper proposes a novel, ensemble deep architecture BERT-MSBiLSTM-Attentions (BMA) which uses the Bidirectional Encoder Representations from Transformers (BERT), Multi-layer Siamese Bi-directional Long Short Term Memory (MSBiLSTM) and dual attention mechanism (Attentions) in order to solve the current question semantic similarity matching problem in medical automatic question answering system. In the preprocessing part, we first obtain token-level and sentence-level embedding vectors that contain rich semantic representations of complete sentences. The fusion of more accurate and adequate semantic features obtained through Siamese recurrent network and dual attention network can effectively eliminate the effect of poor matching results due to the presence of certain non-canonical texts or the diversity of their expression ambiguities. To evaluate our model, we splice the dataset of Ping An Healthkonnect disease QA transfer learning competition and "public AI star” challenge - COVID-19 similar sentence judgment competition. Experimental results with CC19 dataset show that BMA network achieves significant performance improvements compared to existing methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

An AI-enabled research support tool for the classification system of COVID-19.

The outbreak of COVID-19, a little more than 2 years ago, drastically affected all segments of society throughout the world. While at one end, the microbiologists, virologists, and medical practitioners were trying to find the cure for the infection; the Governments were laying emphasis on precautionary measures like lockdowns to lower the spread of the virus. This pandemic is perhaps also the first one of its kind in history that has research articles in all possible areas as like: medicine, sociology, psychology, supply chain management, mathematical modeling, etc. A lot of work is still continuing in this area, which is very important also for better preparedness if such a situation arises in future. The objective of the present study is to build a research support tool that will help the researchers swiftly identify the relevant literature on a specific field or topic regarding COVID-19 through a hierarchical classification system. The three main tasks done during this study are data preparation, data annotation and text data classification through bi-directional long short-term memory (bi-LSTM).

Evaluation of an international health partnership to capacity build emergency, trauma and critical care nurse education and practice in Zambia: An experience from the field.

AIM: To evaluate an international health partnership project to capacity build emergency, trauma and critical care nurse education and practice in Zambia. BACKGROUND: Zambia continues to face a significant workforce challenge and rising burden of communicable and non-communicable diseases, compounded by the COVID-19 pandemic. In response to these, the Zambian Ministry of Health is investing in specialised nurses. Emergency, trauma and critical care nursing education and training were seen as one of the solutions. North-south partnerships have been identified as a force for good to capacity build and develop emerging specialities. SOURCES OF EVIDENCE: We use an evaluative approach, which includes desk research, a rapid literature review and documentary data analysis from published papers, government reports and project documentation. Ethics committee approval was sought and gained in both Zambia and the UK. DISCUSSION: A critical review of the evidence identified three key themes: challenges with changing education and practice, developing Zambian faculty for sustainability and the effect of an international health partnership project on both Zambia and UK. The outcomes from this project are multifaceted; however, the main achievement has been the implementation of emergency, trauma and critical care graduate programmes by the Zambian faculty. CONCLUSION: This experience from the field outlines the benefits and limitations of a north-south partnership and the importance of transparency, shared ownership and collegiate decisions. It has facilitated knowledge exchange and sharing to capacity build emergency, trauma and critical care nursing. IMPLICATIONS FOR NURSING PRACTICE: Lessons learned may be applicable to other international nursing partnerships, these include the need for deep understanding of the context and constraints. Also, the importance of focusing on developing long-term sustainable strategies, based on research, education and practice was noted. IMPLICATIONS FOR NURSING POLICY: This paper outlines the importance of developing nursing education and practice to address the changing burden of disease in line with Zambian national policy, regional and international standards. Also, the value of international nursing partnerships for national and international nursing agendas was described.

Sentiment Analysis for COVID-19 Related Tweets Using Deep Bi-Directional Transformers

This study uses a pre-trained Bi-directional Encoder Representations from Transformers (BERT) with an AdamW optimizer for sentiment analysis of COVID-19 related tweets. This is performed on around 32,000 tweets from an annotated dataset of 190 million tweets. The sentiment of each tweet was predicted between three different classes, negative, neutral, and positive. Under sampling was performed to balance out the data and the model was fine-tuned over 4 epochs. The resulting model was best at predicting negative sentiment and worst at predicting neutral sentiment. The resulting accuracy was found to be 75.15%, however, increasing the amount of data used would likely improve this significantly. © 2022 IEEE.

