Computational Graph-based Framework for Integrating Econometric Models and Machine Learning Algorithms in Emerging Data-Driven Analytical Environments

2021 ◽  
pp. 1-35
Author(s):  
Taehooie Kim ◽  
Xuesong (Simon) Zhou ◽  
Ram M. Pendyala
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Econometric Models ◽  
Machine Learning Algorithms ◽  
Data Driven

scholarly journals An Intelligent and Data-Driven Mobile Platform for Youth Volunteer Management using Machine Learning and Predictive Analytics

2020 ◽  
Author(s):  
Alyssa Huang ◽  
Yu Sun
Keyword(s):  
Machine Learning ◽  
High School ◽  
High School Students ◽  
Predictive Analytics ◽  
Learning Algorithms ◽  
Real Life ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
School Students ◽  
Best Fit

Volunteering is very important to high school students because it not only allows the teens to apply the knowledge and skills they have acquired to real-life scenarios, but it also enables them to make an association between helping others and their own joy of fulfillment. Choosing the right volunteering opportunities to work on can influence how the teens interact with that cause and how well they can serve the community through their volunteering services. However, high school students who look for volunteer opportunities often do not have enough information about the opportunities around them, so they tend to take whatever opportunity that comes across. On the other hand, as organizations who look for volunteers usually lack effective ways to evaluate and select the volunteers that best fit the jobs, they will just take volunteers on a first-come, firstserve basis. Therefore, there is a need to build a platform that serves as a bridge to connect the volunteers and the organizations that offer volunteer opportunities. In this paper, we focus on creating an intelligent platform that can effectively evaluate volunteer performance and predict best-fit volunteer opportunities by using machine learning algorithms to study 1) the correlation between volunteer profiles (e.g. demographics, preferred jobs, talents, previous volunteering events, etc.) and predictive volunteer performance in specific events and 2) the correlation between volunteer profiles and future volunteer opportunities. Two highest-scoring machine learning algorithms are proposed to make predictions on volunteer performance and event recommendations. We demonstrate that the two highest-scoring algorithms are able to make the best prediction for each query. Alongside the practice with the algorithms, a mobile application, which can run on both iPhone and Android platforms is also created to provide a very convenient and effective way for the volunteers and event supervisors to plan and manage their volunteer activities. As a result of this research, volunteers and organizations that look for volunteers can both benefit from this data-driven platform for a more positive overall experience.

scholarly journals Optimizing Fractional Compositions To Achieve Extraordinary Properties

2021 ◽  
Author(s):  
Andrew Falkowski ◽  
Steven Kauwe ◽  
Taylor Sparks
Keyword(s):  
Machine Learning ◽  
Loss Function ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Inverse Design ◽  
Data Driven ◽  
Model Parameters ◽  
Chemical Systems ◽  
Target Property ◽  
Successful Candidate

Traditional, data-driven materials discovery involves screening chemical systems with machine learning algorithms and selecting candidates that excel in a target property. The number of screening candidates grows infinitely large as the fractional resolution of compositions the number of included elements increases. The computational infeasibility and probability of overlooking a successful candidate grow likewise. Our approach shifts the optimization focus from model parameters to the fractions of each element in a composition. Using a pretrained network, CrabNet, and writing a custom loss function to govern a vector of element fractions, compositions can be optimized such that a predicted property is maximized or minimized. Single and multi-property optimization examples are presented that highlight the capabilities and robustness of this approach to inverse design.

scholarly journals Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach

2020 ◽  
Vol 5 ◽  
pp. 56 ◽  
Author(s):  
Rodrigo M. Carrillo-Larco ◽  
Manuel Castillo-Cara
Keyword(s):  
Machine Learning ◽  
Case Fatality Rate ◽  
Learning Algorithms ◽  
Case Fatality ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Mortality Data ◽  
Fatality Rate ◽  
Unsupervised Machine Learning ◽  
Country Level

Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes. Research on the COVID-19 pandemic benefiting from open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic. Methods: Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries; the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage. Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case. Results: The model to define the clusters was developed with 155 countries. The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets. There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number of deaths or case fatality rate. Conclusions: A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases. The model was not able to stratify countries based on COVID-19 mortality data.

