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.

Acknowledgment of patient in sense behaviors using bidirectional ConvLSTM

Recognizing patient activity in real-time from video or images collected by a CCTV camera available in the hospital during a Covid-19 situation has proven challenging. The dilemma of patient activity recognition is identifying and recognizing a patient's various actions in a series of videos. The process presented in our paper needs to achieve unrestricted, generic behavior in videos. Detecting events in any video is often difficult because we use Bidirectional ConvLSTM to create a robust patient in the sense behaviors (PSB) framework capable of eliminating certain barriers. To begin this paper by proposing a new Bidirectional ConvLSTM for establishing a stable PSB scheme. Our proposed model is capable of accurately predicting patient's behaviors like seated, standing, and so on. Using Bidirectional ConvLSTM, learning information from a pre-trained model is an excellent place to start for rapidly developing a new PSB system using a current PSB database, as both the source and target datasets are critical. All parameters are frozen in a pre-trained PSB device. Then, using the UCI and HMDB51 dataset to train the model, variables and local relations are progressively fixed. A novel PSB framework is developed using the target dataset. Relevant tests are conducted using commonly used research indices to assess prediction precision accuracy. They acknowledge six patient's behavior with a weighted accuracy rate of 92%. For recognizing novel activity, laying, the precision of a corresponding prediction is the best, 91%, of all six test results. The proposed work uses bidirectional ConvLSTM with modified activation layers to sense the patients' behavior. This article may be a patient activity recognition system to identify an individual. It takes a clip of COVID-19 patients as input and looks for matches inside the hold-on images.

Diabetes mellitus and COVID-19: A bidirectional relationship?.

The COVID-19 pandemic has generated a public health emergency around the world. The risk, severity, and mortality of the disease has been associated to chronic diseases, such as diabetes mellitus. There are multiple patho-physiological explanations that relate these two entities. The possibility of a bidirectional relationship between COVID-19 and diabetes mellitus type 1 and 2 has been raised. Most studies agree that diabetes mellitus of any etiology is associated with a negative outcome of the infection. Also, CO-VID-19 can cause a worsening of glycemic control and can be a trigger for the development of diabetes mellitus type 1.Copyright © 2023 Universidad de Antioquia.

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.

Assessment of humoral and cellular immunity after bivalent BNT162b2 vaccination and potential association with reactogenicity

Nowadays, new challenges around increasing production quality and productivity, and decreasing energy consumption, are growing in the manufacturing industry. In order to tackle these challenges, it is of vital importance to monitor the health of critical components. In the machine tool sector, one of the main aspects is to monitor the wear of the cutting tools, as it affects directly to the fulfillment of tolerances, production of scrap, energy consumption, etc. Besides, the prediction of the remaining useful life (RUL) of the cutting tools, which is related to their wear level, is gaining more importance in the field of predictive maintenance, being that prediction is a crucial point for an improvement of the quality of the cutting process. Unlike monitoring the current health of the cutting tools in real time, as tool wear diagnosis does, RUL prediction allows to know when the tool will end its useful life. This is a key factor since it allows optimizing the planning of maintenance strategies. Moreover, a substantial number of signals can be captured from machine tools, but not all of them perform as optimum predictors for tool RUL. Thus, this paper focuses on RUL and has two main objectives. First, to evaluate the optimum signals for RUL prediction, a substantial number of them were captured in a turning process and investigated by using recursive feature elimination (RFE). Second, the use of bidirectional recurrent neural networks (BRNN) as regressive models to predict the RUL of cutting tools in machining operations using the investigated optimum signals is investigated. The results are compared to traditional machine learning (ML) models and convolutional neural networks (CNN). The results show that among all the signals captured, the root mean squared (RMS) parameter of the forward force ( F-y ) is the optimum for RUL prediction. As well, the bidirectional long-short term memory (BiLSTM) and bidirectional gated recurrent units (BiGRU), which are two types of BRNN, along with the RMS of F-y signal, achieved the lowest root mean squared error (RMSE) for tool RUL, being also computationally the most demanding ones.

