Machine Learning Algorithms in Heavy Process Manufacturing

No abstract is available for this paper.

• Publication year

  2016

• Venue

  American Journal of Intelligent Systems

• Publication date

  Unknown publication date

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.5923/J.AJIS.20160601.01
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Deep Learning

  2016cited by this paper
• A RETROSPECTIVE

  2016cited by this paper
• Modeling of steelmaking process with effective machine learning techniques

  2015cited by this paper
• A random forest method for real-time price forecasting in New York electricity market

  2014cited by this paper
• A STUDY OF DECISION TREE ENSEMBLES AND FEATURE SELECTION FOR STEEL PLATES FAULTS DETECTION

  2014cited by this paper
• Random Search for Hyper-Parameter Optimization

  2012cited by this paper
• Feature selection via regularized trees

  2012cited by this paper
• Gene selection with guided regularized random forest

  2012cited by this paper
• A fast method of steel surface defect detection using decision trees applied to LBP based features

  2012cited by this paper
• Interpretation of nonlinear relationships between process variables by use of random forests

  2012cited by this paper
• Identification and model predictive control of continuous stirred tank reactor based on artificial neural networks

  2011cited by this paper
• Applications of support vector machines in oil refineries: A survey

  2011cited by this paper
• A Review of Ensemble Methods in Bioinformatics

  2010cited by this paper
• Modeling and characterizing of the thixoforming of steel process parameters – the case of forming load

  2010cited by this paper
• Comparisons of element yield rate prediction using feed-forward neural networks and support vector machine

  2010cited by this paper
• Neural network based predictive control for nonlinear chemical process

  2010cited by this paper
• An Empirical and Formal Analysis of Decision Trees for Ranking

  2008cited by this paper
• Random classification noise defeats all convex potential boosters

  2008cited by this paper
• Curl forecasting for paper quality in papermaking industry

  2008cited by this paper
• A cost model for the effect of setup time reduction in stainless steel strip production

  2007cited by this paper
• An introduction to ROC analysis

  2006cited by this paper
• An empirical comparison of supervised learning algorithms

  2006cited by this paper
• California Center for Population Research On-line Working Paper Series Weather Regime Prediction Using Statistical Learning Weather Regime Prediction Using Statistical Learning

  2005cited by this paper
• Bagging Random Trees for Estimation of Tissue Softness

  2005cited by this paper
• Predicting good probabilities with supervised learning

  2005cited by this paper
• Application of support vector machine method in prediction of Kappa number of kraft pulping process

  2004cited by this paper
• Data mining in metric space: an empirical analysis of supervised learning performance criteria

  2004cited by this paper
• The general inefficiency of batch training for gradient descent learning

  2003cited by this paper
• The support vector machine under test

  2003cited by this paper
• A comprehensive review for industrial applicability of artificial neural networks

  2003cited by this paper
• An introduction to variable and feature selection

  2003cited by this paper
• Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution

  2003influential reference
• Transforming classifier scores into accurate multiclass probability estimates

  2002cited by this paper
• Random Forests

  2001cited by this paper
• Making Decisions in the Oil and Gas Industry

  2001cited by this paper
• Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)

  2001cited by this paper
• A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms

  2000cited by this paper
• Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning

  1999cited by this paper
• Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods

  1999cited by this paper
• A decision-theoretic generalization of on-line learning and an application to boosting

  1997cited by this paper
• Regression Shrinkage and Selection via the Lasso

  1996cited by this paper
• Bagging Predictors

  1996cited by this paper
• Support-Vector Networks

  1995cited by this paper
• STATLOG: COMPARISON OF CLASSIFICATION ALGORITHMS ON LARGE REAL-WORLD PROBLEMS

  1995cited by this paper
• Dealing with missing values in neural network-based diagnostic systems

  1995cited by this paper
• Statistical analysis of the single-layer backpropagation algorithm. II. MSE and classification performance

  1993cited by this paper
• A training algorithm for optimal margin classifiers

  1992cited by this paper
• Approximation by superpositions of a sigmoidal function

  1989cited by this paper
• The Strength of Weak Learnability

  1989cited by this paper
• Pattern recognition using generalized portrait method

  1963cited by this paper

• Spotlights on novel strategic innovations on the artificial intelligence and deep learning driven quality control focuses in transfusion medicine, to optimize blood component safety and efficacy and minimize the potential pitfalls.

