Classic Augmentation based Classifier Generative Adversarial Network (CACGAN) for COVID-19 Diagnosis

Computer-aided diagnosis of COVID-19 from lung medical images has received increasing attention in previous clinical practice and research. However, developing such automatic model is usually challenging due to the requirement of a large amount of data and sufficient computer power. With only 317 training images, this paper presents a Classic Augmentation based Classifier Generative Adversarial Network (CACGAN) for data synthetising. In order to take into account, the feature extraction ability and lightness of the model for lung CT images, the CACGAN network is mainly constructed by convolution blocks. During the training process, each iteration will update the discriminator's network parameters twice and the generator's network parameters once. For the evaluation of CACGAN. This paper organized multiple comparison between each pair from CACGAN synthetic data, classic augmented data, and original data. In this paper, 7 classifiers are built, ranging from simple to complex, and are trained for the three sets of data respectively. To control the variable, the three sets of data use the exact same classifier structure and the exact same validation dataset. The result shows the CACGAN successfully learned how to synthesize new lung CT images with specific labels. © 2023 SPIE.

Implementing a Data Integration Infrastructure for Healthcare Data – A Case Study

Conducting epidemiologic research usually requires a large amount of data to establish the natural history of a disease and achieve meaningful study design, and interpretations of findings. This is, however, a huge task because the healthcare domain is composed of a complex corpus and concepts that result in difficult ways to use and store data. Additionally, data accessibility should be considered because sensitive data from patients should be carefully protected and shared with responsibility. With the COVID-19 pandemic, the need for sharing data and having an integrated view of the data was reaffirmed to identify the best approaches and signals to improve not only treatments and diagnoses but also social answers to the epidemiological scenario. This paper addresses a data integration scenario for dealing with COVID-19 and cardiovascular diseases, covering the main challenges related to integrating data in a common data repository storing data from several hospitals. Conceptual architecture is presented to deal with such approaches and integrate data from a Portuguese hospital into the common repository used to explore data in a standardized way. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Tune Down the Misinformation, Please: Generating Corrective Messages for COVID-19 Misinformation

The ongoing COVID-19 pandemic drastically changed our lives in multiple aspects, one of which is the reliance on social media during quarantine, both for social interaction and information-seeking purposes. However, the wide dissemination of misinformation on social media has impacted public health negatively. Previous studies on COVID-19 misinformation mainly focused on exploration of impacts and explanation of motivations, with few exceptions. In this study, we propose an analytical pipeline that generates corrective messages toward COVID-19 misinformation in a semiautomatic fashion, and then evaluate it against a large amount of data. Both the automated and manual evaluation results suggest the efficiency of the proposed pipeline, which can be used in combination with human intelligence by individuals and public health organizations in fighting COVID-19 misinformation. © 2022 IEEE Computer Society. All rights reserved.

COVID-19 Vaccines Analysis to Predict Its Impact Across the World

In recent years, due to the widespread of COVID-19 pandemic, a large amount of data set is available about the various types of vaccines used by different countries for the protection of their citizens. So it is very important and useful if one is able to perform effective analysis of the same to make the awareness and the effectiveness of each vaccine known to mankind. It is found that COVID-19 vaccines increase the immune system, prepare the body to fight against the virus, and reduce the probability of contracting COVID-19.this can be done with the help of regression techniques such as The MAE, MSE, RMSE values to predict and evaluate the observations with more efficiency. The RMSE technique measures the standard deviation of results and provides more accuracy. This analysis helps to find out how COVID-19 vaccines are provided in various countries and the countries where 80% of the population is vaccinated. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Systematic Literature Survey of Emotion Detection of Students in E-learning

Affective computing is a part of artificial intelligence, which is becoming more important and widely used in education to process and analyze large amounts of data. Consequently, the education system has shifted to an E-learning format because of the COVID-19 epidemic. Then, e-learning is becoming more common in higher education, primarily through Massive Open Online Courses (MOOCs). This study reviewed many prior studies on bolstering educational institutions using AI methods, including deep learning, machine learning, and affective computing. According to the findings, these methods had a very high percentage of success. These studies also helped academic institutions, as well as teachers, understand the emotional state of students in an e-learning environment. © 2022 IEEE.

