A CNN Transfer Learning -Electrocardiogram (ECG) Signal Approach to Predict COVID-19

Deep learning is increasingly gaining traction in cutting-edge medical sciences such as image classification, and genomics due to the high computational performance and accuracy in evaluating medical data. In this study, we investigate the cardiac properties of ECG Images and predict COVID-19 in a binary classification of patients who tested positive for COVID-19 and Normal Persons who tested negative. We analyzed the electrocardiogram (ECG) images by preprocessing the ECG data and building an ECG- Deep Learning- COVID-19 (ECG-DL-COVID) classifier to predict disease. The deep learning models in our experiments constituted CNN, Multi-Layer Perceptron (MLP), and Transfer Learning. Performance evaluation was done to compare the effectiveness of the proposed methodologies with other COVID-19 deep learning-related works. In the three experiments, we achieved an 87% prediction accuracy for MLP, a 90% prediction for CNN and a 93.8% prediction for Transfer Learning. Experimental results and performance evaluation show that the proposed models outperformed previous deep-learning models in the prediction of COVID-19 by a considerable margin. © 2023 IEEE.

Paying Attention to cyber-attacks: A multi-layer perceptron with self-attention mechanism

Cyber-attacks cause huge monetary losses to the institutions that are victims of them. Cyber-attack is becoming increasingly sophisticated. Therefore, the protection system against cyber-attacks has become a highly requested resource by any type of state or private institution. During the pandemic caused by COVID-19, the number of cyber-attacks against both public and private health institutions has increased. Cybersecurity systems have become a necessity. Various protection systems have been proposed using different machine learning algorithms, but deep learning consistently provides the best results. In this work we develop a deep learning model for the detection of different kinds of cyber-attacks, a study is carried out on the relevance of the selection of features in this type of algorithm and the importance of attention mechanisms is analyzed to improve the assessment of features within the same model. We have carried out the experiments using two datasets that are benchmarks in the field of cybersecurity and we have carried out a comparative study with both.

Precision Diagnosis to Discriminate Covid-19 and Pneumonia using Mixed-data Model based on Custom Neural Networks

Classifying Covid-19 and Pneumonia is one of the most important and challenging tasks in the field of the medical sector since manual classification with human assistance can lead to incorrect prediction and diagnosis. Additionally, it is a difficult operation when there is a lot of data that need to be analyzed thoroughly. Due to the similarity in symptoms as well as in chest X-ray images of Covid-19 and Pneumonia diseases, it is difficult to distinguish those. The study presents a technological solution to build a mixed-data model using customized neural networks to discriminate between Covid-19 and Pneumonia. The proposed method is applied to the chest X-ray images and symptoms of patients of Covid-19 and Pneumonia. This helps to perform immediate prediction of Covid-19 and Pneumonia providing fast and specialized treatment to the patients appropriately. This prediction also helps the radiologist or doctors in making quick decisions. In this work, imaging data (such as Chest X-ray images) and text data (such as disease symptoms like cough, body pain, short breathing, fever, etc.) are taken for detecting Covid-19, Pneumonia and Normal patients. Data Synthesis is carried out due to the unavailability of mixed data and it has created dataset of 450 entries of Covid-19, Normal and Pneumonia cases. The goal is to design a system that accurately classifies Covid19, Pneumonia, and Normal patients by utilizing convolutional neural networks (CNN) and multi-layer perceptron (MLP) algorithms. An accuracy of 93.33% is obtained for the mixed-data model using a deep neural network, that is designed by combining custom CNN and MLP architectures. © 2022 IEEE.

Real-time updating of dynamic social networks for COVID-19 vaccination strategies.

Vaccination strategy is crucial in fighting the COVID-19 pandemic. Since the supply is still limited in many countries, contact network-based interventions can be most powerful to set an efficient strategy by identifying high-risk individuals or communities. However, due to the high dimension, only partial and noisy network information can be available in practice, especially for dynamic systems where contact networks are highly time-variant. Furthermore, the numerous mutations of SARS-CoV-2 have a significant impact on the infectious probability, requiring real-time network updating algorithms. In this study, we propose a sequential network updating approach based on data assimilation techniques to combine different sources of temporal information. We then prioritise the individuals with high-degree or high-centrality, obtained from assimilated networks, for vaccination. The assimilation-based approach is compared with the standard method (based on partially observed networks) and a random selection strategy in terms of vaccination effectiveness in a SIR model. The numerical comparison is first carried out using real-world face-to-face dynamic networks collected in a high school, followed by sequential multi-layer networks generated relying on the Barabasi-Albert model emulating large-scale social networks with several communities.

