scholarly journals Predicting Psychological Distress Amid the COVID-19 Pandemic by Machine Learning: Discrimination and Coping Mechanisms of Korean Immigrants in the U.S.

2020 ◽  
Vol 17 (17) ◽  
pp. 6057 ◽  
Author(s):  
Shinwoo Choi ◽  
Joo Young Hong ◽  
Yong Je Kim ◽  
Hyejoon Park
Keyword(s):  
Neural Network ◽  
Psychological Distress ◽  
Online Survey ◽  
Predictive Ability ◽  
Sociodemographic Factors ◽  
Coping Mechanisms ◽  
Korean Immigrants ◽  
Foreign Born ◽  
Artificial Neural Network Ann ◽  
The U.S

The current study examined the predictive ability of discrimination-related variables, coping mechanisms, and sociodemographic factors on the psychological distress level of Korean immigrants in the U.S. amid the COVID-19 pandemic. Korean immigrants (both foreign-born and U.S.-born) in the U.S. above the age of 18 were invited to participate in an online survey through purposive sampling. In order to verify the variables predicting the level of psychological distress on the final sample from 42 states (n = 790), the Artificial Neural Network (ANN) analysis, which is able to examine complex non-linear interactions among variables, was conducted. The most critical predicting variables in the neural network were a person’s resilience, experiences of everyday discrimination, and perception that racial discrimination toward Asians has increased in the U.S. since the beginning of the COVID-19 pandemic.

Comparing Predictive Ability of Classifiers in Forecasting Online Buying Behaviour

2020 ◽  
pp. 1279-1296
Author(s):  
Sanjeev Prashar ◽  
S.K. Mitra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Online Shopping ◽  
Predictive Ability ◽  
Future Research ◽  
Artificial Neural ◽  
Promotional Strategies ◽  
Artificial Neural Network Ann ◽  
Buying Behaviour ◽  
Online Buying

With Internet invading geographic boundaries and diverse demographic strata, online shopping is growing at exponential rate. Expected to grow by 45 per cent to $7.69 billion by the end of 2015, India's ecommerce market has emerged as one of the most anticipated destinations for both multinational and domestic retailers. Since their success will depend on their ability to attract shoppers to buy online, it becomes relevant for them to decipher Indian consumers' attitude and behaviour towards online shopping and to predict online buying potential in India. The effectiveness of marketing and promotional strategies and action plans also will have to be pivoted around the potential available in the market. This empirical study explores the accuracy, precision and recall of four different classifying techniques used in predicting online buying. The forecasting ability of logistic regression (LR), artificial neural network (ANN), support vector machines (SVM) and random forest (RF) in the context of willingness of shoppers' to buy online has been compared. Analysis of the data supported most of the predictions albeit with varying level of accuracy. The outcome of the study reflects the superiority of artificial neural network over the other three models in terms of the predicting power. This paper adds to the knowledge body for online retailers in reducing their vulnerability with respect to market demand and improves their preparedness to handle the market response. Managerial implications of the findings and scope for future research have been deliberated.

Family Structure, Family Processes, and Well-Being Among Asian Americans: Considering Gender and Nativity

2009 ◽  
Vol 31 (3) ◽  
pp. 301-332 ◽  
Author(s):  
Emily Walton ◽  
David T. Takeuchi
Keyword(s):  
Psychological Distress ◽  
Family Structure ◽  
Asian Americans ◽  
Extended Family ◽  
Well Being ◽  
National Sample ◽  
Foreign Born ◽  
Self Rated Health ◽  
Universal Family ◽  
The U.S

This article examines how facets of family structure and processes are linked to self-rated health and psychological distress in a national sample of Asian Americans. The authors find little support for well-established theories predicting the effects of family structure. Marital status does not affect self-rated health and has limited effects on psychological distress. The only effects of family composition are evident among men and the U.S.-born, where the presence of extended family in the home is related to lower levels of psychological distress. The authors find important gender and nativity differences in the effects of family cohesion, which protect the physical and psychological well-being of women and the U.S.-born but not men or foreign-born individuals. Findings suggest that the effects of family structure and processes on well-being are not universal. Family studies among Asian Americans that do not account for gender and nativity differences may be overlooking underlying complexity.

MULTI-PARAMETER ANN MODEL FOR FLAT-END MILLING

2008 ◽  
Vol 32 (3-4) ◽  
pp. 523-536 ◽  
Author(s):  
Hazim El Mounayri ◽  
M. Affan Badar ◽  
Gustavo A. Rengifo
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Cutting Force ◽  
Process Parameters ◽  
End Milling ◽  
Predictive Ability ◽  
Ann Model ◽  
Network Output ◽  
Input Variables ◽  
Artificial Neural Network Ann