  2025cites this paper
• Enhancing dairy processing with machine learning and domain knowledge: A combined analysis of offline and time series data

  2024cites this paper
• Integrating Machine Learning for Network Threat Detection with SmartX Multi-Sec Framework

  2023cites this paper
• Learning with supervised data for anomaly detection in smart manufacturing

  2023cites this paper
• Toward Improved Machine Learning-Based Intrusion Detection for Internet of Things Traffic

  2023cites this paper
• Trustworthy, responsible and ethical artificial intelligence in manufacturing and supply chains: synthesis and emerging research questions

  2023cites this paper
• The Genomics of Industrial Process Through the Qualia of Markovian Behavior

  2022cites this paper
• Machine Learning Applied to Colloidal Properties of Perfluorocarbon Nanoemulsions for Imaging in ARDS/ALI

  2022cites this paper
• An integrated Machine Learning Model for Manufacturing industry

  2022influential citation
• Machine Learning Model Development and Validation for Predicting Outcome in Stage 4 Solid Cancer Patients with Septic Shock Visiting the Emergency Department: A Multi-Center, Prospective Cohort Study

  2022cites this paper
• Monitoring machine learning models: A categorization of challenges and methods

  2022cites this paper
• Prediction of paperboard properties and their variability based on raw material and process data

  2021influential citation
• Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

  2021cites this paper
• Comparison between support vector machines and K-nearest neighbor for time series forecasting

  2020cites this paper
• Monitoring Pneumatic Actuators’ Behavior Using Real-World Data Set

  2020cites this paper
• Application of Big Data and Machine Learning

  2020cites this paper
• Developing a Predictive Model for Asthma-Related Hospital Encounters in Patients With Asthma in a Large, Integrated Health Care System: Secondary Analysis

  2020cites this paper
• Challenges in Deploying Machine Learning: A Survey of Case Studies

  2020cites this paper
• Forecasting Future Asthma Hospital Encounters of Patients With Asthma in an Academic Health Care System: Predictive Model Development and Secondary Analysis Study

  2020cites this paper
• A Machine Learning Approach to Determine Abundance of Inclusions in Stainless Steel

  2019cites this paper
• Development of Smart Technology for Complex Objects Prediction and Control on the Basis of a Distributed Control System and an Artificial Immune Systems Approach

  2019cites this paper
• Machine Learning Based Monitoring of the Pneumatic Actuators' Behavior Through Signal Processing Using Real-World Data Set

  2019cites this paper
• Condition Monitoring and Diagnosis of a Hot Strip Roughing Mill Using an Autoencoder

  2019cites this paper
• Optimized material flow using unsupervised time series clustering : An experimental study on the just in time supermarket for Volvo powertrain production Skövde.

  2019cites this paper
• The 50th CIRP Conference on Manufacturing Systems Towards Industry 4.0 Utilizing Data-Mining Techniques: a Case Study on Quality Improvement

  2018cites this paper
• A new methodology to analyze the functional and physical architecture of existing products for an assembly oriented product family identification Enhancement of clean-in-place procedures in powder production using ultraviolet-induced fluorescence Enhancement of clean-in-place procedures in powder pr

  2018cites this paper
• Comparative Results with Unsupervised Techniques in Cyber Attack Novelty Detection

  2018cites this paper
• D 03 State of the Art InValue Industrial Enterprise Asset Value Enablers

  2017cites this paper
• Improving Design for Manufacturing implementation in knowledge intensive collaborative environments: An analysis of organisational factors in aerospace manufacturing

  2017cites this paper
• Analysis of ROS performance in terms of intelligent monitoring of discrete machinery manufacturing control systems

  2017cites this paper
• Feature Selection and Bleach Time Modelling of Paper Pulp Using Tree Based Learners

  2016cites this paper
• Machine learning in manufacturing: advantages, challenges, and applications

  2016cites this paper
• Study on Correlation-based Feature Selection in an Automatic Quality Inspection System using Support Vector Machine (SVM)

  2016cites this paper