scholarly journals Optical system for on line monitoring of welding: a machine learning approach for optimal set up

ACTA IMEKO ◽  
2016 ◽  
Vol 5 (4) ◽  
pp. 4 ◽  
Author(s):  
David Di Gasbarro ◽  
Giulio D'Emilia ◽  
Emanuela Natale
Keyword(s):  
Machine Learning ◽  
Vision System ◽  
Low Cost ◽  
Laser Sheet ◽  
Measuring System ◽  
P Value ◽  
Learning Approach ◽  
Optical Measuring ◽  
Machine Learning Approach ◽  
On Line

<p class="Abstract">In this paper a methodology is described for continuous checking of the settings of a low cost vision system for automatic geometrical measurement of welding embedded on components of complicated shape. The measurement system is based on a laser sheet. Measuring conditions and the corresponding uncertainty are analyzed by evaluating their p-value and its closeness to an optimal measurement configuration also when working conditions are changed. The method aims to check the holding of optimal measuring conditions by using a machine learning approach for the vision system: based on a such methodology single images can be used to check the settings, therefore allowing a continuous and on line monitoring of the optical measuring system capabilities.</p><p class="Abstract">According to this procedure, the optical measuring system is able to reach and to hold uncertainty levels adequate for automatic dimensional checking of welding and of defects, taking into account the effects of system hardware/software incorrect settings and environmental effects, like varying lighting conditions. The paper also studies the effects of process variability on the method for quantitative evaluation, in order to propose on line solutions for this system.</p>

scholarly journals Modeling the Factors Associated with Mortality in Patients with Breast Cancer: A Machine Learning Approach

2020 ◽  
Author(s):  
Mohammad Asghari Jafarabadi ◽  
Zeynab Iraji ◽  
Roya Dolatkhah ◽  
Tohid Jafari Koshki
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cause Of Death ◽  
Additive Model ◽  
P Value ◽  
Learning Approach ◽  
Time To Event ◽  
Linear Effect ◽  
Factors Associated ◽  
Machine Learning Approach

Abstract Background: Breast cancer (BC) was the fifth leading cause of death worldwide in 2015 and the second leading cause of death in Iran in 2012. This study aimed to model the factors associated with mortality in patients with BC utilizing the machine learning approach.Methods: We used data of patients with primary BC during 2007-2016 in Tabriz, Iran. The data were analyzed using decision tree (DT), boosted tree (BT), random forest (RF), k-nearest neighbors (KNN) and generalized additive model (GAM) with inverse probability of censoring weighting (IPCW) technique to assess the risk factors of mortality. The models were compared by using diagnostic accuracy measures.Results: Accuracy of the models ranged from 76.0 to 93.0%, with sensitivity of 82.5-98.8% and specificity of 72.2-99.4%. The GAM fit the data best with accuracy of 93.0% (95% CI: [90.5, 95.0]), sensitivity of 98.8% (95% CI: [96.9, 99.7]) and specificity of 84.3% (95% CI: [78.8, 88.9]) where non-linear effect of age (p-value = 0.006), grade (p-value = 0.024) and time to event (p-value < 0.001) on mortality were significant. Conclusion: The GAM seems to be an optimal model for classifying the mortality in patients with BC. Considering the time to event, age and grade, as the prognostic factors obtained by GAM, more accurate prevention planning may be designed.

scholarly journals Modeling The Factors Associated with Mortality in Patients with Breast Cancer: A Machine Learning Approach

2020 ◽  
Author(s):  
Mohammad Asghari Jafarabadi ◽  
Zaynab Iraji ◽  
Roya Dolatkhah ◽  
Tohid jafari koshki
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cause Of Death ◽  
Additive Model ◽  
P Value ◽  
Learning Approach ◽  
Time To Event ◽  
Linear Effect ◽  
Factors Associated ◽  
Machine Learning Approach

Abstract Background: Breast cancer (BC) was the fifth leading cause of death worldwide in 2015 and the second leading cause of death in Iran in 2012. This study aimed to model the factors associated with mortality in patients with BC utilizing the machine learning approach.Methods: We used data of patients with primary BC during 2007-2016 in Tabriz, Iran. The data were analyzed using decision tree (DT), boosted tree (BT), random forest (RF), k-nearest neighbors (KNN) and generalized additive model (GAM) with inverse probability of censoring weighting (IPCW) technique to assess the risk factors of mortality. The models were compared by using diagnostic accuracy measures.Results: Accuracy of the models ranged from 76.0 to 93.0%, with sensitivity of 82.5-98.8% and specificity of 72.2-99.4%. The GAM fit the data best with accuracy of 93.0% (95% CI: [90.5, 95.0]), sensitivity of 98.8% (95% CI: [96.9, 99.7]) and specificity of 84.3% (95% CI: [78.8, 88.9]) where non-linear effect of age (p-value = 0.006), grade (p-value = 0.024) and time to event (p-value < 0.001) on mortality were significant. Conclusion: The GAM seems to be an optimal model for classifying the mortality in patients with BC. Considering the time to event, age and grade, as the prognostic factors obtained by GAM, more accurate prevention planning may be designed.

scholarly journals Holistic Prediction of pKa in Diverse Solvents Based on Machine Learning Approach

