Multifactor data analysis to forecast an individual's severity over novel COVID-19 pandemic using extreme gradient boosting and random forest classifier algorithms

AI and machine learning are increasingly often applied in the medical industry. The COVID-19 epidemic will start to spread quickly over the planet around the start of 2020. At hospitals, there were more patients than there were beds. It was challenging for medical personnel to identify the patient who needed treatment right away. A machine learning approach is used to predict COVID-19 pandemic patients at high risk. To provide input data and output results that execute the machine learning model on the backend, a straightforward Python Flask web application is employed. Here, the XGBoost algorithm, a supervised machine learning method, is applied. In order to predict high-risk patients based on their current underlying health issues, the model uses patient characteristics as well as criteria like age, sex, health issues including diabetes, asthma, hypertension, and smoking, among others. The XGBoost model predicts the patient's severity with an accuracy of about 98% after data pre-processing and training. The most important factors to the models are chosen to be age, diabetes, sex, and obesity. Patients and hospital personnel will benefit from this project's assistance in making timely choices and taking appropriate action. This will let medical personnel decide how much time and space to devote to the COVID-19 high-risk patients. providing a treatment that is both efficient and ideal. With this programme and the necessary patient data, hospitals may decide whether a patient need immediate care or not.

A hybrid metaheuristic algorithm for the multi-objective location-routing problem in the early post-disaster stage

Disasters such as earthquakes, typhoons, floods and COVID-19 continue to threaten the lives of people in all countries. In order to cover the basic needs of the victims, emergency logistics should be implemented in time. Location-routing problem (LRP) tackles facility location problem and vehicle routing problem simultaneously to obtain the overall optimization. In response to the shortage of relief materials in the early post-disaster stage, a multi-objective model for the LRP considering fairness is constructed by evaluating the urgency coefficients of all demand points. The objectives are the lowest cost, delivery time and degree of dissatisfaction. Since LRP is a NP-hard problem, a hybrid metaheuristic algorithm of Discrete Particle Swarm Optimization (DPSO) and Harris Hawks Optimization (HHO) is designed to solve the model. In addition, three improvement strategies, namely elite-opposition learning, nonlinear escaping energy, multi-probability random walk, are introduced to enhance its execution efficiency. Finally, the effectiveness and performance of the LRP model and the hybrid metaheuristic algorithm are verified by a case study of COVID-19 in Wuhan. It demonstrates that the hybrid metaheuristic algorithm is more competitive with higher accuracy and the ability to jump out of the local optimum than other metaheuristic algorithms.

Building BPJS Image, Public Trust and Intention to Use JKN Mobile Services During the Covid Pandemic

Purpose: to analyze the ability of the National Health Insurance mobile service quality to build BPJS brand image and public trust to increase intention to use online services during the Covid period. The background of this research is based on the phenomenon in the form of complaints on the quality of online services and research gaps on the effect of service quality on the intention to use online services. Brand image and trust are offered as a mediation for gaps in previous research results. Design/ methodology/approach: The type of research is quantitative, using a pre-existing measurement scale related to mobile service quality, brand image, trust and intention. Involving a sample of 140 BPJS users during the Covid pandemic. It is difficult to identify the population size, the sample size is determined by the formulation of a constant value of 5 multiplied by 28 indicators. The technique of selecting respondents was carried out by means of non-probability random sampling. PLS SEM model as an analysis tool. Findings: The results of this study indicate that the direct relationship of mobile service quality on brand image, trust and intention shows significant positive results. Furthermore, the influence of brand image on trust shows significant results. The influence of brand image and trust on intention is also found to be significantly positive. Practical/implications: although management policies encourage customers to use mobile services more, the public still considers the trustworthy image of BPJS to develop their intention to use mobile application services. The government must remain consistent in ensuring that the quality of mobile service is not compromised because the implications for BPJS image and public trust are at stake. Through the person in charge at BPJS, the government must continue to consistently evaluate and improve the system and educate the public regarding this BPJS health mobile service system. Originality/value: This research offers new insights, filling gaps in studies on national health insurance mobile services during the Covid-19 Pandemic

Spatiotemporal Modeling of the Association between Neighborhood Factors and COVID-19 Incidence Rates in Scotland

