A COVID-19 Infection Prediction Model in Egypt Based on Deep Learning Using Population Mobility Reports

The rapidly spreading COVID-19 disease had already infected more than 190 countries. As a result of this scenario, nations everywhere monitored confirmed cases of infection, cures, and fatalities and made predictions about what the future would hold. In the event of a pandemic, governments had set limit rules for the spread of the virus and save lives. Multiple computer methods existed for forecasting epidemic time series. Deep learning was one of the most promising methods for time-series prediction. In this research, we propose a model for predicting the spread of COVID-19 in Egypt based on deep learning sequence-to-sequence regression, which makes use of data on the population mobility reports. The presented model utilized a new combined dataset from two different sources. The first source is Google population mobility reports, and the second source is the number of infected cases reported daily "world in data” website. The suggested model could predict new cases of COVID-19 infection within 3–7 days with the least amount of prediction error. The proposed model achieved 96.69% accuracy for 3 days of prediction. This study is noteworthy since it is one of the first trials to estimate the daily influx of new COVID-19 infections using population mobility data instead of daily infection rates. © 2023, The Author(s).

Prediction of Dropout Students in Massive Open Online Courses Using Ensemble Learning: A Pilot Study in Post-COVID Academic Session

High dropout rate is a critical problem in MOOCs. The prime objective of this study is to identify possible dropout students at the early stage of the course and reducing the number of dropouts providing proper feedback to address the relevant factor. A prediction model based on stacking ensemble machine learning is proposed to identify whether a learner is at risk of dropping a course. The proposed stacked ensemble model outperformed with an accuracy of 93.4% compared to other popular machine learning classifiers. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Prediction Model for Screening Covid-19 Patients

This study aims to compare the performance of data classifying for COVID-19 patients. In this study, the patients' data acquired from the department of disease control (1,608,923 patients) are collected. They are patients records from January 2020 to October 2021. The study focus on three main data classification techniques: Random forest;Neural Network;and Naïve Bayes. The authors study the comparative performance of the techniques. We apply the split test method to evaluate the performance of data prediction. The data are divided into two parts: training data. The results show that Random Forest has an accuracy of 93.51%. Neural network has an accuracy of 93.02%. Naive Bayes has an accuracy of 27.54%. This presents the Random Forest with the highest accuracy Figure for screening of COVID-19 patients © 2022 IEEE.

Construction and Application of Bid Fraud Prediction Model Based on AdaBoost Algorithm

In recent years, with the development of Internet big data technology and e-commerce platform, many active offline transaction methods have gradually shifted to online. Online auctions have come a long way due to COVID-19, but bidding fraud has seriously disrupted the health of the industry. In this paper, the AdaBoost model is used to build a bidding fraud prediction model, and the prediction performance of the model is verified by data experiments, and it is found that it has a high accuracy for identifying bidding fraud. At present, there are few prediction models for bidding fraud, and it has broad development prospects. © 2022 IEEE.

Covid Wave Prediction using SARIMA Machine Learning Algorithm

A speedy and accurate diagnosis of COVID-19 is made possible by effective SARS-Co V -2 screening, which can also lessen the strain on health care systems. There have been built prediction models that assess the likelihood of infection by combining a number of parameters. These are intended to help medical professionals worldwide prioritize patients, particularly when there are few healthcare resources available. From a dataset of 51,831 tested people, out of which 4,769 were confirmed to have COVID-19 virus, a machine learning method was developed and trained. Records of the following week with 47,401 tested people, of which 3,624 were tested positive was also considered. Our method accurately predicted the COVID-19 test results using eight binary characteristics, including gender, age 60, known contact with an infected person, and the presence of five early clinical signs. © 2023 IEEE.