Health Informatics on Big COVID-19 Pandemic Data via N-Shot Learning

Nowadays, very large amounts of data are generating at a fast rate from a wide variety of rich data sources. Valuable information and knowledge embedded in these big data can be discovered by data science, data mining and machine learning techniques. Biomedical records are examples of the big data. With the technological advancements, more healthcare practice has gradually been supported by electronic processes and communication. This enables health informatics, in which computer science meets the healthcare sector to address healthcare and medical problems. As a concrete example, there have been more than 635 millions cumulative cases of coronavirus disease 2019 (COVID-19) worldwide over the past 3 years since COVID-19 has declared as a pandemic. Hence, effective strategies, solutions, tools and methods - such as artificial intelligence (AI) and/or big data approaches - to tackle the COVID-19 pandemic and possible future pandemics are in demand. In this paper, we present models to analyze big COVID-19 pandemic data and make predictions via N-shot learning. Specifically, our binary model predicts whether patients are COVID-19 or not. If so, the model predicts whether they require hospitalization or not, whereas our multi-class model predicts severity and thus the corresponding levels of hospitalization required by the patients. Our models uses N-shot learning with autoencoders. Evaluation results on real-life pandemic data demonstrate the practicality of our models towards effective allocation of resources (e.g., hospital facilities, staff). These showcase the benefits of AI and/or big data approaches in tackling the pandemic. © 2022 IEEE.

Democratising large scale instrument-based science through e-Infrastructure

Modern scientific instruments are becoming essential for discoveries because they provide unprecedented insight into physical or biological events - often in real time. However, these instruments may generate large amounts of data, and increasingly they require sophisticated e-infrastructure for analysis, storage and archive. The increasing complexity and scale of the data, processing steps and systems has made it difficult for domain scientists to perform their research, narrowing the user base to a select few. In this paper, we present a framework that democratises large-scale instrument-based science, increasing the number of researchers who can engage. We discuss a prototype at the University of Queensland. The system is illustrated through two case studies, one involving light microscopy imaging of the innate immune system, and the other electron microscopy imaging of the SARS-CoV-2 viral proteins. © 2022 IEEE.

Research on several major diseases based on machine learning models

Due to the continuous growth of disease types and past cases, it is more and more difficult to diagnose diseases only by manpower. Machine learning is a model mechanism that is sensitive to data and relies on a large amount of data to complete training. It is very suitable for medical diagnosis. Many scholars have tried to use ML to develop medical diagnosis systems, but they are basically not used in the real world at this stage. This article reviews the work related to medical detection of three major diseases (heart disease, cancer, and COVID-19), aiming to summarize previous experiences to help future scholars conduct research. Specifically, this paper summarizes the research status of the prediction of these three types of diseases based on machine learning methods, evaluate the accuracy and universality of the corresponding prediction models based on time as a clue, and use a comparative method to find out the progress researchers have made in this area and limitations still exist at this stage. And at the end of the article, the results and some potential work fields of the future in these studies are summarized. © 2022 IEEE.

Big Data-Enabled Solutions for Covid-19

In the time of the pandemic like CORONA, Covid-19, everyone is ftghting against this deadly virus. Besides, governments are looking for a barrier that stops spread of virus until the vaccine is made. In modern era, technology plays an important role. This paper brings the way by using a powerful technology called Big data. Big data know for handling a large amount of data and provide powerful insights into the data. Big data integrated with Artificial Intelligence is a powerful tool to ftght against this pandemic. Many countries like Taiwan, China with the use of Big Data stop this pandemic up to some extent. But the collection of data itself comes up with the big challenge of PRIVACY AND SECURITY. In the recent times, the world has seen the effect of data leaking whether by Facebook or by Google. Many European countries due to this big challenge will not be able to use this technology. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Fake News Detection of South African COVID-19 Related Tweets using Machine Learning

Social Media has grown in popularity in recent years comprising of billions of users who in turn exchange and communicate content at a volume and rate impractical to examine manually. Fake News are now being used on these platforms to manipulate and affect societies across the world as was the case in the 2016 United States of America (USA) elections and of recent during the 2019 coronavirus disease (COVID-19) pandemic. South Africa is not immune to the spread of Fake News, particularly, through Social Media platforms such as Twitter, Facebook and TikTok. It is, therefore, important to detect the presence of Fake News computationally in order assist the mitigation of its spread and prevent perceivable negative effects. This study addresses the issue by developing a Machine Learning (ML) model to analyze large amounts of data associated with Social Media. Curated annotated datasets from CONSTRAINT AAAI 2021;COVID-19 Rumour, FNIR and Zenodo's COVID-19 datasets;Google and Polifact Fact Checked websites;were utilized to develop the ML model. Specifically, the model was trained on 36254 data points and applied on a South African related COVID-19 Twitter dataset collected for cursory analysis. In total, 27 ML models were experimented with and the collected South African COVID-19 related Twitter dataset comprised of 976087 tweets from 8 November 2020 until 19 July 2021. The results detected 329107 tweets as being 'Fake' based on the LightGBM Classifier which was chosen as the most feasible model in terms of speed and a balanced accuracy score of 0.82. The proposed model is unique as it is trained on a larger combined dataset and supplements existing efforts to combat misinformation, disinformation and malinformation spread on Twitter. © 2022 IEEE.

