scholarly journals Application of model trees and other machine learning techniques for algal growth prediction in Yongdam reservoir, Republic of Korea

2009 ◽  
Vol 12 (3) ◽  
pp. 262-274 ◽  
Author(s):  
Nahm-Chung Jung ◽  
Ioana Popescu ◽  
Peter Kelderman ◽  
Dimitri P. Solomatine ◽  
Roland K. Price
Keyword(s):  
Machine Learning ◽  
Process Model ◽  
Piecewise Linear ◽  
Algal Growth ◽  
Machine Learning Techniques ◽  
Least Squares Regression ◽  
Linear Regression Models ◽  
M5 Model Tree ◽  
Growth Prediction ◽  
Learning Techniques

A promising new approach for eco-environmental modelling, such as algal growth prediction, is the data-driven modeling using machine learning techniques: an artificial neural network (ANN) being a typical method. Another method growing in popularity, based on the M5 model tree (MT) algorithm, is the use of piecewise linear regression models at the leaf nodes of the tree. M5 MTs using partial least-squares regression (PLSR) proposed in this paper were tested on a particular dataset and then compared to M5 MTs, MLF- and RBF-ANN and k nearest neighbours (kNN). With the dataset partitioned to periods of algal growth and no growth, M5 MTs using PLSR showed better results for algal growth prediction in the reservoir than using the annual dataset and other algorithms. This gives the idea that the M5-PLSR MTs, in spite of the lack of data, more effectively seeks latent vectors between the closely correlated multivariate dataset partitioned using clustering techniques. M5-PLSR MTs is a promising approach when there is a shortage of data required to build a more transparent learning process model, and a combination with clustering is recommended.

scholarly journals A Holistic Quality Assurance Approach for Machine Learning Applications in Cyber-Physical Production Systems

2021 ◽  
Vol 11 (20) ◽  
pp. 9590
Author(s):  
Hajo Wiemer ◽  
Alexander Dementyev ◽  
Steffen Ihlenfeldt
Keyword(s):  
Machine Learning ◽  
Quality Assurance ◽  
Process Model ◽  
Production Systems ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Monitoring Process ◽  
Machine Learning Applications ◽  
Cyber Physical Production Systems ◽  
Automation Technology

With the trend of increasing sensors implementation in production systems and comprehensive networking, essential preconditions are becoming required to be established for the successful application of data-driven methods of equipment monitoring, process optimization, and other relevant automation tasks. As a protocol, these tasks should be performed by engineers. Engineers usually do not have enough experience with data mining or machine learning techniques and are often skeptical about the world of artificial intelligence (AI). Quality assurance of AI results and transparency throughout the IT chain are essential for the acceptance and low-risk dissemination of AI applications in production and automation technology. This article presents a conceptual method of the stepwise and level-wise control and improvement of data quality as one of the most important sources of AI failures. The appropriate process model (V-model for quality assurance) forms the basis for this.

Machine Learning in Fine Wine Price Prediction

2015 ◽  
Vol 10 (2) ◽  
pp. 151-172 ◽  
Author(s):  
Michelle Yeo ◽  
Tristan Fletcher ◽  
John Shawe-Taylor
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Predictive Accuracy ◽  
Time Series Prediction ◽  
Feature Learning ◽  
Gaussian Process Regression ◽  
Machine Learning Techniques ◽  
Linear Regression Models ◽  
Price Prediction ◽  
Learning Techniques

AbstractAdvanced machine learning techniques like Gaussian process regression and multi-task learning are novel in the area of wine price prediction; previous research in this area being restricted to parametric linear regression models when predicting wine prices. Using historical price data of the 100 wines in the Liv-Ex 100 index, the main contributions of this paper to the field are, firstly, a clustering of the wines into two distinct clusters based on autocorrelation. Secondly, an implementation of Gaussian process regression on these wines with predictive accuracy surpassing both the trivial and simple ARMA and GARCH time series prediction benchmarks. Lastly, an implementation of an algorithm which performs multi-task feature learning with kernels on the wine returns as an extension to our optimal Gaussian process regression model. Using the optimal covariance kernel from Gaussian process regression, we achieve predictive results which are comparable to that of Gaussian process regression. Altogether, our research suggests that there is potential in using advanced machine learning techniques in wine price prediction. (JEL Classifications: C6, G12)

scholarly journals An Approach for Variable Selection and Prediction Model for Estimating the Risk-Based Capital (RBC) Based on Machine Learning Algorithms

Risks ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Jaewon Park ◽  
Minsoo Shin
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Business Performance ◽  
Ordinary Least Squares ◽  
Machine Learning Algorithms ◽  
Capital Adequacy ◽  
Machine Learning Techniques ◽  
Insurance Companies ◽  
Least Squares Regression ◽  
Learning Techniques

The risk-based capital (RBC) ratio, an insurance company’s financial soundness system, evaluates the capital adequacy needed to withstand unexpected losses. Therefore, continuous institutional improvement has been made to monitor the financial solvency of companies and protect consumers’ rights, and improvement of solvency systems has been researched. The primary purpose of this study is to find a set of important predictors to estimate the RBC ratio of life insurance companies in a large number of variables (1891), which includes crucial finance and management indices collected from all Korean insurers quarterly under regulation for transparent management information. This study employs a combination of Machine learning techniques: Random Forest algorithms and the Bayesian Regulatory Neural Network (BRNN). The combination of Random Forest algorithms and BRNN predicts the next period’s RBC ratio better than the conventional statistical method, which uses ordinary least-squares regression (OLS). As a result of the findings from Machine learning techniques, a set of important predictors is found within three categories: liabilities and expenses, other financial predictors, and predictors from business performance. The dataset of 23 companies with 1891 variables was used in this study from March 2008 to December 2018 with quarterly updates for each year.

scholarly journals A Comprehensive Survey on Machine Learning

2021 ◽  
Vol 1 (1) ◽  
pp. 1-17
Author(s):  
Astha Singh ◽  
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Machine Learning Techniques ◽  
Linear Regression Models ◽  
Correct Model ◽  
Learning Techniques ◽  
Unseen Data ◽  
Comprehensive Survey ◽  
Statistical Agencies ◽  
High Level

The objective of this briefing is to present an overview of the topic, machine learning techniques currently in use or in consideration at statistical agencies worldwide. It is important to know the main reason why real-world scenarios should start exploring the use of machine learning techniques, terminology, approach and about few popular libraries in python, what regression is, by completely throwing light on simple as well as multiple linear and non-linear regression models and their applications, classification techniques, various clustering techniques. The material presented in this paper is the result of a study based on different models and the study of various datasets (analysis and choice of the correct model are important). While Machine Learning involves concepts of automation, it requires human guidance. Machine Learning involves a high level of generalization to get a system that performs well on yet-unseen data instances. Topics like regression, classification, and clustering, the report covers the insight of various techniques and their applications.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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