The quality, productivity and safety of machining can be significantly improved through the optimization of cutting conditions. The first step in achieving such an objective is the development of accurate and reliable models for predicting the critical process parameters. In this paper, an innovative Artificial Neural Network (ANN) model that predicts both cutting force and surface roughness in end milling is developed and validated. A set of five input variables is selected to represent the machining conditions while twelve quantities representing two key process parameters, namely, cutting force and surface roughness, form the variables of the network output. Full factorial design of experiments is used to generate data for both training and validation. Successful training of the neural network is demonstrated through comparison of simulated and experimental results for four different output variables, namely cutting force, surface roughness, feed marks, and tooth passing frequency. The predictive ability of the model is verified experimentally by comparing simulated output variables with their experimental counterparts. A good agreement is observed.

scholarly journals The COVID-19 Outbreak and Psychological Distress in Healthcare Workers: The Role of Personality Traits, Attachment Styles, and Sociodemographic Factors

2021 ◽  
Vol 13 (9) ◽  
pp. 4992
Author(s):  
Cristina Mazza ◽  
Marco Colasanti ◽  
Eleonora Ricci ◽  
Serena Di Giandomenico ◽  
Daniela Marchetti ◽  
...  
Keyword(s):  
Mental Health ◽  
Psychological Distress ◽  
Personality Traits ◽  
Attachment Style ◽  
Attachment Styles ◽  
Online Survey ◽  
Female Gender ◽  
Sociodemographic Factors ◽  
Depression Score

The COVID-19 outbreak has exposed healthcare professionals (HPs) to increased workloads and a high risk of contagion. The present study aimed at examining the effects of the COVID-19 outbreak on the mental health of HPs in Italy, investigating the role of attachment style, personality traits, and sociodemographic variables. An online survey was administered from 18 to 22 March 2020. Respondents were 296 HPs (77% female, 23% male; aged 21–77 years). The measures employed were a sociodemographic questionnaire, the Personality Inventory for DSM-5-BF (PID-5-BF), the Attachment Style Questionnaire (ASQ), and the Depression, Anxiety and Stress Scale–21 (DASS-21). The findings showed that PID-5-BF Negative Affect, female gender, and ASQ Preoccupation with Relationships predicted high levels of stress, anxiety, and depression, respectively. Furthermore, PID-5-BF Detachment predicted higher psychological distress, as captured in the DASS-21 total score and DASS-21 Depression score, and having an infected loved one was associated with high psychological distress. Overall, the results suggest that HPs are experiencing high rates of psychological distress during the pandemic, and that specific attachment styles and personality traits might be useful in identifying those at greatest risk for developing mental health symptoms.

scholarly journals A survey to evaluate mental well being and coping strategies of adolescents during COVID-19 pandemic in Bangalore, India

2021 ◽  
Vol 10 (2) ◽  
Author(s):  
Arna Bhattacharya ◽  
DR Jigyansa Ipsita Pattnaik ◽  
DR Suhas Chandran
Keyword(s):  
Psychological Distress ◽  
Coping Strategies ◽  
Online Survey ◽  
Well Being ◽  
Development Stage ◽  
Coping Mechanisms ◽  
Cross Sectional ◽  
Brief Cope ◽  
And Coping ◽  
Mental Well Being

Background: The COVID-19 pandemic has brought about significant changes in the lifestyle of adolescents. Adolescence is a development stage of high vulnerability that can impact well-being later in life. Mental health plays an important role in one’s quality of life. Understanding coping mechanisms helps make a person safe and resilient from psychological distress. The present study aims to evaluate the mental well-being and coping strategies used by adolescents in India during the COVID-19 pandemic. Methods: A cross sectional study was conducted in a school in Bangalore, India via an online survey, circulated via the class teacher. Participants included 222 adolescents with ages ranging from 13 to 19. The DASS-21 and Brief COPE scales were used to assess mental well-being and coping mechanisms respectively. The questionnaire concluded with validated general lifestyle related questions. This included inquiring relationships between students and their families, friends, academic performance, social media consumption etc. The data was collected over a period of one month.  Results: In the DASS-21 scoring, 31.9% (n=69), 24.8% (n=55) and 5.4% (n=12) received scores indicating extremely severe depression, anxiety and stress respectively. Females were statistically found to be more depressed and anxious than males. Behavioural disengagement and self blame were commonly used coping mechanisms by those who were found to be psychologically distressed.  Conclusions: Psychological distress has been observed in adolescents in varying degrees during the COVID-19 pandemic. This should be addressed in order to prevent further distress. Keywords: adolescents, covid-19, mental well being, DASS-21, Brief COPE, depression, anxiety, stress

scholarly journals Predictive Ability of Improved Neural Network Models to Simulate Pollutant Dispersion

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Khandaker M. A. Hossain
Keyword(s):  
Neural Network ◽  
Predictive Ability ◽  
Network Models ◽  
Pollutant Dispersion ◽  
Neural Network Models ◽  
Coastal Cities ◽  
Ann Models ◽  
Using Data ◽  
Artificial Neural Network Ann ◽  
Urban Atmospheres

