Prediction of COVID-19 Using the Artificial Neural Network (ANN) with K-Fold Cross-Validation

Background: COVID-19 is a disease that attacks the respiratory system and is highly contagious, so cases of the spread of COVID-19 are increasing every day. The increase in COVID-19 cases cannot be predicted accurately, resulting in a shortage of services, facilities and medical personnel. This number will always increase if the community is not vigilant and actively reduces the rate of adding confirmed cases. Therefore, public awareness and vigilance need to be increased by presenting information on predictions of confirmed cases, recovered cases, and cases of death of COVID-19 so that it can be used as a reference for the government in taking and establishing a policy to overcome the spread of COVID-19. Objective: This research predicts COVID-19 in confirmed cases, recovered cases, and death cases in Lampung Province Method: This study uses the ANN method to determine the best network architecture for predicting confirmed cases, recovered cases, and deaths from COVID-19 using the k-fold cross-validation method to measure predictive model performance. Results: The method used has a good predictive ability with an accuracy value of 98.22% for confirmed cases, 98.08% for cured cases, and 99.05% for death cases. Conclusion: The ANN method with k-fold cross-validation to predict confirmed cases, recovered cases, and COVID-19 deaths in Lampung Province decreased from October 27, 2021, to January 24, 2022. © 2023 The Authors. Published by Universitas Airlangga.

Existing approaches in Ear biometrics

Biometric authentication is a self-sufficient technique to prove one's identity that could be used in various security authentication platforms such as airport immigration control, customer authentication, cyber forensics, and many others. Security and privacy are significant concerns in today's world. Using biometrics traits, we could achieve a superior level of security. The covid-19 virus almost fails the other biometric system. As we have become a mask-wearing society due to which face recognition system was failing, and we know the virus is spread through contact, the fingerprint biometric system also fails. Ear biometrics could have become a promising and helpful field to prove one identity over other biometrics. Various researches have been done with reasonable accuracy but in a constrained environment. Ear biometrics can also come over the significant hurdle of security concerns. A review of many existing techniques is conducted in this paper to determine which algorithm performs better and delivers higher accuracy. This paper contains findings from numerous ear detection studies and suggests a future-related method that will provide good efficient accuracy in ear detection under an unconstraint database. © 2022 IEEE.

A Generic AI-Based Technique for Assessing Student Performance in Conducting Online Virtual and Remote Controlled Laboratories

Due to the COVID-19 pandemic and the development of educational technology, e-learning has become essential in the educational process. However, the adoption of e-learning in sectors such as engineering, science, and technology faces a particular challenge as it needs a special Laboratory Learning Management System (LLMS) capable of supporting online lab activities through virtual and controlled remote labs. One of the most challenging tasks in designing such LLMS is how to assess a student's performance while an experiment is being conducted and how stuttering students can be automatically detected while experimenting and providing the appropriate assistance. For this, a generic technique based on Artificial Intelligence (AI) is proposed in this paper for assessing student performance while conducting online labs and implemented as a performance evaluation module in the LLMS. The performance evaluation module is designed to automatically detect the student performance during the experiment run time and triggers the LLMS virtual assistant service to provide struggling students with the appropriate help when they need it. Also, the proposed performance assessment technique is used during the lab exam sessions to support the automatic grading process conducted by the LLMS Auto-Grading Module. The proposed performance evaluation technique has been developed based on analyzing the student's mouse dynamics to work generally with any type of simulation or control software used by virtual or remote controlled laboratories;without the need for special interfacing. The study has been applied to a novel dataset built by the course instructors and students simulating a circuit on TinkerCad. Using mouse dynamics fetching, the system extracts features and evaluates them to determine if the student has built the experiment steps in the right way or not. A comparison study has been developed between different Machine Learning (ML) models and a number of performance metrics are calculated. The study confirmed that Artificial Neural Network (ANN) and Support Vector Machine (SVM) are the best models to be used for automatically evaluating student performance while conducting the online labs with a precision reaching up to 91%.

