scholarly journals Hybrid Machine Learning Approaches and a Systematic Model Selection Process for Predicting Soot Emissions in Compression Ignition Engines

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7865
Author(s):  
Saeid Shahpouri ◽  
Armin Norouzi ◽  
Christopher Hayduk ◽  
Reza Rezaei ◽  
Mahdi Shahbakhti ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Selection ◽  
Black Box ◽  
Box Model ◽  
Feature Sets ◽  
Machine Learning Methods ◽  
Box Models ◽  
Soot Emissions ◽  
Black Box Models

The standards for emissions from diesel engines are becoming more stringent and accurate emission modeling is crucial in order to control the engine to meet these standards. Soot emissions are formed through a complex process and are challenging to model. A comprehensive analysis of diesel engine soot emissions modeling for control applications is presented in this paper. Physical, black-box, and gray-box models are developed for soot emissions prediction. Additionally, different feature sets based on the least absolute shrinkage and selection operator (LASSO) feature selection method and physical knowledge are examined to develop computationally efficient soot models with good precision. The physical model is a virtual engine modeled in GT-Power software that is parameterized using a portion of experimental data. Different machine learning methods, including Regression Tree (RT), Ensemble of Regression Trees (ERT), Support Vector Machines (SVM), Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Bayesian Neural Network (BNN) are used to develop the black-box models. The gray-box models include a combination of the physical and black-box models. A total of five feature sets and eight different machine learning methods are tested. An analysis of the accuracy, training time and test time of the models is performed using the K-means clustering algorithm. It provides a systematic way for categorizing the feature sets and methods based on their performance and selecting the best method for a specific application. According to the analysis, the black-box model consisting of GPR and feature selection by LASSO shows the best performance with test R2 of 0.96. The best gray-box model consists of SVM-based method with physical insight feature set along with LASSO for feature selection with test R2 of 0.97.

scholarly journals The Role of Textualisation and Argumentation in Understanding the Machine Learning Process

2017 ◽  
Author(s):  
Kacper Sokol ◽  
Peter Flach
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Spatial Perception ◽  
Black Box ◽  
High Dimensional ◽  
Box Models ◽  
Machine Learning Applications ◽  
Black Box Models ◽  
Machine Learning Models

Understanding data, models and predictions is important for machine learning applications. Due to the limitations of our spatial perception and intuition, analysing high-dimensional data is inherently difficult. Furthermore, black-box models achieving high predictive accuracy are widely used, yet the logic behind their predictions is often opaque. Use of textualisation -- a natural language narrative of selected phenomena -- can tackle these shortcomings. When extended with argumentation theory we could envisage machine learning models and predictions arguing persuasively for their choices.

scholarly journals Explainable, Trustworthy, and Ethical Machine Learning for Healthcare: A Survey

2021 ◽  
Author(s):  
Khansa Rasheed ◽  
Adnan Qayyum ◽  
Mohammed Ghaly ◽  
Ala Al-Fuqaha ◽  
Adeel Razi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ethical Issues ◽  
Black Box ◽  
Healthcare Applications ◽  
Ethical Problems ◽  
Clinical Utilization ◽  
Box Models ◽  
Research Problems ◽  
Further Development ◽  
Black Box Models

With the advent of machine learning (ML) applications in daily life, the questions about liability, trust, and interpretability of their outputs are raising, especially for healthcare applications. The black-box nature of ML models is a roadblock for clinical utilization. Therefore, to gain the trust of clinicians and patients, researchers need to provide explanations of how and why the model is making a specific decision. With the promise of enhancing the trust and transparency of black-box models, researchers are in the phase of maturing the field of eXplainable ML (XML). In this paper, we provide a comprehensive review of explainable and interpretable ML techniques implemented for providing the reasons behind their decisions for various healthcare applications. Along with highlighting various security, safety, and robustness challenges that hinder the trustworthiness of ML we also discussed the ethical issues of healthcare ML and describe how explainable and trustworthy ML can resolve these ethical problems. Finally, we elaborate on the limitations of existing approaches and highlight various open research problems that require further development.

scholarly journals Identifying the Machine Learning Family from Black-Box Models

2018 ◽  
pp. 55-65
Author(s):  
Raül Fabra-Boluda ◽  
Cèsar Ferri ◽  
José Hernández-Orallo ◽  
Fernando Martínez-Plumed ◽  
María José Ramírez-Quintana
Keyword(s):  
Machine Learning ◽  
Black Box ◽  
Box Models ◽  
Black Box Models

Real-time daily flow forecasting using black-box models, diffusion processes, and neural networks

