scholarly journals Predictions of the mechanical properties of unidirectional fibre composites by supervised machine learning

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
M. V. Pathan ◽  
S. A. Ponnusami ◽  
J. Pathan ◽  
R. Pitisongsawat ◽  
B. Erice ◽  
...  
Keyword(s):  
Machine Learning ◽  
Constitutive Models ◽  
Principal Component ◽  
Unidirectional Composite ◽  
Transverse Plane ◽  
Supervised Machine Learning ◽  
Physically Based ◽  
Point Correlation ◽  
Point Correlation Function ◽  
Tree Regression Model

Abstract We present an application of data analytics and supervised machine learning to allow accurate predictions of the macroscopic stiffness and yield strength of a unidirectional composite loaded in the transverse plane. Predictions are obtained from the analysis of an image of the material microstructure, as well as knowledge of the constitutive models for fibres and matrix, without performing physically-based calculations. The computational framework is based on evaluating the 2-point correlation function of the images of 1800 microstructures, followed by dimensionality reduction via principal component analysis. Finite element (FE) simulations are performed on 1800 corresponding statistical volume elements (SVEs) representing cylindrical fibres in a continuous matrix, loaded in the transverse plane. A supervised machine learning (ML) exercise is performed, employing a gradient-boosted tree regression model with 10-fold cross-validation strategy. The model obtained is able to accurately predict the homogenized properties of arbitrary microstructures.

scholarly journals The prototype device for non-invasive diagnosis of arteriovenous fistula condition using machine learning methods

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Marcin Grochowina ◽  
Lucyna Leniowska ◽  
Agnieszka Gala-Błądzińska
Keyword(s):  
Machine Learning ◽  
Arteriovenous Fistula ◽  
Low Cost ◽  
Principal Component ◽  
Special Kind ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Signal Acquisition ◽  
Non Invasive ◽  
Prototype Device

Abstract Pattern recognition and automatic decision support methods provide significant advantages in the area of health protection. The aim of this work is to develop a low-cost tool for monitoring arteriovenous fistula (AVF) with the use of phono-angiography method. This article presents a developed and diagnostic device that implements classification algorithms to identify 38 patients with end stage renal disease, chronically hemodialysed using an AVF, at risk of vascular access stenosis. We report on the design, fabrication, and preliminary testing of a prototype device for non-invasive diagnosis which is very important for hemodialysed patients. The system includes three sub-modules: AVF signal acquisition, information processing and classification and a unit for presenting results. This is a non-invasive and inexpensive procedure for evaluating the sound pattern of bruit produced by AVF. With a special kind of head which has a greater sensitivity than conventional stethoscope, a sound signal from fistula was recorded. The proces of signal acquisition was performed by a dedicated software, written specifically for the purpose of our study. From the obtained phono-angiogram, 23 features were isolated for vectors used in a decision-making algorithm, including 6 features based on the waveform of time domain, and 17 features based on the frequency spectrum. Final definition of the feature vector composition was obtained by using several selection methods: the feature-class correlation, forward search, Principal Component Analysis and Joined-Pairs method. The supervised machine learning technique was then applied to develop the best classification model.

scholarly journals Principal Component Analysis Applied to Gradient Fields in Band Gap Optimization Problems for Metamaterials

2021 ◽  
Vol 2015 (1) ◽  
pp. 012047
Author(s):  
Giorgio Gnecco ◽  
Andrea Bacigalupo ◽  
Francesca Fantoni ◽  
Daniela Selvi
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Band Gap ◽  
Optimization Problems ◽  
Principal Component ◽  
Component Analysis ◽  
Physical Models ◽  
Supervised Machine Learning ◽  
Iterative Optimization ◽  
Gradient Based

Abstract A promising technique for the spectral design of acoustic metamaterials is based on the formulation of suitable constrained nonlinear optimization problems. Unfortunately, the straightforward application of classical gradient-based iterative optimization algorithms to the numerical solution of such problems is typically highly demanding, due to the complexity of the underlying physical models. Nevertheless, supervised machine learning techniques can reduce such a computational effort, e.g., by replacing the original objective functions of such optimization problems with more-easily computable approximations. In this framework, the present article describes the application of a related unsupervised machine learning technique, namely, principal component analysis, to approximate the gradient of the objective function of a band gap optimization problem for an acoustic metamaterial, with the aim of making the successive application of a gradient-based iterative optimization algorithm faster. Numerical results show the effectiveness of the proposed method.

scholarly journals Fear in a Handful of Dust: The Epidemiological, Environmental and Economic Drivers of Death by PM2.5 Pollution