Face Completion with Multi-Output Estimators

Since the beginning of the COVID-19 pandemic, images of faces with obscured bottom halves have become more common due to masking. Now more than ever, end-users are looking toward machine learning and data science to create high-quality replacements for missing facial data. For face completion, we evaluate multiple machine learning algorithms, including Decision Trees, K-Nearest Neighbors, and Support Vector Machines. Since most of the existing work in this field uses deep learning, we explore the impact of using multiple deep learning techniques and use them as a point of comparison. Our study indicates that despite the conventional norm that deep learning algorithms outperform their machine learning counterparts, the non-deep learning techniques perform better for this application.11Code is available at https://github.com/nickfons/fcwmoe. © 2022 IEEE.

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.

The illusion of personal health decisions for infectious disease management: disease spread in social contact networks.

Close contacts between individuals provide opportunities for the transmission of diseases, including COVID-19. While individuals take part in many different types of interactions, including those with classmates, co-workers and household members, it is the conglomeration of all of these interactions that produces the complex social contact network interconnecting individuals across the population. Thus, while an individual might decide their own risk tolerance in response to a threat of infection, the consequences of such decisions are rarely so confined, propagating far beyond any one person. We assess the effect of different population-level risk-tolerance regimes, population structure in the form of age and household-size distributions, and different interaction types on epidemic spread in plausible human contact networks to gain insight into how contact network structure affects pathogen spread through a population. In particular, we find that behavioural changes by vulnerable individuals in isolation are insufficient to reduce those individuals' infection risk and that population structure can have varied and counteracting effects on epidemic outcomes. The relative impact of each interaction type was contingent on assumptions underlying contact network construction, stressing the importance of empirical validation. Taken together, these results promote a nuanced understanding of disease spread on contact networks, with implications for public health strategies.

Personalized predictions of adverse side effects of the COVID-19 vaccines.

Background: Misconceptions about adverse side effects are thought to influence public acceptance of the Coronavirus disease 2019 (COVID-19) vaccines negatively. To address such perceived disadvantages of vaccines, a novel machine learning (ML) approach was designed to generate personalized predictions of the most common adverse side effects following injection of six different COVID-19 vaccines based on personal and health-related characteristics. Methods: Prospective data of adverse side effects following COVID-19 vaccination in 19943 participants from Iran and Switzerland was utilized. Six vaccines were studied: The AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and the mRNA-1273 vaccine. The eight side effects were considered as the model output: fever, fatigue, headache, nausea, chills, joint pain, muscle pain, and injection site reactions. The total input parameters for the first and second dose predictions were 46 and 54 features, respectively, including age, gender, lifestyle variables, and medical history. The performances of multiple ML models were compared using Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Results: The total number of people receiving the first dose of the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2, and mRNA-1273 were 6022, 7290, 5279, 802, 277, and 273, respectively. For the second dose, the numbers were 2851, 5587, 3841, 599, 242 and 228. The Logistic Regression model for predicting different side effects of the first dose achieved ROC-AUCs of 0.620-0.686, 0.685-0.716, 0.632-0.727, 0.527-0.598, 0.548-0.655, 0.545-0.712 for the AZD1222, Sputnik V, BBIBP-CorV, COVAXIN, BNT162b2 and mRNA-1273 vaccines, respectively. The second dose models yielded ROC-AUCs of 0.777-0.867, 0.795-0.848, 0.857-0.906, 0.788-0.875, 0.683-0.850, and 0.486-0.680, respectively. Conclusions: Using a large cohort of recipients vaccinated with COVID-19 vaccines, a novel and personalized strategy was established to predict the occurrence of the most common adverse side effects with high accuracy. This technique can serve as a tool to inform COVID-19 vaccine selection and generate personalized factsheets to curb concerns about adverse side effects.

