Intelligent Methods and Models for Assessing Level of Student Adaptation to Online Learning

The problem of introducing online learning is becoming more and more popular in our society. Due to COVID-19 and the war in Ukraine, there is an urgent need for the transition of educational institutions to online learning, so this paper will help people not make mistakes in the process and afterward. The paper's primary purpose is to investigate the effectiveness of machine learning tools that can solve the problem of assessing student adaptation to online learning. These tools include intelligent methods and models, such as classification techniques and neural networks. This work uses data from an online survey of students at different levels: school, college, and university. The survey consists of questions such as gender, age, level of education, whether the student is in the city, class duration, quality of Internet connection, government/non-government educational institution, availability of virtual learning environment, whether the student is familiar with IT, financial conditions, type of Internet connection, a device used for studying, etc. To obtain the results on the effectiveness of online education were used the following machine learning algorithms and models: Random Forest (RF), Extra Trees (ET), Extreme, Light, and Simple Gradient Boosting (GB), Decision Trees (DT), K-neighbors (K-mean), Logistic Regression (LR), Support Vector Machine (SVM), Naїve Bayes (NB) classifier and others. An intelligent neural network model (NNM) was built to address the main issue. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

Prediction of Corona Virus Disease Outcome and Future Forecast the Trend of COVID-19 in India: Observational Study

This work is motivated by the disease caused by the novel corona virus Covid-19, rapid spread in India. An encyclopaedic search from India and worldwide social networking sites was performed between 1 March 2020 and 20 Jun 2020. Nowadays social network platform plays a vital role to track spreading behaviour of many diseases earlier then government agencies. Here we introduced the approach to predict and future forecast the disease outcome spread through corona virus in society to give earlier warning to save from life threats. We compiled daily data of Covid-19 incidence from all state regions in India. Five states (Maharashtra, Delhi, Gujarat, Rajasthan and Madhya-Pradesh) with higher incidence and other states considered for time series analysis to construct a predictive model based on daily incidence training data. In this study we have applied the predictive model building approaches like k-nearest neighbour technique, Random-Forest technique and stochastic gradient boosting technique in COVID-19 dataset and the simulated outcome compared with the observed outcome to validate model and measure the performance of model by accuracy (ACC) and Kappa measures. Further forecast the future trends in number of cases of corona virus deceased patients using the Holt Winters Method. Time series analysis is effective tool for predict the outcome of corona virus disease. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

COVID-19 Media Chatter and Macroeconomic Reflectors on Black Swan: A Spanish and Indian Stock Markets Comparison

Predictive analytics of financial markets in developed and emerging economies during the COVID-19 regime is undeniably challenging due to unavoidable uncertainty and the profound proliferation of negative news on different platforms. Tracking the media echo is crucial to explaining and anticipating the abrupt fluctuations in financial markets. The present research attempts to propound a robust framework capable of channeling macroeconomic reflectors and essential media chatter-linked variables to draw precise forecasts of future figures for Spanish and Indian stock markets. The predictive structure combines Isometric Mapping (ISOMAP), which is a non-linear feature transformation tool, and Gradient Boosting Regression (GBR), which is an ensemble machine learning technique to perform predictive modelling. The Explainable Artificial Intelligence (XAI) is used to interpret the black-box type predictive model to infer meaningful insights. The overall results duly justify the incorporation of local and global media chatter indices in explaining the dynamics of respective financial markets. The findings imply marginally better predictability of Indian stock markets than their Spanish counterparts. The current work strives to compare and contrast the reaction of developed and developing financial markets during the COVID-19 pandemic, which has been argued to share a close resemblance to the Black Swan event when applying a robust research framework. The insights linked to the dependence of stock markets on macroeconomic indicators can be leveraged for policy formulations for augmenting household finance. © 2023 by the authors.

Predictive Attributes for Developing Long COVID-A Study Using Machine Learning and Real-World Data from Primary Care Physicians in Germany.

