scholarly journals A Comparison of Machine-Learning Methods to Select Socioeconomic Indicators in Cultural Landscapes

2018 ◽  
Vol 10 (11) ◽  
pp. 4312 ◽  
Author(s):  
Ana Maldonado ◽  
Darío Ramos-López ◽  
Pedro Aguilera 
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Cultural Landscape ◽  
Cultural Landscapes ◽  
Predictive Accuracy ◽  
Spatial Scales ◽  
Point Of View ◽  
Machine Learning Techniques ◽  
Socioeconomic Indicators ◽  
Linear Regression Models

Cultural landscapes are regarded to be complex socioecological systems that originated as a result of the interaction between humanity and nature across time. Cultural landscapes present complex-system properties, including nonlinear dynamics among their components. There is a close relationship between socioeconomy and landscape in cultural landscapes, so that changes in the socioeconomic dynamic have an effect on the structure and functionality of the landscape. Several numerical analyses have been carried out to study this relationship, with linear regression models being widely used. However, cultural landscapes comprise a considerable amount of elements and processes, whose interactions might not be properly captured by a linear model. In recent years, machine-learning techniques have increasingly been applied to the field of ecology to solve regression tasks. These techniques provide sound methods and algorithms for dealing with complex systems under uncertainty. The term ‘machine learning’ includes a wide variety of methods to learn models from data. In this paper, we study the relationship between socioeconomy and cultural landscape (in Andalusia, Spain) at two different spatial scales aiming at comparing different regression models from a predictive-accuracy point of view, including model trees and neural or Bayesian networks.

scholarly journals Application of Machine Learning Techniques to Predict Mechanical Properties for Polyamide 2200 (PA12) in Additive Manufacturing

2019 ◽  
Author(s):  
Ivanna Baturynska
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Linear Regression ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Tensile Modulus ◽  
Machine Learning Techniques ◽  
Linear Regression Models ◽  
Elongation At Break

Additive manufacturing (AM) is an attractive technology for manufacturing industry due to flexibility in design and functionality, but inconsistency in quality is one of the major limitations that does not allow utilizing this technology for production of end-use parts. Prediction of mechanical properties can be one of the possible ways to improve the repeatability of the results. The part placement, part orientation, and STL model properties (number of mesh triangles, surface, and volume) are used to predict tensile modulus, nominal stress and elongation at break for polyamide 2200 (also known as PA12). EOS P395 polymer powder bed fusion system was used to fabricate 217 specimens in two identical builds (434 specimens in total). Prediction is performed for XYZ, XZY, ZYX, and Angle orientations separately, and all orientations together. The different non-linear models based on machine learning methods have higher prediction accuracy compared with linear regression models. Linear regression models have prediction accuracy higher than 80% only for Tensile Modulus and Elongation at break in Angle orientation. Since orientation-based modeling has low prediction accuracy due to a small number of data points and lack of information about material properties, these models need to be improved in the future based on additional experimental work.

scholarly journals Application of Machine Learning Techniques to Predict the Mechanical Properties of Polyamide 2200 (PA12) in Additive Manufacturing

2019 ◽  
Vol 9 (6) ◽  
pp. 1060
Author(s):  
Ivanna Baturynska
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Linear Regression ◽  
Prediction Accuracy ◽  
Regression Models ◽  
Tensile Modulus ◽  
Machine Learning Techniques ◽  
Linear Regression Models ◽  
Elongation At Break

Additive manufacturing (AM) is an attractive technology for the manufacturing industry due to flexibility in its design and functionality, but inconsistency in quality is one of the major limitations preventing utilizing this technology for the production of end-use parts. The prediction of mechanical properties can be one of the possible ways to improve the repeatability of results. The part placement, part orientation, and STL model properties (number of mesh triangles, surface, and volume) are used to predict tensile modulus, nominal stress, and elongation at break for polyamide 2200 (also known as PA12). An EOS P395 polymer powder bed fusion system was used to fabricate 217 specimens in two identical builds (434 specimens in total). Prediction is performed for XYZ, XZY, ZYX, and Angle orientations separately, and all orientations together. The different non-linear models based on machine learning methods have higher prediction accuracy compared with linear regression models. Linear regression models only have prediction accuracy higher than 80% for Tensile Modulus and Elongation at break in Angle orientation. Since orientation-based modeling has low prediction accuracy due to a small number of data points and lack of information about the material properties, these models need to be improved in the future based on additional experimental work.

