A Review on Challenges in Telerehabilitation and Human Activity Recognition Approaches during Covid-19 Pandemic

Impact of COVID-19 pandemic is widespread imposing limitations on the healthcare services all over the world. Due to this pandemic, governments around the world have imposed restrictions that limit individual freedom and have enforced social distance to prevent the collapse of national health care systems. In such situation, to offer medical care and rehabilitation to the patients, Telerehabilitation (TR) is a promising way of delivering healthcare facilities remotely using telecommunication and internet. Technological advancement has played the vital role to establish this TR technology to remotely assess patient's physical condition and act accordingly during this pandemic. Likewise, Human Activity Recognition (HAR) is a key part of the recovery process for a wide variety of conditions, such as stroke, arthritis, brain injury, musculoskeletal injuries, Parkinson's disease, and others. Different approaches of human activity recognition can be utilized to monitor the health and activity levels of such a patient effectively and TR allows to do this remotely. Therefore, in situations where conventional care is inadequate, combination of telerehabilitation and HAR approaches can be an effective means of providing treatment and these opportunities have become patently apparent during the COVID-19 outbreak. However, this new era of technical progress has significant limitations, and in this paper, our main focus is on the challenges of telerehabilitation and the various human activity recognition approaches. This study will help researchers identify a good activity detection platform for a TR system during and after COVID-19, considering TR and HAR challenges.

Clinical Characteristics of Deceased with COVID-19 Infection: Data from the Largest COVID-19 Dedicated Hospital in Bangladesh

The death t toll of the coronavirus disease 2019 (COVID-19) has been considerable. Several risk factors have been linked to mortality due to COVID-19 in hospitals. This study aimed to describe the clinical characteristics of patients who either died from COVID-19 at Dhaka Medical College Hospital in Bangladesh. In this retrospective study, we reviewed the hospital records of patients who died or recovered and tested positive for COVID-19 from May 3 to August 31, 2020. All patients who died during the study period were included in the analysis. A comparison group of patients who survived COVID-19 at the same hospital during the same period was systematically sampled. All available information was retrieved from the records, including demographic, clinical, and laboratory variables. Of the 3115 patients with confirmed COVID-19 during the study period, 282 died.The mean age of patients who died was higher than that of those who survived (56.7 vs 52.6 years). Approximately three-fourths of deceased patients were male. History of smoking (risk ratio 2.3;95% confidence interval: 1.6-3.4), comorbidities (risk ratio: 1.5;95% confidence interal:1.1-2.1), chronic kidney disease (risk ratio: 3.2;95% confidence interval: 1.7-6.25), and ischemic heart disease (risk ratio:1.8;95% confidence interval: 1.1-2.9) were higher among the deceased than among those who survived. Mean C-reactive protein and D-dimer levels [mean (interquartile range), 34 (21-56) vs. 24 (12-48);and D-dimer [1.43 (1-2.4) vs. 0.8 (0.44-1.55)] were higher among those who died than among those who recovered. Older age, male sex, rural residence, history of smoking, and chronic kidney disease were found to be important predictors of mortality. Early hospitalization should be considered for patients with COVID-19 who are older, male, and have chronic kidney disease. Rapid referral to tertiary care facilities is necessary for high-risk patients in rural settings.Copyright © 2023 Hoque MM.

Student Attention Base Facial Emotion State Recognition Using Convolutional Neural Network

E-learning system advancements give students new opportunities to better their academic performance and access e-learning education. Because it provides benefits over traditional learning, e-learning is becoming more popular. The coronavirus disease pandemic situation has caused educational institution cancelations all across the world. Around all over the world, more than a billion students are not attending educational institutions. As a result, learning criteria have taken on significant growth in e-learning, such as online and digital platform-based instruction. This study focuses on this issue and provides learners with a facial emotion recognition model. The CNN model is trained to assess images and detect facial expressions. This research is working on an approach that can see real-time facial emotions by demonstrating students' expressions. The phases of our technique are face detection using Haar cascades and emotion identification using CNN with classification on the FER 2013 datasets with seven different emotions. This research is showing real-time facial expression recognition and help teachers adapt their presentations to their student's emotional state. As a result, this research detects that emotions' mood achieves 62% accuracy, higher than the state-of-the-art accuracy while requiring less processing. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 impact on tourism inflow in selected Asia-Pacific countries: a gravity model framework