An Efficient COVID-19 Disease Outbreak Prediction Using BI-SSOA-TMLPNN and ARIMA

Globally, people's health and wealth are affected by the outbreak of the corona virus. It is a virus, which infects from common fever to severe acute respiratory syndrome. It has the potency to transmit from one person to another. It is established that this virus spread is augmenting speedily devoid of any symptoms. Therefore, the prediction of this outbreak situation with mathematical modelling is highly significant along with necessary. To produce informed decisions along with to adopt pertinent control measures, a number of outbreak prediction methodologies for COVID-19 are being utilized by officials worldwide. An effectual COVID-19 outbreaks' prediction by employing Squirrel Search Optimization Algorithm centric Tanh Multi-Layer Perceptron Neural Network (MLPNN) (SSOA-TMLPNN) along with Auto-Regressive Integrated Moving Average (ARIMA) methodologies is proposed here. Initially, from the openly accessible sources, the input time series COVID-19 data are amassed. Then, pre-processing is performed for better classification outcomes after collecting the data. Next, by utilizing Sine-centered Empirical Mode Decomposition (S-EMD) methodology, the data decomposition is executed. Subsequently, the data are input to the Brownian motion Intense (BI) - SSOA-TMLPNN classifier. In this, the diseased, recovered, and death cases in the country are classified. After that, regarding the time-series data, the corona-virus's future outbreak is predicted by employing ARIMA. Afterwards, data visualization is conducted. Lastly, to evaluate the proposed model's efficacy, its outcomes are analogized with certain prevailing methodologies. The obtained outcomes revealed that the proposed methodology surpassed the other existing methodologies. © 2023 World Scientific Publishing Company.

Predicting Credit Ratings using Deep Learning Models - An Analysis of the Indian IT Industry

Due to the complexity of transactions and the availability of Big Data, many banks and financial institutions are reviewing their business models. Various tasks get involved in determining the credit worthiness like working with spreadsheets, manually gathering data from customers and corporations, etc. In this research paper, we aim to automate and analyze the credit ratings of the Information and technology industry in India. Various Deep-Learning models are incorporated to predict the credit rankings from highest to lowest separately for each company to find the best fit Margin, inventory valuation, etc., are the parameters that contribute to the credit rating predictions. The data collected for the study spans between the years FY-2015 to FY-2020. As per the research been carried out with efficiencies of different Deep Learning models been tested and compared, MLP gained the highest efficiency for predicting the same. This research contributes to identifying how we can predict the ratings for several IT companies in India based on their Financial risk, Business risk, Industrial risk, and Macroeconomic environment using various neural network models for better accuracy. Also it helps us understand the significance of Artificial Neural Networks in credit rating predictions using unstructured and real time Financial data consisting the influence of COVID-19 in Indian IT industry.

An Efficient COVID-19 Disease Outbreak Prediction Using BI-SSOA-TMLPNN and ARIMA

Globally, people's health and wealth are affected by the outbreak of the corona virus. It is a virus, which infects from common fever to severe acute respiratory syndrome. It has the potency to transmit from one person to another. It is established that this virus spread is augmenting speedily devoid of any symptoms. Therefore, the prediction of this outbreak situation with mathematical modelling is highly significant along with necessary. To produce informed decisions along with to adopt pertinent control measures, a number of outbreak prediction methodologies for COVID-19 are being utilized by officials worldwide. An effectual COVID-19 outbreaks' prediction by employing Squirrel Search Optimization Algorithm centric Tanh Multi-Layer Perceptron Neural Network (MLPNN) (SSOA-TMLPNN) along with Auto-Regressive Integrated Moving Average (ARIMA) methodologies is proposed here. Initially, from the openly accessible sources, the input time series COVID-19 data are amassed. Then, pre-processing is performed for better classification outcomes after collecting the data. Next, by utilizing Sine-centered Empirical Mode Decomposition (S-EMD) methodology, the data decomposition is executed. Subsequently, the data are input to the Brownian motion Intense (BI) - SSOA-TMLPNN classifier. In this, the diseased, recovered, and death cases in the country are classified. After that, regarding the time-series data, the corona-virus's future outbreak is predicted by employing ARIMA. Afterwards, data visualization is conducted. Lastly, to evaluate the proposed model's efficacy, its outcomes are analogized with certain prevailing methodologies. The obtained outcomes revealed that the proposed methodology surpassed the other existing methodologies.

Scaling Up Face Masks Classification Using a Deep Neural Network and Classical Method Inspired Hybrid Technique

Classification of persons wearing and not wearing face masks in images has emerged as a new computer vision problem during the COVID-19 pandemic. In order to address this problem and scale up the research in this domain, in this paper a hybrid technique by employing ResNet-101 and multi-layer perceptron (MLP) classifier has been proposed. The proposed technique is tested and validated on a self-created face masks classification dataset and a standard dataset. On self-created dataset, the proposed technique achieved a classification accuracy of 97.3%. To embrace the proposed technique, six other state-of-the-art CNN feature extractors with six other classical machine learning classifiers have been tested and compared with the proposed technique. The proposed technique achieved better classification accuracy and 1-6% higher precision, recall, and F1 score as compared to other tested deep feature extractors and machine learning classifiers. Copyright © 2022 KSII.