Does COVID-19 Pandemic Motivate Privacy Self-Disclosure in Mobile Fintech Transactions? A Privacy-Calculus-Based Dual-Stage SEM-ANN Analysis

The emergence of mobile financial technology (mobile fintech) services raises numerous public concerns regarding privacy issues;consequently, researchers in mobile technology acceptance have focused on consumers' privacy self-disclosure behaviors under the usual scenario. However, there is still a lack of understanding on how external influences, such as a public health crisis, affect consumers' privacy decision-making process. Therefore, in this article, we examine the effects of privacy- and pandemic-related antecedents on mobile fintech users' information self-disclosure behavior during the coronavirus disease 2019 pandemic. The present research adopts a self-administered questionnaire with 712 effective responses for data collection and a two-stage partial least squares-structural equation modeling-artificial neural network (PLS-SEM-ANN) approach to test the theoretical lens proposed. The results indicate that the significant structural paths in the model are consistent with the proposed hypotheses and existing literature. Surprisingly, face-to-face avoidance (FFA) does not significantly influence consumers' self-disclosure willingness. Infection severity and infection susceptibility were insignificant with FFA. The present research is the first to investigate consumers' privacy-related behavior via integrating the privacy-calculus framework with control agency theory. This research focuses on consumers' decision-making during the pandemic, explicitly highlighting the macroenvironment's role in influencing an individual's behavior.

Ranking Mental Illness among Social Media Users

Currently we are in the era of the internet. We can express our thoughts through social media i.e. Facebook, Twitter, Instagram etc. Now we are in the most challenging stage of this century as well as the rest of the world due to COVID-19 pandemic and the after effects of the virus infection. Some of the survivors are experiencing trauma and other mental health issues. To determine the mental health condition of someone, we performed sentiment analysis on their social media posts. This paper focuses mainly on sentiment analysis of twitter data written in Bengali language and tries to find out whether the user is mentally depressed or not. The expressions of those users are either negative or positive, sometimes neutral. Using different contemporary Machine learning algorithms like Naive Bayes, Support Vector Machines (SVM), we can determine the mental health condition of any user. We also built a model by applying the Random Forest classifier on social media posts and examined them whether they were in a mental condition or not. © 2021 IEEE.

Decision support system to evaluate a vandalized and deteriorated oil pipeline transportation system using artificial intelligence techniques. Part 1: modeling

The oil and gas industry worldwide is experiencing problems of vandalism and mechanical deterioration due to corrosion in its various pipeline transport systems, a drop in the price of hydrocarbons due to the COVID-19, limitation of maintenance processes. This article provides a contribution original to the knowledge and management of a pipeline transportation system (PTS), without an immediate high impact that would help reduce property loss due to corrosion, through the development of intelligent evaluation models that combine field data, laboratory, and cognitive knowledge in a case study in Mexico. The research is divided into Part 1: modeling, a Fuzzy expert system (FES) unified the knowledge of corrosion specialists and mechanical integrity studies (MIS) and identified evolutionary corrosion patterns with reliability of 0.9029. An artificial neural network (ANN) supported by statistics and metallography establishes test reliability of 0.9556 and determines the corrosion inhibition capacity (C) of Mexican hydrocarbon mixtures based on their properties compared to carbon steel. Part 2: analysis of the operational and economic risk of the PTS under corrosive effects, using Monte Carlo simulation (MCS) estimates various financial scenarios considering corrosive profiles of soils, supply, demand, and inflation.

Prediction of crude oil prices in COVID-19 outbreak using real data.

The world has been undergoing a global economic recession for almost two years because of the health crisis stemming from the outbreak and its effects have still continued so far. Especially, COVID-19 reduced consumer spending due to social isolation, lockdown and travel restrictions in 2020. As a result of this, with social and economic life coming to a standstill, oil prices plummeted. With the ongoing uncertainty concerning the COVID-19 pandemic, it has been of great importance for all economic agents to predict crude oil prices. The objective of this paper is to improve a model in order to make more accurate predictions for crude oil price movements. The performance of this model is assessed in terms of some significant criteria comparing our model with its counterparts as well as artificial neural networks (ANNs) and support vector machine (SVM) methods. As for these criteria, root mean square error (RMSE) and mean absolute error (MAE) results show that this model outperforms other models in forecasting crude oil prices. Further, the simulation results for 2021 show that the daily crude oil price forecasts are almost close to the real oil prices. Oil price forecasting has become more and more important for economic agents in COVID-19 period. A consistent model is required to cope with the movements in crude oil prices. A novel method combining fuzzy time series and the greatest integer function is developed. The results show that our model outperforms other counterparts or ANN and SVM methods. We capture non-linearity and volatility in crude oil prices.