TabNet Based Prediction Model for ICU admission in Covid-19 patients

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the responsible virus for coronavirus disease 2019 (COVID-19). It was reported the first time in Wuhan (China) by late December 2019. The COVID-19 pandemic has become a global health risk due to the urgent need for an Intensive Care Unit (ICU) that exceeded its capacity. To cope with this exponential spread the fast adoption of Artificial Intelligence (AI) tools and advanced technology is crucial. For this reason, many research works in AI are conducted. In the current paper, we intend to report AI applications and solutions based on machine learning, deep learning, and data mining algorithms for detecting, predicting, and diagnosing COVID-19. Furthermore, this study aims to develop a new deep learning-based method capable of predicting whether a COVID-19 patient requires admission to an intensive care unit using clinical tabular data from Kaggle. This model will contribute to the optimization of ICU resources. The experimental results showed that combining Synthetic Minority Oversampling Technique (SMOTE) and TabNet classifier improved the prediction performance and surpassed the state-of-the-art models: MLP, RF, LR, and KNN. © 2022 IEEE.

A Predictive Model of COVID-19 mRNA Vaccine Reactivity based on Dual Attention for Federated Learning Scenarios

The base reactivity of the mRNA sequence has a significant impact on the effectiveness of the mRNA vaccine in fighting against the pandemic of COVID-19. The annotation of mRNA sequence reactivity value is a time-consuming and labor-intensive work, which belongs to the private digital assets of each medical institution. It is not practical to train a predictive model by pooling private data from various parties. Fortunately, federated learning techniques can serve to collaboratively train a predictive model among medical institutions while preserving respective digital assets. However, due to the scarcity of data from each participant, conventional sequential prediction mod-els often fail to perform well. To overcome such a challenge, we propose a reactivity value prediction model based on both the self-attention and the convolutional attention mechanisms only requiring a small dataset of labeled samples. Inspired by BERT, we first train a self-attention feature extraction model through self-supervision using both labeled and unlabeled mRNA samples. In this way, the information of mRNA in the semantic space is deeply mined. Then, a convolutional attention block follows the self-attention block, to extract the attention matrix from the base-pair probability matrix and adjacency matrix. By doing so, the attention matrix can compensate for the insensitivity of the self-attention mechanism to the spatial information of mRNA. By using the Open Vaccine RNA database, experiments show that our prediction model for unseen mRNA has a better performance than other state-of-the-art deep learning models that are used to process gene sequences. Further ablation experiments demonstrate that the existence of the dual attention mechanism reduces the risk of overfitting, resulting in an excellent generalization capability of our model. © 2022 IEEE.

Yoga Recommendation System for the Mental Well-Being of Students using Machine Learning

Many expectations placed on students by society have made stress a part of their academic lives. Youth are susceptible to the issues brought on by academic stress since they are going through a phase of transitions in both aspects i.e personal and social. Academic stress has been shown to lower academic achievement and lower motivation toward academics. Therefore, it becomes crucial to develop appropriate and effective intervention options. In recent times, due to COVID, the utilization of online health blogs and sites recommending health, exercise, and yoga has been significantly increased. The blog will provide solution to a problem and then provide precautions to common people but they lack the dynamics to suggest yoga that can be done any person or a personalized yoga by considering their health condition and not a static article. This research work intends to develop an AI model to predict the possible practices a student can do to alleviate their problem by considering their BPM, blood pressure (both systole and diastole), sleep time and some questions related to stress. The proposed stress prediction model has achieved an accuracy of 94.4% and the yoga pose recommendation system has achieved an accuracy of 97.3%. © 2022 IEEE.

Encouraging Crowd Avoidance Behavior using Dynamic Pricing Framework Towards Preventing the Spread of COVID-19

In the COVID-19 epidemic, balancing a trade-off between preventing the spread of infection and maintaining economic activity is a global challenge. Based on the idea that avoiding crowds leads to the prevention of the spread of infection, we propose to leverage a dynamic pricing method to level out congestion with an aim to balance the trade-off between preventing the spread of infection and economic activity. In our method, reward points are provided according to the degree of congestion in stores to encourage customers to visit stores at less crowded times to avoid crowds. Since store congestion is greatly affected by movement restrictions such as a state of emergency, we propose a demand prediction model that takes into account the biases of the data acquisition circumstances. In an offline evaluation, we validated the effectiveness of the proposed unbiased demand prediction model based on the data from an actual campaign conducted for more than 7 months in Kyushu University. The evaluation results showed that our unbiased model reduced the prediction error by up to relatively 25.0% compared with the model that does not consider biases. Our system has been deployed in our closed service since December, 2021. Online evaluation result showed that our application improved conversion rate by 12.0% and reduced cost per acquisition by up to 11.6%. © 2022 IEEE.