COVID-19 Spread Simulation in a Crowd Intelligence Network

In this paper, the Crowd Intelligence Network Model is applied to the simulation of epidemic spread. This model combines the multi-layer coupling network model and the two-stage feedback member model to study the epidemic spread mechanisms under multiple-scene intervention. First, this paper establishes a multi-layer coupled network structure based on the characteristic of Social Network, Information Network, and Monitor Network, namely, the Crowd Intelligence Network structure. Then, based on this structure, the digital-self model, which has a multiple-scene effect and two-stage feedback structure, is designed. It has an emotional state and infection state quantified by using attitude and self-protection levels. This paper uses the attitude level and self-protection level to quantify individual emotions and immune levels, and discusses the impact of individual emotions on epidemic prevention and control. Finally, the availability of the Crowd Intelligence Network Model on the epidemic spread is verified by comparing the simulation trend with the actual spread trend of COVID-19. © The author(s) 2022.

Effects of official information and rumor on resource-epidemic coevolution dynamics

Epidemic-related information and resources have proven to have a significant impact on the spread of the epidemic during the Corona Virus Disease 2019 (COVID-19) pandemic. The various orientation role of information has different effects on the epidemic spreading process, which will affect the individual’ awareness of resources allocation and epidemic spreading scale. Based on this, a three-layer network is established to describe the dynamic coevolution process among information dissemination, resource allocation, and epidemic spreading. In order to analyze dynamic coevolution process, the microscopic Markov chain (MMC) theory is used. Then, the threshold of epidemic spreading is deduced. Our results indicated that the official information orientation intensity inhibits the epidemics spreading, while rumor orientation intensity promotes epidemic spreading. At the same time, the efficiency of resource utilization restrains the expansion of the infection scale. The two kinds of information are combined with resources respectively. Official information will enhance the inhibitory effect of resources epidemics spreading, while rumor will do the opposite.

Systemic risk of multi-layer financial network system under macroeconomic fluctuations

As the global economy continues to integrate, COVID-19 is affecting businesses around the world, causing the financial system to become more complicated. The complicated relationship between various agents in the financial system makes potential hazards more easily transmitted. Most studies of systemic risks have focused on single-layer networks, and macroeconomic fluctuations have not been quantified in multi-layer models of financial networks. In this paper, three different macroeconomic shock scenarios (showing upward, downward, and random trends) are constructed to affect the firm's business activities, and a multi-layer financial network model is developed to simulate systemic risk under macroeconomic fluctuations. Firms with medium and high leverage and small asset sizes, as well as banks with smaller asset sizes and fewer bank-firm credit linkages, are found to be more likely to default. The study also found that average firm leverage exhibits two inflection points, causing banks' default probabilities to "rise, then fall, and then rise, " with the inflection point value being the lowest under the upward trend of macroeconomics. In addition, the higher the ratio of firm loans to total bank assets, the more likely the bank is to default. Appropriate loan maturity extension has also helped to reduce systemic risk, especially in light of the macroeconomic downward trend. Furthermore, improving the capital adequacy ratio can reduce the bank's default probability under macroeconomic fluctuations.

PSO-Optimized CoVID-19 MLP-NARX Mortality Prediction Model

Mortality prediction models localized for Malaysia is limited, warranting a research gap to study further. A predictive model for CoVID-19 mortality prediction is presented in this paper. The model utilized the MLP-NARX structure. Parameters for the model were optimized using PSO. Prediction results yielded average MSE value of 8.1141x10x(-7) with acceptable validation results.

Diagnosis of Covid 19 disease, flu, allergies, colds

In 2020 and by spreading the Covid 19 all over the world, one of the most challenging issues is how to distinguish Covid 19 from similar sicknesses since Covid 19 has lots of similar symptoms in comparison between allergies, colds and flu. Another way to distinguish Covid 19 is testing but it has some challenges, for example in some testing, the accuracy of the test is 50 percent and another test which have more accurate result, some days are needed to provide the result in some developing countries and this can be dangerous if the person has Covid 19. There will be great if we can distinguish the Covid 19 from the patient's symptoms and in this research, an artificial neural network is trained to distinguish the Covid 19 just by the patient's symptoms. For this purpose different types of supervised learning algorithms are implemented to find the best result for classification. We have focused on the symptoms of each patient and assumed them as inputs for our models and the output is the type of sickness. The structure which is used for this neural network is a multilayer feed-forward neural network. © 2022 IEEE.