2000 ◽  
Vol 27 (4) ◽  
pp. 671-682 ◽  
Author(s):  
N Lauzon ◽  
J Rousselle ◽  
S Birikundavyi ◽  
H T Trung
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Diffusion Process ◽  
Diffusion Processes ◽  
Black Box ◽  
Linear Type ◽  
Modeling Approaches ◽  
Box Models ◽  
Almost All ◽  
Black Box Models

The purpose of this study is to compare three modeling approaches used for the prediction of daily natural flows 1-7 days ahead. Linear black-box models, which have been commonly used for modeling flows, constitute the first approach. The second approach, a linear type in the context of our application, is less known in the water resources field and is identified by the term diffusion process. The third approach uses models called neural networks, which have gained interest in many fields. All these approaches were tested on 15 watersheds from the Saguenay - Lac-Saint-Jean hydrographic system, located in the province of Quebec, Canada. Because the watersheds possess different physical characteristics, the models were tested under several runoff conditions. In this article, the focus is on results; all approaches along with their conditions of use have been detailed elsewhere in the literature. The results obtained showed that neural networks constitute, for almost all the watersheds studied, the best approach to forecast daily natural flows. The more flexible structure of neural networks allows a best reproduction of complex runoff conditions. However, neural networks are more sensitive to outliers present in observed natural flow series, which are used as inputs in the three models tested. In practice, to model flows at specific periods of the year, it seems preferable to establish seasonal models. If a neural network has an inadequate structure for the period under consideration, then it may produce less convincing results than the other two modeling approaches tested in this study.Key words: forecasts, flows, black-box model, diffusion process, neural network.

scholarly journals Wine Quality Prediction Using Machine Learning

2021 ◽  
Vol 9 (8) ◽  
pp. 1997-2001
Author(s):  
Vrusha P. Sangodkar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Selection ◽  
Bp Neural Network ◽  
Data Extraction ◽  
Backpropagation Neural Network ◽  
Wine Quality ◽  
Machine Learning Methods ◽  
Culture Consumption

Abstract: Nowadays people are living a luxurious lifestyle, wine has become a part of one's culture. consumption of wine is very common throughout the world so its quality is very important. hence its important to analyse wine quality quality of the wines are usually checked by humans through tasting but it has other physicochemical attributes which affects the taste but the process is slow hence machine learning methods can be used for the same. dataset is taken and feature selection is done using pca feature selection and then accuracy is find using SVM, backpropagation neural network and Random forest algorithm to find which model fits best and gives greater accuracy. Keywords: Data Extraction, PCA, SVM,BP neural network, Randomforest

scholarly journals Use of Machine Learning and Deep Learning to Predict the Outcomes of Major League Baseball Matches

2021 ◽  
Vol 11 (10) ◽  
pp. 4499
Author(s):  
Mei-Ling Huang ◽  
Yun-Zhi Li
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Feature Selection ◽  
Deep Learning ◽  
Prediction Accuracy ◽  
Major League Baseball ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Major League

Major League Baseball (MLB) is the highest level of professional baseball in the world and accounts for some of the most popular international sporting events. Many scholars have conducted research on predicting the outcome of MLB matches. The accuracy in predicting the results of baseball games is low. Therefore, deep learning and machine learning methods were used to build models for predicting the outcomes (win/loss) of MLB matches and investigate the differences between the models in terms of their performance. The match data of 30 teams during the 2019 MLB season with only the starting pitcher or with all pitchers in the pitcher category were collected to compare the prediction accuracy. A one-dimensional convolutional neural network (1DCNN), a traditional machine learning artificial neural network (ANN), and a support vector machine (SVM) were used to predict match outcomes with fivefold cross-validation to evaluate model performance. The highest prediction accuracies were 93.4%, 93.91%, and 93.90% with the 1DCNN, ANN, SVM models, respectively, before feature selection; after feature selection, the highest accuracies obtained were 94.18% and 94.16% with the ANN and SVM models, respectively. The prediction results obtained with the three models were similar, and the prediction accuracies were much higher than those obtained in related studies. Moreover, a 1DCNN was used for the first time for predicting the outcome of MLB matches, and it achieved a prediction accuracy similar to that achieved by machine learning methods.

scholarly journals The importance of ensuring artificial intelligence and machine learning can be understood at the human level

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
S Ram
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Predictive Analytics ◽  
Black Box ◽  
User Engagement ◽  
Box Models ◽  
Black Box Models

