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1.
  • Abbafati, Cristiana, et al. (författare)
  • 2020
  • Tidskriftsartikel (refereegranskat)
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2.
  • Micah, Angela E., et al. (författare)
  • Tracking development assistance for health and for COVID-19 : a review of development assistance, government, out-of-pocket, and other private spending on health for 204 countries and territories, 1990-2050
  • 2021
  • Ingår i: The Lancet. - : Elsevier. - 0140-6736 .- 1474-547X. ; 398:10308, s. 1317-1343
  • Forskningsöversikt (refereegranskat)abstract
    • Background The rapid spread of COVID-19 renewed the focus on how health systems across the globe are financed, especially during public health emergencies. Development assistance is an important source of health financing in many low-income countries, yet little is known about how much of this funding was disbursed for COVID-19. We aimed to put development assistance for health for COVID-19 in the context of broader trends in global health financing, and to estimate total health spending from 1995 to 2050 and development assistance for COVID-19 in 2020. Methods We estimated domestic health spending and development assistance for health to generate total health-sector spending estimates for 204 countries and territories. We leveraged data from the WHO Global Health Expenditure Database to produce estimates of domestic health spending. To generate estimates for development assistance for health, we relied on project-level disbursement data from the major international development agencies' online databases and annual financial statements and reports for information on income sources. To adjust our estimates for 2020 to include disbursements related to COVID-19, we extracted project data on commitments and disbursements from a broader set of databases (because not all of the data sources used to estimate the historical series extend to 2020), including the UN Office of Humanitarian Assistance Financial Tracking Service and the International Aid Transparency Initiative. We reported all the historic and future spending estimates in inflation-adjusted 2020 US$, 2020 US$ per capita, purchasing-power parity-adjusted US$ per capita, and as a proportion of gross domestic product. We used various models to generate future health spending to 2050. Findings In 2019, health spending globally reached $8. 8 trillion (95% uncertainty interval [UI] 8.7-8.8) or $1132 (1119-1143) per person. Spending on health varied within and across income groups and geographical regions. Of this total, $40.4 billion (0.5%, 95% UI 0.5-0.5) was development assistance for health provided to low-income and middle-income countries, which made up 24.6% (UI 24.0-25.1) of total spending in low-income countries. We estimate that $54.8 billion in development assistance for health was disbursed in 2020. Of this, $13.7 billion was targeted toward the COVID-19 health response. $12.3 billion was newly committed and $1.4 billion was repurposed from existing health projects. $3.1 billion (22.4%) of the funds focused on country-level coordination and $2.4 billion (17.9%) was for supply chain and logistics. Only $714.4 million (7.7%) of COVID-19 development assistance for health went to Latin America, despite this region reporting 34.3% of total recorded COVID-19 deaths in low-income or middle-income countries in 2020. Spending on health is expected to rise to $1519 (1448-1591) per person in 2050, although spending across countries is expected to remain varied. Interpretation Global health spending is expected to continue to grow, but remain unequally distributed between countries. We estimate that development organisations substantially increased the amount of development assistance for health provided in 2020. Continued efforts are needed to raise sufficient resources to mitigate the pandemic for the most vulnerable, and to help curtail the pandemic for all. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.
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3.
  • Naghavi, Mohsen, et al. (författare)
  • Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013
  • 2015
  • Ingår i: The Lancet. - 1474-547X .- 0140-6736. ; 385:9963, s. 117-171
  • Tidskriftsartikel (refereegranskat)abstract
    • Background Up-to-date evidence on levels and trends for age-sex-specifi c all-cause and cause-specifi c mortality is essential for the formation of global, regional, and national health policies. In the Global Burden of Disease Study 2013 (GBD 2013) we estimated yearly deaths for 188 countries between 1990, and 2013. We used the results to assess whether there is epidemiological convergence across countries. Methods We estimated age-sex-specifi c all-cause mortality using the GBD 2010 methods with some refinements to improve accuracy applied to an updated database of vital registration, survey, and census data. We generally estimated cause of death as in the GBD 2010. Key improvements included the addition of more recent vital registration data for 72 countries, an updated verbal autopsy literature review, two new and detailed data systems for China, and more detail for Mexico, UK, Turkey, and Russia. We improved statistical models for garbage code redistribution. We used six different modelling strategies across the 240 causes; cause of death ensemble modelling (CODEm) was the dominant strategy for causes with sufficient information. Trends for Alzheimer's disease and other dementias were informed by meta-regression of prevalence studies. For pathogen-specifi c causes of diarrhoea and lower respiratory infections we used a counterfactual approach. We computed two measures of convergence (inequality) across countries: the average relative difference across all pairs of countries (Gini coefficient) and the average absolute difference across countries. To summarise broad findings, we used multiple decrement life-tables to decompose probabilities of death from birth to exact age 15 years, from exact age 15 years to exact age 50 years, and from exact age 50 years to exact age 75 years, and life expectancy at birth into major causes. For all quantities reported, we computed 95% uncertainty intervals (UIs). We constrained cause-specific fractions within each age-sex-country-year group to sum to all-cause mortality based on draws from the uncertainty distributions. Findings Global life expectancy for both sexes increased from 65.3 years (UI 65.0-65.6) in 1990, to 71.5 years (UI 71.0-71.9) in 2013, while the number of deaths increased from 47.5 million (UI 46.8-48.2) to 54.9 million (UI 53.6-56.3) over the same interval. Global progress masked variation by age and sex: for children, average absolute diff erences between countries decreased but relative diff erences increased. For women aged 25-39 years and older than 75 years and for men aged 20-49 years and 65 years and older, both absolute and relative diff erences increased. Decomposition of global and regional life expectancy showed the prominent role of reductions in age-standardised death rates for cardiovascular diseases and cancers in high-income regions, and reductions in child deaths from diarrhoea, lower respiratory infections, and neonatal causes in low-income regions. HIV/AIDS reduced life expectancy in southern sub-Saharan Africa. For most communicable causes of death both numbers of deaths and age-standardised death rates fell whereas for most non-communicable causes, demographic shifts have increased numbers of deaths but decreased age-standardised death rates. Global deaths from injury increased by 10.7%, from 4.3 million deaths in 1990 to 4.8 million in 2013; but age-standardised rates declined over the same period by 21%. For some causes of more than 100 000 deaths per year in 2013, age-standardised death rates increased between 1990 and 2013, including HIV/AIDS, pancreatic cancer, atrial fibrillation and flutter, drug use disorders, diabetes, chronic kidney disease, and sickle-cell anaemias. Diarrhoeal diseases, lower respiratory infections, neonatal causes, and malaria are still in the top five causes of death in children younger than 5 years. The most important pathogens are rotavirus for diarrhoea and pneumococcus for lower respiratory infections. Country-specific probabilities of death over three phases of life were substantially varied between and within regions. Interpretation For most countries, the general pattern of reductions in age-sex specifi c mortality has been associated with a progressive shift towards a larger share of the remaining deaths caused by non-communicable disease and injuries. Assessing epidemiological convergence across countries depends on whether an absolute or relative measure of inequality is used. Nevertheless, age-standardised death rates for seven substantial causes are increasing, suggesting the potential for reversals in some countries. Important gaps exist in the empirical data for cause of death estimates for some countries; for example, no national data for India are available for the past decade.
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4.