2021 ◽  
Vol 18 (16) ◽  
pp. 8688
Author(s):  
James Chen ◽  
Mira Zovko ◽  
Nika Šimurina ◽  
Vatroslav Zovko
Keyword(s):  
Machine Learning ◽  
European Union ◽  
Principal Component ◽  
Well Being ◽  
Morbidity And Mortality ◽  
Supervised Machine Learning ◽  
Economic Damage ◽  
Kuznets Curve ◽  
The European Union ◽  
Pm2.5 Exposure

This study evaluates numerous epidemiological, environmental, and economic factors affecting morbidity and mortality from PM2.5 exposure in the 27 member states of the European Union. This form of air pollution inflicts considerable social and economic damage in addition to loss of life and well-being. This study creates and deploys a comprehensive data pipeline. The first step consists of conventional linear models and supervised machine learning alternatives. Those regression methods do more than predict health outcomes in the EU-27 and relate those predictions to independent variables. Linear regression and its machine learning equivalents also inform unsupervised machine learning methods such as clustering and manifold learning. Lower-dimension manifolds of this dataset’s feature space reveal the relationship among EU-27 countries and their success (or failure) in managing PM2.5 morbidity and mortality. Principal component analysis informs further interpretation of variables along economic and health-based lines. A nonlinear environmental Kuznets curve may describe the fuller relationship between economic activity and premature death from PM2.5 exposure. The European Union should bridge the historical, cultural, and economic gaps that impair these countries’ collective response to PM2.5 pollution.

scholarly journals An Ensemble-based Supervised Machine Learning Framework for Android Ransomware Detection

2021 ◽  
Vol 18 (3A) ◽  
Author(s):  
Shweta Sharma ◽  
Rama Krishna ◽  
Rakesh Kumar
Keyword(s):  
Machine Learning ◽  
Graphics Processing Unit ◽  
Principal Component ◽  
Supervised Machine Learning ◽  
Computational Time ◽  
Processing Unit ◽  
Central Processing ◽  
Analysis Technique ◽  
Malicious Apps ◽  
Time Required

With latest development in technology, the usage of smartphones to fulfill day-to-day requirements has been increased. The Android-based smartphones occupy the largest market share among other mobile operating systems. The hackers are continuously keeping an eye on Android-based smartphones by creating malicious apps housed with ransomware functionality for monetary purposes. Hackers lock the screen and/or encrypt the documents of the victim’s Android based smartphones after performing ransomware attacks. Thus, in this paper, a framework has been proposed in which we (1) utilize novel features of Android ransomware, (2) reduce the dimensionality of the features, (3) employ an ensemble learning model to detect Android ransomware, and (4) perform a comparative analysis to calculate the computational time required by machine learning models to detect Android ransomware. Our proposed framework can efficiently detect both locker and crypto ransomware. The experimental results reveal that the proposed framework detects Android ransomware by achieving an accuracy of 99.67% with Random Forest ensemble model. After reducing the dimensionality of the features with principal component analysis technique; the Logistic Regression model took least time to execute on the Graphics Processing Unit (GPU) and Central Processing Unit (CPU) in 41 milliseconds and 50 milliseconds respectively

Unsupervised Machine Learning Approach for Identifying Biomechanical Influences on Protein-Ligand Binding Affinity

2021 ◽  
Author(s):  
Arjun Singh
Keyword(s):  
Machine Learning ◽  
Binding Affinity ◽  
Principal Component ◽  
3D Models ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Drug Candidates ◽  
Machine Learning Approach ◽  
Novel Drug ◽  
Future Work

Abstract Drug discovery is incredibly time-consuming and expensive, averaging over 10 years and $985 million per drug. Calculating the binding affinity between a target protein and a ligand is critical for discovering viable drugs. Although supervised machine learning (ML) can predict binding affinity accurately, models experience severe overfitting due to an inability to identify informative properties of protein-ligand complexes. This study used unsupervised ML to reveal underlying protein-ligand characteristics that strongly influence binding affinity. Protein-ligand 3D models were collected from the PDBBind database and vectorized into 2422 features per complex. Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), K-Means Clustering, and heatmaps were used to identify groups of complexes and the features responsible for the separation. ML benchmarking was used to determine the features’ effect on ML performance. The PCA heatmap revealed groups of complexes with binding affinity of pKd < 6 and pKd > 8, and identified the number of CCCH and CCCCCH fragments in the ligand as the most responsible features. A high correlation of 0.8337, their ability to explain 18% of the binding affinity’s variance, and an error increase of 0.09 in Decision Trees when trained without the two features suggests that the fragments exist within a larger ligand substructure that significantly influences binding affinity. This discovery is a baseline for informative ligand representations to be generated so that ML models overfit less and can more reliably identify novel drug candidates. Future work will focus on validating the ligand substructure’s presence and discovering more informative intra-ligand relationships.

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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