SVM and ANN classification using GLCM and HOG features for COVID-19 and Pneumonia detection from Chest X-rays

Due to the coronavirus pandemic and the lack of an automatic COVID-19 diagnostic system to relieve congestion in health centers and to support the traceability of this disease, this article exposes the implementation of algorithms for automatic diagnosis of lung diseases such as COVID-19 and Pneumonia from chest X-rays (CXR) through GLCM and HOG features extraction using 6300 patches. Then, selecting the best features and different classifiers such as an Support Vector Machine (SVM) and Artificial Neural Network (ANN) to obtain a system maximum accuracy of 93,73% for SVM. © 2021 IEEE.

Performance Evaluation of Soft Computing Approaches for Forecasting COVID-19 Pandemic Cases.

An unexpected outbreak of deadly Covid-19 in later part of 2019 not only endangered the economies of the world but also posed threats to the cultural, social and psychological barriers of mankind. As soon as the virus emerged, scientists and researchers from all over the world started investigating the dynamics of this disease. Despite extensive investments in research, no cure has been officially found to date. This uncertain situation rises severe threats to the survival of mankind. An ultimate need of the time is to investigate the course of disease transfer and suggest a future projection of the disease transfer to be enabled to effectively tackle the always evolving situations ahead. In the present study daily new cases of COVID-19 was predicted using different forecasting techniques; Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing/Error Trend Seasonality (ETS), Artificial Neural Network Models (ANN), Gene Expression Programming (GEP), and Long Short-Term Memory (LSTM) in four countries; Pakistan, USA, India and Brazil. The dataset of new daily confirmed cases of COVID-19 from the date on which first case was registered in the respective country to 30 November 2020 is analyzed through these five forecasting models to forecast the new daily cases up to 31st January 2020. The forecasting efficiency of each model was evaluated using well known statistical parameters R 2, RMSE, and NSE. A comparative analysis of all above-mentioned models was performed. Finally, the study concluded that Long Short-Term Memory (LSTM) neural network-based forecasting model projected the future cases of COVID-19 pandemic best in all the selected four stations. The accuracy of the model ranges from coefficient of determination value of 0.85 in Brazil to 0.96 in Pakistan. NSE value for the model in India is 0. 99, 0.98 in USA and Pakistan and 0.97 in Brazil. This high-accuracy forecast of COVID-19 cases enables the projection of possible peaks in near future in the aforementioned countries and, therefore, prove to be helpful in formulating strategies to get prepared for the potential hard times ahead.

Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study.

Early and self-identification of locomotive degradation facilitates us with awareness and motivation to prevent further deterioration. We propose the usage of nine squat and four one-leg standing exercise features as input parameters to Machine Learning (ML) classifiers in order to perform lower limb skill assessment. The significance of this approach is that it does not demand manpower and infrastructure, unlike traditional methods. We base the output layer of the classifiers on the Short Test Battery Locomotive Syndrome (STBLS) test used to detect Locomotive Syndrome (LS) approved by the Japanese Orthopedic Association (JOA). We obtained three assessment scores by using this test, namely sit-stand, 2-stride, and Geriatric Locomotive Function Scale (GLFS-25). We tested two ML methods, namely an Artificial Neural Network (ANN) comprised of two hidden layers with six nodes per layer configured with Rectified-Linear-Unit (ReLU) activation function and a Random Forest (RF) regressor with number of estimators varied from 5 to 100. We could predict the stand-up and 2-stride scores of the STBLS test with correlation of 0.59 and 0.76 between the real and predicted data, respectively, by using the ANN. The best accuracies (R-squared values) obtained through the RF regressor were 0.86, 0.79, and 0.73 for stand-up, 2-stride, and GLFS-25 scores, respectively.

Predictive value of the preliminary findings in the severity of COVID-19 disease and the effect on therapeutic approaches.

In this retrospective multicenter case series study, the predictive value of initial findings of confirm COVID-19 cases in determining outcome of the disease was assessed. Patients were divided into two groups based on the outcome: low risk (hospitalization in the infectious disease ward and discharge) and high risk (hospitalization in ICU or death). A total of 164 patients with positive PCR-RT were enrolled in this study. About 36 patients (22%) were in the high-risk group and 128 (78%) were in the low-risk group. Results of statistical analysis revealed a significant relationship between age, fatigue, history of cerebrovascular disease, organ failure, white blood cells (WBC), neutrophil-to-lymphocyte ratio (NLR), and derived neutrophil-to-lymphocyte ratio (dNLR) with increased risk of disease. The artificial neural network (ANN) could predict the high-risk group with an accuracy of 87.2%. Preliminary findings of COVID-19 patients can be used in predicting their outcome and ANN can determine the outcome of patients with appropriate accuracy (87.2%). Most treatment in Covid-19 are supportive and depend on the severity of the disease and its complications. The first step in treatment is to determine the severity of the disease. This study can improve the treatment of patients by predicting the severity of the disease using the initial finding of patients and improve the management of disease with differentiating high-risk from low-risk groups.

