scholarly journals Digital conversations about depression among Hispanics and non-Hispanics in the US: A big‐data, machine learning analysis

2021 ◽  
Author(s):  
Ruby Castilla-Puentes ◽  
Anjali Dagar ◽  
Dinorah Villanueva ◽  
Laura Jimenez-Parrado ◽  
Liliana. Gil Valleta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Open Source ◽  
Source Analysis ◽  
Clinical Settings ◽  
Learning Technology ◽  
The Us ◽  
Negative Tone ◽  
Online Conversation ◽  
Learning Analysis

Abstract Background Digital conversations can offer unique information into the attitudes of Hispanics with depression outside of formal clinical settings and help generate useful information for medical treatment planning. Our study aimed to explore the big data from open-source digital conversations among Hispanics with regard to depression, specifically attitudes toward depression comparing Hispanics and non-Hispanics using machine learning technology. Methods Advanced machine‐learning empowered methodology was used to mine and structure open‐source digital conversations of self‐identifying Hispanics and non-Hispanics who endorsed suffering from depression and engaged in conversation about their tone, topics, and attitude towards depression. The search was limited to 12 months originating from US internet protocol (IP) addresses. Results A total of 441, 000 unique conversations about depression, including 43,000 (9.8%) for Hispanics, were posted. Source analysis revealed that 48% of conversations originated from topical sites compared to 16% on social media. Several critical differences were noted between Hispanics and non-Hispanics. In a higher percentage of Hispanics, their conversations portray “negative tone” due to depression (66% vs 39% non-Hispanics), show a resigned/hopeless attitude (44% vs. 30%) and were about ‘living with’ depression (44% vs. 25%). There were important differences in the author's determined sentiments behind the conversations among Hispanics and non-Hispanics. Conclusion In this first of its kind big data analysis of nearly a half-million digital conversations about depression using machine learning we found that Hispanics engage in an online conversation about negative, resigned, and hopeless attitude towards depression more often than non-Hispanic.

scholarly journals Digital conversations about depression among Hispanics and non-Hispanics in the US: a big‐data, machine learning analysis identifies specific characteristics of depression narratives in Hispanics

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Ruby Castilla-Puentes ◽  
Anjali Dagar ◽  
Dinorah Villanueva ◽  
Laura Jimenez-Parrado ◽  
Liliana Gil Valleta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Open Source ◽  
Cross Sectional Study ◽  
Source Analysis ◽  
Learning Technology ◽  
Cross Sectional ◽  
The Us ◽  
Negative Tone ◽  
Online Conversation

Abstract Background Digital conversations can offer unique information into the attitudes of Hispanics with depression outside of formal clinical settings and help generate useful information for medical treatment planning. Our study aimed to explore the big data from open‐source digital conversations among Hispanics with regard to depression, specifically attitudes toward depression comparing Hispanics and non-Hispanics using machine learning technology. Methods Advanced machine‐learning empowered methodology was used to mine and structure open‐source digital conversations of self‐identifying Hispanics and non-Hispanics who endorsed suffering from depression and engaged in conversation about their tone, topics, and attitude towards depression. The search was limited to 12 months originating from US internet protocol (IP) addresses. In this cross-sectional study, only unique posts were included in the analysis and were primarily analyzed for their tone, topic, and attitude towards depression between the two groups using descriptive statistical tools. Results A total of 441,000 unique conversations about depression, including 43,000 (9.8%) for Hispanics, were posted. Source analysis revealed that 48% of conversations originated from topical sites compared to 16% on social media. Several critical differences were noted between Hispanics and non-Hispanics. In a higher percentage of Hispanics, their conversations portray “negative tone” due to depression (66% vs 39% non-Hispanics), show a resigned/hopeless attitude (44% vs. 30%) and were about ‘living with’ depression (44% vs. 25%). There were important differences in the author's determined sentiments behind the conversations among Hispanics and non-Hispanics. Conclusion In this first of its kind big data analysis of nearly a half‐million digital conversations about depression using machine learning, we found that Hispanics engage in an online conversation about negative, resigned, and hopeless attitude towards depression more often than non-Hispanic.

The Rain Prediction in Australia Based Big Data Analysis and Machine Learning Technology

2021 ◽  
Author(s):  
Bohdan Polishchuk ◽  
Andrii Berko ◽  
Lyubomyr Chyrun ◽  
Myroslava Bublyk ◽  
Vadim Schuchmann
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Learning Technology

Model of predicting customer behavior based on big data analysis technologies

2021 ◽  
Vol 1 (2 (6)) ◽  
Author(s):  
Anastasiia Ivanitska ◽  
Dmytro Ivanov ◽  
Ludmila Zubik
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Customer Behavior ◽  
Learning Technologies ◽  
User Preferences ◽  
Data Sets ◽  
Learning Technology ◽  
Behavior Prediction ◽  
Network Information ◽  
Selection Of