  • Vos, Theo, et al. (författare)
  • Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990-2013: a systematic analysis for the Global Burden of Disease Study 2013
  • 2015
  • Ingår i: The Lancet. - 1474-547X .- 0140-6736. ; 386:9995, s. 743-800
  • Tidskriftsartikel (refereegranskat)abstract
    • Background Up-to-date evidence about levels and trends in disease and injury incidence, prevalence, and years lived with disability (YLDs) is an essential input into global, regional, and national health policies. In the Global Burden of Disease Study 2013 (GBD 2013), we estimated these quantities for acute and chronic diseases and injuries for 188 countries between 1990 and 2013. Methods Estimates were calculated for disease and injury incidence, prevalence, and YLDs using GBD 2010 methods with some important refinements. Results for incidence of acute disorders and prevalence of chronic disorders are new additions to the analysis. Key improvements include expansion to the cause and sequelae list, updated systematic reviews, use of detailed injury codes, improvements to the Bayesian meta-regression method (DisMod-MR), and use of severity splits for various causes. An index of data representativeness, showing data availability, was calculated for each cause and impairment during three periods globally and at the country level for 2013. In total, 35 620 distinct sources of data were used and documented to calculated estimates for 301 diseases and injuries and 2337 sequelae. The comorbidity simulation provides estimates for the number of sequelae, concurrently, by individuals by country, year, age, and sex. Disability weights were updated with the addition of new population-based survey data from four countries. Findings Disease and injury were highly prevalent; only a small fraction of individuals had no sequelae. Comorbidity rose substantially with age and in absolute terms from 1990 to 2013. Incidence of acute sequelae were predominantly infectious diseases and short-term injuries, with over 2 billion cases of upper respiratory infections and diarrhoeal disease episodes in 2013, with the notable exception of tooth pain due to permanent caries with more than 200 million incident cases in 2013. Conversely, leading chronic sequelae were largely attributable to non-communicable diseases, with prevalence estimates for asymptomatic permanent caries and tension-type headache of 2.4 billion and 1.6 billion, respectively. The distribution of the number of sequelae in populations varied widely across regions, with an expected relation between age and disease prevalence. YLDs for both sexes increased from 537.6 million in 1990 to 764.8 million in 2013 due to population growth and ageing, whereas the age-standardised rate decreased little from 114.87 per 1000 people to 110.31 per 1000 people between 1990 and 2013. Leading causes of YLDs included low back pain and major depressive disorder among the top ten causes of YLDs in every country. YLD rates per person, by major cause groups, indicated the main drivers of increases were due to musculoskeletal, mental, and substance use disorders, neurological disorders, and chronic respiratory diseases; however HIV/AIDS was a notable driver of increasing YLDs in sub-Saharan Africa. Also, the proportion of disability-adjusted life years due to YLDs increased globally from 21.1% in 1990 to 31.2% in 2013. Interpretation Ageing of the world's population is leading to a substantial increase in the numbers of individuals with sequelae of diseases and injuries. Rates of YLDs are declining much more slowly than mortality rates. The non-fatal dimensions of disease and injury will require more and more attention from health systems. The transition to non-fatal outcomes as the dominant source of burden of disease is occurring rapidly outside of sub-Saharan Africa. Our results can guide future health initiatives through examination of epidemiological trends and a better understanding of variation across countries.
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5.
  • Rashid, Md Utba, et al. (författare)
  • Quality of life (QoL) among COVID-19 recovered healthcare workers in Bangladesh
  • 2022
  • Ingår i: BMC Health Services Research. - : Springer Science and Business Media LLC. - 1472-6963. ; 22:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Background The Coronavirus Disease 2019 (COVID-19) caused by the SARS-CoV-2 virus has taken the lives of more than 100,000 healthcare workers (HCWs) so far. Those who survived continuously work under immense physical and psychological pressure, and their quality of life (QoL) is impacted. The study aimed to assess the QoL among HCWs in Bangladesh who recovered from COVID-19. Methods This cross-sectional, telephonic interview-based study was conducted among 322 randomly selected HCWs from Bangladesh who were positive for COVID-19 and recovered from the infection before the interview. Data were collected from June to November 2020. We examined the impact of COVID on the QoL of the participants using the validated Bangladesh version of the World Health Organization (WHO) Quality of life questionnaire brief (WHOQOL-BREF). All analyses were done by STATA (Version 16.1). Results More than half of the health care professionals were male (56.0%), aged between 26-35 years (51%), and completed graduation (49%). The majority of the study participants in the four domains were married (n = 263, 81%) and living in Dhaka. The average score of the participants was 70.91 +/- 13.07, 62.68 +/- 14.99, 66.93 +/- 15.14, and 63.56 +/- 12.11 in physical, psychological, social relationship and environmental domains, respectively. HCWs in urban areas enjoyed 2.4 times better socially stable lives (OR: 2.42, 95% CI: 1.18-4.96) but 72% less psychologically satisfactory lives. Conclusion HCWs' post-COVID quality of life depended on variable interaction of demographic socioeconomic, including old age, female sex, graduation, and higher monthly income. The findings indicate the issues which should be addressed to improve the quality of life of frontline workers who fight against the pandemic.
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6.
  • Bravo, L, et al. (författare)
  • 2021
  • swepub:Mat__t
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7.
  • Tabiri, S, et al. (författare)
  • 2021
  • swepub:Mat__t
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8.
  • Arfizurrahmanl, Mohammad, et al. (författare)
  • Real-time non-intrusive driver fatigue detection system using belief rule-based expert system
  • 2021
  • Ingår i: Journal of Internet Services and Information Security (JISIS). - : Innovative Information Science and Technology Research Group. - 2182-2069 .- 2182-2077. ; 11:4, s. 44-60
  • Tidskriftsartikel (refereegranskat)abstract
    • This paper presents a non-intrusive system for detecting driver fatigue in real-time. To determine the level of fatigue the system uses various visual features, namely head nodding, eye closure duration and yawning. A state-of-the-art facial landmark detector ’IntraFace’ has been adopted to determine the eye state, mouth state and head pose estimation. However, different forms of uncertainties such as vagueness, imprecision, ambiguity and incompleteness are involved in calculating these visual parameters. Therefore, a Belief Rule-Based Expert System (BRBES) is employed, which has the ability to handle the uncertainties. The information of the visual parameters is sent to BRBES as input to determine the level of fatigue. An optimal learning model has been developed to improve the performance and accuracy of the BRBES. A comparison between the system and the fuzzy rulebased expert system has been carried out. The system generates more effective and reliable results than the fuzzy rule-based expert system.
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9.
  • Dey, Polash, et al. (författare)
  • Comparative Analysis of Recurrent Neural Networks in Stock Price Prediction for Different Frequency Domains
  • 2021
  • Ingår i: Algorithms. - Basel, Switzerland : MDPI. - 1999-4893. ; 14:8, s. 1-20
  • Tidskriftsartikel (refereegranskat)abstract
    • Investors in the stock market have always been in search of novel and unique techniques so that they can successfully predict stock price movement and make a big profit. However, investors continue to look for improved and new techniques to beat the market instead of old and traditional ones. Therefore, researchers are continuously working to build novel techniques to supply the demand of investors. Different types of recurrent neural networks (RNN) are used in time series analyses, especially in stock price prediction. However, since not all stocks’ prices follow the same trend, a single model cannot be used to predict the movement of all types of stock’s price. Therefore, in this research we conducted a comparative analysis of three commonly used RNNs—simple RNN, Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU)—and analyzed their efficiency for stocks having different stock trends and various price ranges and for different time frequencies. We considered three companies’ datasets from 30 June 2000 to 21 July 2020. The stocks follow different trends of price movements, with price ranges of $30, $50, and $290 during this period. We also analyzed the performance for one-day, three-day, and five-day time intervals. We compared the performance of RNN, LSTM, and GRU in terms of R2 value, MAE, MAPE, and RMSE metrics. The results show that simple RNN is outperformed by LSTM and GRU because RNN is susceptible to vanishing gradient problems, while the other two models are not. Moreover, GRU produces lesser errors comparing to LSTM. It is also evident from the results that as the time intervals get smaller, the models produce lower errors and higher reliability. 
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10.
  • Gupta, Dipankar, et al. (författare)
  • A Digital Personal Assistant using Bangla Voice Command Recognition and Face Detection
  • 2019
  • Ingår i: 2019 IEEE International Conference on Robotics, Automation, Artificial- Intelligence and Internet-of-Things. - : IEEE. ; , s. 116-121
  • Konferensbidrag (refereegranskat)abstract
    • Though speech recognition has been a common interest of researchers over the last couple of decades, but very few works have been done on Bangla voice recognition. In this research, we developed a digital personal assistant for handicapped people which recognizes continuous Bangla voice commands. We employed the cross-correlation technique which compares the energy of Bangla voice commands with prerecorded reference signals. After recognizing a Bangla command, it executes a task specified by that command. Mouse cursor can also be controlled using the facial movement of a user. We validated our model in three different environments (noisy, moderate and noiseless) so that the model can act naturally. We also compared our proposed model with a combined model of MFCC & DTW, and another model which combines crosscorrelation with LPC. Results indicate that the proposed model achieves a huge accuracy and smaller response time comparing to the other two techniques.
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11.
