Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees
Published 2019 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees
Authors
Keywords
-
Journal
Materials
Volume 12, Issue 9, Pages 1544
Publisher
MDPI AG
Online
2019-05-13
DOI
10.3390/ma12091544
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Landslide susceptibility modelling using different advanced decision trees methods
- (2019) Binh Thai Pham et al. CIVIL ENGINEERING AND ENVIRONMENTAL SYSTEMS
- Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete
- (2019) Dong Dao et al. Materials
- Engineering characteristics of nanosilica/polymer-modified bitumen and predicting their rheological properties using multilayer perceptron neural network model
- (2019) Nur Izzi Md. Yusoff et al. CONSTRUCTION AND BUILDING MATERIALS
- Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches
- (2019) Dong Dao et al. Applied Sciences-Basel
- Microstructure evolution and mechanical properties of ceramic shell moulds for investment casting of turbine blades by selective laser sintering
- (2018) Qian Wei et al. CERAMICS INTERNATIONAL
- Electrical and thermal conductivities of MWCNT/polymer composites fabricated by selective laser sintering
- (2018) Shangqin Yuan et al. COMPOSITES PART A-APPLIED SCIENCE AND MANUFACTURING
- A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran
- (2018) Khabat Khosravi et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Pipe failure modelling for water distribution networks using boosted decision trees
- (2018) Daniel Winkler et al. Structure and Infrastructure Engineering
- A Short-Term Photovoltaic Power Prediction Model Based on the Gradient Boost Decision Tree
- (2018) Jidong Wang et al. Applied Sciences-Basel
- Process of selective laser sintering of polymer powders: Modeling, simulation, and validation
- (2018) Aoulaiche Mokrane et al. COMPTES RENDUS MECANIQUE
- Assessment of computational fracture models using Bayesian method
- (2018) K.M. Hamdia et al. ENGINEERING FRACTURE MECHANICS
- Support optimization for overhanging parts in direct metal laser sintering
- (2018) Zafer Cagatay Oter et al. OPTIK
- Improving the Diagnosis of Liver Disease Using Multilayer Perceptron Neural Network and Boosted Decision Trees
- (2017) Moloud Abdar et al. Journal of Medical and Biological Engineering
- Neural networks for the prediction of polymer permeability to gases
- (2017) Hanaa Hasnaoui et al. JOURNAL OF MEMBRANE SCIENCE
- Predicting minor nutrient requirements of hazelnut shoot cultures using regression trees
- (2017) Meleksen Akin et al. PLANT CELL TISSUE AND ORGAN CULTURE
- Identifying Risk Factors for Drug Use in an Iranian Treatment Sample: A Prediction Approach Using Decision Trees
- (2017) Alireza Amirabadizadeh et al. SUBSTANCE USE & MISUSE
- Neural network classifier optimization using Differential Evolution with Global Information and Back Propagation algorithm for clinical datasets
- (2016) N. Leema et al. APPLIED SOFT COMPUTING
- Stochastic continuum modeling of random interphases from atomistic simulations. Application to a polymer nanocomposite
- (2016) T.T. Le et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Uncertainty quantification in computational linear structural dynamics for viscoelastic composite structures
- (2016) R. Capillon et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction
- (2016) Maciej Zięba et al. EXPERT SYSTEMS WITH APPLICATIONS
- Uncertainty quantification for multiscale modeling of polymer nanocomposites with correlated parameters
- (2015) N. Vu-Bac et al. COMPOSITES PART B-ENGINEERING
- Geometric consideration of support structures in part overhang fabrications by electron beam additive manufacturing
- (2015) Bo Cheng et al. COMPUTER-AIDED DESIGN
- A gradient boosting method to improve travel time prediction
- (2015) Yanru Zhang et al. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
- Uncertainties propagation in metamodel-based probabilistic optimization of CNT/polymer composite structure using stochastic multi-scale modeling
- (2014) Hamid Ghasemi et al. COMPUTATIONAL MATERIALS SCIENCE
- Design optimization of supports for overhanging structures in aluminum and titanium alloys by selective laser melting
- (2014) F. Calignano MATERIALS & DESIGN
- Using methods from the data-mining and machine-learning literature for disease classification and prediction: a case study examining classification of heart failure subtypes
- (2013) Peter C. Austin et al. JOURNAL OF CLINICAL EPIDEMIOLOGY
- Comparative evaluation of text classification techniques using a large diverse Arabic dataset
- (2013) Mohammad S. Khorsheed et al. Language Resources and Evaluation
- Prediction of properties of polymer concrete composite with tire rubber using neural networks
- (2013) Rodica-Mariana Diaconescu et al. Materials Science and Engineering B-Advanced Functional Solid-State Materials
- Partial Least Square Discriminant Analysis for bankruptcy prediction
- (2012) Carlos Serrano-Cinca et al. DECISION SUPPORT SYSTEMS
- A working guide to boosted regression trees
- (2008) J. Elith et al. JOURNAL OF ANIMAL ECOLOGY
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now