Multi-modal approach for COVID-19 detection using coughs and self-reported symptoms

COVID-19 (Coronavirus Disease of 2019) is one of the most challenging healthcare crises of the twenty-first century. The pandemic causes many negative impacts on all aspects of life and livelihoods. Although recent developments of relevant vaccines, such as Pfizer/BioNTechmRNA, AstraZeneca, or Moderna, the emergence of newvirus mutations and their fast infection rate yet pose significant threats to public health. In this context, early detection of the disease is an important factor to reduce its effect and quickly control the spread of pandemic. Nevertheless, many countries still rely on methods that are either expensive and time-consuming (i.e., Reverse-transcription polymerase chain reaction) or uncomfortable and difficult for self-testing (i.e., Rapid Antigen Test Nasal). Recently, deep learning methods have been proposed as a potential solution for COVID-19 analysis. However, previous works usually focus on a single symptom, which can omit critical information for disease diagnosis. Therefore, in this study, we propose a multi-modal method to detect COVID-19 using cough sounds and self-reported symptoms. The proposed method consists of five neural networks to deal with different input features, including CNN-biLSTM for MFCC features, EfficientNetV2 for Mel spectrogram images, MLP for self-reported symptoms, C-YAMNet for cough detection, and RNNoise for noise-canceling. Experimental results demonstrated that our method outperformed the other state-of-the-art methods with a high AUC, accuracy, and F1-score of 98.6%, 96.9%, and 96.9% on the testing set.

Aspect-Based Sentiment Analysis with Semi-Supervised Approach on Taiwan Social Distancing App User Reviews

Sentiment analysis has a critical role to reveal an opinion in a text-based form. Therefore, we exploit this analysis to discover the sentiment polarity of Taiwan Social Distancing mobile application. This paper proposes a semi-supervised scheme for annotating this mobile application's reviews. The semi-supervised scheme utilized a combination of numeric rating and lexicon-based sentiment. In addition, we also perform the sentiment analysis on an aspect-based level. Based on the experiment, we decide to select three aspects to be analyzed. This paper also evaluates the proposed scheme by implementing bidirectional encoder representations from transformers (BERT) and multilayer perceptron (MLP) as the classification model using the sentiment label of the proposed scheme. The result shows that the annotation of the proposed scheme outperforms the data annotation using counterpart models. © 2023 IEEE.

Predictive Analytics of Stock Market as a Time Series

This paper presents a new methodology and a comparative study using past stock market data that can help businesses take investing or divesting decisions in critical situations in the future. These may be like the COVID-19 pandemic, where market volatility is extremely high, thus creating an urgent need for better decision support systems to minimise loss and ensure better profits. The results of the study are based on the comparison of different configurations of ARIMAX, Prophet, LSTM and Bidirectional LSTM Models trained on historical NSE data. By understanding the correlation and variations in the data processing and model training parameters, we have successfully proposed a LSTM neural network model training and optimising method which could successfully help businesses take both long and short term profitable decisions before and after big financial and market crises with a respective accuracy of 98.60 percent and 96.97 percent. © 2022 IEEE.

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.

Leveraging natural language processing and geospatial time series model to analyze COVID-19 vaccination sentiment dynamics on Tweets.