UltrasonicG: Highly Robust Gesture Recognition on Ultrasonic Devices

In the current critical situation of novel coronavirus, the use of contactless gesture recognition method can reduce human contact and decrease the probability of virus transmission. In this context, ultrasound-based sensing has been widely concerned for its slow propagation speed, low sampling rate, and easy access to devices. However, limited by the complexity of gestural movements and insufficient training data, the accuracy and robustness of gesture recognition are low. To solve this problem, we propose UltrasonicG, a system for highly robust gesture recognition on ultrasonic devices. The system first converts a single audio signal into a Doppler shift and subsequently extracts the feature values using the Residual Neural Network (ResNet34) and uses Bi-directional Long Short-Term Memory (Bi-LSTM) for gesture recognition. The method effectively improves the accuracy of gesture recognition by combining the information of feature dimension with time dimension. To overcome the challenge of insufficient dataset, we use data extension to expand the dataset. We have conducted extensive experiments and evaluations on UltrasonicG in a variety of real scenarios. The experimental results show that UltrasonicG can recognize 15 kinds of gestures with a recognition distance of 0.5 m. And it has a high accuracy and robustness with a comprehensive recognition rate of 98.8% under different environments and influencing factors. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Aspect based sentiment analysis using multi-criteria decision-making and deep learning under COVID-19 pandemic in India.

The COVID-19 pandemic has a significant impact on the global economy and health. While the pandemic continues to cause casualties in millions, many countries have gone under lockdown. During this period, people have to stay within walls and become more addicted towards social networks. They express their emotions and sympathy via these online platforms. Thus, popular social media (Twitter and Facebook) have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues. We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases. The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus. India-specific COVID-19 tweets have been annotated, for analysing the sentiment of common public. To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35% for Lockdown and 83.33% for Unlock data set. The suggested method outperforms many of the contemporary approaches (long short-term memory, Bi-directional long short-term memory, Gated Recurrent Unit etc.). This study highlights the public sentiment on lockdown and stepwise unlocks, imposed by the Indian Government on various aspects during the Corona outburst.

Novel Data-Based Model for Future Epidemiology

Epidemiological studies aim at predicting the outbreak of diseases as epidemics and pandemics. This goal is often realized using closed form expressions that significantly utilize systems of differential equations. These systems of equations are often derived by groups of researchers working in global collaborative efforts. However, groups of researchers can experience a high workload in scenarios when there is large number of non-active participating researchers in a collaborative study. In addition, data explosion and increased availability can also undermine the efforts of hardworking researchers. This is because of the high velocity and variety associated with big data availability. Hence, an approach that helps researchers to address these challenges is required. The discussion in this research proposes a suitable solution in this regard. The proposed solution introduces the notion of cognitive epidemiology and epidemiological crawlers in a novel computing framework. In the proposed computing framework, crawlers and existing closed form expressions used in epidemiological studies interact. Prior to this interaction, epidemiological closed form expressions are formatted in a manner to enable the incorporation of intelligence capability. The research presents execution paths and discusses the incorporation alongside the integration of the proposed mechanism in a manner suitable for integration with the internet. © 2022 IEEE.

Analysis of public sentiment tendency in COVID-19 period based on GA-BiLSTM

As the Internet becomes the main source of information for the public, grasping the emotional polarity of online public opinion is particularly important for relevant departments to supervise online public opinion. In order to more accurately determine the emotional polarity of public opinion in the epidemic, this paper proposes a public sentiment analysis model based on Word2vec, genetic algorithm and Bi-directional Long Short-Term Memory (Bi-LSTM) algorithm. The Word2vec model converts the comment text into an n-dimensional vector, uses the Bi-LSTM algorithm to analyze the sentiment polarity, and uses the genetic algorithm to analyze the number of Bi-LSTM layers and the number of fully connected layers and the number of neurons in each layer of Bi-LSTM optimization. The experimental results show that the accuracy of the above model is compared with the accuracy of the Word2vec model and the LSTM model separately, and the accuracy is increased by 11.0% and 7.7%, respectively. © 2022 ACM.

Predicting Organization Performance Changes: A Sequential Data-Based Framework.

The business environment is increasingly uncertain due to the rapid development of disruptive information technologies, the changing global economy, and the COVID-19 pandemic. This brings great uncertainties to investors to predict the performance changes and risks of companies. This research proposes a sequential data-based framework that aggregates data from multiple sources including both structured and unstructured data to predict the performance changes. It leverages data generated from the early risk warning system in China stock market to measure and predict organization performance changes based on the risk warning status changes of public companies. Different from the models in existing literature that focus on the prediction of risk warning of companies, our framework predicts a portfolio of organization performance changes, including business decline and recovery, thus helping investors to not only predict public company risks, but also discover investment opportunities. By incorporating sequential data, our framework achieves 92.3% macro-F1 value on real-world data from listed companies in China, outperforming other static models.

