scholarly journals Black-Box Marine Vehicle Identification with Regression Techniques for Random Manoeuvres

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 492 ◽  
Author(s):  
Raul Moreno ◽  
David Moreno-Salinas ◽  
Joaquin Aranda
Keyword(s):  
System Identification ◽  
Symbolic Regression ◽  
Black Box ◽  
Machine Learning Techniques ◽  
Control Structures ◽  
Marine Vehicles ◽  
Building Models ◽  
Learning Techniques ◽  
Marine System ◽  
Regression Techniques

As a critical step to efficiently design control structures, system identification is concerned with building models of dynamical systems from observed input–output data. In this paper, a number of regression techniques are used for black-box marine system identification of a scale ship. Unlike other works that train the models using specific manoeuvres, in this work the data have been collected from several random manoeuvres and trajectories. Therefore, the aim is to develop general and robust mathematical models using real experimental data from random movements. The techniques used in this work are ridge, kernel ridge and symbolic regression, and the results show that machine learning techniques are robust approaches to model surface marine vehicles, even providing interpretable results in closed form equations using techniques such as symbolic regression.

scholarly journals Analytical Statistics Techniques of Classification and Regression in Machine Learning

2020 ◽  
Author(s):  
Pramod Kumar ◽  
Sameer Ambekar ◽  
Manish Kumar ◽  
Subarna Roy
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Machine Learning Techniques ◽  
Statistical Machine Learning ◽  
Building Models ◽  
Learning Techniques ◽  
Implementation Techniques ◽  
Regression Techniques ◽  
The Common ◽  
Classification And Regression

This chapter aims to introduce the common methods and practices of statistical machine learning techniques. It contains the development of algorithms, applications of algorithms and also the ways by which they learn from the observed data by building models. In turn, these models can be used to predict. Although one assumes that machine learning and statistics are not quite related to each other, it is evident that machine learning and statistics go hand in hand. We observe how the methods used in statistics such as linear regression and classification are made use of in machine learning. We also take a look at the implementation techniques of classification and regression techniques. Although machine learning provides standard libraries to implement tons of algorithms, we take a look on how to tune the algorithms and what parameters of the algorithm or the features of the algorithm affect the performance of the algorithm based on the statistical methods.

scholarly journals Nonlinear System Identification of a Furuta Pendulum Using Machine Learning Techniques

PAMM ◽  
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Tobias Rückwald ◽  
Svenja Drücker ◽  
Daniel-André Dücker ◽  
Robert Seifried
Keyword(s):  
Machine Learning ◽  
System Identification ◽  
Nonlinear System ◽  
Nonlinear System Identification ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Intersections of machine learning and epidemiological methods for health services research

2020 ◽  
Author(s):  
Sherri Rose
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health Services Research ◽  
Health Services ◽  
Machine Learning Techniques ◽  
Epidemiological Methods ◽  
Learning Tools ◽  
Services Research ◽  
Learning Techniques ◽  
Regression Techniques

Abstract The field of health services research is broad and seeks to answer questions about the health care system. It is inherently interdisciplinary, and epidemiologists have made crucial contributions. Parametric regression techniques remain standard practice in health services research with machine learning techniques currently having low penetrance in comparison. However, studies in several prominent areas, including health care spending, outcomes and quality, have begun deploying machine learning tools for these applications. Nevertheless, major advances in epidemiological methods are also as yet underleveraged in health services research. This article summarizes the current state of machine learning in key areas of health services research, and discusses important future directions at the intersection of machine learning and epidemiological methods for health services research.

scholarly journals Explanation Generation Mechanism for Black Box Recommendation Model

2020 ◽  
Vol 9 (8) ◽  
pp. 275-279
Keyword(s):  
Recommender System ◽  
Semantic Network ◽  
Black Box ◽  
Machine Learning Techniques ◽  
New Approach ◽  
Semantic Web Technology ◽  
Box Models ◽  
Learning Techniques ◽  
Available Information ◽  
Black Box Models

The recommender system is everywhere, and even streaming platform they have been looking for a maze of user available information handling products and services. Unfortunately, these black box systems do not have sufficient transparency, as they provide littlie description about the their prediction. In contrast, the white box system by its nature can produce a brief description. However, their predictions are less accurate than complex black box models. Recent research has shown that explanations are an important component in bringing powerful big data predictions and machine learning techniques to a mass audience without compromising trust.This paper proposes a new approach using semantic web technology to generate an explanation for the output of a black box recommender system. The developed model is trained to make predictions accompanied by explanations that are automatically extracted from the semantic network.

scholarly journals Compression Strength Prediction Using Machine Learning Techniques

2021 ◽  
Vol 10 (1) ◽  
pp. 301-307
Keyword(s):  
Random Forest ◽  
Compression Strength ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Mathematical Relationship ◽  
Random Forest Regression ◽  
Learning Techniques ◽  
Regression Techniques ◽  
Compressive Strength Of Concrete ◽  
Advanced Computing

The advanced computing techniques and its applications on other engineering disciplines accelerated the different aspects and phases in engineering process. Nowadays there are so many computer aided methods widely used in civil engineering domain. The mathematical relationship between ratios of different concrete components and other influencing factors with its compression strength need to be analyzed for different engineering needs. This paper aims to develop a mathematical relationship after analyzing the above factors and to foresee the compressive strength of concrete by applying various regression techniques such as linear regression, support vector regression, decision tree regression and random forest regression on assumeddata set., It was found that the accuracy of the random forest regression was considerable as per the result after applying the various regression techniques.