Purpose: The main objective of this paper is to examine the impact of COVID-19 on the tourism flows of eight Asia-Pacific Countries: Australia, Hong Kong, Malaysia, New Zealand, the Philippines, Singapore, Taiwan and Thailand. Design/methodology/approach: Using monthly data from 2019M1 to 2021M10 and 48 origin and eight destination countries in a panel Poisson pseudo-maximum likelihood (PPML) estimation technique and gravity equation framework, this paper finds that after controlling for gravity determinants, COVID-19 periods have a 0.689% lower tourism inflow than in non-COVID-19 periods. The total observations in this paper are 12,138. Findings: A 1% increase in COVID-19 transmission in the origin country leads to a 0.037% decline in tourism flow in the destination country, while the reduction is just 0.011% from the destination. On the mortality side, the corresponding decline in tourism flows from origin countries is 0.030%, whereas it is 0.038% from destination countries. A 1% increase in vaccine intensity in the destination country leads to a 0.10% improvement in tourism flows, whereas vaccinations at the source have no statistically significant effect. The results are also robust at a 1% level in a pooled OLS and random-effects specification for the same model. Research limitations/implications: The findings provide insights into managing tourism flows concerning transmission, death and vaccination coverage in destination and origin countries. Practical implications: The COVID-19-induced tourism decline may also be considered another channel through which the global recession has been aggravated. If we convert this decline in terms of loss of GDP, the global figure will be huge, and airline industries will have to cut down many service products for a long time to recover from the COVID-19-induced tourism decline. Social implications: It is to be realized by the policymaker and politicians that infectious diseases have no national boundary, and the problem is not local or national. That's why it is to be faced globally with cooperation from all the countries. Originality/value: This is the first paper to address tourism disruption due to COVID-19 in eight Asia-Pacific countries using a gravity model framework. Highlights: Asia-Pacific countries are traditionally globalized through tourism channels This pattern was severely affected by COVID-19 transmission and mortality and improved through vaccination The gravity model can be used to quantify the loss in the tourism sector due to COVID-19 shocks Transmission and mortality should be controlled both at the origin and the destination countries Vaccinations in destination countries significantly raise tourism flows. © 2023, Emerald Publishing Limited.

Hand-breathe: Non-Contact Monitoring of Breathing Abnormalities from Hand Palm

In post-covid19 world, radio frequency (RF)-based non-contact methods, e.g., software-defined radios (SDR)-based methods have emerged as promising candidates for intelligent remote sensing of human vitals, and could help in containment of contagious viruses like covid19. To this end, this work utilizes the universal software radio peripherals (USRP)-based SDRs along with classical machine learning (ML) methods to design a non-contact method to monitor different breathing abnormalities. Under our proposed method, a subject rests his/her hand on a table in between the transmit and receive antennas, while an orthogonal frequency division multiplexing (OFDM) signal passes through the hand. Subsequently, the receiver extracts the channel frequency response (basically, fine-grained wireless channel state information), and feeds it to various ML algorithms which eventually classify between different breathing abnormalities. Among all classifiers, linear SVM classifier resulted in a maximum accuracy of 88.1%. To train the ML classifiers in a supervised manner, data was collected by doing real-time experiments on 4 subjects in a lab environment. For label generation purpose, the breathing of the subjects was classified into three classes: normal, fast, and slow breathing. Furthermore, in addition to our proposed method (where only a hand is exposed to RF signals), we also implemented and tested the state-of-the-art method (where full chest is exposed to RF radiation). The performance comparison of the two methods reveals a trade-off, i.e., the accuracy of our proposed method is slightly inferior but our method results in minimal body exposure to RF radiation, compared to the benchmark method. IEEE

Characterization and antibiogram study of pneumonia causing bacteria isolated from blood culture of children in northern Bangladesh