This study aims to investigate the association between neighborhood-level factors and COVID-19 incidence in Scotland from a spatiotemporal perspective. The outcome variable is the COVID-19 incidence in Scotland. Based on the identification of the wave peaks for COVID-19 cases between 2020 and 2021, confirmed COVID-19 cases in Scotland can be divided into four phases. To model the COVID-19 incidence, sixteen neighborhood factors are chosen as the predictors. Geographical random forest models are used to examine spatiotemporal variation in major determinants of COVID-19 incidence. The spatial analysis indicates that proportion of religious people is the most strongly associated with COVID-19 incidence in southern Scotland, whereas particulate matter is the most strongly associated with COVID-19 incidence in northern Scotland. Also, crowded households, prepandemic emergency admission rates, and health and social workers are the most strongly associated with COVID-19 incidence in eastern and central Scotland, respectively. A possible explanation is that the association between predictors and COVID-19 incidence might be influenced by local context (e.g., people's lifestyles), which is spatially variant across Scotland. The temporal analysis indicates that dominant factors associated with COVID-19 incidence also vary across different phases, suggesting that pandemic-related policy should take spatiotemporal variations into account. © 2023 by American Association of Geographers.

DEEP LEARNING BASED AN EFFICIENT HYBRID PREDICTION MODEL FOR COVID-19 CROSS-COUNTRY SPREAD AMONG E7 AND G7 COUNTRIES

The COVID-19 pandemic has caused the death of many people around the world and has also caused economic problems for all countries in the world. In the literature, there are many studies to analyze and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyze the cross-country spread in the world. In this study, a deep learning based hybrid model was developed to predict and analysis of COVID-19 cross-country spread and a case study was carried out for Emerging Seven (E7) and Group of Seven (G7) countries. It is aimed to reduce the workload of healthcare professionals and to make health plans by predicting the daily number of COVID-19 cases and deaths. Developed model was tested extensively using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R Squared (R2). The experimental results showed that the developed model was more successful to predict and analysis of COVID-19 cross-country spread in E7 and G7 countries than Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The developed model has R2 value close to 0.9 in predicting the number of daily cases and deaths in the majority of E7 and G7 countries. © 2023 by the authors.

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)

New Paradigm: HR Professional Transformation in Manufacturing Company: Acces la Success

This study aimed to optimize the line managers performances in the human resources (HR) division in answering the role of the HR management function problem in Medan City Manufacturing Company. The novelty proposed is a concept of HR management called "Human Resources Professional Transformation". Specifically, this concept discussed the ability of HR division line managers to make adaptive changes to the company's business-oriented functional divisions with managerial competence, commitment, innovation capability, and readiness for changes towards work performance. The population of this research was the line manager of the HR division, totaling 185 respondents. The sampling technique used a probability sampling approach with simple random sampling through the slovin formula, totaling 126 respondents. The analytical tool used is structural equation software through the SmartPLS application program. The results showed that managerial competence, commitment, innovation capability had a positive and significant effect through the HR professional transformation on the performance of line managers in the HR division. Meanwhile, readiness for change has a positive and insignificant effect on the HR Professional Transformation. Readiness for change also has a positive and insignificant effect on the Line Managers Performances in the Human Resources Division through HR Professional Transformation. Based on the suitability test of the research model, it proved that the HR Professional Transformation can answer the problem of the role of the management function to improve the line managers performances in the HR division with managerial competence, commitment, innovation capability, and readiness for change of 0.907.

Social cash assistance for food security during a disaster: lesson learned from Indonesia

There was a change in the environment and food security threat during the COVID-19 pandemic. Many countries, including Indonesia, are forced to allocate funds to reduce the risk of this disaster. The Government Republic of Indonesia, through the Ministry of Social Affairs, has launched a Social Cash Assistance Program for 10 million families affected by COVID-19. This study aims to identify how families affected by COVID-19 take advantage of this social cash assistance. The study was conducted on the beneficiaries of social assistance, in cash transfer of IDR 600,000 (USD 40), per month, for three months. This study involved 2290 beneficiaries as respondents spread across 12 provinces. The sampling technique was the Cohen Manion Morrison Table by proportional stratified random sampling. The findings show that (1) 99% of cash assistance is used for basic needs, especially for food, and (2) cash assistance could be used for basic needs for around two to three weeks, thereby strengthening food security. Recommendations are submitted based on the results of this study related to social cash assistance and food security. The first is that this assistance still needs to be continued until the COVID-19 pandemic is over. It is to help families affected by the COVID-19 pandemic meet their daily needs. Second, most respondents do not have a fixed income during the pandemic, so providing capital and business startups are needed to increase family income sustainably to maintain food security.