  • Gupta, Dipankar, et al. (författare)
  • An Interactive Computer System with Gesture-Based Mouse and Keyboard
  • 2021
  • Ingår i: Intelligent Computing and Optimization. - Cham : Springer Nature. ; , s. 894-906
  • Konferensbidrag (refereegranskat)abstract
    • Researchers around the world are now focused on to make our devices more interactive and trying to make the devices operational with minimal physical contact. In this research, we propose an interactive computer system which can operate without any physical keyboard and mouse. This system can be beneficial to everyone, especially to the paralyzed people who face difficulties to operate physical keyboard and mouse. We used computer vision so that user can type on virtual keyboard using a yellow-colored cap on his fingertip, and can also navigate to mouse controlling system. Once the user is in mouse controlling mode, user can perform all the mouse operations only by showing different number of fingers. We validated both module of our system by a 52 years old paralyzed person and achieved around 80% accuracy on average.
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12.
  • Hawlader, Mohammad Delwer Hossain, et al. (författare)
  • The art of forming habits : applying habit theory in changing physical activity behaviour
  • 2023
  • Ingår i: Journal of Public Health. - : Springer Nature. - 2198-1833 .- 1613-2238. ; 31:12, s. 2045-2057
  • Tidskriftsartikel (refereegranskat)abstract
    • Background Habits are obtained as a consequence of cue-contingent behavioural repetition. Context cues stimulate strong habits without an individual contemplating that action has been initiated. Because of its health-enhancing effects, making physical activity a part of one's life is essential. This study examined the associations of physical activity (PA) behaviours with PA habits and the role of autonomous motivation in developing PA habits. Methods This study used a cross-sectional design. A structured questionnaire was implemented through emails to 226 university students, where PA levels, habits and autonomous motivation were self-reported. Results Binary logistic regression identified age groups, gender and participants who were trying to lose weight as the significant predictors in meeting physical activity guidelines. Path analysis showed that moderate-intensity physical activity (beta = 0.045, CI = 0.069-0.248) and strength training exercises (beta = 0.133, CI = 0.148-0.674) were significantly associated with PA habits (p < 0.01). Autonomous motivation was directly associated with PA habits (beta = 0.062, CI = [0.295-0.541], p < 0.01) and was also significantly related to moderate-intensity physical activity (beta = 0.243, CI = [0.078-0.266], p < 0.01) and strength training exercises (beta = 0.202, CI = [0.033-0.594], p < 0.05). Conclusions The emphasis on experiment-based logic and interest in habit formation in the research community is extensive. As the college years offer an excellent opportunity to establish healthy behavioural interventions, encouraging students in regular PA and exhibiting an autonomous motivation towards PA may be necessary.
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13.
  • Islam, Md. Aminul, et al. (författare)
  • A 30-day follow-up study on the prevalence of SARS-COV-2 genetic markers in wastewater from the residence of COVID-19 patient and comparison with clinical positivity
  • 2023
  • Ingår i: Science of the Total Environment. - : Elsevier BV. - 0048-9697 .- 1879-1026. ; 858, s. 159350-
  • Tidskriftsartikel (refereegranskat)abstract
    • Wastewater based epidemiology (WBE) is an important tool to fight against COVID-19 as it provides insights into the health status of the targeted population from a small single house to a large municipality in a cost-effective, rapid, and non-invasive way. The implementation of wastewater based surveillance (WBS) could reduce the burden on the public health system, management of pandemics, help to make informed decisions, and protect public health. In this study, a house with COVID-19 patients was targeted for monitoring the prevalence of SARS-CoV-2 genetic markers in wastewa-ter samples (WS) with clinical specimens (CS) for a period of 30 days. RT-qPCR technique was employed to target non-structural (ORF1ab) and structural-nucleocapsid (N) protein genes of SARS-CoV-2, according to a validated experimental protocol. Physiological, environmental, and biological parameters were also measured following the American Public Health Association (APHA) standard protocols. SARS-CoV-2 viral shedding in wastewater peaked when the highest number of COVID-19 cases were clinically diagnosed. Throughout the study period, 7450 to 23,000 gene copies/1000 mL were detected, where we identified 47 % (57/120) positive samples from WS and 35 % (128/360) from CS. When the COVID-19 patient number was the lowest (2), the highest CT value (39.4; i.e., lowest copy number) was identified from WS. On the other hand, when the COVID-19 patients were the highest (6), the lowest CT value (25.2 i.e., highest copy numbers) was obtained from WS. An advance signal of increased SARS-CoV-2 viral load from the COVID-19 patient was found in WS earlier than in the CS. Using customized primer sets in a traditional PCR approach, we confirmed that all SARS-CoV-2 variants identified in both CS and WS were Delta variants (B.1.617.2). To our knowledge, this is the first follow-up study to determine a temporal relationship be-tween COVID-19 patients and their discharge of SARS-CoV-2 RNA genetic markers in wastewater from a single house including all family members for clinical sampling from a developing country (Bangladesh), where a proper sewage system is lacking. The salient findings of the study indicate that monitoring the genetic markers of the SARS-CoV-2 virus in wastewater could identify COVID-19 cases, which reduces the burden on the public health system during COVID-19 pandemics.
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14.
  • Jakariya, Md, et al. (författare)
  • Wastewater-based epidemiological surveillance to monitor the prevalence of SARS-CoV-2 in developing countries with onsite sanitation facilities
  • 2022
  • Ingår i: Environmental Pollution. - : Elsevier BV. - 0269-7491 .- 1873-6424. ; 311
  • Tidskriftsartikel (refereegranskat)abstract
    • Wastewater-based epidemiology (WBE) has emerged as a valuable approach for forecasting disease outbreaks in developed countries with a centralized sewage infrastructure. On the other hand, due to the absence of well-defined and systematic sewage networks, WBE is challenging to implement in developing countries like Bangladesh where most people live in rural areas. Identification of appropriate locations for rural Hotspot Based Sampling (HBS) and urban Drain Based Sampling (DBS) are critical to enable WBE based monitoring system. We investigated the best sampling locations from both urban and rural areas in Bangladesh after evaluating the sanitation infrastructure for forecasting COVID-19 prevalence. A total of 168 wastewater samples were collected from 14 districts of Bangladesh during each of the two peak pandemic seasons. RT-qPCR commercial kits were used to target ORF1ab and N genes. The presence of SARS-CoV-2 genetic materials was found in 98% (165/168) and 95% (160/168) wastewater samples in the first and second round sampling, respectively. Although waste-water effluents from both the marketplace and isolation center drains were found with the highest amount of genetic materials according to the mixed model, quantifiable SARS-CoV-2 RNAs were also identified in the other four sampling sites. Hence, wastewater samples of the marketplace in rural areas and isolation centers in urban areas can be considered the appropriate sampling sites to detect contagion hotspots. This is the first complete study to detect SARS-CoV-2 genetic components in wastewater samples collected from rural and urban areas for monitoring the COVID-19 pandemic. The results based on the study revealed a correlation between viral copy numbers in wastewater samples and SARS-CoV-2 positive cases reported by the Directorate General of Health Services (DGHS) as part of the national surveillance program for COVID-19 prevention. The findings of this study will help in setting strategies and guidelines for the selection of appropriate sampling sites, which will facilitate in development of comprehensive wastewater-based epidemiological systems for surveillance of rural and urban areas of low-income countries with inadequate sewage infrastructure.
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16.
  • Kocarnik, J. M., et al. (författare)
  • Cancer Incidence, Mortality, Years of Life Lost, Years Lived With Disability, and Disability-Adjusted Life Years for 29 Cancer Groups From 2010 to 2019 A Systematic Analysis for the Global Burden of Disease Study 2019
  • 2022
  • Ingår i: Jama Oncology. - : American Medical Association (AMA). - 2374-2437 .- 2374-2445. ; 8:3, s. 420-488
  • Tidskriftsartikel (refereegranskat)abstract
    • IMPORTANCE The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019) provided systematic estimates of incidence, morbidity, and mortality to inform local and international efforts toward reducing cancer burden. OBJECTIVE To estimate cancer burden and trends globally for 204 countries and territories and by Sociodemographic Index (SDI) quintiles from 2010 to 2019. EVIDENCE REVIEW The GBD 2019 estimation methods were used to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life years (DALYs) in 2019 and over the past decade. Estimates are also provided by quintiles of the SDI, a composite measure of educational attainment, income per capita, and total fertility rate for those younger than 25 years. Estimates include 95% uncertainty intervals (UIs). FINDINGS In 2019, there were an estimated 23.6 million (95% UI, 22.2-24.9 million) new cancer cases (17.2 million when excluding nonmelanoma skin cancer) and 10.0 million (95% UI, 9.36-10.6 million) cancer deaths globally, with an estimated 250 million (235-264 million) DALYs due to cancer. Since 2010, these represented a 26.3%(95% UI, 20.3%-32.3%) increase in new cases, a 20.9%(95% UI, 14.2%-27.6%) increase in deaths, and a 16.0% (95% UI, 9.3%-22.8%) increase in DALYs. Among 22 groups of diseases and injuries in the GBD 2019 study, cancer was second only to cardiovascular diseases for the number of deaths, years of life lost, and DALYs globally in 2019. Cancer burden differed across SDI quintiles. The proportion of years lived with disability that contributed to DALYs increased with SDI, ranging from 1.4%(1.1%-1.8%) in the low SDI quintile to 5.7%(4.2%-7.1%) in the high SDI quintile. While the high SDI quintile had the highest number of new cases in 2019, the middle SDI quintile had the highest number of cancer deaths and YDALYs. From 2010 to 2019, the largest percentage increase in the numbers of cases and deaths occurred in the low and low-middle SDI quintiles. CONCLUSIONS AND RELEVANCE The results of this systematic analysis suggest that the global burden of cancer is substantial and growing, with burden differing by SDI. These results provide comprehensive and comparable estimates that can potentially inform efforts toward equitable cancer control around the world.