Children are usually affected by pneumonia, which is a common ailment caused by Pathogenic Streptococcus pneumoniae. This study's objective was to isolate and identify S. pneumoniae, which was recovered from blood samples of suspected paediatric pneumonia patients using conventional techniques, such as antibiotic sensitivity profiles and molecular approaches. In this study, forty (40) samples from three major hospitals in the Dinajpur region of Bangladesh were collected and assessed using various bacteriological, biochemical, antibiotic susceptibility test, and molecular techniques. 37.5% of the 40 samples tested positive for pneumonia, and 15 isolates were discovered. In terms of age, pneumonia was more common in children aged 3-5 years (50%) than in those aged 6 to 8 (33.33%), 9 to 11 (25%) and 12 to 15 (20%). According to the results of the current study, the study area had no statistically significant impact (P > 0.05), while age and socioeconomic status had a significant impact on the prevalence of pneumonia in patients with pneumonia (P 0.05). The age group for which pneumonia was most prevalent (at 50%) was that for children between the ages of 3-5. Poor socioeconomic status was associated with the highest prevalence of pneumonia (54.54%). By sequencing the 16S rRNA gene, S. pneumoniae was identified as S. pneumoniae NBRC102642. In the antibiotic investigation, S. pneumoniae was found to be extremely resistant to ciprofloxacin, amikacin, vancomycin, and cefexime, but responsive to erythromycin and azithromycin, as well as neomycin, kanamycin, streptomycin, and bacitracin. S. pneumoniae causes serious complications in paediatric patients, and this scenario requires prevention through vaccination and the development of new, efficient antibiotic therapies for pneumonia. If specific laboratory features of paediatric patients with pneumonia are understood, sepsis will be easier to detect early, treat, and reduce mortality.

Impact Prediction of Online Education During COVID-19 Using Machine Learning: A Case Study

The transition from traditional to online education is challenging and has many obstacles in various situations. Due to the Covid-19 situation, we use digital blended education from the traditional system. However, in some cases, it can harm our student's academic performance. In this research, we aim to identify the factors that impact the student's academic performance in online education. On the other hand, this study also finds the student Cumulative Grade Point Average (CGPA) fluctuation using machine learning classifiers. To achieve this, we survey to gather data perspective of Bangladesh private university, and this data allows us to analyze and classify using machine learning techniques such as Logistic Regression (LR), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Decision Tree (DT), and Random Forest (RF). This study finds Random Forest (RF) outperforms the other state-of-art classifiers. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Rejuvenating multiple true-false: proposing fairer scoring methods.

INTRODUCTION: The COVID-19 pandemic has reached a phase where many have been infected at least once. Healthcare workers were not spared from being infected. This study aimed to determine the period prevalence of COVID-19 among the paediatric healthcare workers in Negeri Sembilan as the country transitioned into an endemic phase of the pandemic. Additionally, we investigate potential sociodemographic and occupational characteristics associated with SARS-CoV-2 infection among healthcare workers. MATERIALS AND METHODS: A cross-sectional study was conducted among the healthcare workers in the paediatric department at three public specialist hospitals in Negeri Sembilan between 15 and 21 April 2022. Data were collected through a self-administered questionnaire. RESULTS: Out of the 504 eligible healthcare workers, 493 participated in this study (response rate 97.8%). The overall prevalence of COVID-19 (11 March 2020-15 April 2022) among healthcare workers was 50.9%. The majority (80.1%) were infected during the Omicron wave two months before the survey. Household contacts accounted for 35.9% of infection sources. The proportion of non-doctors in the COVID-19-infected group was significantly higher compared to the non-infected group (74.1% vs 64.0%, p=0.016). The COVID-19-infected group had a higher proportion of schoolgoing children (44.6% vs 30.6%, p=0.001) and children who attended pre-school/sent to the babysitter (49.0% vs 24.4%, p<0.001). There were no significant differences between infection rates among the healthcare workers working in the tertiary hospital and the district hospitals. There were also no significant differences in the proportion of COVID-19- infected doctors and nurses when analysed by seniority. CONCLUSION: Our study provided an estimate on the prevalence of COVID-19 among paediatric healthcare workers in Negeri Sembilan and the factors associated with infection, which captures the extent and magnitude of this pandemic on the state's paediatric department. Most infections resulted from household contact, with a higher proportion of infected healthcare workers having young children.