Perceptions of COVID-19 Vaccines Side Effects

BACKGROUND: Vaccines are one of the best interventions developed for eradicating COVID-19. In Albania, COVID-19 vaccination uses different types of vaccines: Pfizer, AstraZeneca, CoronaVac, and Sputnik V. Like any other vaccine, these have side effects too. AIM: This study was carried out to identify the perception of the side effects of vaccines. METHOD(S): A quantitative study using a cross-sectional survey was conducted between April and September 2021 to collect data on the effects of the COVID-19 vaccine among individuals in Shkodra region. Data were collected online through a self-administered survey created on Google Forms which had been randomly delivered to individuals (aged >=18 years) using social media sites (Email and WhatsApp). All data collected were analyzed with Microsoft Office Excel 2010, using the exact Fisher's test and x2 test. RESULT(S): This study included 292 citizens, out of which 200 were female and 92 were male;62% were from urban areas and 38% from rural areas of Shkodra region. The random sample of the citizens who took part in this study is 44.5% (18-30 years old). A massive percentage of the participants, 66.4%, had received the second dose of the vaccine. Our study shows that 55.8% of these citizens have had side effects after the first vaccination dose, and only 43.8% have had side effects after the second dose. About 80.6% of the participants were well informed about the type of vaccine they got. CONCLUSION(S): Side effects from vaccines were reported. Injection site pain and fatigue were the most common first dose side effects (55.8%). The same side effects were reported for the second dose. The side effects were presented during the first 12 h after the vaccination in most cases. Side effects were more prevalent in people >50 years old. Older people have a higher probability to have more side effects from the COVID vaccine. There is no statistically significant relationship between gender and the presence of the side effect from the COVID vaccine. People living in urban areas have a higher probability to have side effect from COVID vaccine comparing with people living in rural areas. People being vaccinated with Pfizer vaccine have a higher probability to admit the presence of side effects.Copyright: © 2022 Zamira Shabani, Arketa Guli, Julian Kraja, Arlinda Ramaj, Nertila Podgorica.

Comparison of machine learning algorithms with regression analysis to predict the COVID-19 outbreak in Thailand

Coronavirus disease (COVID-19) is a public health problem in Thailand. Currently, there are more than 5 million infected people and the rate has been increasing at some point. It is therefore important to forecast the number of new cases over a short period of time to assist in strategic planning for the response to COVID-19. The purpose of this research paper was to compare the efficiency and prediction of the number of COVID-19 cases in Thailand using machine learning of 8 models using a regression analysis method. Using the 475-day dataset of COVID-19 cases in Thailand, the results showed that the predictive accuracy model (R2 score) from the testing dataset was the random forest (RF) model, which was 99.06%, followed by K-nearest neighbor (KNN), XGBoost. And the decision tree (DT) had the precision of 98.97, 98.67, and 98.64, respectively. And the results of the comparison of the number of infected people obtained from the prediction The models that predicted the number of real infections were the decision tree, random forest, and XGBoost, which were effective at predicting the number of infections correctly in the 2-4 day period. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

A Classification Approach to Detect Public Sentiments towards COVID-19 Vaccines

In this paper, team University of Botswana Computer Science (UBCS) investigate the opinions of Twitter users towards vaccine uptake. In particular, we build three different text classifiers to detect people's opinions and classify them as provax-for opinions that are for vaccination, antivax for opinions against vaccination and neutral-for opinions that are neither for or against vaccination. Two different datasets obtained from Twitter, 1 by Cotfas and the other by Fire2022 Organizing team were merged to and used for this study. The dataset contained 4392 tweets. Our first classifier was based on the basic BERT model and the other 2 were machine learning models, Random Forest and Multinomial Naive Bayes models. Naive Bayes classifier outperformed other classifiers with a macro-F1 score of 0.319. © 2022 Copyright for this paper by its authors.

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.

The Women's Role in the Resilience of Farmers' Households during the COVID-19 Pandemic in Gubugklakah Village, Poncokusumo District, Malang Regency

The cause of rural changes, in terms of demographic, technological developments, climate changes, and the Covid-19 pandemic potential to cause vulnerabilities, especially for women as individuals in household members. These must be responded with livelihood resilience by involving the women's role to contribute in the agricultural and non-agricultural sectors. This study aims to (1) describe the vulnerabilities of farmers' households and (2) analyze women's role in household resilience through the use of livelihood assets during the Covid-19 pandemic. This research was conducted in Gubugklakah village, Malang regency as a tourist village affected by the closure of TNBTS tourist visits due to the Covid-19 pandemic. This research used the simple random sampling technique, with total sample of 64 women farmers. Data were analyzed using WarpPLS software. The results showed that farmers' households experienced several vulnerabilities by that the households' livelihood assets: natural, physic, human, social and financial capital can be optimized to achieve a degree of resilience. The women's role in resilience efforts is as the core of the household, because all financial cycles involve housewives' role, such as reducing consumption expenditures, selling jewelry assets, taking savings, involving in farm worker, and others.