(1) In the present study, we used data comprising patient medical histories from a panel of primary care practices in Germany to predict post-COVID-19 conditions in patients after COVID-19 diagnosis and to evaluate the relevant factors associated with these conditions using machine learning methods. (2) Methods: Data retrieved from the IQVIATM Disease Analyzer database were used. Patients with at least one COVID-19 diagnosis between January 2020 and July 2022 were selected for inclusion in the study. Age, sex, and the complete history of diagnoses and prescription data before COVID-19 infection at the respective primary care practice were extracted for each patient. A gradient boosting classifier (LGBM) was deployed. The prepared design matrix was randomly divided into train (80%) and test data (20%). After optimizing the hyperparameters of the LGBM classifier by maximizing the F2 score, model performance was evaluated using several test metrics. We calculated SHAP values to evaluate the importance of the individual features, but more importantly, to evaluate the direction of influence of each feature in our dataset, i.e., whether it is positively or negatively associated with a diagnosis of long COVID. (3) Results: In both the train and test data sets, the model showed a high recall (sensitivity) of 81% and 72% and a high specificity of 80% and 80%; this was offset, however, by a moderate precision of 8% and 7% and an F2-score of 0.28 and 0.25. The most common predictive features identified using SHAP included COVID-19 variant, physician practice, age, distinct number of diagnoses and therapies, sick days ratio, sex, vaccination rate, somatoform disorders, migraine, back pain, asthma, malaise and fatigue, as well as cough preparations. (4) Conclusions: The present exploratory study describes an initial investigation of the prediction of potential features increasing the risk of developing long COVID after COVID-19 infection by using the patient history from electronic medical records before COVID-19 infection in primary care practices in Germany using machine learning. Notably, we identified several predictive features for the development of long COVID in patient demographics and their medical histories.

Predicting the COVID-19 mortality among Iranian patients using tree-based models: A cross-sectional study.

Background and Aims: To explore the use of different machine learning models in prediction of COVID-19 mortality in hospitalized patients. Materials and Methods: A total of 44,112 patients from six academic hospitals who were admitted for COVID-19 between March 2020 and August 2021 were included in this study. Variables were obtained from their electronic medical records. Random forest-recursive feature elimination was used to select key features. Decision tree, random forest, LightGBM, and XGBoost model were developed. Sensitivity, specificity, accuracy, F-1 score, and receiver operating characteristic (ROC)-AUC were used to compare the prediction performance of different models. Results: Random forest-recursive feature elimination selected following features to include in the prediction model: Age, sex, hypertension, malignancy, pneumonia, cardiac problem, cough, dyspnea, and respiratory system disease. XGBoost and LightGBM showed the best performance with an ROC-AUC of 0.83 [0.822-0.842] and 0.83 [0.816-0.837] and sensitivity of 0.77. Conclusion: XGBoost, LightGBM, and random forest have a relatively high predictive performance in prediction of mortality in COVID-19 patients and can be applied in hospital settings, however, future research are needed to externally confirm the validation of these models.

Deep neural network for monitoring the growth of COVID-19 epidemic using meteorological covariates

Growth of an epidemic is influenced by the natural variation in climatic conditions and enforcement variation in government stringency policies. Though these variations do not prompt an instant change in the growth of an epidemic, effects of climatic conditions and stringency policies become apparent over time. Time-lagged relationships and functional dynamic connectivity among meteorological covariates and stringency levels generate many lagged features deemed to be important for prediction of reproduction rate, a measure for growth of an epidemic. This empirical study examines the importance scores of lagged features and implements distributed lag inspired feature selection with back testing for model selection and forecasting. A verification forecasting scheme is developed for continuous monitoring of the growth of an epidemic. We have demonstrated the monitoring process by computing a week ahead expected target of the reproduction rate and then by computing a one day ahead verification forecast to evaluate the progress towards the expected target. This evaluation procedure will aid the analysts with a decision making tool for any early adjustment of control options to suppress the transmission.

The role of booster vaccination in decreasing COVID-19 age-adjusted case fatality rate: Evidence from 32 countries.