The economics of COVID-19: a systematic literature review

Purpose: A systematic, PRISMA-guided literature review was conducted using four databases (ProQuest, PubMed, EconLit and Scopus) to analyze research published between February 2020 and August 2021. This review included 31 studies out of 1,248 that were identified. Design/methodology/approach: In addition to the serious health issues it causes, severe acute respiratory syndrome coronavirus 2 (COVID-19) has a destructive impact on the global economy. The objectives of this study are (1) to examine the growing literature on variations of economic factors due to COVID-19 (2) to review the literature on the governmental response to the pandemic and (3) to discover the perspective and the gaps and outline the future avenues for further research. Findings: All selected studies (31) have used the macroeconomic, household and health economic factors to analyze the economic impacts of the COVID-19 pandemic. Among these studies, 22 articles examined the economic consequences and macroeconomic activities, 7 analyzed microeconomic costs and healthcare trade-offs and 2 studies reviewed economic uncertainty and macroeconomic expectations. Research limitations/implications: This study comprises the most relevant research articles to measure the economic consequences of COVID-19. As a result of the lockdown and other containment initiatives, price levels, employment and consumption patterns have all suffered. Practical implications: Therefore, the government's requirement to develop policy tools and approaches to ensure a full recovery from the pandemic should lead to greater long-term economic resilience. Originality/value: This study examines the economic implications of COVID-19, with the aim of not only analysing COVID-19's negative economic effects but also, those measures that provide new directions in the form of short-run economic impacts and policy decisions. © 2022, Emerald Publishing Limited.

Publisher Correction: Additive manufacturing for biomedical applications: a review on classification, energy consumption, and its appreciable role since COVID-19 pandemic (Progress in Additive Manufacturing, (2022), 10.1007/s40964-022-00373-9)

The publication of this article unfortunately contained mistakes. The funding note was not correct. The corrected funding note is given below. Funding The current study was funded by;The National Key Research and Development Program of China [Grant No. 2019QY(Y)0502];The Key Research and Development Program of Shaanxi Province [Grant No. 2020ZDLSF04- 07];The National Natural Science Foundation of China [Grant No. 51905438];The Fundamental Research Funds for the Central Universities [Grant No. 31020190502009];The Innovation Platform of Bio fabrication [Grant No. 17SF0002];and China postdoctoral Science Foundation [Grant No. 2020M673471]. The original article has been corrected. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

Additive manufacturing for biomedical applications: a review on classification, energy consumption, and its appreciable role since COVID-19 pandemic

The exponential rise of healthcare problems like human aging and road traffic accidents have developed an intrinsic challenge to biomedical sectors concerning the arrangement of patient-specific biomedical products. The additively manufactured implants and scaffolds have captured global attention over the last two decades concerning their printing quality and ease of manufacturing. However, the inherent challenges associated with additive manufacturing (AM) technologies, namely process selection, level of complexity, printing speed, resolution, biomaterial choice, and consumed energy, still pose several limitations on their use. Recently, the whole world has faced severe supply chain disruptions of personal protective equipment and basic medical facilities due to a respiratory disease known as the coronavirus (COVID-19). In this regard, local and global AM manufacturers have printed biomedical products to level the supply-demand equation. The potential of AM technologies for biomedical applications before, during, and post-COVID-19 pandemic alongwith its relation to the industry 4.0 (I4.0) concept is discussed herein. Moreover, additive manufacturing technologies are studied in this work concerning their working principle, classification, materials, processing variables, output responses, merits, challenges, and biomedical applications. Different factors affecting the sustainable performance in AM for biomedical applications are discussed with more focus on the comparative examination of consumed energy to determine which process is more sustainable. The recent advancements in the field like 4D printing and 5D printing are useful for the successful implementation of I4.0 to combat any future pandemic scenario. The potential of hybrid printing, multi-materials printing, and printing with smart materials, has been identified as hot research areas to produce scaffolds and implants in regenerative medicine, tissue engineering, and orthopedic implants.