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17.
  • Morshed, Muhammad Sarwar Jahan, et al. (författare)
  • Integration of wireless hand-held devices with the cloud architecture : Security and privacy issues
  • 2011
  • Ingår i: Proc. - Int. Conf. P2P, Parallel, Grid, Cloud Internet Comput., 3PGCIC. - 9780769545318 ; , s. 83-88
  • Konferensbidrag (refereegranskat)abstract
    • Use of wireless hand held devices like mobile, PDA, laptop etc. is increasing rapidly. Many advanced users want more functionality with these wireless devices to manage their daily schedule. But most of the wireless hand-held devices have limited resource capability for robust functionality. Therefore cloud computing environment could be an alternative solution for these devices to support resource consuming applications. If the wireless hand held device is connected with the cloud, user can use more resource consuming applications and private data (stored in the cloud) from those devices. But privacy and security of the personal information and data make the user concern for using the cloud. The aim of the paper is to identify the security and privacy related risks and threats of the mass users as well as corporate users, if the wireless hand-held devices will be integrated with the cloud.
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18.
  • Rahman, Mahfil Ara, et al. (författare)
  • Quality of life among health care workers with and without prior COVID-19 infection in Bangladesh
  • 2022
  • Ingår i: BMC Health Services Research. - : Springer Science and Business Media LLC. - 1472-6963. ; 22:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Background Health care workers have been facing difficulties in coping with the COVID-19 infection from the beginning. The study aimed to compare Quality of Life (QOL) among health care workers (HCWs) with and without prior COVID-19 disease. Methods This study was conducted from July 2020 to January 2021 among 444 HCWs. We randomly interviewed 3244 participants for our earlier nationwide survey from a list of COVID-19 positive cases after their recovery, and we found 222 HCWs among the respondents. We randomly chose 222 HCWs unaffected by COVID as a comparison group from our selected hospitals. We measured QOL using World Health Organization's WHOQOL-BREF tool. Physical, psychological, environmental, and social ties were the four areas assessed on a 5-point Likert scale where a higher score suggests better QOL. Due to pandemic restrictions, we used telephonic interviews for data collection. Results A higher QOL score was observed in HCWs with prior COVID-19 infection in all four domains than HCWs without previous COVID-19 conditions. Comorbidity was negatively associated with QOL scores of the physical (p = 0.001) and (p < 0.001) and psychological (p = 0.05, and (p < 0.05) domains for non-COVID and COVID-affected groups, respectively. Current smoking was significantly associated with lower psychological (p = 0.019) and environmental (p = 0.007) QOL scores among HCWs with prior COVID-19 infection. Hospitalization history due to COVID infection was a contributing factor for lower physical QOL scores (p = 0.048). Environmental (p = 0.016) QOL scores were significantly associated with the monthly income in the prior COVID-19 infection group, and physical scores were significantly associated (p = 0.05) with a monthly income in the non-COVID group. Conclusion Governmental and non-governmental stakeholders should focus on potentially modifiable factors to improve health care workers' quality of life.
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19.
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20.
  • Saha, Amit, et al. (författare)
  • Vaccine specific immune response to an inactivated oral cholera vaccine and EPI vaccines in a high and low arsenic area in Bangladeshi children
  • 2013
  • Ingår i: Vaccine. - : Elsevier BV. - 0264-410X. ; 31:4, s. 647-652
  • Tidskriftsartikel (refereegranskat)abstract
    • Immune responses to the inactivated oral whole cell cholera toxin B (CTB) subunit cholera vaccine, Dukoral(®), as well as three childhood vaccines in the national immunization system were compared in children living in high and low arsenic contaminated areas in Bangladesh. In addition, serum complement factors C3 and C4 levels were evaluated among children in the two areas. VACCINATIONS: Toddlers (2-5 years) were orally immunized with two doses of Dukoral 14 days apart. Study participants had also received diphtheria, tetanus and measles vaccines according to the Expanded Program on Immunization (EPI) in Bangladesh. RESULTS: The mean level of arsenic in the urine specimens in the children of the high arsenic area (HAA, Shahrasti, Chandpur) was 291.8μg/L while the level was 6.60μg/L in the low arsenic area (LAA, Mirpur, Dhaka). Cholera specific vibriocidal antibody responses were significantly increased in the HAA (87%, P<0.001) and the LAA (75%, P<0.001) children after vaccination with Dukoral, but no differences were found between the two groups. Levels of CTB specific IgA and IgG antibodies were comparable between the two groups, whereas LPS specific IgA and IgG were higher in the LAA group, although response rates were comparable. Diphtheria and tetanus vaccine specific IgG responses were significantly higher in the HAA compared to the LAA group (P<0.001, P=0.048 respectively), whereas there were no differences in the measles specific IgG responses between the groups. Complement C3 and C4 levels in sera were higher in participants from the HAA than the LAA groups (P<0.001, P=0.049 respectively). CONCLUSIONS: The study demonstrates that the oral cholera vaccine as well as the EPI vaccines studied are immunogenic in children in high and low arsenic areas in Bangladesh. The results are encouraging for the potential use of cholera vaccines as well as the EPI vaccines in arsenic endemic areas.
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21.
  • Sakib, Najmuj, et al. (författare)
  • Psychometric Validation of the Bangla Fear of COVID-19 Scale : Confirmatory Factor Analysis and Rasch Analysis
  • 2022
  • Ingår i: International Journal of Mental Health and Addiction. - : Springer. - 1557-1874 .- 1557-1882. ; 20:5, s. 2623-2634
  • Tidskriftsartikel (refereegranskat)abstract
    • The recently developed Fear of COVID-19 Scale (FCV-19S) is a seven-item uni-dimensional scale that assesses the severity of fears of COVID-19. Given the rapid increase of COVID-19 cases in Bangladesh, we aimed to translate and validate the FCV-19S in Bangla. The forward-backward translation method was used to translate the English version of the questionnaire into Bangla. The reliability and validity properties of the Bangla FCV-19S were rigorously psychometrically evaluated (utilizing both confirmatory factor analysis and Rasch analysis) in relation to socio-demographic variables, national lockdown variables, and response to the Bangla Health Patient Questionnaire. The sample comprised 8550 Bangladeshi participants. The Cronbach α value for the Bangla FCV-19S was 0.871 indicating very good internal reliability. The results of the confirmatory factor analysis showed that the uni-dimensional factor structure of the FCV-19S fitted well with the data. The FCV-19S was significantly correlated with the nine-item Bangla Patient Health Questionnaire (PHQ-90) (r = 0.406, p < 0.001). FCV-19S scores were significantly associated with higher worries concerning lockdown. Measurement invariance of the FCV-19S showed no differences with respect to age or gender. The Bangla version of FCV-19S is a valid and reliable tool with robust psychometric properties which will be useful for researchers carrying out studies among the Bangla speaking population in assessing the psychological impact of fear from COVID-19 infection during this pandemic.
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22.