ModViz: A Modular and Extensible Architecture for Drill-Down and Visualization of Complex Data

Analysis of data sets that may be changing often or in real-time, consists of at least three important synchronized components: i) figuring out what to infer (objectives), ii) analysis or computation of those objectives, and iii) understanding of the results which may require drill-down and/or visualization. There is considerable research on the first two of the above components whereas understanding actionable inferences through visualization has not been addressed properly. Visualization is an important step towards both understanding (especially by non-experts) and inferring the actions that need to be taken. As an example, for Covid-19, knowing regions (say, at the county or state level) that have seen a spike or are prone to a spike in the near future may warrant additional actions with respect to gatherings, business opening hours, etc. This paper focuses on a modular and extensible architecture for visualization of base as well as analyzed data. This paper proposes a modular architecture of a dashboard for user interaction, visualization management, and support for complex analysis of base data. The contributions of this paper are: i) extensibility of the architecture providing flexibility to add additional analysis, visualizations, and user interactions without changing the workflow, ii) decoupling of the functional modules to ease and speed up development by different groups, and iii) supporting concurrent users and addressing efficiency issues for display response time. This paper uses Multilayer Networks (or MLNs) for analysis. To showcase the above, we present the architecture of a visualization dashboard, termed CoWiz++ (for Covid Wizard), and elaborate on how web-based user interaction and display components are interfaced seamlessly with the back-end modules. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

LHSPred: A web based application for predicting lung health severity.

Background and objectives: The computed tomography (CT) scan facilities are crucial for diagnosis of pulmonary diseases and are overburdened during the current pandemic of novel coronavirus disease 2019 (COVID-19). LHSPred (Lung Health Severity Prediction) is a web based tool that enables users to determine a score that evaluates CT scans, without radiologist intervention, and predict risk of pneumonia with features of blood examination and age of patient. It can help in early assessment of lung health severity of patients without CT-scan results and also enable monitoring of post-COVID lung health for recovered patients. Methods: This tool uses Support Vector Regression (SVR) and Multi-Layer Perceptron Regression (MLPR), trained on COVID-19 patient data reported in the literature. It allows to compute a score (CT severity score) that evaluates the involvement of lesions in lung lobes and to predict risk of pneumonia. A web application was implemented that uses the trained regression models. Results: The application has proven to be effective and user friendly in a clinical setting for pulmonary disease treatment. The SVR model achieved Pearson correlation coefficient (PCC) of 0.77 and mean absolute error (MAE) of 2.239 while determining the computed tomography (CT) severity score. The MLPR model achieved PCC of 0.77 and MAE of 2.309. Thus, it can be applied as a useful tool in predicting pneumonia in the post COVID-19 era. Conclusion: LHSPred can be used as a decision support system by the clinicians and as a tool for self-assessment by the patients with only six blood test input features.

Prediction of COVID-19 Based on Genomic Biomarkers of Metagenomic Next-Generation Sequencing Data Using Artificial Intelligence Technology

Objective: The primary aim of this study was to use metagenomic next-generation sequencing (mNGS) data to identify coronavirus 2019 (COVID-19)-related biomarker genes and to construct a machine learning model that could successfully differentiate patients with COVID-19 from healthy controls. Materials and Methods: The mNGS dataset used in the study demonstrated expression of 15,979 genes in the upper airway in 234 patients who were COVID-19 negative and COVID-19 positive. The Boruta method was used to select qualitative biomarker genes associated with COVID-19. Random forest (RF), gradient boosting tree (GBT), and multi-layer perceptron (MLP) models were used to predict COVID-19 based on the selected biomarker genes. Results: The MLP (0.936) model outperformed the GBT (0.851), and RF (0.809) models in predicting COVID-19. The three most important biomarker candidate genes associated with COVID-19 were IFI27, TPTI, and FAM83A. Conclusion: The proposed model (MLP) was able to predict COVID-19 successfully. The results showed that the generated model and selected biomarker candidate genes can be used as diagnostic models for clinical testing or potential therapeutic targets and vaccine design.