Machine Learning Model Comparison for Leak Detection in Noisy Industrial Pipelines

2020 ◽  
Author(s):  
Dimitrios Kampelopoulos ◽  
George N. Papastavrou ◽  
George P. Kousiopoulos ◽  
Nikolaos Karagiorgos ◽  
Sotirios K. Goudos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Model Comparison ◽  
Leak Detection ◽  
Learning Model ◽  
Machine Learning Model

Fraud Detection with Machine Learning - Model Comparison

2023 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Guilherme Ferreira Pelucio Salome ◽  
Jo�ão Luiz Chela ◽  
Jo�ão Carlos Pacheco Junior
Keyword(s):  
Machine Learning ◽  
Model Comparison ◽  
Fraud Detection ◽  
Learning Model ◽  
Machine Learning Model

scholarly journals Machine Learning Model Comparison for Automatic Segmentation of Intracoronary Optical Coherence Tomography and Plaque Cap Thickness Quantification

2020 ◽  
Vol 123 (2) ◽  
pp. 631-646 ◽  
Author(s):  
Caining Zhang ◽  
Xiaopeng Guo ◽  
Xiaoya Guo ◽  
David Molony ◽  
Huaguang Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Optical Coherence Tomography ◽  
Model Comparison ◽  
Automatic Segmentation ◽  
Learning Model ◽  
Optical Coherence ◽  
Machine Learning Model

A Machine Learning Model Comparison and Selection Framework for Software Defect Prediction Using VIKOR

2021 ◽  
pp. 890-898
Author(s):  
Miguel Ángel Quiroz Martinez ◽  
Byron Alcívar Martínez Tayupanda ◽  
Sulay Stephanie Camatón Paguay ◽  
Luis Andy Briones Peñafiel
Keyword(s):  
Machine Learning ◽  
Model Comparison ◽  
Learning Model ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Machine Learning Model ◽  
Selection Framework

scholarly journals Feature Engineering and Machine Learning Model Comparison for Malicious Activity Detection in the DNS-Over-HTTPS Protocol

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Matthew Behnke ◽  
Nathan Briner ◽  
Drake Cullen ◽  
Katelynn Schwerdtfeger ◽  
Jackson Warren ◽  
...  
Keyword(s):  
Machine Learning ◽  
Model Comparison ◽  
Learning Model ◽  
Feature Engineering ◽  
Activity Detection ◽  
Malicious Activity ◽  
Machine Learning Model

A Novel Machine Learning Model for Early Operational Anomaly Detection Using LWD/MWD Data

2019 ◽  
Author(s):  
Mohammed Al-Ghazal ◽  
Viranchi Vedpathak
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Learning Model ◽  
Machine Learning Model

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search

2018 ◽  
Author(s):  
Steen Lysgaard ◽  
Paul C. Jennings ◽  
Jens Strabo Hummelshøj ◽  
Thomas Bligaard ◽  
Tejs Vegge
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Au Nanoparticles ◽  
Learning Model ◽  
Energy Calculations ◽  
Atomic Distribution ◽  
Machine Learning Model ◽  
Fold Reduction ◽  
Computational Materials

A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

Smart-ML: A System for Machine Learning Model Exploration using Pipeline Graph

2020 ◽  
Author(s):  
Dhaval Patel ◽  
Shrey Shrivastava ◽  
Wesley Gifford ◽  
Stuart Siegel ◽  
Jayant Kalagnanam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Machine Learning Model

An efficient machine learning model for malicious activities recognition in water‐based industrial internet of things

2021 ◽  
Author(s):  
Gamal E. I. Selim ◽  
Ezz El‐Din Hemdan ◽  
Ahmed M. Shehata ◽  
Nawal A. El‐Fishawy
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Learning Model ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Machine Learning Model ◽  
Water Based ◽  
Efficient Machine
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