Machine learning-based marker length estimation for garment mass customization

2021 ◽  
Author(s):  
Yanni Xu ◽  
Sébastien Thomassey ◽  
Xianyi Zeng
Keyword(s):  
Machine Learning ◽  
Mass Customization ◽  
Length Estimation

scholarly journals ModelTeller: Model Selection for Optimal Phylogenetic Reconstruction Using Machine Learning

2020 ◽  
Vol 37 (11) ◽  
pp. 3338-3352
Author(s):  
Shiran Abadi ◽  
Oren Avram ◽  
Saharon Rosset ◽  
Tal Pupko ◽  
Itay Mayrose
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Selection Criteria ◽  
Sequence Data ◽  
Phylogenetic Reconstruction ◽  
Branch Length ◽  
Model Parameters ◽  
Model Selection Criteria ◽  
Learning Framework ◽  
Length Estimation

Abstract Statistical criteria have long been the standard for selecting the best model for phylogenetic reconstruction and downstream statistical inference. Although model selection is regarded as a fundamental step in phylogenetics, existing methods for this task consume computational resources for long processing time, they are not always feasible, and sometimes depend on preliminary assumptions which do not hold for sequence data. Moreover, although these methods are dedicated to revealing the processes that underlie the sequence data, they do not always produce the most accurate trees. Notably, phylogeny reconstruction consists of two related tasks, topology reconstruction and branch-length estimation. It was previously shown that in many cases the most complex model, GTR+I+G, leads to topologies that are as accurate as using existing model selection criteria, but overestimates branch lengths. Here, we present ModelTeller, a computational methodology for phylogenetic model selection, devised within the machine-learning framework, optimized to predict the most accurate nucleotide substitution model for branch-length estimation. We demonstrate that ModelTeller leads to more accurate branch-length inference than current model selection criteria on data sets simulated under realistic processes. ModelTeller relies on a readily implemented machine-learning model and thus the prediction according to features extracted from the sequence data results in a substantial decrease in running time compared with existing strategies. By harnessing the machine-learning framework, we distinguish between features that mostly contribute to branch-length optimization, concerning the extent of sequence divergence, and features that are related to estimates of the model parameters that are important for the selection made by current criteria.

scholarly journals Node Detection and Internode Length Estimation of Tomato Seedlings Based on Image Analysis and Machine Learning

Sensors ◽  
2016 ◽  
Vol 16 (7) ◽  
pp. 1044 ◽  
Author(s):  
Kyosuke Yamamoto ◽  
Wei Guo ◽  
Seishi Ninomiya
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Internode Length ◽  
Tomato Seedlings ◽  
Length Estimation

Adaptive Interaction for Mass Customisation

2012 ◽  
pp. 2101-2116
Author(s):  
Gulden Uchyigit
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Ubiquitous Computing ◽  
Mass Customization ◽  
New Technologies ◽  
State Of The Art ◽  
Computing Technologies ◽  
One To One ◽  
Marketing Service ◽  
Innovative Techniques

The popularisation of mass customization and the need for integration of the user needs into the design, production and marketing phases has called for more innovative methods to be introduced into this area. At present the continuous growth of the world wide web and its rapid integration into people’s everyday lives and the popularisation of new technologies such as ubiquitous computing making possible the computing everywhere paradigm, offers a more desirable alternative for vendors in reaching their customers using more innovative techniques in an attempt to provide each customer with a one-to-one design, manufacturing and marketing service. The integration of ubiquitous computing technologies with machine learning and data mining techniques, which has been popular in personalization techniques, will serve to bring about innovative changes in this area. In future years this may revolutionise the way in which vendors can reach their customers offering every customer a tailored one-to-one service from design, to manufacturing, to delivery. This chapter will present the state of the art techniques to enable the combination of machine learning, data mining and ubiquitous computing technologies which will serve to provide innovative techniques applications and user interfaces for mass customization systems. This is currently a field of intense research and development activity and some technologies are already on the path to practical application. This chapter will present a state of the art survey of these technologies and their applications.

scholarly journals Double Mammary In Situ: Predicting Feasibility of Right Mammary Artery In Situ for Circumflex Coronary Artery System

2020 ◽  
Author(s):  
Rafik Margaryan ◽  
Daniele Della Latta ◽  
Giacomo Bianchi ◽  
Nicola Martini ◽  
Andrea Gori ◽  
...  
Keyword(s):  
Machine Learning ◽  
Positive Outcome ◽  
Positive Outcomes ◽  
X Rays ◽  
Circumflex Coronary Artery ◽  
Arterial Revascularization ◽  
Length Estimation ◽  
Percent Accuracy ◽  
The Right