Network-side 5G mmWave Channel Signatures for Pandemic Contact Tracing

Contact tracing is a key mechanism to help contain the COVID-19 pandemic and other pandemics in the future. In this work, we propose using 5G channel signatures - specifically, mm-Wave channel signatures - to perform contact tracing and infer early sources of the infection. Our network-side approach is motivated by the density of mm-Wave base stations, coupled with the large amount of data about mobile device signals already being collected by cellular operators. We model the contact tracing problem as a graph mining problem, and develop machine learning models to estimate contacts between UEs based on 5G channel signatures such as received power. These contacts are also used to infer the original sources of the infection. Simulations of our proposed method using the ns-3 5G mmWave module suggest that contact can be inferred with a recall of 85% and specificity of 94%. Our infection sources estimation method can accurately rank the most likely infection sources, with the true infection sources lying in the top 25% of the ranked list on average. These methods represent a first step towards network-based contact tracing, and can complement other contact tracing methods to help reduce the spread of disease. © 2022 IEEE.

Dynamic Analysis of Demographic Sentiment

There is no doubt that big data analysis has a very positive impact on economics, security, and other aspects for countries and enterprises alike. Where we have recently noticed the frantic competition between companies to increase their profits by analyzing the largest amount of data as quickly as possible. Especially analyzing data related to Covid-19 to make the most of information in all areas. Covid-19 has drastically affected many lives in recent years but, even in these hard times, businesses can leverage the current pandemic to make a profit. In this paper, we investigate a variety of tweets using MapReduce, Spark, and Machine Learning methods to determine the sentiment of a given tweet based on the information provided by the dataset. With this information, businesses could learn how to present Covid-19 and pandemic related goods and information in a way that will be well received by its audience. To take this a step further, we will investigate trends in sentiment across demographics tweeting about the virus. This information in sentiment is dynamically useful to understand how specific audiences feel about the pandemic. We explore which Machine Learning methods produce the best results such as Multi-Layer Perceptron neural networks and Logistic Regression. © 2022 IEEE.

Covid19 Vaccination and Lifestyle Counsel using Machine Learning algorithms

Machine Learning is a predominant area in Artificial Intelligence. It gets the ability to make predictions by learning the past observed values and information. This learning process is Machine Learning. A large amount of data is accessed and processed to gain more accurate results. Nowadays anyone around the world can use any Machine Learning algorithm to obtain competitive and accurate results. The main objective of this project is to recommend the Life style modification of the people after covid19 and to predict whether the particular person needs for the vaccination intake or not by accessing thousands of patient details. Hence the accuracy rate is very high compared to other predicting processes. These techniques are used to predict the current health conditions of the people. © 2022 IEEE.

Multilabel Emotion Recognition Technique considering the Characteristics of Unstructured Conversation Data

Due to the recent increase in non-face-To-face services due to COVID-19, the number of users communicating through messengers or SNS (social networking service) is increasing. As a large amount of data is generated by users, research on recognizing emotions by analyzing user information or opinions is being actively conducted. Conversation data such as SNS is freely created by users, so there is no set format. Due to these characteristics, it is difficult to analyze using AI (artificial intelligence), which leads to a decrease in the performance of the emotion recognition technique. Therefore, a processing method suitable for the characteristics of unstructured data is required. Among the unstructured data, most emotion recognition in Korean conversation recognizes a single emotion by analyzing emotion keywords or vocabulary. However, since multiple emotions exist complexly in a single sentence, research on multilabel emotion recognition is needed. Therefore, in this paper, the characteristics of unstructured conversation data are considered and processed for more accurate emotion recognition. In addition, we propose a multilabel emotion recognition technique that understands the meaning of dialogue and recognizes inherent and complex emotions. A deep learning model was compared and tested as a method to verify the usefulness of the proposed technique. As a result, performance was improved when it was processed in consideration of the characteristics of unstructured conversation data. Also, when the attention model was used, accuracy showed the best performance with 65.9%. The proposed technique can contribute to improving the accuracy and performance of conversational emotion recognition. © 2022 Myungjin Lim et al.