  • Sbarra, AN, et al. (författare)
  • Mapping routine measles vaccination in low- and middle-income countries
  • 2021
  • Ingår i: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 589:7842, s. 415-
  • Tidskriftsartikel (refereegranskat)abstract
    • The safe, highly effective measles vaccine has been recommended globally since 1974, yet in 2017 there were more than 17 million cases of measles and 83,400 deaths in children under 5 years old, and more than 99% of both occurred in low- and middle-income countries (LMICs)1–4. Globally comparable, annual, local estimates of routine first-dose measles-containing vaccine (MCV1) coverage are critical for understanding geographically precise immunity patterns, progress towards the targets of the Global Vaccine Action Plan (GVAP), and high-risk areas amid disruptions to vaccination programmes caused by coronavirus disease 2019 (COVID-19)5–8. Here we generated annual estimates of routine childhood MCV1 coverage at 5 × 5-km2pixel and second administrative levels from 2000 to 2019 in 101 LMICs, quantified geographical inequality and assessed vaccination status by geographical remoteness. After widespread MCV1 gains from 2000 to 2010, coverage regressed in more than half of the districts between 2010 and 2019, leaving many LMICs far from the GVAP goal of 80% coverage in all districts by 2019. MCV1 coverage was lower in rural than in urban locations, although a larger proportion of unvaccinated children overall lived in urban locations; strategies to provide essential vaccination services should address both geographical contexts. These results provide a tool for decision-makers to strengthen routine MCV1 immunization programmes and provide equitable disease protection for all children.
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23.
  • Tarek, Iftakher Hasan Mohammad, et al. (författare)
  • A Hybrid Hotel Recommendation Using Collaborative, Content Based and Knowledge Based Approach
  • 2023. - 1
  • Ingår i: Intelligent Computing &amp; Optimization. - Cham : Springer. ; , s. 1049-1057
  • Bokkapitel (refereegranskat)abstract
    • Everybody plans vacations, and the first step in that process is to book a hotel. With the hospitality sector being so competitive, it’s critical to maintain best practices and stay on top of client demands and wants. They want individualized experiences, one-of-a-kind amenities, and a general sense of well-being on all levels. A consumer of a hotel recommendation system frequently encounters challenges in obtaining and fulfilling his or her wishes. Content-based filtering and collaborative filtering are two well-known strategies for creating a recommender system. Content-based filtering does not use human opinions to produce predictions, whereas collaborative filtering does, resulting in more accurate predictions. Collaborative filtering, on the other hand, cannot forecast objects that have never been rated by anyone. Both approaches can be merged with a hybrid methodology to cover the disadvantages of each approach while gaining the benefits of the other. This research employed Item-Item collaborative filtering (CF) and content-based filtering (CB) to calculate hotel similarity in our suggested method. It uses cosine similarity to calculate user similarity. For content-based filtering, natural language processing (NLP) is also employed. Our model employs a knowledge-based approach for Cold-User scenarios. Precision, recall and f1 used to evaluate the recommendation system.
  •  
24.
  • Uddin Ahmed, Tawsin, et al. (författare)
  • An Integrated Real-Time Deep Learning and Belief Rule Base Intelligent System to Assess Facial Expression Under Uncertainty
  • 2020
  • Ingår i: 2020 Joint 9th International Conference on Informatics, Electronics &amp; Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision &amp; Pattern Recognition (icIVPR). - : IEEE.
  • Konferensbidrag (refereegranskat)abstract
    • Nowadays, the recognition of facial expression draws significant attention in various domains. In view of this, a realtime facial expression recognition system has been developed using a Deep Learning approach, which can classify ten emotions, including angry, disgust, fear, happy, mockery, neutral, sad, surprise, think, and wink. In addition, an integrated expert system has also been developed by integrating Deep Learning with a Belief Rule Base to support the assessment of the overall mental state of a person over a period of time from video streaming data under uncertainty. In this research, data-driven and knowledge-driven approaches are integrated together to assess the mental state of an individual. Such a system could enable the identification of a suspect before committing any crime beforehand by the law enforcement agency. The performance of this integrated system is found reliable than existing methods of facial expression assessment. Contribution- The paper presents a noble method of computing the overall mental condition of a person by integrating CNN and BRBES under uncertainty. Contribution- The paper presents a noble method of computing the overall mental condition of a person by integrating CNN and BRBES under uncertainty.
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25.
  • Uddin, Md Bashir, et al. (författare)
  • Molecular Detection of Colistin Resistance mcr-1 Gene in Multidrug-Resistant Escherichia coli Isolated from Chicken
  • 2022
  • Ingår i: Antibiotics. - : MDPI AG. - 2079-6382. ; 11:1
  • Tidskriftsartikel (refereegranskat)abstract
    • Zoonotic and antimicrobial-resistant Escherichia coli (hereafter, E. coli) is a global public health threat which can lead to detrimental effects on human health. Here, we aim to investigate the antimicrobial resistance and the presence of mcr-1 gene in E. coli isolated from chicken feces. Ninety-four E. coli isolates were obtained from samples collected from different locations in Bangladesh, and the isolates were identified using conventional microbiological tests. Phenotypic disk diffusion tests using 20 antimicrobial agents were performed according to CLSI-EUCAST guidelines, and minimum inhibitory concentrations (MICs) were determined for a subset of samples. E. coli isolates showed high resistance to colistin (88.30%), ciprofloxacin (77.66%), trimethoprim/sulfamethoxazole (76.60%), tigecycline (75.53%), and enrofloxacin (71.28%). Additionally, the pathotype eaeA gene was confirmed in ten randomly selected E. coli isolates using primer-specific polymerase chain reaction (PCR). The presence of mcr-1 gene was confirmed using PCR and sequencing analysis in six out of ten E. coli isolates. Furthermore, sequencing and phylogenetic analyses revealed a similarity between the catalytic domain of Neisseria meningitidis lipooligosaccharide phosphoethanolamine transferase A (LptA) and MCR proteins, indicating that the six tested isolates were colistin resistant. Finally, the findings of the present study showed that E. coli isolated from chicken harbored mcr-1 gene, and multidrug and colistin resistance. These findings accentuate the need to implement strict measures to limit the imprudent use of antibiotics, particularly colistin, in agriculture and poultry farms.
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26.
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27.
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28.
  • Afroze, Tasnim, et al. (författare)
  • Glaucoma Detection Using Inception Convolutional Neural Network V3
  • 2021
  • Ingår i: Applied Intelligence and Informatics. - Cham : Springer. ; , s. 17-28
  • Konferensbidrag (refereegranskat)abstract
    • Glaucoma detection is an important research area in intelligent system and it plays an important role to medical field. Glaucoma can give rise to an irreversible blindness due to lack of proper diagnosis. Doctors need to perform many tests to diagnosis this threatening disease. It requires a lot of time and expense. Sometime affected people may not have any vision loss, at the early stage of glaucoma. For detecting glaucoma, we have built a model to lessen the time and cost. Our work introduces a CNN based Inception V3 model. We used total 6072 images. Among this image 2336 were glaucomatous and 3736 were normal fundus image. For training our model we took 5460 images and for testing we took 612 images. After that we obtained an accuracy of 0.8529 and a value of 0.9387 for AUC. For comparison, we used DenseNet121 and ResNet50 algorithm and got an accuracy of 0.8153 and 0.7761 respectively.
  •  
29.
  • Ahmed, Faisal, et al. (författare)
  • Comparative Performance of Tree Based Machine Learning Classifiers in Product Backorder Prediction
  • 2023. - 1
  • Ingår i: Intelligent Computing &amp; Optimization. - Cham : Springer. ; , s. 572-584
  • Bokkapitel (refereegranskat)abstract
    • Early prediction of whether a product will go to backorder or not is necessary for optimal management of inventory that can reduce the losses in sales, establish a good relationship between the supplier and customer and maximize the revenues. In this study, we have investigated the performance and effectiveness of tree based machine learning algorithms to predict the backorder of a product. The research methodology consists of preprocessing of data, feature selection using statistical hypothesis test, imbalanced learning using the random undersampling method and performance evaluating and comparing of four tree based machine learning algorithms including decision tree, random forest, adaptive boosting and gradient boosting in terms of accuracy, precision, recall, f1-score, area under the receiver operating characteristic curve and area under the precision and recall curve. Three main findings of this study are (1) random forest model without feature selection and with random undersampling method achieved the highest performance in terms of all performance measure metrics, (2) feature selection cannot contribute to the performance enhancement of the tree based classifiers, and (3) random undersampling method significantly improves performance of tree based classifiers in product backorder prediction.
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30.