What sound sources trigger misophonia? Not just chewing and breathing

2020 ◽  
Author(s):  
Heather Hansen ◽  
Andrew B. Leber ◽  
Zeynep M. Saygin
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Individual Variability ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Sound Sources ◽  
Environmental Sounds

Misophonia is a condition that affects approximately one out of five individuals, yet is relatively understudied. Misophonia involves an aversion to particular environmental sounds; previous work focuses primarily on aversions to human mouth and nose sounds (e.g., chewing, sniffling), but there exists considerable individual variability in sounds reported as bothersome, warranting an objective and data-driven investigation. What types of sounds trigger aversive reactions in misophonia, and can we identify individuals as having misophonia based on their aversiveness ratings to certain sounds? Experiment 1 asked whether human-produced oral/nasal sounds were more aversive than human-produced non-oral/nasal sounds and nonhuman/nature sounds. Experiment 2 replicated these findings using a large public sound bank and different participants, and additionally asked whether machine-learning algorithms could predict the presence and severity of misophonia. Results showed that sounds from all three source categories – not just oral/nasal sounds – were rated as significantly more aversive to individuals with misophonia compared to controls; further, modeling all source categories classified misophonia with 89% accuracy and predicted severity of misophonia with a correlation of 0.75. This work suggests that misophonia should be conceptualized as more than an aversion to human oral/nasal sounds, which has implications for future diagnostics and experimental consistency moving forward.

Towards data-driven decision-making for breast cancer patients undergoing mastectomy and reconstruction: Prediction of individual patient-reported outcomes at two-year follow-up using machine learning.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 520-520 ◽  
Author(s):  
André Pfob ◽  
Babak Mehrara ◽  
Jonas Nelson ◽  
Edwin G. Wilkins ◽  
Andrea Pusic ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Decision Making ◽  
Patient Reported Outcomes ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Group Level ◽  
Patient Reported

520 Background: Post-surgical satisfaction with breasts is a key outcome for women undergoing cancer-related mastectomy and reconstruction. Current decision making relies on group-level evidence, which may not offer optimal choice of treatment for individuals. We developed and validated machine learning algorithms to predict individual post-surgical breast-satisfaction. We aim to facilitate individualized data-driven decision making in breast cancer. Methods: We collected clinical, perioperative, and patient-reported data from 3058 women who underwent breast reconstruction due to breast cancer across 11 sites in North America. We trained and evaluated four algorithms (regularized regression, Support Vector Machine, Neural Network, Regression Tree) to predict significant changes in satisfaction with breasts at 2-year follow up using the validated BREAST-Q measure. Accuracy and area under the receiver operating characteristics curve (AUC) were used to determine algorithm performance in the test sample. Results: Machine learning algorithms were able to accurately predict changes in women’s satisfaction with breasts (see table). Baseline satisfaction with breasts was the most informative predictor of outcome, followed by radiation during or after reconstruction, nipple-sparing and mixed mastectomy, implant-based reconstruction, chemotherapy, unilateral mastectomy, lower psychological well-being, and obesity. Conclusions: We reveal the crucial role of patient-reported outcomes in determining post-operative outcomes and that Machine Learning algorithms are suitable to identify individuals who might benefit from alternative treatment decisions than suggested by group-level evidence. We provide a web-based tool for individuals considering mastectomy and reconstruction. importdemo.com . Clinical trial information: NCT01723423 . [Table: see text]

scholarly journals A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Dazhong Wu ◽  
Connor Jennings ◽  
Janis Terpenny ◽  
Robert X. Gao ◽  
Soundar Kumara
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Tool Wear ◽  
Random Forests ◽  
Manufacturing Systems ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Tool Wear Prediction

Manufacturers have faced an increasing need for the development of predictive models that predict mechanical failures and the remaining useful life (RUL) of manufacturing systems or components. Classical model-based or physics-based prognostics often require an in-depth physical understanding of the system of interest to develop closed-form mathematical models. However, prior knowledge of system behavior is not always available, especially for complex manufacturing systems and processes. To complement model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. While previous research has demonstrated the effectiveness of data-driven methods, most of these prognostic methods are based on classical machine learning techniques, such as artificial neural networks (ANNs) and support vector regression (SVR). With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. The objective of this research is to introduce a random forests (RFs)-based prognostic method for tool wear prediction as well as compare the performance of RFs with feed-forward back propagation (FFBP) ANNs and SVR. Specifically, the performance of FFBP ANNs, SVR, and RFs are compared using an experimental data collected from 315 milling tests. Experimental results have shown that RFs can generate more accurate predictions than FFBP ANNs with a single hidden layer and SVR.

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