COVID-19 Along with Other Chest Infection Diagnoses Using Faster R-CNN and Generative Adversarial Network

The rapid spreading of coronavirus (COVID-19) caused severe respiratory infections affecting the lungs. Automatic diagnosis helps to fight against COVID-19 in community outbreaks. Medical imaging technology can reinforce disease monitoring and detection facilities with the advancement of computer vision. Unfortunately, deep learning models are facing starvation of more generalized datasets as the data repositories of COVID-19 are not rich enough to provide significant distinct features. To address the limitation, this article describes the generation of synthetic images of COVID-19 along with other chest infections with distinct features by empirical top entropy-based patch selection approach using the generative adversarial network. After that, a diagnosis is performed through a faster region-based convolutional neural network using 6,406 synthetic as well as 3,933 original chest X-ray images of different chest infections, which also addressed the data imbalance problems and not recumbent to a particular class. The experiment confirms a satisfactory COVID-19 diagnosis accuracy of 99.16% in a multi-class scenario.

Consumption pattern of dairy products in Sabah, Malaysia during Covid-19

Dairy production has a considerable effect on climate change due to emissions of greenhouse gases, but dairy products are meals that are well-known for their pleasant taste and nutritional value. During the Covid-19 outbreak, there were shortages of dairy goods on the shelves of grocery stores. This study investigated the consumption patterns of dairy products in Sabah. Using a pre-tested questionnaire, data were collected through online survey during Covid-19 outbreaks from 64 households comprising 16 from rural, 25 from town and 23 from city areas. The surveyed households were classified into 5 groups based on monthly household income: (i) ≤RM2000, (ii) RM2001-RM3000, (iii) RM3001-RM4000 and (iv) >RM4000. Among the participated households, 75% of respondents were female and 25% were male. There was a significant relationship among household income groups for fresh milk consumption. Regardless of areas and household incomes, the average monthly consumption for evaporated milk, fresh milk, condensed milk, powder milk, sweetmeats, yogurt, butter and ice cream per household were 1018g, 1425ml, 978g, 815g, 527g, 468g, 522g, and 650g, respectively. 28% of respondents monthly consumed 0.5-1.0 L fresh milk per household. 42%, 39%, 39%, 63%, 58%, 64% and 50% of respondents-- respectively-- monthly consumed evaporated milk, condensed milk, powder milk, sweetmeats, yogurt, butter and ice cream, where the amount of each component was not more than 500g per household. Results showed that 38% of respondents liked more on butter followed by cheese (30%), yogurt (20%), cream (9%) and condensed milk (3%). The 25% and 45% of respondents had reduced their consumption and expenditure behaviour, respectively. Results indicated that individual of city areas consumed more dairy products. Although cows add methane to our environment, organic dairy farming and husbandry methods can significantly reduce greenhouse gas emission.

How SARS-CoV-2 Omicron droplets transport and deposit in realistic extrathoracic airways

The SARS-CoV-2 Omicron variant is more highly transmissible and causes a higher mortality rate compared to the other eleven variants despite the high vaccination rate. The Omicron variant also establishes a local infection at the extrathoracic airway level. For better health risk assessment of the infected patients, it is essential to understand the transport behavior and the toxicity of the Omicron variant droplet deposition in the extrathoracic airways, which is missing in the literature. Therefore, this study aims to develop a numerical model for the Omicron droplet transport to the extrathoracic airways and to analyze that transport behavior. The finite volume method and ANSYS Fluent 2020 R2 solver were used for the numerical simulation. The Lagrangian approach, the discrete phase model, and the species transport model were employed to simulate the Omicron droplet transport and deposition. Different breathing rates, the mouth and nose inhalation methods were employed to analyze the viral toxicity at the airway wall. The results from this study indicated that there was a 33% of pressure drop for a flow rate at 30 l/min, while there was only a 3.5% of pressure drop for a 7.5 l/min. The nose inhalation of SARS-CoV-2 Omicron droplets is significantly more harmful than through the mouth due to a high deposition rate at the extrathoracic airways and high toxicity in the nasal cavities. The findings of this study would potentially improve knowledge of the health risk assessment of Omicron-infected patients. Published under a nonexclusive license by AIP Publishing.

Moving from long case to scenario-based clinical examination: Proposals for making it feasible.