  • Ahmed, Faisal, et al. (författare)
  • Machine Learning-Based Tomato Leaf Disease Diagnosis Using Radiomics Features
  • 2023
  • Ingår i: Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering - TCCE 2022. - : Springer Science and Business Media Deutschland GmbH. - 9789811994821 - 9789811994838 ; , s. 25-35
  • Konferensbidrag (refereegranskat)abstract
    • Tomato leaves can be infected with various infectious viruses and fungal diseases that drastically reduce tomato production and incur a great economic loss. Therefore, tomato leaf disease detection and identification are crucial for maintaining the global demand for tomatoes for a large population. This paper proposes a machine learning-based technique to identify diseases on tomato leaves and classify them into three diseases (Septoria, Yellow Curl Leaf, and Late Blight) and one healthy class. The proposed method extracts radiomics-based features from tomato leaf images and identifies the disease with a gradient boosting classifier. The dataset used in this study consists of 4000 tomato leaf disease images collected from the Plant Village dataset. The experimental results demonstrate the effectiveness and applicability of our proposed method for tomato leaf disease detection and classification.
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31.
  • Ahmed, Tawsin Uddin, et al. (författare)
  • A Deep Learning Approach with Data Augmentation to Recognize Facial Expressions in Real Time
  • 2022
  • Ingår i: Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering. - Singapore : Springer Nature. ; , s. 487-500
  • Konferensbidrag (refereegranskat)abstract
    • The enormous use of facial expression recognition in various sectors of computer science elevates the interest of researchers to research this topic. Computer vision coupled with deep learning approach formulates a way to solve several real-world problems. For instance, in robotics, to carry out as well as to strengthen the communication between expert systems and human or even between expert agents, it is one of the requirements to analyze information from visual content. Facial expression recognition is one of the trending topics in the area of computer vision. In our previous work, a facial expression recognition system is delivered which can classify an image into seven universal facial expressions—angry, disgust, fear, happy, neutral, sad, and surprise. This is the extension of our previous research in which a real-time facial expression recognition system is proposed that can recognize a total of ten facial expressions including the previous seven facial expressions and additional three facial expressions—mockery, think, and wink from video streaming data. After model training, the proposed model has been able to gain high validation accuracy on a combined facial expression dataset. Moreover, the real-time validation of the proposed model is also promising.
  •  
32.
  • Ahmed, Tawsin Uddin, et al. (författare)
  • An Integrated Deep Learning and Belief Rule Base Intelligent System to Predict Survival of COVID-19 Patient under Uncertainty
  • 2022
  • Ingår i: Cognitive Computation. - : Springer. - 1866-9956 .- 1866-9964. ; 14:2, s. 660-676
  • Tidskriftsartikel (refereegranskat)abstract
    • The novel Coronavirus-induced disease COVID-19 is the biggest threat to human health at the present time, and due to the transmission ability of this virus via its conveyor, it is spreading rapidly in almost every corner of the globe. The unification of medical and IT experts is required to bring this outbreak under control. In this research, an integration of both data and knowledge-driven approaches in a single framework is proposed to assess the survival probability of a COVID-19 patient. Several neural networks pre-trained models: Xception, InceptionResNetV2, and VGG Net, are trained on X-ray images of COVID-19 patients to distinguish between critical and non-critical patients. This prediction result, along with eight other significant risk factors associated with COVID-19 patients, is analyzed with a knowledge-driven belief rule-based expert system which forms a probability of survival for that particular patient. The reliability of the proposed integrated system has been tested by using real patient data and compared with expert opinion, where the performance of the system is found promising.
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33.
  • Akhtar, Zubair, et al. (författare)
  • Undiagnosed SARS-CoV-2 infection and outcome in patients with acute MI and no COVID-19 symptoms
  • 2021
  • Ingår i: Open heart. - : BMJ Publishing Group Ltd. - 2053-3624. ; 8:1
  • Tidskriftsartikel (refereegranskat)abstract
    • OBJECTIVE: We aimed to determine the prevalence and outcome of occult infection with SARS-CoV-2 and influenza in patients presenting with myocardial infarction (MI) without COVID-19 symptoms.METHODS: We conducted an observational study from 28 June to 11 August 2020, enrolling patients admitted to the National Institute of Cardiovascular Disease Hospital, Dhaka, Bangladesh, with ST-segment elevation MI (STEMI) or non-ST-segment elevation MI who did not meet WHO criteria for suspected COVID-19. Samples were collected by nasopharyngeal swab to test for SARS-CoV-2 and influenza virus by real-time reverse transcriptase PCR. We followed up patients at 3 months (13 weeks) postadmission to record adverse cardiovascular outcomes: all-cause death, new MI, heart failure and new percutaneous coronary intervention or stent thrombosis. Survival analysis was performed using the Kaplan-Meier method.RESULTS: We enrolled 280 patients with MI, 79% male, mean age 54.5±11.8 years, 140 of whom were diagnosed with STEMI. We found 36 (13%) to be infected with SARS-CoV-2 and 1 with influenza. There was no significant difference between mortality rate observed among SARS-CoV-2 infected patients compared with non-infected (5 (14%) vs 26 (11%); p=0.564). A numerically shorter median time to a recurrent cardiovascular event was recorded among SARS-CoV-2 infected compared with non-infected patients (21 days, IQR: 8-46 vs 27 days, IQR: 7-44; p=0.378).CONCLUSION: We found a substantial rate of occult SARS-CoV-2 infection in the studied cohort, suggesting SARS-CoV-2 may precipitate MI. Asymptomatic patients with COVID-19 admitted with MI may contribute to disease transmission and warrants widespread testing of hospital admissions.
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34.
  • Al Arafat, Md. Mahedi, et al. (författare)
  • Neural Network-Based Obstacle and Pothole Avoiding Robot
  • 2023
  • Ingår i: Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering - TCCE 2022. - : Springer Science and Business Media Deutschland GmbH. - 9789811994821 - 9789811994838 ; , s. 173-184
  • Konferensbidrag (refereegranskat)abstract
    • The main challenge of any mobile robot is to detect and avoid obstacles and potholes. This paper presents the development and implementation of a novel mobile robot. An Arduino Uno is used as the processing unit of the robot. A Sharp distance measurement sensor and Ultrasonic sensors are used for taking inputs from the environment. The robot trains a neural network based on a feedforward backpropagation algorithm to detect and avoid obstacles and potholes. For that purpose, we have used a truth table. Our experimental results show that our developed system can ideally detect and avoid obstacles and potholes and navigate environments.
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35.
  • Alvarez, E. M., et al. (författare)
  • The global burden of adolescent and young adult cancer in 2019: a systematic analysis for the Global Burden of Disease Study 2019
  • 2022
  • Ingår i: Lancet Oncology. - : Elsevier BV. - 1470-2045. ; 23:1, s. 27-52
  • Tidskriftsartikel (refereegranskat)abstract
    • Background In estimating the global burden of cancer, adolescents and young adults with cancer are often overlooked, despite being a distinct subgroup with unique epidemiology, clinical care needs, and societal impact. Comprehensive estimates of the global cancer burden in adolescents and young adults (aged 15-39 years) are lacking. To address this gap, we analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, with a focus on the outcome of disability-adjusted life-years (DALYs), to inform global cancer control measures in adolescents and young adults. Methods Using the GBD 2019 methodology, international mortality data were collected from vital registration systems, verbal autopsies, and population-based cancer registry inputs modelled with mortality-to-incidence ratios (MIRs). Incidence was computed with mortality estimates and corresponding MIRs. Prevalence estimates were calculated using modelled survival and multiplied by disability weights to obtain years lived with disability (YLDs). Years of life lost (YLLs) were calculated as age-specific cancer deaths multiplied by the standard life expectancy at the age of death. The main outcome was DALYs (the sum of YLLs and YLDs). Estimates were presented globally and by Socio-demographic Index (SDI) quintiles (countries ranked and divided into five equal SDI groups), and all estimates were presented with corresponding 95% uncertainty intervals (UIs). For this analysis, we used the age range of 15-39 years to define adolescents and young adults. Findings There were 1.19 million (95% UI 1.11-1.28) incident cancer cases and 396 000 (370 000-425 000) deaths due to cancer among people aged 15-39 years worldwide in 2019. The highest age-standardised incidence rates occurred in high SDI (59.6 [54.5-65.7] per 100 000 person-years) and high-middle SDI countries (53.2 [48.8-57.9] per 100 000 person-years), while the highest age-standardised mortality rates were in low-middle SDI (14.2 [12.9-15.6] per 100 000 person-years) and middle SDI (13.6 [12.6-14.8] per 100 000 person-years) countries. In 2019, adolescent and young adult cancers contributed 23.5 million (21.9-25.2) DALYs to the global burden of disease, of which 2.7% (1.9-3.6) came from YLDs and 97.3% (96.4-98.1) from YLLs. Cancer was the fourth leading cause of death and tenth leading cause of DALYs in adolescents and young adults globally. Interpretation Adolescent and young adult cancers contributed substantially to the overall adolescent and young adult disease burden globally in 2019. These results provide new insights into the distribution and magnitude of the adolescent and young adult cancer burden around the world. With notable differences observed across SDI settings, these estimates can inform global and country-level cancer control efforts. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.