Background: The global COVID-19 pandemic is still ongoing, and cross-country and cross-period variation in COVID-19 age-adjusted case fatality rates (CFRs) has not been clarified. Here, we aimed to identify the country-specific effects of booster vaccination and other features that may affect heterogeneity in age-adjusted CFRs with a worldwide scope, and to predict the benefit of increasing booster vaccination rate on future CFR. Method: Cross-temporal and cross-country variations in CFR were identified in 32 countries using the latest available database, with multi-feature (vaccination coverage, demographic characteristics, disease burden, behavioral risks, environmental risks, health services and trust) using Extreme Gradient Boosting (XGBoost) algorithm and SHapley Additive exPlanations (SHAP). After that, country-specific risk features that affect age-adjusted CFRs were identified. The benefit of booster on age-adjusted CFR was simulated by increasing booster vaccination by 1-30% in each country. Results: Overall COVID-19 age-adjusted CFRs across 32 countries ranged from 110 deaths per 100,000 cases to 5,112 deaths per 100,000 cases from February 4, 2020 to Jan 31, 2022, which were divided into countries with age-adjusted CFRs higher than the crude CFRs and countries with age-adjusted CFRs lower than the crude CFRs (n = 9 and n = 23) when compared with the crude CFR. The effect of booster vaccination on age-adjusted CFRs becomes more important from Alpha to Omicron period (importance scores: 0.03-0.23). The Omicron period model showed that the key risk factors for countries with higher age-adjusted CFR than crude CFR are low GDP per capita and low booster vaccination rates, while the key risk factors for countries with higher age-adjusted CFR than crude CFR were high dietary risks and low physical activity. Increasing booster vaccination rates by 7% would reduce CFRs in all countries with age-adjusted CFRs higher than the crude CFRs. Conclusion: Booster vaccination still plays an important role in reducing age-adjusted CFRs, while there are multidimensional concurrent risk factors and precise joint intervention strategies and preparations based on country-specific risks are also essential.

Early Highway Construction Cost Estimation: Selection of Key Cost Drivers

Cost estimates in the early stages of project development are essential for making the right decisions, but they are a huge challenge and risk for owners and potential contractors due to limited information about the characteristics of a future highway project. Whereas previous studies were mainly focused on achieving the highest possible estimation accuracy, this paper aims to propose cost-estimation models that can provide satisfactory accuracy with the least possible effort and to compare the perspectives of owners and contractors as the key stakeholders on projects. To determine cost drivers (CDs) that have a high influence on highway-construction costs and require low effort for their establishment, a questionnaire survey was conducted. Based on the key stakeholders' perceptions and collected data set, cost-estimation models were developed using multiple-regression analysis, artificial neural networks, and XGBoost. The results show that reasonable cost-estimation accuracy can be achieved with relatively low effort for three CDs for the owners' perspective and five CDs for the contractors' perspective. Additional inclusion of input CDs in models does not necessarily imply an increase in accuracy. Also, the questionnaire results show that owners are more concerned about environmental issues, whereas contractors are more concerned about the possible changes in resource prices (especially after recent high increases caused by COVID-19 and the Russia-Ukraine war). These findings can help owners and potential contractors in intelligent decision-making in the early stages of future highway-construction projects.

Predictive Six Sigma for Turkish manufacturers: utilization of machine learning tools in DMAIC

PurposeThis study aims to emphasize utilization of Predictive Six Sigma to achieve process improvements based on machine learning (ML) techniques embedded in define, measure, analyze, improve, control (DMAIC). With this aim, this study presents selection and utilization of ML techniques, including multiple linear regression (MLR), artificial neural network (ANN), random forests (RF), gradient boosting machines (GBM) and k-nearest neighbors (k-NN) in the analyze and improve phases of Six Sigma DMAIC.Design/methodology/approachA data set containing 320 observations with nine input and one output variables is used. To achieve the objective which was to decrease the number of fabric defects, five ML techniques were compared in terms of prediction performance and best tools were selected. Next, most important causes of defects were determined via these tools. Finally, parameter optimization was conducted for minimum number of defects.FindingsAmong five ML tools, ANN, GBM and RF are found to be the best predictors. Out of nine potential causes, "machine speed” and "fabric width” are determined as the most important variables by using these tools. Then, optimum values for "machine speed” and "fabric width” for fabric defect minimization are determined both via regression response optimizer and ANN surface optimization. Ultimately, average defect number was decreased from 13/roll to 3/roll, which is a considerable decrease attained through utilization of ML techniques in Six Sigma.Originality/valueAddressing an important gap in Six Sigma literature, in this study, certain ML techniques (i.e. MLR, ANN, RF, GBM and k-NN) are compared and the ones possessing best performances are used in the analyze and improve phases of Six Sigma DMAIC.