INTRODUCTION: Our faculty used one long case (LC) and three short cases for the clinical component of the final professional examinations. During the COVID-19 pandemic, the LC had to be replaced with scenario-based clinical examination (SBCE) due to the impracticability of using recently hospitalised patients. While keeping the short case component as usual, the LC had to be replaced with SBCE in 2020 for the first time at a short notice. To evaluate the positive and negative aspects of SBCE and LC to determine the feasibility of replacing LC with SBCE in future examinations. MATERIALS AND METHODS: We compared the LC scores of three previous years with those of the SBCE and studied the feedback of the three stakeholders: students, examiners, and simulated patients (SPs), regarding their experience with SBCE and the suitability of SBCE as an alternative for LC in future examinations. RESULTS: The SBCE scores were higher than those of the LC. Most of the examiners and students were not in favour of SBCE replacing LC, as such. The SPs were more positive about the proposition. The comments of the three stakeholders brought out the plus and minus points of LC and SBCE, which prompted our proposals to make SBCE more practical for future examinations. CONCLUSION: Having analysed the feedback of the stakeholders, and the positive and negative aspects of LC and SBCE, it was evident that SBCE needed improvements. We have proposed eight modifications to SBCE to make it a viable alternative for LC.

Post-Infection Irritable Bowel Syndrome in Sars-Cov-2 Patients: The Gi-Covid19 Study

Introduction: SARS-CoV-2 infection, known as COVID-19, may lead to persistent gastrointestinal dysfunction resembling aspects of post-infection disorders of gut-brain interaction (DGBI). However, the long-term consequences of COVID-19 on the gastrointestinal tract remain unclear. Aims & Methods: We aimed to evaluate the prevalence of gastrointestinal symptoms and post-infection disorders of gut-brain interaction (DGBI) up to 12 months after hospitalization and the factors associated with their presence. The GI-COVID19 is a prospective, multicenter, controlled study. Patients with and without COVID-19 diagnosis were assessed at hospital admission and followed up after 1, 6, and 12 months to assess gastrointestinal symptoms using the Gastrointestinal Symptoms Rating Scale, the Rome IV Diagnostic Questionnaire for Functional Gastrointestinal Disorders in Adults, and the hospital anxiety and depression scale. ClinicalTrials. gov number, NCT04691895. Result(s): The study included2183 hospitalized patients. After excluding patients with pre-existing gastrointestinal symptoms and/or surgery, a total of 883 patients (614 COVID-19 and 269 controls) were included in the primary analysis, of whom 435 COVID-19 and 188 controls completed 12 months of follow-up. At enrollment, gastrointestinal symptoms occurred more frequently in COVID-19 patients than in the control group (59.3% vs. 39.7%, P<0.001). Symptoms more frequently complained by COVID-19 patients at enrollment were nausea, diarrhea, loose stool, and urgency. At 1-month follow-up evaluation, nausea and acid regurgitation were significantly more prevalent in COVID-19 patients than in the control group (8.7% vs. 1.7%, P=0.015 and 8.4% vs. 2.1%, P=0.006, respectively). At 6 months, COVID-19 patients reported lower rates of flatus (17.6% vs. 19.1%, P=0.024), constipation (8.9% vs. 17.1%, P<0.001) and hard stools (9.6 vs. 17.2%, P=0.030) as compared with the control group. At 12 months, constipation and hard stools were significantly less prevalent in COVID-19 patients than in the control group (9.6% vs. 16%, P=0.019 and 10.9% vs. 17.7%, P=0.011, respectively). COVID-19 patients reported higher rates of DGBI during follow-up compared to controls (Table), although statistically significant differences were found only for irritable bowel syndrome (IBS) according to Rome III criteria (4.4% vs 1.1%, P=0.036) and Rome IV criteria (3.2% vs 0.5%, P=0.045). The rate of COVID-19 patients depressed at 6 months and with anxiety at 12 months was higher compared to controls (4.1% vs 2.7%, P=0.014 and 4.5% vs 1.1%, P=0.088, respectively). Factors significantly associated with IBS diagnosis were anamnestic allergies (OR 10.024, 95% CI 1.766-56.891, P=0.009), chronic intake of proton pump inhibitors (OR 4.816, 95% CI 1.447-16.025, P=0.010) and dyspnea (OR 4.157, 95% CI 1.336-12.934, P=0.014). Conclusion(s): Hospitalized COVID-19 patients complain less constipation and hard stools than control at 12 months after acute infection. COVID-19 patients are also more likely to develop IBS.