Objective: To develop and apply a natural language processing (NLP)-based approach to analyze public sentiments on social media and their geographic pattern in the United States toward coronavirus disease 2019 (COVID-19) vaccination. We also aim to provide insights to facilitate the understanding of the public attitudes and concerns regarding COVID-19 vaccination. Methods: We collected Tweet posts by the residents in the United States after the dissemination of the COVID-19 vaccine. We performed sentiment analysis based on the Bidirectional Encoder Representations from Transformers (BERT) and qualitative content analysis. Time series models were leveraged to describe sentiment trends. Key topics were analyzed longitudinally and geospatially. Results: A total of 3 198 686 Tweets related to COVID-19 vaccination were extracted from January 2021 to February 2022. 2 358 783 Tweets were identified to contain clear opinions, among which 824 755 (35.0%) expressed negative opinions towards vaccination while 1 534 028 (65.0%) demonstrated positive opinions. The accuracy of the BERT model was 79.67%. The key hashtag-based topics include Pfizer, breaking, wearamask, and smartnews. The sentiment towards vaccination across the states showed manifest variability. Key barriers to vaccination include mistrust, hesitancy, safety concern, misinformation, and inequity. Conclusion: We found that opinions toward the COVID-19 vaccination varied across different places and over time. This study demonstrates the potential of an analytical pipeline, which integrates NLP-enabled modeling, time series, and geospatial analyses of social media data. Such analyses could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccination, help address the concerns of vaccine skeptics, and provide support for developing tailored policies and communication strategies to maximize uptake.

Atherosclerosis, Cardiovascular Disease, and COVID-19: A Narrative Review.

Atherosclerosis is a chronic inflammatory and degenerative process that mainly occurs in large- and medium-sized arteries and is morphologically characterized by asymmetric focal thickenings of the innermost layer of the artery, the intima. This process is the basis of cardiovascular diseases (CVDs), the most common cause of death worldwide. Some studies suggest a bidirectional link between atherosclerosis and the consequent CVD with COVID-19. The aims of this narrative review are (1) to provide an overview of the most recent studies that point out a bidirectional relation between COVID-19 and atherosclerosis and (2) to summarize the impact of cardiovascular drugs on COVID-19 outcomes. A growing body of evidence shows that COVID-19 prognosis in individuals with CVD is worse compared with those without. Moreover, various studies have reported the emergence of newly diagnosed patients with CVD after COVID-19. The most common treatments for CVD may influence COVID-19 outcomes. Thus, their implication in the infection process is briefly discussed in this review. A better understanding of the link among atherosclerosis, CVD, and COVID-19 could proactively identify risk factors and, as a result, develop strategies to improve the prognosis for these patients.

A Comparison of LSTM and BiLSTM for Forecasting the Air Pollution Index and Meteorological Conditions in Jakarta

Jakarta is the capital city of Indonesia where air pollution becomes one of the problems that must be properly handled. The historical data of the air pollution index is beneficial for developing models for forecasting future values. One of the advantages of forecasting air pollution is to help people to arrange future plans to reduce the dangerous effect on health. Analyzing a record of meteorological conditions can be used to understand climate change. This paper reports the comparison of Long Short Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models for multivariate forecasting of the air pollution index and meteorological conditions in Jakarta. It also informs the performance of those algorithms for forecasting the observed variables before and during the Coronavirus disease (Covid-19) outbreak to analyze the effect of the pandemic on the environment. The experiments use a historical time series dataset from 2010-2021. The experimental results show that LSTM and BiLSTM work well to forecast PM10, temperature, humidity, and wind speed. In this case study, there are no significant differences in the performance of LSTM and BiLSTM. © 2022 IEEE.

Predicting aspect-based sentiment using deep learning and information visualization: The impact of COVID-19 on the airline industry

This study investigates customer satisfaction through aspect-level sentiment analysis and visual analytics. We collected and examined the flight reviews on TripAdvisor from January 2016 to August 2020 to gauge the impact of COVID-19 on passenger travel sentiment in several aspects. Till now, information systems, management, and tourism research have paid little attention to the use of deep learning and word embedding techniques, such as bidirectional encoder representations from transformers, especially for aspect-level sentiment analysis. This paper aims to identify perceived aspect-based sentiments and predict unrated sentiments for various categories to address this research gap. Ultimately, this study complements existing sentiment analysis methods and extends the use of data-driven and visual analytics approaches to better understand customer satisfaction in the airline industry and within the context of the COVID-19. Our proposed method outperforms baseline comparisons and therefore contributes to the theoretical and managerial literature. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Analysis of Government Policy Sentiment Regarding Vacation during the COVID-19 Pandemic Using the Bidirectional Encoder Representation from Transformers (BERT)