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36.
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37.
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38.
  • Biswas, Munmun, et al. (författare)
  • A Belief Rule Base Expert System for staging Non-Small Cell Lung Cancer under Uncertainty
  • 2019
  • Ingår i: BECITHCON 2019. - : IEEE. ; , s. 47-52
  • Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
    • Non small cell Lung cancer (NSCLC) is one of the most well-known types of Lung cancer which is reason for cancer related demise in Bangladesh. The early detection stage of NSCLC is required for improving the survival rate by taking proper decision for surgery and radiotherapy. The most common factors for staging NSCLC are age, tumor size, lymph node distance, Metastasis and Co morbidity. Moreover, physicians' diagnosis is unable to give more reliable outcome due to some uncertainty such as ignorance, incompleteness, vagueness, randomness, imprecision. Belief Rule Base Expert System (BRBES) is fit to deal with above mentioned uncertainty by applying both Belief Rule base and Evidential Reasoning approach. Therefore, this paper represents the architecture, development and interface for staging NSCLC by incorporating belief rule base as well as evidential reasoning with the capability of handling uncertainty. At last, a comparative analysis is added which indicate that the outcomes of proposed expert system is more reliable and efficient than the outcomes generated from traditional human expert as well as Support Vector Machine (SVM) or Fuzzy Rule Base Expert System (FRBES).
  •  
39.
  • Bryazka, D., et al. (författare)
  • Population-level risks of alcohol consumption by amount, geography, age, sex, and year: a systematic analysis for the Global Burden of Disease Study 2020
  • 2022
  • Ingår i: Lancet. - 0140-6736. ; 400:10347, s. 185-235
  • Tidskriftsartikel (refereegranskat)abstract
    • Background The health risks associated with moderate alcohol consumption continue to be debated. Small amounts of alcohol might lower the risk of some health outcomes but increase the risk of others, suggesting that the overall risk depends, in part, on background disease rates, which vary by region, age, sex, and year. Methods For this analysis, we constructed burden-weighted dose-response relative risk curves across 22 health outcomes to estimate the theoretical minimum risk exposure level (TMREL) and non-drinker equivalence (NDE), the consumption level at which the health risk is equivalent to that of a non-drinker, using disease rates from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2020 for 21 regions, including 204 countries and territories, by 5-year age group, sex, and year for individuals aged 15-95 years and older from 1990 to 2020. Based on the NDE, we quantified the population consuming harmful amounts of alcohol. Findings The burden-weighted relative risk curves for alcohol use varied by region and age. Among individuals aged 15-39 years in 2020, the TMREL varied between 0 (95% uncertainty interval 0-0) and 0.603 (0.400-1.00) standard drinks per day, and the NDE varied between 0.002 (0-0) and 1.75 (0.698-4.30) standard drinks per day. Among individuals aged 40 years and older, the burden-weighted relative risk curve was J-shaped for all regions, with a 2020 TMREL that ranged from 0.114 (0-0.403) to 1.87 (0.500-3.30) standard drinks per day and an NDE that ranged between 0.193 (0-0.900) and 6.94 (3.40-8.30) standard drinks per day. Among individuals consuming harmful amounts of alcohol in 2020, 59.1% (54.3-65.4) were aged 15-39 years and 76.9% (7.0-81.3) were male. Interpretation There is strong evidence to support recommendations on alcohol consumption varying by age and location. Stronger interventions, particularly those tailored towards younger individuals, are needed to reduce the substantial global health loss attributable to alcohol. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd.
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40.
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41.
  • Chowdhury, Rumman Rashid, et al. (författare)
  • Analyzing Sentiment of Movie Reviews in Bangla by Applying Machine Learning Techniques
  • 2019
  • Ingår i: Proceedings of the International Conference on Bangla Speech and Language Processing. - : IEEE.
  • Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
    • This paper proposes a process of sentiment analysis of movie reviews written in Bangla language. This process can automate the analysis of audience’s reaction towards a specific movie or TV show. With more and more people expressing their opinions openly in the social networking sites, analyzing the sentiment of comments made about a specific movie can indicate how well the movie is being accepted by the general public. The dataset used in this experiment was collected and labeled manually from publicly available comments and posts from social media websites. Using Support Vector Machine algorithm, this model achieves 88.90% accuracy on the test set and by using Long Short Term Memory network [1] the model manages to achieve 82.42% accuracy. Furthermore, a comparison with some other machine learning approaches is presented in this paper.
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42.
  • Chowdhury, Rumman Rashid, et al. (författare)
  • Bangla Handwritten Character Recognition using Convolutional Neural Network with Data Augmentation
  • 2019
  • Ingår i: Joint 2019 8th International Conference on Informatics, Electronics and Vision (ICIEV) &amp; 3rd International Conference on Imaging, Vision &amp; Pattern Recognition (IVPR) with International Conference on Activity and Behavior Computing (ABC). - : IEEE. ; , s. 318-323
  • Konferensbidrag (refereegranskat)abstract
    • This paper proposes a process of Handwritten Character Recognition to recognize and convert images of individual Bangla handwritten characters into electronically editable format, which will create opportunities for further research and can also have various practical applications. The dataset used in this experiment is the BanglaLekha-Isolated dataset [1]. Using Convolutional Neural Network, this model achieves 91.81% accuracy on the alphabets (50 character classes) on the base dataset, and after expanding the number of images to 200,000 using data augmentation, the accuracy achieved on the test set is 95.25%. The model was hosted on a web server for the ease of testing and interaction with the model. Furthermore, a comparison with other machine learning approaches is presented.
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43.
  • Chowdury, Mohammad Salah Uddin, et al. (författare)
  • IoT Based Real-time River Water Quality Monitoring System
  • 2019
  • Ingår i: Procedia Computer Science. - : Elsevier. - 1877-0509. ; 155, s. 161-168
  • Tidskriftsartikel (refereegranskat)abstract
    • Current water quality monitoring system is a manual system with a monotonous process and is very time-consuming. This paper proposes a sensor-based water quality monitoring system. The main components of Wireless Sensor Network (WSN) include a microcontroller for processing the system, communication system for inter and intra node communication and several sensors. Real-time data access can be done by using remote monitoring and Internet of Things (IoT) technology. Data collected at the apart site can be displayed in a visual format on a server PC with the help of Spark streaming analysis through Spark MLlib, Deep learning neural network models, Belief Rule Based (BRB) system and is also compared with standard values. If the acquired value is above the threshold value automated warning SMS alert will be sent to the agent. The uniqueness of our proposed paper is to obtain the water monitoring system with high frequency, high mobility, and low powered. Therefore, our proposed system will immensely help Bangladeshi populations to become conscious against contaminated water as well as to stop polluting the water.
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44.
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45.
  • Farshid, Mohammad, et al. (författare)
  • IoMT-based Android Application for Monitoring COVID-19 Patients Using Real-Time Data
  • 2023
  • Ingår i: Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering - TCCE 2022. - : Springer Science and Business Media Deutschland GmbH. - 9789811994821 - 9789811994838 ; , s. 145-157
  • Konferensbidrag (refereegranskat)abstract
    • Surviving three years of the pandemic since December 2019, monitoring COVID-19 patients in a projected way is still challenging. Even after testing negative for coronavirus, people face a lot of post-covid stresses and symptoms. Scarcity of hospital beds, shortage of medical equipment like oxygen, ventilation, etc. have made the situation worse as people failed to receive proper treatment. In this regard, this work proposes an IoMT-based wearable checking device for assessing COVID-19-identified imperative signals. Furthermore, by continuously monitoring data, the device promptly warns concerned clinical personnel about any breach of isolation for possibly contaminated patients. The data from the body-wearable sensor is processed and broken down by an edge node in the IoMT cloud to characterize the condition of health. A puttable IoMT sensor layer, a cloud layer with Application Peripheral Interface (API), and an Android-based cell prototype are part of the proposed system. Each layer has its own function; for example, the data from the IoMT sensor layer is used to characterize the wellness of the side effects. The Android portable application layer is in charge of informing and cautioning possibly infected patient family members, the nearest hospital, and the patient’s signed doctor about the potential contamination. Two APIs and a variety of applications are synchronized in the integrated system to predict and disrupt the situation. In a word, the target is to monitor this data and send it to the cloud through the IoMT gateway and monitor these parameters using the Android app. The doctor and the patient’s relative could also observe the monitor system through the app using the device id from this app. Because there are fewer available beds in hospitals, more people are dying as a result of inadequate care.
