The investigation of the effect of particle size on wear performance of AA7075/Al2O3 composites using statistical analysis and different machine learning methods

2021 ◽  
Author(s):  
Fatih Aydin
Keyword(s):  
Machine Learning ◽  
Particle Size ◽  
Statistical Analysis ◽  
Learning Methods ◽  
Wear Performance ◽  
Machine Learning Methods ◽  
Effect Of Particle Size

Data in Construction Grammar

2013 ◽  
Author(s):  
Stefan Th. Gries
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Computational Linguistic ◽  
Statistical Analysis ◽  
Construction Grammar ◽  
Data Types ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
New Directions ◽  
Corpus Data

This chapter examines the types of data used in constructionist approaches and the parameters along which data types can be classified. It discusses different kinds of quantitative observational/corpus data (frequencies, probabilities, association measures) and their statistical analysis. In addition, it provides a survey of a variety of different experimental data (novel word/construction learning, priming, sorting, etc.). Finally, the chapter discusses computational-linguistic/machine-learning methods as well as new directions for the development of new data and methods in Construction Grammar.

Supervised Machine Learning Algorithms for Evaluation of Solid Lipid Nanoparticles and Particle Size

2019 ◽  
Vol 21 (9) ◽  
pp. 693-699 ◽  
Author(s):  
A. Alper Öztürk ◽  
A. Bilge Gündüz ◽  
Ozan Ozisik
Keyword(s):  
Machine Learning ◽  
Particle Size ◽  
High Speed ◽  
Mean Absolute Error ◽  
Mixing Time ◽  
Learning Algorithms ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods

Aims and Objectives: Solid Lipid Nanoparticles (SLNs) are pharmaceutical delivery systems that have advantages such as controlled drug release, long-term stability etc. Particle Size (PS) is one of the important criteria of SLNs. These factors affect drug release rate, bio-distribution etc. In this study, the formulation of SLNs using high-speed homogenization technique has been evaluated. The main emphasis of the work is to study whether the effect of mixing time and formulation ingredients on PS can be modeled. For this purpose, different machine learning algorithms have been applied and evaluated using the mean absolute error metric. Materials and Methods: SLNs were prepared by high-speed homogenizaton. PS, size distribution and zeta potential measurements were performed on freshly prepared samples. In order to model the formulation of the particles in terms of mixing time and formulation ingredients and evaluate the predictability of PS depending on these parameters, different machine learning algorithms were applied on the prepared dataset and the performances of the algorithms were also evaluated. Results: PS of SLNs obtained was in the range of 263-498nm. The results present that PS of SLNs can be best estimated by decision tree based methods, among which Random Forest has the least mean absolute error value with 0.028. As a result, the estimation of machine learning algorithms demonstrates that particle size can be estimated by both decision rule-based machine learning methods and function fitting machine learning methods. Conclusion: Our findings present that machine learning methods can be highly useful for determining formulation parameters for further research.

scholarly journals Data-driven monitoring of the gearbox using multifractal analysis and machine learning methods

2019 ◽  
Vol 252 ◽  
pp. 06006
Author(s):  
Andrzej Puchalski ◽  
Iwona Komorska
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Statistical Analysis ◽  
Multifractal Analysis ◽  
Diagnostic Methods ◽  
Data Driven ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Wavelet Leaders

Data-driven diagnostic methods allow to obtain a statistical model of time series and to identify deviations of recorded data from the pattern of the monitored system. Statistical analysis of time series of mechanical vibrations creates a new quality in the monitoring of rotating machines. Most real vibration signals exhibit nonlinear properties well described by scaling exponents. Multifractal analysis, which relies mainly on assessing local singularity exponents, has become a popular tool for statistical analysis of empirical data. There are many methods to study time series in terms of their fractality. Comparing computational complexity, a wavelet leaders algorithm was chosen. Using Wavelet Leaders Multifractal Formalism, multifractal parameters were estimated, taking them as diagnostic features in the pattern recognition procedure, using machine learning methods. The classification was performed using neural network, k-nearest neighbours’ algorithm and support vector machine. The article presents the results of vibration acceleration tests in a demonstration transmission system that allows simulations of assembly errors and teeth wear.

scholarly journals Salivary cystatin S levels in children with early childhood caries in comparison with caries-free children; statistical analysis and machine learning

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Maryam Koopaie ◽  
Mahsa Salamati ◽  
Roshanak Montazeri ◽  
Mansour Davoudi ◽  
Sajad Kolahdooz
Keyword(s):  
Machine Learning ◽  
Early Childhood ◽  
Statistical Analysis ◽  
Roc Curve ◽  
Early Childhood Caries ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Whole Saliva ◽  
Significant Difference ◽  
Childhood Caries

Abstract Background Early childhood caries is the most common infectious disease in childhood, with a high prevalence in developing countries. The assessment of the variables that influence early childhood caries as well as its pathophysiology leads to improved control of this disease. Cystatin S, as one of the salivary proteins, has an essential role in pellicle formation, tooth re-mineralization, and protection. The present study aims to assess salivary cystatin S levels and demographic data in early childhood caries in comparison with caries-free ones using statistical analysis and machine learning methods. Methods A cross-sectional, case–control study was undertaken on 20 cases of early childhood caries and 20 caries-free children as a control. Unstimulated whole saliva samples were collected by suction. Cystatin S concentrations in samples were determined using human cystatin S ELISA kit. The checklist was collected from participants about demographic characteristics, oral health status, and dietary habits by interviewing parents. Regression and receiver operating characteristic (ROC) curve analysis were done to evaluate the potential role of cystatin S salivary level and demographic using statistical analysis and machine learning. Results The mean value of salivary cystatin S concentration in the early childhood caries group was 191.55 ± 81.90 (ng/ml) and in the caries-free group was 370.06 ± 128.87 (ng/ml). T-test analysis showed a statistically significant difference between early childhood caries and caries-free groups in salivary cystatin S levels (p = 0.032). Investigation of the area under the curve (AUC) and accuracy of the ROC curve revealed that the logistic regression model based on salivary cystatin S levels and birth weight had the most and acceptable potential for discriminating of early childhood caries from caries-free controls. Furthermore, using salivary cystatin S levels enhanced the capability of machine learning methods to differentiate early childhood caries from caries-free controls. Conclusion Salivary cystatin S levels in caries-free children were higher than the children with early childhood caries. Results of the present study suggest that considering clinical examination, demographic and socioeconomic factors, along with the salivary cystatin S levels, could be usefull for early diagnosis ofearly childhood caries in high-risk children; furthermore, cystatin S is a protective factor against dental caries.

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