Machine Learning in Fine Wine Price Prediction

2015 ◽  
Vol 10 (2) ◽  
pp. 151-172 ◽  
Author(s):  
Michelle Yeo ◽  
Tristan Fletcher ◽  
John Shawe-Taylor
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Predictive Accuracy ◽  
Time Series Prediction ◽  
Feature Learning ◽  
Gaussian Process Regression ◽  
Machine Learning Techniques ◽  
Linear Regression Models ◽  
Price Prediction ◽  
Learning Techniques

AbstractAdvanced machine learning techniques like Gaussian process regression and multi-task learning are novel in the area of wine price prediction; previous research in this area being restricted to parametric linear regression models when predicting wine prices. Using historical price data of the 100 wines in the Liv-Ex 100 index, the main contributions of this paper to the field are, firstly, a clustering of the wines into two distinct clusters based on autocorrelation. Secondly, an implementation of Gaussian process regression on these wines with predictive accuracy surpassing both the trivial and simple ARMA and GARCH time series prediction benchmarks. Lastly, an implementation of an algorithm which performs multi-task feature learning with kernels on the wine returns as an extension to our optimal Gaussian process regression model. Using the optimal covariance kernel from Gaussian process regression, we achieve predictive results which are comparable to that of Gaussian process regression. Altogether, our research suggests that there is potential in using advanced machine learning techniques in wine price prediction. (JEL Classifications: C6, G12)

scholarly journals A Comprehensive Survey on Machine Learning

2021 ◽  
Vol 1 (1) ◽  
pp. 1-17
Author(s):  
Astha Singh ◽  
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Machine Learning Techniques ◽  
Linear Regression Models ◽  
Correct Model ◽  
Learning Techniques ◽  
Unseen Data ◽  
Comprehensive Survey ◽  
Statistical Agencies ◽  
High Level

The objective of this briefing is to present an overview of the topic, machine learning techniques currently in use or in consideration at statistical agencies worldwide. It is important to know the main reason why real-world scenarios should start exploring the use of machine learning techniques, terminology, approach and about few popular libraries in python, what regression is, by completely throwing light on simple as well as multiple linear and non-linear regression models and their applications, classification techniques, various clustering techniques. The material presented in this paper is the result of a study based on different models and the study of various datasets (analysis and choice of the correct model are important). While Machine Learning involves concepts of automation, it requires human guidance. Machine Learning involves a high level of generalization to get a system that performs well on yet-unseen data instances. Topics like regression, classification, and clustering, the report covers the insight of various techniques and their applications.

scholarly journals Examining the Association of Socioeconomic Position with Microcephaly and Delayed Childhood Neurodevelopment among Children with Prenatal Zika Virus Exposure

Viruses ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1342
Author(s):  
Grace M. Power ◽  
Suzanna C. Francis ◽  
Nuria Sanchez Clemente ◽  
Zilton Vasconcelos ◽  
Patricia Brasil ◽  
...  
Keyword(s):  
Socioeconomic Position ◽  
Zika Virus ◽  
Early Life ◽  
Regression Models ◽  
Social Protection ◽  
Socioeconomic Indicators ◽  
Linear Regression Models ◽  
Neurodevelopmental Outcomes ◽  
Individual Level ◽  
Virus Exposure

Increased rates of Zika virus have been identified in economically deprived areas in Brazil at the population level; yet, the implications of the interaction between socioeconomic position and prenatal Zika virus exposure on adverse neurodevelopmental outcomes remains insufficiently evaluated at the individual level. Using data collected between September 2015 and September 2019 from 163 children with qRT-PCR and/or IgM-confirmed prenatal exposure to Zika virus participating in a prospective cohort study in Rio de Janeiro, Brazil (NCT03255369), this study evaluated the relationships of socioeconomic indicators with microcephaly at birth and Bayley-III neurodevelopmental scores during the early life course. Adjusted logistic regression models indicated increased odds of microcephaly in children born to families with lower household income (OR, 95% CI: 3.85, 1.43 to 10.37) and higher household crowding (OR, 95% CI: 1.83, 1.16 to 2.91), while maternal secondary and higher education appeared to have a protective effect for microcephaly compared to primary education (OR, 95% CI: 0.33, 0.11 to 0.98 and 0.10, 0.03 to 0.36, respectively). Consistent with these findings, adjusted linear regression models indicated lower composite language (−10.78, 95% CI: −19.87 to −1.69), motor (−10.45, 95% CI: −19.22 to −1.69), and cognitive (−17.20, 95% CI: −26.13 to −8.28) scores in children whose families participated in the Bolsa Família social protection programme. As such, the results from this investigation further emphasise the detrimental effects of childhood disadvantage on human health and development by providing novel evidence on the link between individual level socioeconomic indicators and microcephaly and delayed early life neurodevelopment following prenatal Zika virus exposure.

scholarly journals Heart Failure Diagnosis, Readmission, and Mortality Prediction Using Machine Learning and Artificial Intelligence Models

2020 ◽  
Vol 7 (4) ◽  
pp. 212-219 ◽  
Author(s):  
Aixia Guo ◽  
Michael Pasque ◽  
Francis Loh ◽  
Douglas L. Mann ◽  
Philip R. O. Payne
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Outcome Prediction ◽  
Predictive Accuracy ◽  
Imbalanced Data ◽  
Mortality Prediction ◽  
Machine Learning Techniques ◽  
Patient Specific ◽  
Learning Models ◽  
Machine Learning Models