AbstractArterial revascularization of the myocardium is well established and it is the gold standard.Double mammary artery in situ revascularisation seems less attractive to surgeons because of limited mammary length, scare, or no means of its length estimation. Here we hypothesized that the right mammary artery length is relative to chest anatomy, mainly to its type. We sought to estimate the feasibility, hence the mammary artery length using machine learning (neural networks). The predictive model was able to predict a positive outcome with 95% percent accuracy. (p < 0.01). Model’s sensitivity and specificity were 96% and 95% respectively.Neural networks can be used to predict double mammary feasibility using chest x-rays. Model is capable of predicting positive outcomes with 95% accuracy. Last update date “2020-12-04 15:43:45”

scholarly journals Feature Selection for Machine Learning Based Step Length Estimation Algorithms

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 778
Author(s):  
Stef Vandermeeren ◽  
Herwig Bruneel ◽  
Heidi Steendam
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Step Length ◽  
Indoor Positioning ◽  
Test Person ◽  
Positioning Systems ◽  
Estimation Algorithms ◽  
Length Estimation

An accurate step length estimation can provide valuable information to different applications such as indoor positioning systems or it can be helpful when analyzing the gait of a user, which can then be used to detect various gait impairments that lead to a reduced step length (caused by e.g., Parkinson’s disease or multiple sclerosis). In this paper, we focus on the estimation of the step length using machine learning techniques that could be used in an indoor positioning system. Previous step length algorithms tried to model the length of a step based on measurements from the accelerometer and some tuneable (user-specific) parameters. Machine-learning-based step length estimation algorithms eliminate these parameters to be tuned. Instead, to adapt these algorithms to different users, it suffices to provide examples of the length of multiple steps for different persons to the machine learning algorithm, so that in the training phase the algorithm can learn to predict the step length for different users. Until now, these machine learning algorithms were trained with features that were chosen intuitively. In this paper, we consider a systematic feature selection algorithm to be able to determine the features from a large collection of features, resulting in the best performance. This resulted in a step length estimator with a mean absolute error of 3.48 cm for a known test person and 4.19 cm for an unknown test person, while current state-of-the-art machine-learning-based step length estimators resulted in a mean absolute error of 4.94 cm and 6.27 cm for respectively a known and unknown test person.

Adaptive Interaction for Mass Customisation

2010 ◽  
pp. 133-148
Author(s):  
Gulden Uchyigit
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Ubiquitous Computing ◽  
Mass Customization ◽  
New Technologies ◽  
State Of The Art ◽  
Computing Technologies ◽  
One To One ◽  
Marketing Service ◽  
Innovative Techniques

The popularisation of mass customization and the need for integration of the user needs into the design, production and marketing phases has called for more innovative methods to be introduced into this area. At present the continuous growth of the world wide web and its rapid integration into people’s everyday lives and the popularisation of new technologies such as ubiquitous computing making possible the computing everywhere paradigm, offers a more desirable alternative for vendors in reaching their customers using more innovative techniques in an attempt to provide each customer with a one-to-one design, manufacturing and marketing service. The integration of ubiquitous computing technologies with machine learning and data mining techniques, which has been popular in personalization techniques, will serve to bring about innovative changes in this area. In future years this may revolutionise the way in which vendors can reach their customers offering every customer a tailored one-to-one service from design, to manufacturing, to delivery. This chapter will present the state of the art techniques to enable the combination of machine learning, data mining and ubiquitous computing technologies which will serve to provide innovative techniques applications and user interfaces for mass customization systems. This is currently a field of intense research and development activity and some technologies are already on the path to practical application. This chapter will present a state of the art survey of these technologies and their applications.

scholarly journals ModelTeller: model selection for optimal phylogenetic reconstruction using machine learning

2020 ◽  
Author(s):  
Shiran Abadi ◽  
Oren Avram ◽  
Saharon Rosset ◽  
Tal Pupko ◽  
Itay Mayrose
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Selection Criteria ◽  
Sequence Data ◽  
Phylogenetic Reconstruction ◽  
Branch Length ◽  
Complex Model ◽  
Estimation Accuracy ◽  
Model Selection Criteria ◽  
Length Estimation

AbstractStatistical criteria have long been the standard for selecting the best model for phylogenetic reconstruction and downstream statistical inference. While model selection is regarded as a fundamental step in phylogenetics, existing methods for this task consume computational resources for long processing time, they are not always feasible, and sometimes depend on preliminary assumptions which do not hold for sequence data. Moreover, while these methods are dedicated to revealing the processes that underlie the sequence data, in most cases they do not produce the most accurate trees. Notably, phylogeny reconstruction consists of two related tasks, topology reconstruction and branch-length estimation. It was previously shown that in many cases the most complex model, GTR+I+G, leads to topologies that are as accurate as using existing model selection criteria, but overestimates branch lengths. Here, we present ModelTeller, a computational methodology for phylogenetic model selection, devised within the machine-learning framework, optimized to predict the most accurate model for branch-length estimation accuracy. ModelTeller relies on a readily implemented machine-learning model and thus the prediction according to features extracted from the sequence data results in a substantial decrease in running time compared to existing strategies. We show that on datasets simulated under simple homogenous substitution models ModelTeller leads to branch-length estimation that is as accurate as the statistical model selection criteria. We then demonstrate that ModelTeller outperforms these criteria when more intricate patterns – that aim at mimicking realistic processes – are considered.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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