  •  
46.
  • Gosh, Subasish, et al. (författare)
  • Recommendation System for E-commerce Using Alternating Least Squares (ALS) on Apache Spark
  • 2021
  • Ingår i: Intelligent Computing and Optimization. - Cham : Springer Nature. ; , s. 880-893
  • Konferensbidrag (refereegranskat)abstract
    • Recommendation system can predict the ratings of users to items by leveraging machine learning algorithms. The use of recommendation systems is common in e-commerce websites now-a-days. Since enormous amounts of data including users’ click streams, purchase history, demographics, social networking comments and user-item ratings are stored in e-commerce systems databases, the volume of the data is getting bigger at high speed, and the data is sparse. However, the recommendations and predictions must be made in real time, enabling to bring enormous benefits to human beings. Apache spark is well suited for applications which require high speed query of data, transformation and analytics results. Therefore, the recommendation system developed in this research is implemented on Apache Spark. Also, the matrix factorization using Alternating Least Squares (ALS) algorithm which is a type of collaborative filtering is used to solve overfitting issues in sparse data and increases prediction accuracy. The overfitting problem arises in the data as the user-item rating matrix is sparse. In this research a recommendation system for e-commerce using alternating least squares (ALS) matrix factorization method on Apache Spark MLlib is developed. The research shows that the RMSE value is significantly reduced using ALS matrix factorization method and the RMSE is 0.870. Consequently, it is shown that the ALS algorithm is suitable for training explicit feedback data set where users provide ratings for items.
  •  
47.
  • Hossain, Emam, et al. (författare)
  • A Novel Deep Learning Approach to Predict Air Quality Index
  • 2021
  • Ingår i: Proceedings of International Conference on Trends in Computational and Cognitive Engineering. - Singapore : Springer. ; , s. 367-381
  • Konferensbidrag (refereegranskat)abstract
    • In accordance with the World Health Organization’s instruction, the air quality in Bangladesh is considered perilous. A productive and precise air quality index (AQI) is a must and one of the obligatory conditions for helping the society to be viable in lieu of the consequences of air contamination. If we know the index of air quality in advance, then it would be of a great help saving our health from air contamination. This study introduces an air quality index prediction model for two mostly polluted cities in Bangladesh: Dhaka and Chattogram. Gated recurrent unit (GRU), long short-term memory (LSTM) are the two robust variation of recurrent neural network (RNN). This model combines these two together. We have used GRU as first hidden layer and LSTM as the second hidden layer of the model, followed by two dense layers. After collecting and processing the data, the model was trained on 80% of the data and then validated against the remaining data. We have evaluated the performance of the model considering MSE, RMSE, and MAE to see how much error does the model produce. Results reflect that our model can follow the actual AQI trends for both cities. At last, we have juxtaposed the performance of our proposed hybrid model against a standalone GRU model and a standalone LSTM model. Results also show that combining these two models improves the overall model’s performance.
  •  
48.
  • Hossain, Emam, et al. (författare)
  • Machine learning with Belief Rule-Based Expert Systems to predict stock price movements
  • 2022
  • Ingår i: Expert systems with applications. - : Elsevier. - 0957-4174 .- 1873-6793. ; 206
  • Tidskriftsartikel (refereegranskat)abstract
    • Price prediction of financial assets has been a key interest for researchers over the decades. Numerous techniques to predict the price movements have been developed by the researchers over the years. But a model loses its credibility once a large number of traders start using the same technique. Therefore, the traders are in continuous search of new and efficient prediction techniques. In this research, we propose a novel machine learning technique using technical analysis with Belief Rule-Based Expert System (BRBES), and incorporating the concept of Bollinger Band to forecast stock price in the next five days. A Bollinger Event is triggered when the closing price of the stock goes down the Lower Bollinger Band. The BRBES approach has never been applied to stock markets, despite its potential and the appetite of the financial markets for expert systems. We predict the price movement of the Swedish company TELIA as a proof of concept. The knowledge base of the initial BRBES is constructed by simulating the historical data and then the learning parameters are optimized using MATLAB’s fmincon function. We evaluate the performance of the trained BRBES in terms of Accuracy, Area Under ROC Curve, Root Mean Squared Error, type I error, type II error,  value, and profit/loss ratio. We compare our proposed model against a similar rule-based technique, Adaptive Neuro-Fuzzy Inference System (ANFIS), to understand the significance of the improved rule base of BRBES. We also compare the performance against Support Vector Machine (SVM), one of the most popular machine learning techniques, and a simple heuristic model. Finally, the trained BRBES is compared against recent state-of-the-art deep learning approaches to show how competitive the performance of our proposed model is. The results show that the trained BRBES produces better performance than the non-trained BRBES, ANFIS, SVM, and the heuristic approaches. Also, it indicates better or competitive performance against the deep learning approaches. Thus BRBES exhibits its potential in predicting financial asset price movement.
  •  
49.
  • Hossain Mazumder, Shazzad, et al. (författare)
  • A Belief Rule-Based Expert System to Assess Multiple Human Reaction in the Context of Facebook Posts under Uncertainty
  • 2021
  • Ingår i: 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD). - : IEEE. ; , s. 389-394
  • Konferensbidrag (refereegranskat)abstract
    • Human may have multiple reactions at a time. Social media is a real-life example where people can express their reactions or opinions. For example, Facebook has become a widely used social media where users express their opinion on different posts such as status or photo post. Therefore, user opinions can easily be achieved through social media and then analyzed and applied in different practical fields. Reaction assessment of Social media can be an excellent source of information. Therefore, an accurate assessment of human reaction is a must. Facebook provides six emoticons for each of the posts of its users. The six emoticons define six types of reactions. The user, who wants to react to any post, have to choose only one of the six emoticons. There is no scope for a user to express multiple reactions at a time. Moreover, if a user selects an emoticon, then the system takes that input as 100% of that corresponding reaction, or 0% if not selected, thus the reaction assessment system becomes a Boolean system. Therefore, the assessment of multiple reactions of the user cannot be measured with 100% certainty due to the existence of various types of uncertainties such as vagueness, imprecision, randomness, ignorance, incompleteness, and ambiguity in the system. Therefore, to assess multiple human reactions, an expert system is needed to handle all these uncertainties. The system design, development process, and applications of an expert system to assess multiple human reactions are described in this paper. For the development of the expert system, the Belief Rule-Based Inference Methodology using the Evidential Reasoning (RIMER) approach has been used and the system is named as a Belief Rule-Based Expert System (BRBES). The developed BRBES can mitigate all the uncertainties mentioned above.
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50.
  • Islam, Md. Saiful, et al. (författare)
  • A Review on Recent Advancements in FOREX Currency Prediction
  • 2020
  • Ingår i: Algorithms. - : MDPI. - 1999-4893. ; 13:8
  • Forskningsöversikt (refereegranskat)abstract
    • In recent years, the foreign exchange (FOREX) market has attracted quite a lot of scrutiny from researchers all over the world. Due to its vulnerable characteristics, different types of research have been conducted to accomplish the task of predicting future FOREX currency prices accurately. In this research, we present a comprehensive review of the recent advancements of FOREX currency prediction approaches. Besides, we provide some information about the FOREX market and cryptocurrency market. We wanted to analyze the most recent works in this field and therefore considered only those papers which were published from 2017 to 2019. We used a keyword-based searching technique to filter out popular and relevant research. Moreover, we have applied a selection algorithm to determine which papers to include in this review. Based on our selection criteria, we have reviewed 39 research articles that were published on “Elsevier”, “Springer”, and “IEEE Xplore” that predicted future FOREX prices within the stipulated time. Our research shows that in recent years, researchers have been interested mostly in neural networks models, pattern-based approaches, and optimization techniques. Our review also shows that many deep learning algorithms, such as gated recurrent unit (GRU) and long short term memory (LSTM), have been fully explored and show huge potential in time series prediction.
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