2020 ◽  
Author(s):  
Qi Yang ◽  
Yao Li ◽  
Jin-Dong Yang ◽  
Yidi Liu ◽  
Long Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Current Model ◽  
Structural Features ◽  
Acid Dissociation ◽  
Learning Approach ◽  
Acid Dissociation Constant ◽  
Holistic Model ◽  
Solvent Models ◽  
Machine Learning Approach ◽  
On Line

The acid dissociation constant p<i>K</i><sub>a</sub> dictates a molecule’s ionic status, and is a critical physicochemical property in rationalizing acid-base chemistry in solution and in many biological contexts. Although numerous theoretic approaches have been developed for predicating aqueous p<i>K</i><sub>a</sub>, fast and accurate prediction of non-aqueous p<i>K</i><sub>a</sub>s has remained a major challenge. On the basis of <i>i</i>BonD experimental p<i>K</i><sub>a</sub> database curated across 39 solvents, a holistic p<i>K</i><sub>a</sub> prediction model was established by using machine learning approach. Structural and physical organic parameters combined descriptors (SPOC) were introduced to represent the electronic and structural features of molecules. With SPOC and ionic status labelling (ISL), the holistic models trained with neural network or XGBoost algorithm showed the best prediction performance <a>with MAE value as low as 0.87</a> p<i>K</i><sub>a</sub> unit. The holistic model showed better performance than all the tested single-solvent models (SSMs), verifying the transfer learning features. The capability of prediction in diverse solvents allows for a comprehensive mapping of all the possible p<i>K</i><sub>a</sub> correlations between different solvents. The <i>i</i>BonD holistic model was validated by prediction of aqueous p<i>K</i><sub>a</sub> and micro-p<i>K</i><sub>a</sub> of pharmaceutical molecules and p<i>K</i><sub>a</sub>s of organocatalysts in DMSO and MeCN with high accuracy. An on-line prediction platform (<a href="http://pka.luoszgroup.com/">http://pka.luoszgroup.com</a>) was constructed based on the current model.

scholarly journals Machine Learning Identification of Capsid Mutations to Improve AAV Production Fitness

2021 ◽  
Author(s):  
Georgios Mikos ◽  
Weitong Chen ◽  
Junghae Suh
Keyword(s):  
Machine Learning ◽  
Domain Knowledge ◽  
Structural Data ◽  
P Value ◽  
Learning Approach ◽  
Wild Type ◽  
Adeno Associated Virus ◽  
Threefold Increase ◽  
Machine Learning Approach ◽  
Experimental Approaches

The adeno-associated virus (AAV) holds great potential for gene therapy efforts by providing a viable vector. However, current efforts are constrained by a lack of AAV variants that exhibit specific tropisms or immunogenicity and a lack of sustainable industrial projection. Departing from experimental approaches to addressing these issues, we built a model to predict residue mutations to improve AAV production fitness. Our model leverages the evolutionary paradigm and microenvironment characteristics by analyzing structural AAV data without needing domain knowledge or experimental fitness data for AAV as inputs. When testing our model's predictions for AAV2 residue mutations, we found a threefold increase in the percent of mutations yielding variants with better production fitness than wild type compared to random mutations, achieving a p-value of 7.46×10-12. Given these results, our machine learning approach of using structural data to approximate fitness data has the potential to accelerate AAV development.

scholarly journals Holistic Prediction of pKa in Diverse Solvents Based on Machine Learning Approach

2020 ◽  
Author(s):  
Qi Yang ◽  
Yao Li ◽  
Jin-Dong Yang ◽  
Yidi Liu ◽  
Long Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Current Model ◽  
Structural Features ◽  
Acid Dissociation ◽  
Learning Approach ◽  
Acid Dissociation Constant ◽  
Holistic Model ◽  
Solvent Models ◽  
Machine Learning Approach ◽  
On Line

The acid dissociation constant p<i>K</i><sub>a</sub> dictates a molecule’s ionic status, and is a critical physicochemical property in rationalizing acid-base chemistry in solution and in many biological contexts. Although numerous theoretic approaches have been developed for predicating aqueous p<i>K</i><sub>a</sub>, fast and accurate prediction of non-aqueous p<i>K</i><sub>a</sub>s has remained a major challenge. On the basis of <i>i</i>BonD experimental p<i>K</i><sub>a</sub> database curated across 39 solvents, a holistic p<i>K</i><sub>a</sub> prediction model was established by using machine learning approach. Structural and physical organic parameters combined descriptors (SPOC) were introduced to represent the electronic and structural features of molecules. With SPOC and ionic status labelling (ISL), the holistic models trained with neural network or XGBoost algorithm showed the best prediction performance <a>with MAE value as low as 0.87</a> p<i>K</i><sub>a</sub> unit. The holistic model showed better performance than all the tested single-solvent models (SSMs), verifying the transfer learning features. The capability of prediction in diverse solvents allows for a comprehensive mapping of all the possible p<i>K</i><sub>a</sub> correlations between different solvents. The <i>i</i>BonD holistic model was validated by prediction of aqueous p<i>K</i><sub>a</sub> and micro-p<i>K</i><sub>a</sub> of pharmaceutical molecules and p<i>K</i><sub>a</sub>s of organocatalysts in DMSO and MeCN with high accuracy. An on-line prediction platform (<a href="http://pka.luoszgroup.com/">http://pka.luoszgroup.com</a>) was constructed based on the current model.

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