Research Topics (Social Sciences) | ||||
2019-ncov | Coronavirus infections | Immunization schedule | Pneumonia | Software |
Adolescent | Covid-19 | Incidence | Politics | Standards |
Adolescent behavior | Cross-sectional studies | Industrial ecology | Population surveillance | State of the art |
Adult | Decision making | Infant | Practice guideline | Statistical model |
Advisory committees | Deep learning | Innovation | Practice guidelines as topic | Statistics and numerical data |
Age distribution | Deep neural networks | Institutional framework | Prediction | Structural equation modeling |
Aged | Delivery | Internet | Pregnancy | Students |
Algorithm | Demography | Internet of things | Pregnancy complication | Supervised learning |
Analgesics | Disease spread | Isolation and purification | Preschool child | Surveys |
Ancestry group | Drug overdose | Land use change | Prevalence | Sustainability |
Anthropogenic effect | Economic growth | Learning | Prevention and control | Sustainable cities |
Artificial intelligence | Economic impact | Learning algorithms | Priority journal | Sustainable development |
Artificial neural network | Economics | Learning systems | Procedures | Sustainable development goals |
Augmented reality | Education | Literature review | Psychological model | Systematic review |
Automation | Educational technology | Machine learning | Psychology | Teaching |
Betacoronavirus | E-learning | Machine learning models | Psycinfo | Theoretical study |
Big data | Emission | Major clinical study | Public health | time factors |
Birth certificate | Engineering education | Male | Public health service | Tobacco |
Birth order | Environmental economics | Mapping | Publishing | tobacco dependence |
Birth rates | Environmental impact | Marital status | Regression analysis | tobacco products |
Birth weight | Environmental protection | Marriage | Research agenda | tobacco use disorder |
Carbon emission | Epidemic | Maternal age | Research design | Tourism |
Cause of death | Epidemiology | Maternal and infant health | Research work | Tourism economics |
Centers for disease control and prevention (u.s.) | Ethnic groups | Maternal characteristics | Resilience | Tourism market |
Chemistry | Ethnology | Mental disorders | Respiratory disease | Tourist behavior |
Child | Europe | Mental health | Risk | Tourist destination |
Chronic pain | Forecasting | Meta analysis | Risk assessment | Traffic management |
Circular economy | Future prospect | meta-analysis | Risk factors | Treatment outcome |
Classification | Gender | Methodology | Sars-cov-2 | Trends |
Classification (of information) | Gestational age | Middle aged | Scenario analysis | Urbanization |
climate change | Global perspective | Models | schools | Utilization |
Cognition | Greenhouse gas | Mortality | Semi- supervised learning | Vaccination |
Comparative study | Health | Multiple birth offspring | Severe acute respiratory syndrome coronavirus 2 | Vaccines |
Complication | Health care disparity | Narcotic analgesic agent | Severity of illness index | Very elderly |
Computational linguistics | Health care personnel | Network architecture | Sex distribution | Viral disease |
Computer simulation | Health personnel | Neural networks | Sex ratio | Virology |
Conceptual framework | Health surveys | Newborn | Simulation | Virtual reality |
Continental population groups | Healthcare disparities | Numerical model | Smart city | Virus pneumonia |
Controlled study | Hispanic | Obstetric delivery | Smoking | Vital statistics |
Convolution | Hispanic americans | Online learning | smoking cessation | Young adult |
Convolutional neural network | Hospitalization | Organization and management | Social media | |
Convolutional neural networks | Human experiment | Pandemics | Social status | |
Coronavirus | Humans | Perception | Socioeconomic factors | |
Coronavirus disease 2019 | Immunization | Planning | Socioeconomics |