Abstract Purpose of Review One in five people will develop heart failure (HF), and 50% of HF patients die in 5 years. The HF diagnosis, readmission, and mortality prediction are essential to develop personalized prevention and treatment plans. This review summarizes recent findings and approaches of machine learning models for HF diagnostic and outcome prediction using electronic health record (EHR) data. Recent Findings A set of machine learning models have been developed for HF diagnostic and outcome prediction using diverse variables derived from EHR data, including demographic, medical note, laboratory, and image data, and achieved expert-comparable prediction results. Summary Machine learning models can facilitate the identification of HF patients, as well as accurate patient-specific assessment of their risk for readmission and mortality. Additionally, novel machine learning techniques for integration of diverse data and improvement of model predictive accuracy in imbalanced data sets are critical for further development of these promising modeling methodologies.

From machine learning to sustainable taxation: GPS traces of trucks circulating in Belgium

2020 ◽  
Author(s):  
Arnaud Adam ◽  
Isabelle Thomas
Keyword(s):  
Machine Learning ◽  
Point Of View ◽  
Machine Learning Techniques ◽  
Spatial Effects ◽  
Space And Time ◽  
Learning Techniques ◽  
Other Information ◽  
Gps Traces ◽  
The Individual ◽  
Taxation System

<p>Transport geography has always been characterized by a lack of accurate data, leading to surveys often based on samples that are spatially not representative. However, the current deluge of data collected through sensors promises to overpass this scarcity of data. We here consider one example: since April 1<sup>st</sup> 2016, a GPS tracker is mandatory within each truck circulating in Belgium for kilometre taxes. Every 30 seconds, this tracker collects the position of the truck (as well as some other information such as speed or direction), leading to an individual taxation of trucks. This contribution uses a one-week exhaustive database containing the totality of trucks circulating in Belgium, in order to understand transport fluxes within the country, as well as the spatial effects of the taxation on the circulation of trucks.</p><p>Machine learning techniques are applied on over 270 million of GPS points to detect stops of trucks, leading to transform GPS sequences into a complete Origin-Destination matrix. Using machine learning allows to accurately classify stops that are different in nature (leisure stop, (un-)loading areas, or congested roads). Based on this matrix, we firstly propose an overview of the daily traffic, as well as an evaluation of the number of stops made in every Belgian place. Secondly, GPS sequences and stops are combined, leading to characterise sub-trajectories of each truck (first/last miles and transit) by their fiscal debit. This individual characterisation, as well as its variation in space and time, are here discussed: is the individual taxation system always efficient in space and time?</p><p>This contribution helps to better understand the circulation of trucks in Belgium, the places where they stopped, as well as the importance of their locations in a fiscal point of view. What are the potential modifications of the trucks routes that would lead to a more sustainable kilometre taxation? This contribution illustrates that combining big-data and machine learning open new roads for accurately measuring and modelling transportation.</p>

scholarly journals GP.6 Improving Triaging of EEG Referrals for Rule out Infantile Spasms (ITERIS)

2021 ◽  
Vol 48 (s3) ◽  
pp. S13-S13
Author(s):  
D Djordjevic ◽  
J Tracey ◽  
M Alqahtani ◽  
J Boyd ◽  
C Go
Keyword(s):  
Machine Learning ◽  
Predictive Factors ◽  
Predictive Accuracy ◽  
Learning Model ◽  
Infantile Spasms ◽  
Low Risk ◽  
Machine Learning Techniques ◽  
Risk Category ◽  
Point System ◽  
Machine Learning Model

Background: Infantile spasms (IS) is a devastating pediatric seizure disorder for which EEG referrals are prioritized at the Hospital for Sick Children, representing a resource challenge. The goal of this study was to improve the triaging system for these referrals. Methods: Part 1: descriptive analysis was performed retrospectively on EEG referrals. Part 2: prospective questionnaires were used to determine relative risk of various predictive factors. Part 3: electronic referral form was amended to include 5 positive predictive factors. A triage point system was tested by assigning EEGs as high risk (3 days), standard risk (1 week), or low risk (2 weeks). A machine learning model was developed. Results: Most EEG referrals were from community pediatricians with a low yield of IS diagnoses. Using the 5 predictive factors, the proposed triage system accurately diagnosed all IS within 3 days. No abnormal EEGs were missed in the low-risk category. The machine learning model had over 90% predictive accuracy and will be prospectively tested. Conclusions: Improving EEG triaging for IS may be possible to prioritize higher risk patients. Machine Learning techniques can potentially be applied to help with predictions. We hope that our findings will ultimately improve resource utilization and patient care.

Sign in / Sign up

Export Citation Format

Share Document