Detecting Ransomware Attacks in Cloud Environment Using Machine Learning-Based Intelligence System in COVID-19 Chaos

2020-2022 provided nearly ideal circumstances for cybercriminals, with confusion and uncertainty dominating the planet due to COVID-19. Our way of life was altered by the COVID-19 pandemic, which also sparked a widespread shift to digital media. However, this change also increased people's susceptibility to cybercrime. As a result, taking advantage of the COVID-19 events' exceedingly unusual circumstances, cybercriminals launched widespread Phishing, Identity theft, Spyware, Trojan-horse, and Ransomware attacks. Attackers choose their victims with the intention of stealing their information, money, or both. Therefore, if we wish to safeguard people from these frauds at a time when millions have already fallen into poverty and the remaining are trying to survive, it is imperative that we put an end to these attacks and assailants. This manuscript proposes an intelligence system for identifying ransomware attacks using nature-inspired and machine-learning algorithms. To classify the network traffic in less time and with enhanced accuracy, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), two widely used algorithms are coupled in the proposed approach for Feature Selection (FS). Random Forest (RF) approach is used for classification. The system's effectiveness is assessed using the latest ransomware-oriented dataset of CIC-MalMem-2022. The performance is evaluated in terms of accuracy, model building, and testing time and it is found that the proposed method is a suitable solution to detect ransomware attacks. © 2022 IEEE.

An Interpretable Fake News Detection Method Based on Commonsense Knowledge Graph

Existing deep learning-based methods for detecting fake news are uninterpretable, and they do not use external knowledge related to the news. As a result, the authors of the paper propose a graph matching-based approach combined with external knowledge to detect fake news. The approach focuses on extracting commonsense knowledge from news texts through knowledge extraction, extracting background knowledge related to news content from a commonsense knowledge graph through entity extraction and entity disambiguation, using external knowledge as evidence for news identification, and interpreting the final identification results through such evidence. To achieve the identification of fake news containing commonsense errors, the algorithm uses random walks graph matching and compares the commonsense knowledge embedded in the news content with the relevant external knowledge in the commonsense knowledge graph. The news is then discriminated as true or false based on the results of the comparative analysis. From the experimental results, the method can achieve 91.07%, 85.00%, and 89.47% accuracy, precision, and recall rates, respectively, in the task of identifying fake news containing commonsense errors.

Coagulation Parameters Changes Associated with Covid-19 Vaccination

A global spreading corona virus at 2019 (COVID-19) declared as emergent worldwide, due to its quick spreading and high rates of mortality that serious disruptions. The objective of this research is to explore further into effect of different types of covid-19 vaccinations (Pfizer, AstraZeneca and Sinopharm) on some coagulation parameters from random samples of students in college of pharmacy/ university of Ker-bala. A case-control study was carried out with Iraqis living in Kerbala city particularly college students in Kerbala University/ College of Pharmacy from 2021/1/16 to 2022/4/16. This study was done to encompass quantitative and qualitative analysis of covid 19 vaccination types and possible thrombosis that occur after vaccination. The enrolled sixty participants of male and female were aged 18years and above. A questionnaire was made questions pertaining age were inquired to make sure participants fulfilled the criteria for in-clusion, past medical history, previous infection with covid 19 were incorporated into the survey. The scien-tific and ethical committee provided their ethical approval in college of pharmacy at University of Kerbala. Our results in this study indicate significant differences in coagulation parameters readings of (Pt, Ptt) between vaccination groups and control by using ANOVA statistical analysis of SPSS. Our study showed that the difference between the vaccinated and unvaccinated groups was considerable (Pfizer, AstraZeneca & Sinopharm Covid 19 vaccines) and control group in thrombotic measurements time and platelet mean value. The most effective and economical method of preventing COVID-19 infection is still vaccination. A number of COVID-19 vaccines have been developed quickly, but more research needs to be done on any side effects that may appear.Copyright © 2023, Colegio de Farmaceuticos de la Provincia de Buenos Aires. All rights reserved.