An Optimized Ensemble Model for COVID Detection

The entire world has gone through a pandemic situation due to the spread of novel corona virus. In this paper, the authors have proposed an ensemble learning model for the classification of the subjects to be infected by coronavirus. For this purpose, five types of symptoms are considered. The dataset contains 2889 samples with six attributes and is collected from the Kaggle database. Three different types of classifiers such as Support vector machine (SVM), Gradient boosting, and extreme gradient boosting (XGBoost) are considered for classification purposes. For improving the learning strategy and performance of the proposed models subjected to accuracy, the learning rates are varied for each node of the tree-based ensemble classifiers. Also, the hyperparameters of the XGBoost model are optimized by applying the Bayesian optimization (BO) technique. The best accuracy in SVM classifier is found as 91.69%. 96.58% accuracy is obtained in the modified gradient boosting model. The optimized XGBoost model is providing 100% accuracy which is better than other. © 2022 IEEE.

Dense Feature Memory Augmented Transformers for COVID-19 Vaccination Search Classification

With the devastating outbreak of COVID-19, vaccines are one of the crucial lines of defense against mass infection in this global pandemic. Given the protection they provide, vaccines are becoming mandatory in certain social and professional settings. This paper presents a classification model for detecting COVID-19 vaccination related search queries, a machine learning model that is used to generate search insights for COVID-19 vaccinations. The proposed method combines and leverages advancements from modern state-of-the-art (SOTA) natural language understanding (NLU) techniques such as pretrained Transformers with traditional dense features. We propose a novel approach of considering dense features as memory tokens that the model can attend to. We show that this new modeling approach enables a significant improvement to the Vaccine Search Insights (VSI) task, improving a strong well-established gradient-boosting baseline by relative +15% improvement in F1 score and +14% in precision. © 2022 Association for Computational Linguistics.

Machine learning approach for predicting inhalation injury in patients with burns.

BACKGROUND: The coronavirus disease pandemic has had a tangible impact on bronchoscopy for burn inpatients due to isolation and triage measures. We utilised the machine-learning approach to identify risk factors for predicting mild and severe inhalation injury and whether patients with burns experienced inhalation injury. We also examined the ability of two dichotomous models to predict clinical outcomes including mortality, pneumonia, and duration of hospitalisation. METHODS: A retrospective 14-year single-centre dataset of 341 intubated patients with burns with suspected inhalation injury was established. The medical data on day one of admission and bronchoscopy-diagnosed inhalation injury grade were compiled using a gradient boosting-based machine-learning algorithm to create two prediction models: model 1, mild vs. severe inhalation injury; and model 2, no inhalation injury vs. inhalation injury. RESULTS: The area under the curve (AUC) for model 1 was 0·883, indicating excellent discrimination. The AUC for model 2 was 0·862, indicating acceptable discrimination. In model 1, the incidence of pneumonia (P < 0·001) and mortality rate (P < 0·001), but not duration of hospitalisation (P = 0·1052), were significantly higher in patients with severe inhalation injury. In model 2, the incidence of pneumonia (P < 0·001), mortality (P < 0·001), and duration of hospitalisation (P = 0·021) were significantly higher in patients with inhalation injury. CONCLUSIONS: We developed the first machine-learning tool for differentiating between mild and severe inhalation injury, and the absence/presence of inhalation injury in patients with burns, which is helpful when bronchoscopy is not available immediately. The dichotomous classification predicted by both models was associated with the clinical outcomes.

