Construction of Virtual Metrology Cloud Platform with Machine Learning Tools for Providing Factory-Wide Manufacturing Service

2020 ◽  
pp. 47-59
Author(s):  
Tang-Hsuan O ◽  
Min-Hsiung Hung ◽  
Yu-Chuan Lin ◽  
Chao-Chun Chen
Keyword(s):  
Machine Learning ◽  
Learning Tools ◽  
Cloud Platform ◽  
Virtual Metrology

A Comparative Study of Different Machine Learning Tools

2019 ◽  
Vol 7 (4) ◽  
pp. 184-190
Author(s):  
Himani Maheshwari ◽  
Pooja Goswami ◽  
Isha Rana
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Learning Tools

Improved nutrient management in cereals using Nutrient Expert and machine learning tools: Productivity, profitability and nutrient use efficiency

2021 ◽  
Vol 192 ◽  
pp. 103181
Author(s):  
Jagadish Timsina ◽  
Sudarshan Dutta ◽  
Krishna Prasad Devkota ◽  
Somsubhra Chakraborty ◽  
Ram Krishna Neupane ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nutrient Management ◽  
Nutrient Use Efficiency ◽  
Learning Tools ◽  
Nutrient Use ◽  
Use Efficiency

Paper2Wire – A Case Study of User-Centred Development of Machine Learning Tools for UX Designers

i-com ◽  
2021 ◽  
Vol 20 (1) ◽  
pp. 19-32
Author(s):  
Daniel Buschek ◽  
Charlotte Anlauff ◽  
Florian Lachner
Keyword(s):  
Machine Learning ◽  
Development Process ◽  
User Study ◽  
Concept Development ◽  
Lessons Learned ◽  
Design Tool ◽  
Learning Tools ◽  
Interface Elements ◽  
Industry Partner

Abstract This paper reflects on a case study of a user-centred concept development process for a Machine Learning (ML) based design tool, conducted at an industry partner. The resulting concept uses ML to match graphical user interface elements in sketches on paper to their digital counterparts to create consistent wireframes. A user study (N=20) with a working prototype shows that this concept is preferred by designers, compared to the previous manual procedure. Reflecting on our process and findings we discuss lessons learned for developing ML tools that respect practitioners’ needs and practices.

scholarly journals Organizing workers and machine learning tools for a less oppressive workplace

2021 ◽  
Vol 59 ◽  
pp. 102353
Author(s):  
Amber Grace Young ◽  
Ann Majchrzak ◽  
Gerald C. Kane
Keyword(s):  
Machine Learning ◽  
Learning Tools

Alignment and Identification of the Relationships between Key Performance Indicators in a Multilevel Tree Structure to Support Transportation Agency Decisions

2021 ◽  
pp. 036119812110190
Author(s):  
Hector Donaldo Mata ◽  
Mohammed Hadi ◽  
David Hale
Keyword(s):  
Machine Learning ◽  
Performance Indicators ◽  
Business Processes ◽  
Key Performance Indicators ◽  
Ordinary Least Squares ◽  
Learning Tools ◽  
Least Squares Regression ◽  
Upper Level ◽  
Selection Operator ◽  
Transportation Agencies

Transportation agencies utilize key performance indicators (KPIs) to measure the performance of their traffic networks and business processes. To make effective decisions based on these KPIs, there is a need to align the KPIs at the strategic, tactical, and operational decision levels and to set targets for these KPIs. However, there has been no known effort to develop methods to ensure this alignment producing a correlative model to explore the relationships to support the derivation of the KPI targets. Such development will lead to more realistic target setting and effective decisions based on these targets, ensuring that agency goals are met subject to the available resources. This paper presents a methodology in which the KPIs are represented in a tree-like structure that can be used to depict the association between metrics at the strategic, tactical, and operational levels. Utilizing a combination of business intelligence and machine learning tools, this paper demonstrates that it is possible not only to identify such relationships but also to quantify them. The proposed methodology compares the effectiveness and accuracy of multiple machine learning models including ordinary least squares regression (OLS), least absolute shrinkage and selection operator (LASSO), and ridge regression, for the identification and quantification of interlevel relationships. The output of the model allows the identification of which metrics have more influence on the upper-level KPI targets. The analysis can be performed at the system, facility, and segment levels, providing important insights on what investments are needed to improve system performance.

scholarly journals Phenotyping Women Based on Dietary Macronutrients, Physical Activity, and Body Weight Using Machine Learning Tools

Nutrients ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1681 ◽  
Author(s):  
Ramyaa Ramyaa ◽  
Omid Hosseini ◽  
Giri P. Krishnan ◽  
Sridevi Krishnan
Keyword(s):  
Physical Activity ◽  
Machine Learning ◽  
Cluster Analysis ◽  
Body Weight ◽  
Energy Balance ◽  
Numerical Data ◽  
Support Vector ◽  
Learning Tools ◽  
K Nearest Neighbor ◽  
Numerical Predictions

Nutritional phenotyping can help achieve personalized nutrition, and machine learning tools may offer novel means to achieve phenotyping. The primary aim of this study was to use energy balance components, namely input (dietary energy intake and macronutrient composition) and output (physical activity) to predict energy stores (body weight) as a way to evaluate their ability to identify potential phenotypes based on these parameters. From the Women’s Health Initiative Observational Study (WHI OS), carbohydrates, proteins, fats, fibers, sugars, and physical activity variables, namely energy expended from mild, moderate, and vigorous intensity activity, were used to predict current body weight (both as body weight in kilograms and as a body mass index (BMI) category). Several machine learning tools were used for this prediction. Finally, cluster analysis was used to identify putative phenotypes. For the numerical predictions, the support vector machine (SVM), neural network, and k-nearest neighbor (kNN) algorithms performed modestly, with mean approximate errors (MAEs) of 6.70 kg, 6.98 kg, and 6.90 kg, respectively. For categorical prediction, SVM performed the best (54.5% accuracy), followed closely by the bagged tree ensemble and kNN algorithms. K-means cluster analysis improved prediction using numerical data, identified 10 clusters suggestive of phenotypes, with a minimum MAE of ~1.1 kg. A classifier was used to phenotype subjects into the identified clusters, with MAEs <5 kg for 15% of the test set (n = ~2000). This study highlights the challenges, limitations, and successes in using machine learning tools on self-reported data to identify determinants of energy balance.

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