Deep Learning-Based Sentiment Analysis on COVID-19 News Videos

Coronavirus disease (COVID-19) has adversely affected all walks of human life. The whole world is confronting this deadly virus, and no country in this world remains untouched during this pandemic. There are several online news videos related to COVID-19 that are shared on various online platforms such as YouTube, DailyMotion, and Vimeo. There were several arguments on the genuineness of the contents, people watch them, share them, and most importantly express their views and opinions as comments on those platforms. Analyzing these comments can unearth the patterns hidden in them to study people's responses to videos on COVID-19. This paper proposes a deep learning-based sentiment analysis approach to people's response toward online COVID-19 video news. This work implements different deep learning approaches such as LSTM, Bi-LSTM, CNN, and GRU to classify sentiment from the comments collected from YouTube. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Bi-directional Long Short-Term Memory Network for Fake News Detection from Social Media

These days’ web-based media is one of the main news hotspots for individuals throughout the planet for its minimal expense, simple openness, and quick spreading. This web-based media can in some cases include uncertain messages and has a critical danger of openness to counterfeit or fake news, which may elude the pursuers. Therefore, finding fake news from social media is one of the important natural language processing tasks. In this work, we have proposed a bi-directional long short-term memory (Bi-LSTM) network to identify COVID-19 fake news posted on Twitter. The performance of the proposed Bi-LSTM network is compared to six different popular classical machine learning classifiers such as Naïve Bayes, KNN, Decision Tree, Gradient Boosting, Random Forest, and AdaBoost. In the case of classical machine learning classifiers uni-gram, bi-gram, and tri-gram word TF-IDF features are used whereas in the case of the Bi-LSTM model word embedding features are used. The proposed Bi-LSTM network performed best in comparison to other implemented models and achieved a weighted F1-score of 0.94 in identifying COVID-19 fake news from Twitter. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Research on the Throughput Prediction of Coastal Ports in China

Coastal ports play a pivotal role in ensuring the healthy development of China's national economy and society and the smooth flow of global supply chain. The novel coronavirus pneumonia epidemic is impacting the global supply chain system. In this context, China's coastal port throughput prediction has become the focus of attention. Therefore, it is necessary to establish a model to accurately predict the throughput of coastal ports in China. This paper constructs the economy-industry bi-directional prediction model. It also provides a possibility for forecasting the throughput of China's coastal ports during the 14th Five-Year Plan period. On the one hand, build a model to measure the relationship between economic growth and coastal port throughput growth. On the other hand, the model is constructed to measure the correlation between major goods such as coal, iron ore and crude oil. Thus, another result of coastal port throughput is obtained. Given the corresponding weights of the two results, the final prediction results are obtained. It is concluded that on the basis of 2020, China's coastal port throughput will reach 10.7 billion tons by 2025, with an average annual increase of 2.5% during the 14th Five-Year Plan period. China's coastal port throughput growth rate will slow down. The scale of transportation demand should be fully demonstrated in the future port infrastructure construction. © 2021 IEEE.

COVID-19 CT image recognition algorithm based on transformer and CNN.

Novel corona virus pneumonia (COVID-19) broke out in 2019, which had a great impact on the development of world economy and people's lives. As a new mainstream image processing method, deep learning network has been constructed to extract medical features from chest CT images, and has been used as a new detection method in clinical practice. However, due to the medical characteristics of COVID-19 CT images, the lesions are widely distributed and have many local features. Therefore, it is difficult to diagnose directly by using the existing deep learning model. According to the medical features of CT images in COVID-19, a parallel bi-branch model (Trans-CNN Net) based on Transformer module and Convolutional Neural Network module is proposed by making full use of the local feature extraction capability of Convolutional Neural Network and the global feature extraction advantage of Transformer. According to the principle of cross-fusion, a bi-directional feature fusion structure is designed, in which features extracted from two branches are fused bi-directionally, and the parallel structures of branches are fused by a feature fusion module, forming a model that can extract features of different scales. To verify the effect of network classification, the classification accuracy on COVIDx-CT dataset is 96.7%, which is obviously higher than that of typical CNN network (ResNet-152) (95.2%) and Transformer network (Deit-B) (75.8%). These results demonstrate accuracy is improved. This model also provides a new method for the diagnosis of COVID-19, and through the combination of deep learning and medical imaging, it promotes the development of real-time diagnosis of lung diseases caused by COVID-19 infection, which is helpful for reliable and rapid diagnosis, thus saving precious lives.

A Cross-Platform Blood Donation Application with a Real-Time, Intelligent, and Rational Recommendation System

Blood or plasma transmission is one of the most effective treatments for critical diseases like Covid 19. Nowadays, voluntary blood donation has become the major source of blood supply. Several mobile applications are currently available to establish the initial communication between blood donors and receivers. Recommending the right potential donor during a blood search can save the life of a critical patient with an immediate response from the donor. However, the requirement of an advanced recommendation system has not been addressed by any of the existing mobile applications. In our research work, we have designed a real-time, intelligent, and rational recommendation system using sentiment analysis of the user's feedback, response rate of the donor, and the current geo-location information and finally develop a cross-platform application for blood collection and distribution system. To process and generate features from the user feedback, we have designed a Bi-directional LSTM-based deep learning model. The quality of the recommendation of the potential donors has significantly improved. Moreover, we have conducted rigorous requirement analysis from real users and evaluated the performance of our application through both indoor and outdoor testing. © 2021 IEEE.