scholarly journals Build Orientation Optimization for Strength Enhancement of FDM Parts Using Machine Learning based Algorithm

2019 ◽  
Author(s):  
Manoj Malviya ◽  
Kaushal A Desai
Keyword(s):  
Machine Learning ◽  
Brute Force ◽  
Initial Sample ◽  
Bayesian Algorithm ◽  
Build Orientation ◽  
Optimization Framework ◽  
Sample Data ◽  
Artificial Neural Network Ann ◽  
Brute Force Approach ◽  
Orientation Optimization

The layered fabrication approach induces directional anisotropy and impacts mechanical strength of FDM components significantly. This paper proposes generalized machine learning based parameter optimization framework to determine optimal build orientation for FDM components. The algorithm determines ideal build orientation by maximizing the minimum Factor of Safety (FoS) for the component under prescribed loading conditions ensuring its even distribution. An Artificial Neural Network (ANN) coupled with Bayesian algorithm has been employed to accelerate the optimization process. The algorithm begins with an initial sample data collected using brute force approach; uses single layered ANN for approximation and optimization is achieved using Bayesian algorithm. A series of computational experiments considering five different test components has been devised to evaluate the performance and efficacy of the proposed algorithm. These experiments demonstrated that the proposed algorithm can determine the optimum building orientation effectively with certain limitations

scholarly journals Build Orientation Optimization for Strength Enhancement of 3-D Printed Parts Using Machine Learning based Algorithm

2019 ◽  
Author(s):  
Manoj Malviya
Keyword(s):  
Machine Learning ◽  
Brute Force ◽  
Initial Sample ◽  
Bayesian Algorithm ◽  
Build Orientation ◽  
Optimization Framework ◽  
Sample Data ◽  
Artificial Neural Network Ann ◽  
Brute Force Approach ◽  
Orientation Optimization

The layered fabrication approach induces directional anisotropy and impacts the mechanical strength of FDM components significantly. This paper proposes a generalized machine learning based parameter optimization framework to determine optimal build orientation for FDM components. The algorithm determines ideal build orientation by maximizing the minimum Factor of Safety (FoS) for the component under prescribed loading conditions ensuring its even distribution. An Artificial Neural Network (ANN) coupled with Bayesian algorithm has been employed to accelerate the optimization process. The algorithm begins with an initial sample data collected using a brute force approach; uses single layered ANN for approximation and optimization is achieved using a Bayesian algorithm. A series of computational experiments considering five different test components have been devised to evaluate the performance and efficacy of the proposed algorithm. These experiments demonstrated that the proposed algorithm can determine the optimum building orientation effectively with certain limitations.

scholarly journals Feature partitioning for robust tree ensembles and their certification in adversarial scenarios

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Stefano Calzavara ◽  
Claudio Lucchese ◽  
Federico Marcuzzi ◽  
Salvatore Orlando
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Brute Force ◽  
Accuracy Improvement ◽  
Safe Environment ◽  
Feature Partitioning ◽  
Feature Based ◽  
Brute Force Approach ◽  
The Given ◽  
Certification Method

AbstractMachine learning algorithms, however effective, are known to be vulnerable in adversarial scenarios where a malicious user may inject manipulated instances. In this work, we focus on evasion attacks, where a model is trained in a safe environment and exposed to attacks at inference time. The attacker aims at finding a perturbation of an instance that changes the model outcome.We propose a model-agnostic strategy that builds a robust ensemble by training its basic models on feature-based partitions of the given dataset. Our algorithm guarantees that the majority of the models in the ensemble cannot be affected by the attacker. We apply the proposed strategy to decision tree ensembles, and we also propose an approximate certification method for tree ensembles that efficiently provides a lower bound of the accuracy of a forest in the presence of attacks on a given dataset avoiding the costly computation of evasion attacks.Experimental evaluation on publicly available datasets shows that the proposed feature partitioning strategy provides a significant accuracy improvement with respect to competitor algorithms and that the proposed certification method allows ones to accurately estimate the effectiveness of a classifier where the brute-force approach would be unfeasible.

Diseño de algoritmo compuesto por Machine Learning y un modelo probabilístico para la detección de diabetes

2021 ◽  
Author(s):  
◽  
C. E. Cañedo Figueroa
Keyword(s):  
Neural Network ◽  
Diabetes Mellitus ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Metabolic Disorder ◽  
Bayesian Algorithm ◽  
Chronic Hyperglycemia ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Prediction Of Diabetes

Diabetes mellitus (DM) is a type of metabolic disorder which causes chronic hyperglycemia. This alteration usually occurs due to an inadequate secretion of insulin. In the present work, a set of algorithms for the detection and prediction of diabetes was carried out using Pimas database. The algorithms used were: a Naive Bayesian algorithm with an 79.67% F1, a KNN algorithm with an 79.64% F1, an Artificial Neural Network (ANN) with an 74.07% F1 and an algorithm composed of the three previous algorithms with an 80.32% F1.

scholarly journals Machine Learning-Based Prediction of Air Quality

2020 ◽  
Vol 10 (24) ◽  
pp. 9151
Author(s):  
Yun-Chia Liang ◽  
Yona Maimury ◽  
Angela Hsiang-Ling Chen ◽  
Josue Rodolfo Cuevas Juarez
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Random Forest ◽  
Prediction Models ◽  
Superior Performance ◽  
Support Vector ◽  
Economic Activities ◽  
Adaptive Boosting ◽  
Series Of Experiments ◽  
Artificial Neural Network Ann

Air, an essential natural resource, has been compromised in terms of quality by economic activities. Considerable research has been devoted to predicting instances of poor air quality, but most studies are limited by insufficient longitudinal data, making it difficult to account for seasonal and other factors. Several prediction models have been developed using an 11-year dataset collected by Taiwan’s Environmental Protection Administration (EPA). Machine learning methods, including adaptive boosting (AdaBoost), artificial neural network (ANN), random forest, stacking ensemble, and support vector machine (SVM), produce promising results for air quality index (AQI) level predictions. A series of experiments, using datasets for three different regions to obtain the best prediction performance from the stacking ensemble, AdaBoost, and random forest, found the stacking ensemble delivers consistently superior performance for R2 and RMSE, while AdaBoost provides best results for MAE.

scholarly journals Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

Materials ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4068
Author(s):  
Xu Huang ◽  
Mirna Wasouf ◽  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Machine Learning ◽  
Search Algorithm ◽  
Weather Conditions ◽  
Prediction Performance ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Support Vector ◽  
Self Healing ◽  
Artificial Neural Network Ann

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

scholarly journals LHC physicists embrace brute-force approach to particle hunt

Nature ◽  
2018 ◽  
Vol 560 (7718) ◽  
pp. 293-294
Author(s):  
Davide Castelvecchi
Keyword(s):  
Brute Force ◽  
Brute Force Approach
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