Systematic Literature Review: Machine Learning Prediction Model for Covid-19 Spreading

The dataset, methods, and machine learning prediction framework on the Covid-19 theme have been published widely and complex. Special publications on the spread of virus infection 19 in the form of a time series need to be mapped more comprehensively. This literature review aims to identify and analyze research trends, datasets, and methods used in predicting Covid-19 with Machine Learning Engineering research between 2019 and 2021. Identifying the need, specifying the research question evaluating review protocol, searching for papers, scanning papers, and reporting results are the eight major steps of this systematic literature review. The most critical aspect of systematic analysis is defining the research questions. The PICOC techniques are used to identify research questions. Journal candidates were filtered out using inclusion and exclusion criteria techniques to shrink the SLR scope area. based on a literature study it was found that research in 2019-2021 on the Covid-19 distribution prediction system used variables: susceptibility, infection, mortality, geography, weather, and patient clinical data to be processed into ANFIS machine learning prediction models and neural networks are several models. A classification model that is widely used for hybrid processing in calculating covid-19 infection prediction. The datasets that are often used do not fully meet the epidemiological aspects that trigger the spread of COVID-19 infections. ANFIS and NN are several classification methods that are widely used for hybrid processing in calculating predictions of the spread of COVID-19 infection. © 2022 IEEE.

Covid severity prediction: Who cares about the data quality?

COVID-19 is an ongoing pandemic disrupting daily life and overwhelming the healthcare infrastructure. Since the outburst of the pandemic, researchers have used various techniques to predict many aspects of the disease, including mortality rate and severity. The reproducibility of this research is challenging due to varying methodologies used to collect data, data quality, vague description of methodological approach to training prediction models, over-relying on data imputation, and over-fitting. This paper focuses on these challenges and provides a short yet comprehensive review of research on COVID mortality and severity prediction. The emphasis is on the reproducibility of the results and data quality issues. To further elaborate on the issue, we report the development of severity prediction models using two data sets. CRISP-DM is used as a methodological approach. We analyze and criticize the quality of the used data sets and how they affect the performance and limitations of the trained models. We conclude this paper with comments on data quality issues, the importance of reproducibility, and suggestions to improve reproducibility. © 2022 IEEE.

High-performance association rule mining: Mortality prediction model for cardiovascular patients with COVID-19 patterns

On a global scale, 213 countries and territories have been affected by the coronavirus outbreak. According to researchers, underlying co-morbidity, which includes conditions like diabetes, hypertension, cancer, cardiovascular disease, and chronic respiratory disease, impacts mortality. The current situation requires for immediate delivery of solutions. The diagnosis should therefore be more accurate. Therefore, it's essential to determine each person's level of risk in order to prioritise testing for those who are subject to greater risk. The COVID-19 pandemic's onset and the cases of COVID-19 patients who have cardiovascular illness require specific handling. The paper focuses on defining the symptom rule for COVID-19 sickness in cardiovascular patients. The patient's chronic condition was taken into account while classifying the symptoms and determining the likelihood of fatality. The study found that a large proportion of people with fever, sore throats, and coughs have a history of stroke, high cholesterol, diabetes, and obesity. Patients with stroke were more likely to experience chest discomfort, hypertension, diabetes, and obesity. Additionally, the strategy scales well for large datasets and the computing time required for the entire rule extraction procedure is faster than the existing state-of-the-art method. © 2023 Faculty of Engineering, Alexandria University

Survival Analysis and Its Application of Discharge Time Likelihood Prediction Using Clinical Data on COVID-19 Patients-Machine-Learning Approaches