The analysis of the available methods and models of formation of recommendations for the potential buyer in network information systems for the purpose of development of effective modules of selection of advertising is executed. The effectiveness of the use of machine learning technologies for the analysis of user preferences based on the processing of data on purchases made by users with a similar profile is substantiated. A model of recommendation formation based on machine learning technology is proposed, its work on test data sets is tested and the adequacy of the RMSE model is assessed. Keywords: behavior prediction; advertising based on similarity; collaborative filtering; matrix factorization; big data; machine learning

scholarly journals Differential and Combined Effects of Physical Activity Profiles and Prohealth Behaviors on Diabetes Prevalence among Blacks and Whites in the US Population: A Novel Bayesian Belief Network Machine Learning Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Azizi A. Seixas ◽  
Dwayne A. Henclewood ◽  
Aisha T. Langford ◽  
Samy I. McFarlane ◽  
Ferdinand Zizi ◽  
...  
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Total Sample ◽  
Diabetes Prevalence ◽  
The Us ◽  
Interview Survey ◽  
Physical Activity Recommendations ◽  
Active Lifestyles ◽  
Blacks And Whites ◽  
Learning Analysis

The current study assessed the prevalence of diabetes across four different physical activity lifestyles and infer through machine learning which combinations of physical activity, sleep, stress, and body mass index yield the lowest prevalence of diabetes in Blacks and Whites. Data were extracted from the National Health Interview Survey (NHIS) dataset from 2004–2013 containing demographics, chronic diseases, and sleep duration (N = 288,888). Of the total sample, 9.34% reported diabetes (where the prevalence of diabetes was 12.92% in Blacks/African Americans and 8.68% in Whites). Over half of the sample reported sedentary lifestyles (Blacks were more sedentary than Whites), approximately 20% reported moderately active lifestyles (Whites more than Blacks), approximately 15% reported active lifestyles (Whites more than Blacks), and approximately 6% reported very active lifestyles (Whites more than Blacks). Across four different physical activity lifestyles, Blacks consistently had a higher diabetes prevalence compared to their White counterparts. Physical activity combined with healthy sleep, low stress, and average body weight reduced the prevalence of diabetes, especially in Blacks. Our study highlights the need to provide alternative and personalized behavioral/lifestyle recommendations to generic national physical activity recommendations, specifically among Blacks, to reduce diabetes and narrow diabetes disparities between Blacks and Whites.

scholarly journals A survey of open source tools for machine learning with big data in the Hadoop ecosystem

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Sara Landset ◽  
Taghi M. Khoshgoftaar ◽  
Aaron N. Richter ◽  
Tawfiq Hasanin
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Open Source ◽  
Hadoop Ecosystem

scholarly journals Research on Pump Inspection Cycle Early Warning Method Based on Big Data

2021 ◽  
Vol 245 ◽  
pp. 02026
Author(s):  
Du Lihong ◽  
Liu Yufang ◽  
Cao Fei ◽  
Li Fang ◽  
Min Guizhi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Influencing Factors ◽  
Early Warning ◽  
Learning Technology ◽  
Inspection Period ◽  
Engineering Technology ◽  
Early Warning Model ◽  
Single Well ◽  
Warning Model

At present, the existing indicator diagram can only be used for expost judgment and can not give early warning, and the influencing factors of pump inspection period are nonlinear, multi constrained and multi variable. In this paper, big data machine learning method is used to carry out relevant research. Firstly, around the influencing factors of pump inspection cycle, relevant data are collected and the evaluation index of pump inspection cycle is designed. Then, based on feature engineering technology, the production parameters of oil wells in different pump inspection periods are calculated to form the analysis sample set of pump inspection period. Finally, the early warning model of pump inspection period is established by using machine learning technology. The experimental results show that: the pump inspection cycle early warning model established by stochastic forest algorithm can identify the pump inspection status of single well, and the accuracy rate is about 85%.

scholarly journals Uso de Ferramentas Livres para Recuperação de Conteúdos Textuais em Ambientes Big Data

2015 ◽  
Author(s):  
Brunno Attorre ◽  
Leandro Silva
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Open Source

Com o aumento do volume de dados na Web, a tarefa de construir um mecanismo de busca com alta precisão se torna cada vez mais difícil. Como uma alternativa para melhorar esses resultados, o desenvolvimento de uma ferramenta de recomendação baseada no conteúdo dos documentos a serem buscados pode se tornar bastante útil. Nesse contexto, objetivo desse trabalho é analisar como algoritmos de indexação, Machine Learning e análise textual podem melhorar os resultados de busca e, através do conteúdo buscado em cada documento, construir uma aplicação de busca e recomendação usando as tecnologias Open Source disponíveis no mercado.

scholarly journals 01 - Understanding suicidality among teenagers and adults suffering epilepsy using a big data, machine learning analysis of digital conversations

2019 ◽  
Author(s):  
Tatiana Falcone ◽  
Ruby Castilla-Puentes ◽  
Caroline Brethenoux ◽  
Liliana. Gil Valleta ◽  
Amit Anand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Learning Analysis
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