Season, not lockdown, improved air quality during COVID-19 State of Emergency in Nigeria.

Globally, ambient air pollution claims ~9 million lives yearly, prompting researchers to investigate changes in air quality. Of special interest is the impact of COVID-19 lockdown. Many studies reported substantial improvements in air quality during lockdowns compared with pre-lockdown or as compared with baseline values. Since the lockdown period coincided with the onset of the rainy season in some tropical countries such as Nigeria, it is unclear if such improvements can be fully attributed to the lockdown. We investigate whether significant changes in air quality in Nigeria occurred primarily due to statewide COVID-19 lockdown. We applied a neural network approach to derive monthly average ground-level fine aerosol optical depth (AODf) across Nigeria from year 2001-2020, using the Multi-angle Implementation of Atmospheric Correction (MAIAC) AODs from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) satellites, AERONET aerosol optical properties, meteorological and spatial parameters. During the year 2020, we found a 21% or 26% decline in average AODf level across Nigeria during lockdown (April) as compared to pre-lockdown (March), or during the easing phase-1 (May) as compared to lockdown, respectively. Throughout the 20-year period, AODf levels were highest in January and lowest in May or June, but not April. Comparison of AODf levels between 2020 and 2019 shows a small decline (1%) in pollution level in April of 2020 compare to 2019. Using a linear time-lag model to compare changes in AODf levels for similar months from 2002 to 2020, we found no significant difference (Levene's test and ANCOVA; α = 0.05) in the pollution levels by year, which indicates that the lockdown did not significantly improve air quality in Nigeria. Impact analysis using multiple linear regression revealed that favorable meteorological conditions due to seasonal change in temperature, relative humidity, planetary boundary layer height, wind speed and rainfall improved air quality during the lockdown.

Use of Machine Learning and Artificial Intelligence to predict SARS-CoV-2 infection from Full Blood Counts in a population.

Since December 2019 the novel coronavirus SARS-CoV-2 has been identified as the cause of the pandemic COVID-19. Early symptoms overlap with other common conditions such as common cold and Influenza, making early screening and diagnosis are crucial goals for health practitioners. The aim of the study was to use machine learning (ML), an artificial neural network (ANN) and a simple statistical test to identify SARS-CoV-2 positive patients from full blood counts without knowledge of symptoms or history of the individuals. The dataset included in the analysis and training contains anonymized full blood counts results from patients seen at the Hospital Israelita Albert Einstein, at São Paulo, Brazil, and who had samples collected to perform the SARS-CoV-2 rt-PCR test during a visit to the hospital. Patient data was anonymised by the hospital, clinical data was standardized to have a mean of zero and a unit standard deviation. This data was made public with the aim to allow researchers to develop ways to enable the hospital to rapidly predict and potentially identify SARS-CoV-2 positive patients. We find that with full blood counts random forest, shallow learning and a flexible ANN model predict SARS-CoV-2 patients with high accuracy between populations on regular wards (AUC = 94-95%) and those not admitted to hospital or in the community (AUC = 80-86%). Here, AUC is the Area Under the receiver operating characteristics Curve and a measure for model performance. Moreover, a simple linear combination of 4 blood counts can be used to have an AUC of 85% for patients within the community. The normalised data of different blood parameters from SARS-CoV-2 positive patients exhibit a decrease in platelets, leukocytes, eosinophils, basophils and lymphocytes, and an increase in monocytes. SARS-CoV-2 positive patients exhibit a characteristic immune response profile pattern and changes in different parameters measured in the full blood count that are detected from simple and rapid blood tests. While symptoms at an early stage of infection are known to overlap with other common conditions, parameters of the full blood counts can be analysed to distinguish the viral type at an earlier stage than current rt-PCR tests for SARS-CoV-2 allow at present. This new methodology has potential to greatly improve initial screening for patients where PCR based diagnostic tools are limited.