COVID-19 has significant fatality rate since its appearance in December 2019 as a respiratory ailment that is extremely contagious. As the number of cases in reduction zones rises, highly health officials are control that authorized treatment centers may become overrun with corona virus patients. Artificial neural networks (ANNs) are machine coding that can be used to find complicate relationships between datasets. They enable the detection of category in complicated biological datasets that would be impossible to identify with traditional linear statistical analysis. To study the survival characteristics of patients, several computational techniques are used. Men and older age groups had greater mortality rates than women, according to this study. COVID-19 patients discharge times were predicted;also, utilizing various machine learning and statistical tools applied technically. In medical research, survival analysis is a regularly used technique for identifying relevant predictors of adverse outcomes and developing therapy guidelines for patients. Historically, demographic statistics have been used to predict outcomes in such patients. These projections, on the other hand, have little meaning for the individual patient. We present the training of neural networks to predict outcomes for individual patients at one institution, as well as their predictive performance using data from another institution in a different region. The research output show that the Gradient boosting longevity model beats the all other different models, also in this research study for predicting patient longevity. This study aims to assist health officials in making more informed decisions during the outbreak. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Policy Driven Epidemiological (PDE) Model for Prediction of COVID-19 in India

The fast spread of COVID-19 has made it a global issue. Despite various efforts, proper forecasting of COVID-19 spread is still in question. Government lockdown policies play a critical role in controlling the spread of coronavirus. However, existing prediction models have ignored lockdown policies and only focused on other features such as age, sex ratio, travel history, daily cases etc. This work proposes a Policy Driven Epidemiological (PDE) Model with Temporal, Structural, Profile, Policy and Interaction Features to forecast COVID-19 in India and its 6 states. PDE model integrates two models: Susceptible-Infected-Recovered-Deceased (SIRD) and Topical affinity propagation (TAP) model to predict the infection spread within a network for a given set of infected users. The performance of PDE model is assessed with respect to linear regression model, three epidemiological models (Susceptible-Infectious-Recovered-Model (SIR), Susceptible-Exposed-Infectious-Recovered-Model (SEIR) and SIRD) and two diffusion models (Time Constant Cascade Model and Time Decay Feature Cascade Model). Experimental evaluation for India and six Indian states with respect to different government policies from 15th June to 30th June, i.e., Maharashtra, Gujarat, Tamil Nadu, Delhi, Rajasthan and Uttar Pradesh divulge that prediction accuracy of PDE model is in close proximity with the real time for the considered time frame. Results illustrate that PDE model predicted the COVID-19 cases up to 94% accuracy and reduced the Normalize Mean Squared Error (NMSE) up to 50%, 35% and 42% with respect to linear regression, epidemiological models and diffusion models, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Arduino based Anti-Face-Touching Device and prediction model

Due of the fast pace at which COVID-19 may spread through respiratory illness, the terrible condition it was in heightened public tension. The WHO's primary recommendations advised against often touching your face in order to avoid the transmission of viruses through your lips, eyes, and nose. According to research, the typical person was discovered to touch their face about 20 times each hour since it is everyone's unconscious behavior. In order to cope with this, the study suggests a hardware model that recognizes hand motions that are made in the direction of the user's face and alerts them to such movements using both aural and visual sensory feedback modalities. In order to create a model for the prediction of facial touch motions, the study analyses deep learning architectures in more detail. The FaceGuard device, which is a deep learning-based prediction model used to determine whether or not a hand movement would result in face contact, is compared to the accuracy of the suggested hardware model in the paper 'FaceGuard: A Wearable System To Avoid Face Touching1.' It alerts the user through vibrotactile, aural, and visual sensory modalities. After investigation, it was discovered that the hardware model had less accuracy than the deep learning model and required shorter time to respond to vibro tactile sensory data. © 2022 IEEE.

Use of telemedicine and artificial intelligence in Eye and ENT: A boon for developing countries

Developing countries like Nepal face challenges in accessing health services due to sparse distribution in communities, difficult geographic terrain, limited transportation, poverty, and lack of health human expertise in rural areas. The COVID-19 pandemic added woes to the wound. To address this gap, the Hospital for Children, Eye, ENT, and Rehabilitation Services adopted an innovative approach to remote rural patient care using telehealth and artificial intelligence in close coordination with IT professionals and healthcare professionals. We developed a deep learning-based disease prediction model that incorporates telemedicine with AI for screening and diagnosing Eye and ENT diseases using nonspecialist health workers. Deep learning-based disease prediction models in Diabetic Retinopathy (DR) and Glaucoma added quality specialized services to telehealth. This paper presents the adoption of digital innovations and the incorporation of telehealth to tackle various diseases. To predict DR, 61,458 colorful retinal photographs from fundus photography and 1500 for Glaucoma were used. To reduce the biases, EyePACS data sets were also incorporated. Inception V3 transfer learning model was used for DR and employed DenseNet architecture for Glaucoma. An accuracy of more than 90 %in both models was achieved. Accurate specialized diagnosis, better medical care, patient monitoring, limited specialized hospital visits, and easier with shorter wait times are now possible. In the future, this successful model can be replicated nationally and in other developing countries. © 2022 IEEE.