This paper describes the ability of artificial neural network (ANN) models to simulate the pollutant dispersion characteristics in varying urban atmospheres at different regions. ANN models are developed based on twelve meteorological (including rainfall/precipitation) and six traffic parameters/variables that have significant influence on emission/pollutant dispersion. The models are trained to predict concentration of carbon monoxide and particulate matters in urban atmospheres using field meteorological and traffic data. Training, validation, and testing of ANN models are conducted using data from the Dhaka city of Bangladesh. The models are used to simulate concentration of pollutants as well as the effect of rainfall on emission dispersion throughout the year and inversion condition during the night. The predicting ability and robustness of the models are then determined by using data of the coastal cities of Chittagong and Dhaka. ANN models based on both meteorological and traffic variables exhibit the best performance and are capable of resolving patterns of pollutant dispersion to the atmosphere for different cities.

scholarly journals Prediction-Based Maintenance of Existing Bridges Using Neural Network and Sensitivity Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Pengyong Miao
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Predictive Ability ◽  
Relative Effect ◽  
Analysis Method ◽  
Ann Model ◽  
Sensitivity Analysis Method ◽  
Artificial Neural Network Ann ◽  
Existing Bridges ◽  
Inspection Data

Bridge deterioration is affected by various factors. However, neither the relationships between these factors and deterioration are explicitly determined, nor the relative effect of each factor on deterioration is well understood. This study proposed a methodology to resolve these issues by integrating an artificial neural network (ANN) and sensitivity analysis method. The ANN was used to predict deterioration, and the sensitivity analysis method was applied to evaluate the influence of each factor on deterioration. Testing the methodology with 3,368 bridge inspection data pieces indicates that (1) the developed ANN obtained an accuracy of about 65%; and (2) seven factors were identified affecting deterioration. The established ANN model has equivalent performance for three deterioration grades and four types of bridges. Two sensitivity analysis (the Shapley value and the Sobol indices) methods were compared, and they identified the same five most important factors. Consequently, the methodology can effectively avoid the uncertainty of factors on deterioration by providing a relative importance list of factors. The methodology’s predictive ability and factor importance identification ability make it suitable for decision-makers to understand the deterioration situations and to schedule a further inspection and corresponding maintenance strategies.

Abstract P086: The Association between Psychological Distress and Diabetes Prevalence among Foreign-Born Blacks in the United States: An Examination of the 2010-2014 National Health Interview Survey

Circulation ◽  
2017 ◽  
Vol 135 (suppl_1) ◽  
Author(s):  
Nwakaego Ukonu ◽  
Ruth-Alma Turkson-Ocran ◽  
Yvonne Commodore-Mensah
Keyword(s):  
Psychological Distress ◽  
National Health ◽  
National Health Interview Survey ◽  
Diabetes Prevalence ◽  
Outcome Variable ◽  
Foreign Born ◽  
Health Interview Survey ◽  
Health Interview ◽  
Interview Survey ◽  
The U.S

Background: Psychological distress, a leading cause of disability globally, is highly prevalent in people diagnosed with diabetes. Blacks in the U.S. are disproportionately affected by chronic conditions such as diabetes. Taken together, psychological distress and diabetes constitute an immense health burden and result in poor health outcomes, increased mortality and decreased quality of life. While the association between psychological distress and diabetes is documented among U.S.-born Blacks, this relationship remains poorly examined among the growing foreign-born Black population in the U.S. Hypothesis: We hypothesized that psychological distress (non-specific) would be associated with a higher prevalence of diabetes among a sample of foreign-born Blacks in the U.S. Methods: We analyzed data on adult foreign-born Blacks in the 2010-2014 National Health Interview Survey which is a national in-person survey of non-institutionalized persons in the U.S. The main independent variable was psychological distress which was defined as a score of ≥ 12 on the Kessler Psychological Distress Scale (K-6 Scale) . The main outcome variable was diabetes. Multivariable logistic regression was performed to examine the association between psychological distress and diabetes prevalence adjusting for known confounders. Results: A total of 2,974 foreign-born Blacks were included in this study. The mean age (±SE) was 43.9 (±15.3)and nearly half (53.3%) were female. Among the individuals who received the K-6 Scale, 13.3% indicated experiencing symptoms of depression within the last 30 days and 10% had diabetes. After adjusting for age, sex, body mass index, poverty status, and marital status, we observed that foreign-born Blacks with higher levels of psychological distress had 2.30 (95% CI: 1.69-3.12) higher odds of being diabetic in comparison to those without psychological distress. Conclusion: In a sample of contemporary foreign-born Blacks in the U.S., we observed that psychological distress was associated with prevalence of diabetes, such that individuals with elevated levels of psychological distress were twice as likely to be diabetic. Additional barriers may be associated with managing diabetes when a co-morbid mental health condition such as depression is present. Thus, culturally-tailored behavioral health interventions should be developed and utilized among foreign-born Black sub-populations to help promote adherence to complex behavioral and medical regimens associated with diabetes management.

Sign in / Sign up

Export Citation Format

Share Document