Abstract With rapid developments in big data technology and the prevalence of large-scale datasets from diverse sources, the healthcare predictive analytics (HPA) field is witnessing a dramatic surge in interest. In healthcare, it is not only important to provide accurate predictions, but also critical to provide reliable explanations to the underlying black-box models making the predictions. Such explanations can play a crucial role in not only supporting clinical decision-making but also facilitating user engagement and patient safety. If users and decision makers do not have faith in the HPA model, it is highly likely that they will reject its use. Furthermore, it is extremely risky to blindly accept and apply the results derived from black-box models, which might lead to undesirable consequences or life-threatening outcomes in domains with high stakes such as healthcare. As machine learning and artificial intelligence systems are becoming more capable and ubiquitous, explainable artificial intelligence and machine learning interpretability are garnering significant attention among practitioners and researchers. The introduction of policies such as the General Data Protection Regulation (GDPR), has amplified the need for ensuring human interpretability of prediction models. In this talk I will discuss methods and applications for developing local as well as global explanations from machine learning and the value they can provide for healthcare prediction.

scholarly journals Implementation of Black Box Models for Internal Ballistics Optimization Using an Artificial Neural Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jie Chen ◽  
JingYin Li ◽  
ShuangXi Li ◽  
YunXiang You
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rotational Speed ◽  
Black Box ◽  
Optimization Techniques ◽  
Low Energy ◽  
Internal Ballistics ◽  
Box Models ◽  
Artificial Neural ◽  
Black Box Models

The process of UUV delivery is a typical nonlinear transient dynamic phenomenon, which is generally described by the internal ballistic model. Evaluation of optimal internal ballistics parameters is a key step for promoting ballistic weapon performance under given launch constraints. Hence, accurate and efficient optimization techniques are required in ballistics technology. In this study, an artificial neural network (ANN) is used to simplify the process of regression analysis. To this end, an internal ballistics model is built in this study as a black box for a classic underwater launching system, such as a torpedo launcher, based on ANN parameter identification. The established black box models are mainly employed to calculate the velocity of a ballistic body and the torque of a launching pump. Typical internal ballistics test data are adopted as samples for training the ANN. Comparative results demonstrate that the developed black box models can accurately reflect changes in internal ballistics parameters according to rotational speed variations. Therefore, the proposed approach can be fruitfully applied to the task of internal ballistics optimization. The optimization of internal ballistics precision control, optimal control of the launching pump, and optimal low-energy launch control were, respectively, realized in conjunction with the established model using the SHERPA search algorithm. The results demonstrate that the optimized internal ballistics rotational speed curve can achieve the optimization objectives of low-energy launch and peak power while meeting the requirements of optimization constraints.

scholarly journals A Short Survey on Machine Learning Explainability: An Application to Periocular Recognition

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1861
Author(s):  
João Brito ◽  
Hugo Proença
Keyword(s):  
Machine Learning ◽  
Medical Image ◽  
Visual Cues ◽  
Medical Image Analysis ◽  
Black Box ◽  
Future Directions ◽  
The Core ◽  
Box Models ◽  
Black Box Models ◽  
Periocular Recognition

Interpretability has made significant strides in recent years, enabling the formerly black-box models to reach new levels of transparency. These kinds of models can be particularly useful to broaden the applicability of machine learning-based systems to domains where—apart from the predictions—appropriate justifications are also required (e.g., forensics and medical image analysis). In this context, techniques that focus on visual explanations are of particular interest here, due to their ability to directly portray the reasons that support a given prediction. Therefore, in this document, we focus on presenting the core principles of interpretability and describing the main methods that deliver visual cues (including one that we designed for periocular recognition in particular). Based on these intuitions, the experiments performed show explanations that attempt to highlight the most important periocular components towards a non-match decision. Then, some particularly challenging scenarios are presented to naturally sustain our conclusions and thoughts regarding future directions.

scholarly journals Contextualized LORE for Fuzzy Attributes

2021 ◽  
Author(s):  
Najlaa Maaroof ◽  
Antonio Moreno ◽  
Mohammed Jabreel ◽  
Aida Valls
Keyword(s):  
Machine Learning ◽  
Contextual Information ◽  
Black Box ◽  
New Method ◽  
Learning Models ◽  
Box Models ◽  
Black Boxes ◽  
Linguistic Labels ◽  
Black Box Models ◽  
Machine Learning Models

Despite the broad adoption of Machine Learning models in many domains, they remain mostly black boxes. There is a pressing need to ensure Machine Learning models that are interpretable, so that designers and users can understand the reasons behind their predictions. In this work, we propose a new method called C-LORE-F to explain the decisions of fuzzy-based black box models. This new method uses some contextual information about the attributes as well as the knowledge of the fuzzy sets associated to the linguistic labels of the fuzzy attributes to provide actionable explanations. The experimental results on three datasets reveal the effectiveness of C-LORE-F when compared with the most relevant related works.

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