COVID-19 Patient Recovery Prediction Using Efficient Logistic Regression Model

This research develops a COVID-19 patient recovery prediction model using machine learning. A publicly available data of infected patients is taken and pre-processed to prepare 450 patients' data for building a prediction model with 20.27% recovered cases and 79.73% not recovered/dead cases. An efficient logistic regression (ELR) model is built using the stacking of random forest (RF) and logistic regression (LR) classifiers. Further, the proposed model is compared with state-of-art models such as logistic regression (LR), support vector machine (SVM), decision tree (C5.0), and random forest (RF). All the models are evaluated with different metrics and statistical tests. The results show that the proposed ELR model is good in predicting not recovered/dead cases and handling imbalanced data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Staging Prediction Model for COVID-19 Pandemic under Strong Public Health Interventions

With the extensive implementation of the strong public health interventions in China, many models proposed to predict COVID-19 epidemic are no longer applicable to the current epidemic development. In this paper, a COVID-19 prediction method is proposed based on a staging SEITR model with consideration of strong public health interventions in China. The method simulates preventive and control measures such as mass nucleic acid testing and quarantine of close contacts by introducing the role of Isolates and the transformation of Exposed to Isolated. The experimental evaluation uses real epidemic data from six cities including Nanjing, Yangzhou, and etc. The accuracy of prediction for total number of infections reaches 95.8% with the data of the first 15 days of the outbreak. In addition, the prediction accuracy of the end of the pandemic is 95.07%. These show that the proposed method can effectively predict the course of the epidemic and it is practical for relevant departments to formulate reasonable prevention and control measures. © 2022 IEEE.

Analysis and Prediction of COVID-19 Spread in Ernakulam District, Kerala

The world is witnessing a pandemic of SARS-CoV2 infection since the first quarter of the twenty-first century. Ever since the first case was reported in Wuhan city of China in December 2019, the virus has spread over 223 countries. Understanding and predicting the dynamics of COVID-19 spread through data analysis will empower our administrations with insights for better planning and response against the burden inflicted on our health care infrastructure and economy. The aim of the study was to analyze and predict COVID-19 spread in Ernakulam district of Kerala. Data was extracted from lab data management system (LDMS), a government portal to enter all the COVID-19 testing details. Using the EpiModel package of R-mathematical modeling of infectious disease dynamics, the predictive analysis for hospitalization rate, percentage of patients requiring oxygen and ICU admission, percentage of patients getting admitted, duration of hospital stay, case fatality rate, age group and gender-wise fatality rate, and hospitalization rate were computed. While calculating the above-said variables, the percentage of vaccinated population, breakthrough infections, and percentage of hospitalization among the vaccinated was also taken into consideration. The time trend of patients in ICU showed men outnumbered women. Positive cases were more among 20–30 years, while 61–70 years age group had more risk for ICU admission. An increase in CFR with advancing age and also a higher CFR among males were seen. Conclusions: Analyzing and predicting the trend of COVID-19 would help the governments to better utilize their limited healthcare resources and adopt timely measures to contain the virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Prediction of COVID-19 Severity Using Patient's PHR

A significant health crisis, including the current COVID-19 outbreak, presents us for an opportunity to think about it and focus on how we may improve the way we handle health care in the future to make us humans better prepared and capable of dealing with such an incident.Since the COVID-19 trend has swayed irregularly, they have remained in the dark, unsure how much resources they will have even in the future week.At these instances, difficult period to be capable of predicting exactly what sort of resources a person have it necessary now of a positive test, or perhaps even earlier, would take place extremely beneficial to organizations, as they will be able to get or make preparations with their resource required to save that patient's existence.The aim of the work is to devise a system that would be both outlay and reliable. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.