To address the COVID-19 situation in Indonesia, the Indonesian government has adopted a number of policies. One of them is a vacation-related policy. Government measures with regard to this vacation policy have produced a wide range of viewpoints in society, which have been extensively shared on social media, including YouTube. However, there has not been any computerized system developed to date that can assess people's social media reactions. Therefore, this paper provides a sentiment analysis application to this government policy by employing a bidirectional encoder representation from transformers (BERT) approach. The study method began with data collecting, data labeling, data preprocessing, BERT model training, and model evaluation. This study created a new dataset for this topic. The data were collected from the comments section of YouTube, and were categorized into three categories: positive, neutral, and negative. This research yielded an F-score of 84.33%. Another contribution from this study regards the methodology for processing sentiment analysis in Indonesian. In addition, the model was created as an application using the Python programming language and the Flask framework. The government can learn the extent to which the public accepts the policies that have been implemented by utilizing this research. © 2023 by the authors.

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.

Sentiment Analysis of COVID-19 Tweets Using BiLSTM and CNN-BiLSTM

In a society where people express almost every thought they have on social media, analysing social media for sentiment has become very significant in order to understand what the masses are thinking. Especially microblogging website like twitter, where highly opinionated individuals come together to discuss ongoing socioeconomic and political events happening in their respective countries or happening around the world. For analysing such vast amounts of data generated every day, a model with high efficiency, i.e., less running time and high accuracy, is needed. Sentiment analysis has become extremely useful in this regard. A model trained on a dataset of tweets can help determine the general sentiment of people towards a particular topic. This paper proposes a bidirectional long short-term memory (BiLSTM) and a convolutional bidirectional long short-term memory (CNN-BiLSTM) to classify tweet sentiment;the tweets were divided into three categories—positive, neutral and negative. Specialized word embeddings such as Word2Vec or term frequency—inverse document frequency (tf-idf) were avoided. The aim of this paper is to analyse the performance of deep neural network (DNN) models where traditional classifiers like logistic regression and decision trees fail. The results show that the BiLSTM model can predict with an accuracy of 0.84, and the CNN-BiLSTM model can predict with an accuracy of 0.80. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations

Global natural and manmade events are exposing the fragility of the tourism industry and its impact on the global economy. Prior to the COVID-19 pandemic, tourism contributed 10.3% to the global GDP and employed 333 million people but saw a significant decline due to the pandemic. Sustainable and smart tourism requires collaboration from all stakeholders and a comprehensive understanding of global and local issues to drive responsible and innovative growth in the sector. This paper presents an approach for leveraging big data and deep learning to discover holistic, multi-perspective (e.g., local, cultural, national, and international), and objective information on a subject. Specifically, we develop a machine learning pipeline to extract parameters from the academic literature and public opinions on Twitter, providing a unique and comprehensive view of the industry from both academic and public perspectives. The academic-view dataset was created from the Scopus database and contains 156,759 research articles from 2000 to 2022, which were modelled to identify 33 distinct parameters in 4 categories: Tourism Types, Planning, Challenges, and Media and Technologies. A Twitter dataset of 485,813 tweets was collected over 18 months from March 2021 to August 2022 to showcase the public perception of tourism in Saudi Arabia, which was modelled to reveal 13 parameters categorized into two broader sets: Tourist Attractions and Tourism Services. The paper also presents a comprehensive knowledge structure and literature review of the tourism sector based on over 250 research articles. Discovering system parameters are required to embed autonomous capabilities in systems and for decision-making and problem-solving during system design and operations. The work presented in this paper has significant theoretical and practical implications in that it improves AI-based information discovery by extending the use of scientific literature, Twitter, and other sources for autonomous, holistic, dynamic optimizations of systems, promoting novel research in the tourism sector and contributing to the development of smart and sustainable societies. © 2023 by the authors.