Evaluation of Machine Learning Approaches for Prediction of Dengue Fever

Dengue is a mosquito-borne, deadly viral disease that is a major threat to public health all over the world. Dengue and covid-19 symptoms are almost same, and sometimes, people are confused about which disease they are infected with. This year in Bangladesh dengue and covid-19 patients have been increasing at an alarming rate, and most of the time people didn’t properly recognize the disease. A developing country like Bangladesh has faced many difficulties to handle this situation. The target of this research work is to analyze the symptoms and predict the chances to get infected with dengue fever. Machine learning techniques are widely utilized in the health industry to detect fraud in treatment at lower cost, predictive analysis, cure the disease. Four machine learning algorithms are used which are support vector machine, decision tree, K-nearest neighbor, random forest to predict dengue fever based on symptoms. The results were compared for percentage split and K-fold cross-validation method for before and after applying principal component analysis. The experimental result shows that the support vector machine algorithm provides the highest performance compared to others algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Comparison of the Performance of Machine Learning-based Algorithms for Predicting Depression and Anxiety among University Students in Bangladesh: A Result of the First Wave of the COVID-19 Pandemic

Introduction: The purpose of this research was to predict mental illness among university students using various machine learning (ML) algorithms. Methods: A structured questionnaire-based online survey was conducted on 2121 university students (private and public) living in Bangladesh. After obtaining informed consent, the participants completed a web-based survey examining sociodemographic variables and behavioral tests (including the Patient Health Questionnaire (PHQ-9) scale and the Generalized Anxiety Disorder Assessment-7 scale). This study applied six well-known ML algorithms, namely logistic regression, random forest (RF), support vector machine (SVM), linear discriminate analysis, K-nearest neighbors, Naive Bayes, and which were used to predict mental illness among university students from Dhaka city in Bangladesh. Results: Of the 2121 eligible respondents, 45% were male and 55% were female, and approximately 76.9% were 21-25 years old. The prevalence of severe depression and severe anxiety was higher for women than for men. Based on various performance parameters, the results of the accuracy assessment showed that RF outperformed other models for the prediction of depression (89% accuracy), while SVM provided the best result than other models for the prediction of anxiety (91.49% accuracy). Conclusion: Based on these findings, we recommend that the RF algorithm and the SVM algorithm were more moderate than any other ML algorithm used in this study to predict the mental health status of university students in Bangladesh (depression and anxiety, respectively). Finally, this study proposes to apply RF and SVM classification when the prediction of mental illness status is the core interest.

MKRF Stacking-Voting: A Data Mining Technique for Predicting Educational Satisfaction Level of Bangladeshis Student During Pandemic

Data mining is most efficient when used deliberately to achieve a corporate goal, answer business or research questions, or contribute to a problem-solving solution. Data mining aids in the accurate prediction of outcomes, the recognition of patterns and anomalies, and frequently inform forecasts. Online education is becoming more popular all around the world because of the COVID-19 pandemic. The main goal of this research is to Predict Educational Satisfaction Level of Bangladeshis Students During the Pandemic using data mining approaches by only filling up with some basic questionnaires which are related to the satisfaction level of online education collected through a public survey. By surveying 1004 students from various academic institutions, schools, colleges, and universities on the quality of online education in COVID-19 pandemic scenarios, we were able to determine how productive it would be. Influence how online learning is measured and how satisfied people are with it. To achieve our aim of predicting satisfaction levels, we used a total of eight classifiers, six of which were based classifiers, which we combined with the best three top-scoring classifiers to build a novel ensemble approach called MKRF Stacking and MKRF Voting ensemble classifier. Among those classifiers, the Random Forest classifier outperforms the other six base classifiers with 97.21% accuracy. Our proposed data mining ensemble approaches MKRF Stacking and MKRF Voting outperform applied classifiers. Typically, voting ensemble classifiers outperform voting ensemble classifiers, but in this case, MKRF Stacking defeated MKRF Voting and all applied classifiers with a supreme accuracy of 97.68% (Average). The proposed method would be used in a framework where education counselors find the root causes and minor explanations for dissatisfaction in online education among students so that they can better understand all aspects and provide them with the best advice and solutions to their problems. © 2022 IEEE.