Analysis of Psychological Distress During COVID-19 Among Professionals

The aftermath of the lockdown caused by the current pandemic generates many challenges and opportunities for the professionals as well as for organizations. Several organizations forced the people to work on-site whereas many of the organizations have been allowing work from home. However, both ways of working are challenging and cause psychological distress. The present work analyses the psychological distress among professionals residing in India during the COVID-19 pandemic. The work considers both the scenarios of working professionals: professionals working from home and professionals working onsite. The work introduces a novel hybrid machine learning approach called GBETRR. GBETRR combines two approaches, namely gradient-boosting classifier and extra-trees regressor repressor. The present work also uses a hybrid parameter optimization algorithm. Multiple performance metrics are used to evaluate the performance evaluation. Results revealed that the professionals with work from home are more stressed as compared to the professionals working onsite. Copyright © 2022 IGI Global.

Using Tree-Based Gradient Boosting to Distinguish Between Lymphoma and COVID-19

Over 600,000 new lymphoma cases and around 280,000 lymphoma-related deaths were reported in 2020. The delayed diagnosis of lymphoma has long been a problem. However, the advent of the COVID-19 pandemic, which disrupted healthcare services worldwide, may have caused more significant delays in lymphoma diagnoses. Since lymphomas can sometimes present with symptoms like COVID-19 and can affect the lungs, there is also a risk of misdiagnosis. We collected 505 lymphoma and 180 COVID-19 case reports from ScienceDirect and applied boosting methods to classify each patient as having COVID-19 or lymphoma based on the patient's age, gender and reported symptoms. LightGBM had the highest ROC AUC (0.89), meaning it best differentiates between the two diseases. Therefore, this model can be used as a screening tool to reduce the delay in lymphoma diagnosis and improve the patients' chances of survival. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Understanding nonlinear and synergistic effects of the built environment on urban vibrancy in metro station areas

Transit-oriented development (TOD) has long been recognized as a significant model for prospering urban vibrancy. However, most studies on TOD and urban vibrancy do not consider temporal differences or the nonlinear effects involved. This study applies the gradient boosting decision tree (GBDT) model to metro station areas in Wuhan to explore the nonlinear and synergistic effects of the built-environment features on urban vibrancy during different times. The results show that (1) the effects of the built-environment features on the vibrancy around metro stations differ over time;(2) the most critical features affecting vibrancy are leisure facilities, floor area ratio, commercial facilities, and enterprises;(3) there are approximately linear or complex nonlinear relationships between the built-environment features and the vibrancy;and (4) the synergistic effects suggest that multimodal is more effective at leisure-dominated stations, high-density development is more effective at commercial-dominated stations, and mixed development is more effective at employment-oriented stations. The findings suggest improved planning recommendations for the organization of rail transport to improve the vibrancy of metro station areas.

Clinical data-driven approach to identifying COVID-19 and influenza from a gradient-boosting model

Corona Virus Disease 2019 (COVID-19) and influenza are both caused by viruses, seriously affect human health, and are highly infectious. However, because the clinical manifestations of these two groups of diseases have almost identical symptoms, separate Polymerase Chain Reaction (PCR) tests must be used for patients in each disease group. This study proposes an automatic data-processing model based on artificial intelligence and gradient boosting to identifying COVID-19 and influenza. The model can learn directly from raw data without the need for human input to delete empty data. Methodology and techniques operate in two stages: first, it evaluates and processes data to reduce the dataset's complexity using the light gradient boosting machine (LightGBM);then, in the second stage, it builds a classification model for each disease group based on the extreme gradient boosting (XGBoost) method. The research tools showed that combining two gradient-boosting models both LightGBM and XGBoost to generate automatic COVID-19 and influenza classifiers from clinical data produced strong results and a superior performance versus one model alone, with an overall accuracy of over 99.96%. In the future, the developed model will enable patients to be diagnosed simply and accurately and thereby reduce countries' testing costs for COVID-19 and similar pandemics that may arise. © 2023 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.