Research on Supply Chain Shortage of Electronic Technology Companies under the Pandemic Situation: A Case Study of Apple Inc. and Xiaomi Inc

Supply management plays an important role in the business. In our research, we make the introduction with research background in COVID-19, in the APPLE Inc. and large amount of data is constructed. Several methods are used during the task, for example, literature review, data analysis, quantitative analysis and comparative analysis. The research shows the current problems faced by electronic technology companies, which are instability of the supply chains, material shortage and regional policies, after which the research reasons for deep causes and provides feasible improvements of the company, which are production concentration, purchase limit strategy, online commodity physical models, price reduction, and comprehensive capital. © 2022 ACM.

Artificial Intelligence and Machine Learning Techniques for COVID-19 Prediction

An Intelligent data processing is essential to create a large amount of data in Internet of things. We progress the consistent smooth and computerized uses of artificial intelligence, machine learning, deep Learning. To analyze the data using deep learning that is subcategory of machine learning techniques. This investigation designed and implemented the intelligent system that is used to detect the rise of Covid-19 cases using various artificial intelligent algorithms through machine learning. Here best algorithm is chosen for prediction of Covid 19 Omicron cases based on their accuracy of performance metrics. © 2022 IEEE.

A Review About the Main Technologies to Fight COVID-19

This work presents a review about research published from the last five months (January–May 2020) about technologies used during the pandemic to fight the disease caused by the SARS-CoV-2 virus, termed COVID-19. Through an analysis of these studies, Telemedicine was considered as a viable option to decrease the dissemination of the virus and identify infected people. However, to implant Telemedicine worldwide, it is necessary a fast and reliable way to transfer of large amounts of data, such as the new fifth generation of mobile communications (5G). Thus, new concepts as Internet of Medical Things (IoMT) and the Electronic Health (e-Health) can be used, which have the necessary structures and tools for fast communication, with high-resolution, between patients and health professionals. © 2022, Springer Nature Switzerland AG.

The effects of regularisation on RNN models for Covid-19 time series forecasting

Many time series forecasting models applied to the COVID-19 pandemic data have been limited to the amount of locations that they operate on. To improve the efficiency of a model it is desirable to have one model produce outputs for as many different locations as possible. Another drawback of previous models is that most operate on large amounts of data. However, during the initial states of the spread of the disease, before the epidemic became a pandemic, there was not enough data for the models therefore the proposed model not only has to produce forecasts for multiple locations at once, but they must also be accurate based on small amounts of data. This work proposes a multi-output recurrent neural network capable of producing forecasts for 187 different locations even when trained on only 28 days of time series data for each location. Regularisation methods were used to reduce the noise in the model during training. Applying regularisers helped the model better generalise its predictions for the multiple locations the results show that the model using the Long-Short Term Memory network combined with 20% Dropout performed, on average, 3% better than its baseline without the regularisers the improvement was measured using the Root Mean Squared Error. Previously proposed models were not capable of producing forecasts on a global scale without training multiple versions of the same model. This work proposes one model capable of making predictions on a global scale after only the first four weeks of the epidemic. © 2021 IEEE.

Epidemic Data Visualization Analysis of the Covid-19 Development in China

Corona Virus Disease 2019 (Covid-19) is a war between all humans and viruses. The outbreak of the Covid-19 epidemic has produced a large amount of data related to case information. Current related visualization studies are difficult to analyze these data, so a visualization analysis method for the Covid-19 epidemic situation in China is proposed. In this study, we present an effort to compile and analyze epidemiological outbreak information of Covid-19 based on the epidemic news and data in China after January 10, 2020. Through the analysis of data, it is concluded that the Covid-19 has the characteristics of a high infection rate and rapid transmission rate, and it also reflects the great contribution made by the Chinese government in controlling the epidemic. This study can obtain the hidden value behind the data, facilitate the understanding of the results of data analysis, and provide a reference for the government. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Coronavirus: A Curse or a bless ?

Nowadays crime is one of the major threats that affect human lives. The current pandemic has a great impact on changing the criminal landscape. Extensive investigations for crime and criminal behaviors have revealed new crime patterns and led to the generation of a large amount of data and relations that need to be presented in a proper model. In this paper, we conduct several experiments on different datasets representing some major cities in the USA to study the effect of the current pandemic on crime types, rates, and intensity which can be used in crime prediction and prevention. we also introduce an ontology model with its underlying description logics as the knowledge representation model to represent crime information. © 2021 Association for Computing Machinery.