Towards Detection of Cyberbullying in Hinglish Code Mixed Data

Covid-19 eruption and lockdown situation have increased the usages of online platforms which have impacted the users. Cyberbullying is one of the negative outcomes of using social media platforms which leads to mental and physical distress. This study proposes a machine learning-based approach for the detection of cyberbullying in Hinglish text. We use the Hinglish Code-Mixed Corpus, which consists of over 6,000 tweets, for our experiments. We use various machine learning algorithms, including Logistic regression (LR), Multinomial Naive Bayes (MNB), Support vector machine (SVM), Random Forest (RF), to train our models. We evaluate the performance of the models using standard evaluation metrics such as precision, recall, and F1-score. Our experiments show that the LR with Term Frequency-Inverse Document Frequency (TFIDF) outperforms the other models, achieving 92% accuracy. Our study demonstrates that machine learning models can be effective for cyberbullying detection in Hinglish text, and the proposed approach can help identify and prevent cyberbullying on social media platforms. © 2023 Bharati Vidyapeeth, New Delhi.

An Exploratory Analysis of Delhi Air Quality Using Statistics and Machine Learning Models

Air pollution is one of the most significant concerns of the present era, which has severe and alarming effects on human health and the environment, thereby escalating the climate change issue. Hence, in-depth analysis of air pollution data and accurate air quality forecasting is crucial in controlling the growing pollution levels. It also aids in designing appropriate policies to prevent exposure to toxic pollutants and taking necessary precautionary measures. Air quality in Delhi, the capital of India, is inferior compared to other major cities in the world. In this study, daily and hourly concentrations of air pollutants in the Delhi region were collected and analyzed using various methods. A comparative analysis is performed based on months, seasons, and the topography of different stations. The effect of the Covid-19 lockdown on the reduction of pollutant levels is also studied. A correlation analysis is performed on the available data to show the relationships and dependencies among different pollutants, their relationship with weather parameters, and the correlations between the stations. Various machine learning models were used for air quality forecasting, like Linear Regression, Vector Auto Regression, Gradient Boosting Machine, Random Forest, and Decision Tree Regression. The performance of these models was compared using RMSE, MAE, and MAPE metrics. This study is focused on the dire state of air pollution in Delhi, the primary reasons behind it, and the efficacy of calculated lockdowns in bringing down pollution levels. It also highlights the potential of Linear Regression and Decision Tree Regression models in predicting the air quality for different time intervals. © 2022 IEEE.

Utilization and effect of telehealth for the treatment of haemangioma before and after COVID

Many patients treated at Stanford for haemangiomas must travel from the rural Central Valley or Central Coast to receive care. Because of COVID-19, there was an increased use of telehealth which shifted the management of haemangiomas. Our study aimed to identify the implications of this change and its impact on access to care for patients who live far away. Using the Stanford Research Repository, we established two cohorts of patients seen at Stanford dermatology clinics with a haemangioma diagnosis: one from 2018 and one from 2022. We took a random sample of 50 patients from each and collected data on haemangioma treatment prescriptions, age at diagnosis, age at dermatology encounters, and distance travelled to clinic. We subdivided the 2022 cohort into in-person visits and telehealth appointments. While no patients utilized telehealth in the 2018 cohort, 69% of patients in the 2022 cohort utilized telehealth for their first Dermatology visit. In the 2022 cohort, 52% of patients utilized telehealth for at least one dermatology appointment. The average age at presentation for the 2018, 2022 in-person, 2022 telehealth groups were 121 , 208 , and 116 days, respectively. Average age at diagnosis was significantly younger for the telehealth cohort compared to the 2022 in-person cohort, and there was an increase in prescriptions for treatment in the telehealth cohort. These results show that increased telehealth utilization as a result of the pandemic has allowed patients to be seen by a dermatologist at an earlier age and receive a prescription for treatment for haemangiomas.

Some properties of branching processes with random control functions and affected by viral infectivity in random environments.

In this paper, a model of branching processes with random control functions and affected by viral infectivity in independent and identically distributed random environments is established, and the Markov property of the model and a sufficient condition for the model to be certainly extinct under some conditions are discussed. Then, the limit properties of the model are studied. Under the normalization factor {Sn:n∈N}, the normalization processes {Wˆn:n∈N} are studied, and the sufficient conditions of {Wˆn:n∈N} a.s., L1 and L2 convergence are given; A sufficient condition and a necessary condition for convergence to a nondegenerate at zero random variable are obtained. Under the normalization factor {In:n∈N}, the normalization processes {W¯n:n∈N} are studied, and the sufficient conditions of {W¯n:n∈N} a.s., and L1 convergence are obtained.