Performance Analysis of Machine Learning Classification Approaches for Monkey Pox Disease Prediction

After covid-19 pandemic, many countries have the possibility of getting affected by Monkey Pox Virus. Monkey pox has the same symptoms of smallpox, chicken pox, and measles virus. In this work, the computational models are construed to predict the presence or absence of monkey pox virus. Eight different Classification algorithms including Decision Tree (DT), Random Forest Classification (RF), Naïve Bayes (NB), K-Nearest Neighbor algorithms (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Ada Boosting algorithm (AB), Gradient Boosting (GB) algorithm are used for the Classification of Monkey Pox disease. Four evaluation measures are used in this work to compute the accuracy of classification. Four measures F-Score, Accuracy, Precision, and Recall are used to compare the eight different types of classification algorithms. Based on experimental analysis, it was observed that highest accuracy of 71% is achieved by Gradient Boosting algorithm when compared to other algorithms. © 2022 IEEE.

Comparison of Machine Learning Models for predicting Covid-19 patients' recovery in India

The pandemic Covid-19 is a name coined by WHO on 31st December 2019. This devastating illness was carried on by a new coronavirus known as SARS-COV-2. Most of the research has focused on estimating the total number of cases and mortality rate of COVID-19. Due to this, people across the world were stressed out by observing the growing number of cases every day. As a means of maintaining equilibrium, this paper aims to identify the best way to predict the number of recovered cases of Coronavirus in India. Dataset was divided into two parts: training and testing. The training dataset utilised 70% of the dataset, and the testing dataset utilised 30%. In this paper, we applied 10 machine learning techniques i.e. Random Forest Classifier (RF), Naive Bayes (NB), Quadratic Discriminant Analysis (QDA), Gradient Boosting Classifier (GBM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), K Neighbour Classifier (KNN), Decision Tree Classifier (DT), SVM - Linear and Ada-Boost Classifier in order to predict recovered patients in India. Our study suggests that Random Forest Classifier outperforms other machine learning models for predicting the recovered Coronavirus patients having an accuracy of 0.9632, AUC of 0.9836, Recall of 0.9640, Precision of 0.9680, F1 Score of 0.9617 and Kappa of 0.9558. © 2022 IEEE.

A segmented machine learning modeling approach of social media for predicting occupancy

Purpose: This study aims to predict hotel demand through text analysis by investigating keyword series to increase demand predictions' precision. To do so, this paper presents a framework for modeling hotel demand that incorporates machine learning techniques. Design/methodology/approach: The empirical forecasting is conducted by introducing a segmented machine learning approach of leveraging hierarchical clustering tied to machine learning and deep learning techniques. These features allow the model to yield more precise estimates. This study evaluates an extensive range of social media-derived words with the most significant probability of gradually establishing an understanding of an optimal outcome. Analyzes were performed on a major hotel chain in an urban market setting within the USA. Findings: The findings indicate that while traditional methods, being the naive approach and ARIMA models, struggled with forecasting accuracy, segmented boosting methods (XGBoost) leveraging social media predict hotel occupancy with greater precision for all examined time horizons. Additionally, the segmented learning approach improved the forecasts' stability and robustness while mitigating common overfitting issues within a highly dimensional data set. Research limitations/implications: Incorporating social media into a segmented learning framework can augment the current generation of forecasting methods' accuracy. Moreover, the segmented learning approach mitigates the negative effects of market shifts (e.g. COVID-19) that can reduce in-production forecasts' life-cycles. The ability to be more robust to market deviations will allow hospitality firms to minimize development time. Originality/value: The results are expected to generate insights by providing revenue managers with an instrument for predicting demand. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Predicting Happiness Score during Covid-19 Using Machine Learning

The COVID-19 pandemic has been a bad dream for many people. People suffered from job losses, leading to a low level of happiness. Happiness is the key to a healthy life, and predicting the happiness score of 156 countries will give the idea of a happiness index around the world during the COVID-19 pandemic. An open dataset of the happiness index has been picked from the World Happiness Report, which is manifested already in a United Nations conference. The available dataset splits into training data and testing data, respectively. The training data have fitted into different machine learning algorithms. After that, the prediction score has observed based on testing data. After applying a large number of algorithms, the highest accuracy of the resulting regression model is 97 percent. © 2022 IEEE.