A Statistical Scrutiny of Three Prominent Machine-Learning Techniques to Forecast Machining Performance Parameters of Inconel 690

2018 ◽  
pp. 104-120
Author(s):  
Binayak Sen ◽  
Uttam Kumar Mandal ◽  
Sankar Prasad Mondal
Keyword(s):  
Machine Learning ◽  
Population Based ◽  
Black Box ◽  
Machine Learning Techniques ◽  
Percentage Error ◽  
Machining Parameters ◽  
Ann Model ◽  
Machining Performance ◽  
Learning Techniques ◽  
Inconel 690

Computational approaches like “Black box” predictive modeling approaches are extensively used technique applied in machine learning operations of today. Considering the latest trends, present study compares capabilities of two different “Black box” predictive model like ANFIS and ANN with a population-based evolutionary algorithm GEP for forecasting machining parameters of Inconel 690 material, machined in a CNC-assisted 3-axis milling machine. The aims of this article are to represent considerable data showing, every techniques performance under the criteria of root mean square error (RSME), Correlational coefficient R and Mean absolute percentage error (MAPE). In this chapter, we vigorously demonstrate that the performance of the GEP model is far superior to ANFIS and ANN model.

scholarly journals Winsorization for Robust Bayesian Neural Networks

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1546
Author(s):  
Somya Sharma ◽  
Snigdhansu Chatterjee
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Black Box ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Versatile Technique ◽  
Aberrant Observations ◽  
Probabilistic Machine Learning ◽  
Different Levels

With the advent of big data and the popularity of black-box deep learning methods, it is imperative to address the robustness of neural networks to noise and outliers. We propose the use of Winsorization to recover model performances when the data may have outliers and other aberrant observations. We provide a comparative analysis of several probabilistic artificial intelligence and machine learning techniques for supervised learning case studies. Broadly, Winsorization is a versatile technique for accounting for outliers in data. However, different probabilistic machine learning techniques have different levels of efficiency when used on outlier-prone data, with or without Winsorization. We notice that Gaussian processes are extremely vulnerable to outliers, while deep learning techniques in general are more robust.

scholarly journals Binding Affinity Regression Models from Repeats Mutation in Polyglutamine Disease

2018 ◽  
Author(s):  
P R Asha ◽  
M S Vijaya
Keyword(s):  
Spinocerebellar Ataxia ◽  
Regression Models ◽  
Protein Complexes ◽  
Genetic Disorders ◽  
Research Work ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Regression Techniques ◽  
Framework Energy

AbstractDiagnosing and curing neurodegenarative disorder such as spinocerebellar ataxia is complicated when there is differences in formation of protein sequences and structures. Affinity prediction plays vital role to identify drugs for various genetic disorders. Spinocerebellar ataxia occurs but mainly it occurs due to polyglutamine repeats. This research work aims in predicting the affinity of spinocerebellar ataxia from the protein complexes by extracting the well-defined descriptors. Regression models are built to predict the affinity through machine learning techniques coded in python using the Scikit-Learn framework. Energy complexes and protein sequence descriptors are defined and extracted from the complex and sequences. Results show that the SVR is found to predict the affinity with high accuracy of 98% for spinocerebellar ataxia. This paper also deliberates the results of statistical learning carried out with the same set of complexes with various regression techniques.

Review on Various Machine Learning and Deep Learning Techniques for Prediction and Classification of Quotidian Datasets

2020 ◽  
pp. 296-323
Author(s):  
Anisha M. Lal ◽  
B. Koushik Reddy ◽  
Aju D.
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Supervised Methods ◽  
Regression Techniques

Machine learning can be defined as the ability of a computer to learn and solve a problem without being explicitly coded. The efficiency of the program increases with experience through the task specified. In traditional programming, the program and the input are specified to get the output, but in the case of machine learning, the targets and predictors are provided to the algorithm make the process trained. This chapter focuses on various machine learning techniques and their performance with commonly used datasets. A supervised learning algorithm consists of a target variable that is to be predicted from a given set of predictors. Using these established targets is a function that plots targets to a given set of predictors. The training process allows the system to train the unknown data and continues until the model achieves a desired level of accuracy on the training data. The supervised methods can be usually categorized as classification and regression. This chapter discourses some of the popular supervised machine learning algorithms and their performances using quotidian datasets. This chapter also discusses some of the non-linear regression techniques and some insights on deep learning with respect to object recognition.

scholarly journals Prediction Modeling Methodology

2018 ◽  
pp. 101-120 ◽  
Author(s):  
Frank J. W. M. Dankers ◽  
Alberto Traverso ◽  
Leonard Wee ◽  
Sander M. J. van Kuijk
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Machine Learning Techniques ◽  
Prediction Modeling ◽  
Modeling Methodology ◽  
Learning Techniques ◽  
Regression Techniques

AbstractIn the previous chapter, you have learned how to prepare your data before you start the process of generating a predictive model. In this chapter, you will learn how to make a predictive model using very common regression techniques and how to evaluate the performance of a model. In the next chapter we will then look at more advanced machine learning techniques that have become increasingly popular in recent years.

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