scholarly journals Estimating the Autotrophic and Heterotrophic Respiration in the US Crop Fields using Knowledge Guided Machine Learning

2021 ◽  
Author(s):  
Licheng LIU ◽  
Wang Zhou ◽  
Zhenong Jin ◽  
Jinyun Tang ◽  
Xiaowei Jia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heterotrophic Respiration ◽  
Crop Fields ◽  
The Us

Predicting the price of Vietnamese shrimp products exported to the US market using machine learning

2021 ◽  
Author(s):  
Nguyen Minh Khiem ◽  
Yuki Takahashi ◽  
Khuu Thi Phuong Dong ◽  
Hiroki Yasuma ◽  
Nobuo Kimura
Keyword(s):  
Machine Learning ◽  
The Us

scholarly journals Analysis of the effectiveness of face-coverings on the death ratio of COVID-19 using machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ali Lafzi ◽  
Miad Boodaghi ◽  
Siavash Zamani ◽  
Niyousha Mohammadshafie ◽  
Veeraraghava Raju Hasti
Keyword(s):  
Machine Learning ◽  
West Coast ◽  
Economic Condition ◽  
Preventive Measure ◽  
Accuracy Score ◽  
Machine Learning Classification ◽  
Monthly Average ◽  
The Us ◽  
Recent Outbreak ◽  
Us West

AbstractThe recent outbreak of the COVID-19 led to death of millions of people worldwide. To stave off the spread of the virus, the authorities in the US employed different strategies, including the mask mandate order issued by the states’ governors. In the current work, we defined a parameter called average death ratio as the monthly average of the number of daily deaths to the monthly average number of daily cases. We utilized survey data to quantify people’s abidance by the mask mandate order. Additionally, we implicitly addressed the extent to which people abide by the mask mandate order, which may depend on some parameters such as population, income, and education level. Using different machine learning classification algorithms, we investigated how the decrease or increase in death ratio for the counties in the US West Coast correlates with the input parameters. The results showed that for the majority of counties, the mask mandate order decreased the death ratio, reflecting the effectiveness of such a preventive measure on the West Coast. Additionally, the changes in the death ratio demonstrated a noticeable correlation with the socio-economic condition of each county. Moreover, the results showed a promising classification accuracy score as high as 90%.

scholarly journals Russian Twitter Accounts and the Partisan Polarization of Vaccine Discourse, 2015–2017

2020 ◽  
Vol 110 (5) ◽  
pp. 718-724 ◽  
Author(s):  
Dror Walter ◽  
Yotam Ophir ◽  
Kathleen Hall Jamieson
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Health Disparities ◽  
Network Analysis ◽  
Internet Research ◽  
Unsupervised Machine Learning ◽  
Health Implications ◽  
Vaccination Rates ◽  
Partisan Polarization ◽  
The Us

Objectives. To understand how Twitter accounts operated by the Russian Internet Research Agency (IRA) discussed vaccines to increase the credibility of their manufactured personas. Methods. We analyzed 2.82 million tweets published by 2689 IRA accounts between 2015 and 2017. Combining unsupervised machine learning and network analysis to identify “thematic personas” (i.e., accounts that consistently share the same topics), we analyzed the ways in which each discussed vaccines. Results. We found differences in volume and valence of vaccine-related tweets among 9 thematic personas. Pro-Trump personas were more likely to express antivaccine sentiment. Anti-Trump personas expressed support for vaccination. Others offered a balanced valence, talked about vaccines neutrally, or did not tweet about vaccines. Conclusions. IRA-operated accounts discussed vaccines in manners consistent with fabricated US identities. Public Health Implications. IRA accounts discussed vaccines online in ways that evoked political identities. This could exacerbate recently emerging partisan gaps relating to vaccine misinformation, as differently valenced messages were targeted at different segments of the US public. These sophisticated targeting efforts, if repeated and increased in reach, could reduce vaccination rates and magnify health disparities.

scholarly journals Epidemic Dynamics via Wavelet Theory and Machine Learning with Applications to Covid-19

Biology ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 477
Author(s):  
Tô Tat Dat ◽  
Protin Frédéric ◽  
Nguyen T. T. Hang ◽  
Martel Jules ◽  
Nguyen Duc Thang ◽  
...  
Keyword(s):  
Machine Learning ◽  
New York ◽  
Wavelet Theory ◽  
The Czech Republic ◽  
Federal States ◽  
Epidemic Dynamics ◽  
Johns Hopkins University ◽  
Special Cases ◽  
The Us ◽  
Novel Method

We introduce the concept of epidemic-fitted wavelets which comprise, in particular, as special cases the number I(t) of infectious individuals at time t in classical SIR models and their derivatives. We present a novel method for modelling epidemic dynamics by a model selection method using wavelet theory and, for its applications, machine learning-based curve fitting techniques. Our universal models are functions that are finite linear combinations of epidemic-fitted wavelets. We apply our method by modelling and forecasting, based on the Johns Hopkins University dataset, the spread of the current Covid-19 (SARS-CoV-2) epidemic in France, Germany, Italy and the Czech Republic, as well as in the US federal states New York and Florida.

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 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.

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