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A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 27209

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Special Issue Editor

Dr. Shengkun Xie

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Guest Editor

Global Management Studies, Ted Rogers School of Management, Toronto Metropolitan University, Toronto, ON M5G 2C3, Canada
Interests: statistical machine learning; explainable data analytics; risk modeling; rate making; multivariate statistical methods; time series analysis; predictive analytics; health informatics; biosignal analysis
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Special Issue Information

Dear Colleagues,

Explainable AI, explainable data analysis, and explainable data analytics are now playing an important role in machine learning and artificial intelligence. This is because many machine learning techniques are highly technical, and the models involved are complicated so that it is not easy to understand how the input data are processed. When data visualization or understanding of the key features extracted using complicated statistical and mathematical approaches are crucial to real-world applications, improving data interpretability becomes necessary and essential. For example, the visualization of low dimensional extracted features is typically crucial in computer-aided medical diagnosis. Further, the study of high dimensional data for business decision making is rapidly growing since it often leads to more accurate information so that the decision is more reliable than others. To better understand the natural variation and pattern, attempts to improve the data interpretability have been an ongoing challenging problem, mainly in the area of complex statistical data analysis.

Recently, research on explainable computational intelligence has gained much attention in many fields of study, including engineering, science, and social science. Further, in machine learning, novel dimension reduction and feature extraction methods are particularly needed to facilitate data classification or clustering, depending on the availability of data labels. This Special Issue aims at promoting advanced mathematical, statistical, and computational techniques, which help to improve explainable data analysis or understanding the models that we consider. The techniques include but are not limited to:

Sparse statistical methods;
Feature extraction and data fusion;
Explainable artificial neural networks;
Data dimension reduction;
Functional data analysis;
Time–frequency domain approaches.

Both theoretical development and applied work, including application and methodological development, are welcome.

Dr. Shengkun Xie
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Dimension reduction
Feature extraction
Sparsity
Machine learning
Explainable AI
Explainable data analytics

Published Papers (7 papers)

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Research

14 pages, 1111 KiB

Open AccessArticle

Application of Trust in Recommender Systems—Utilizing Naive Bayes Classifier

by Korab Rrmoku, Besnik Selimi and Lule Ahmedi

Computation 2022, 10(1), 6; https://0-doi-org.brum.beds.ac.uk/10.3390/computation10010006 - 12 Jan 2022

Cited by 21 | Viewed by 4205

Abstract

Receiving a recommendation for a certain item or a place to visit is now a common experience. However, the issue of trustworthiness regarding the recommended items/places remains one of the main concerns. In this paper, we present an implementation of the Naive Bayes classifier, one of the most powerful classes of Machine Learning and Artificial Intelligence algorithms in existence, to improve the accuracy of the recommendation and raise the trustworthiness confidence of the users and items within a network. Our approach is proven as a feasible one, since it reached the prediction accuracy of 89%, with a confidence of approximately 0.89, when applied to an online dataset of a social network. Naive Bayes algorithms, in general, are widely used on recommender systems because they are fast and easy to implement. However, the requirement for predictors to be independent remains a challenge due to the fact that in real-life scenarios, the predictors are usually dependent. As such, in our approach we used a larger training dataset; hence, the response vector has a higher selection quantity, thus empowering a higher determining accuracy. Full article

(This article belongs to the Special Issue Explainable Computational Intelligence, Theory, Methods and Applications)

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16 pages, 939 KiB

Open AccessArticle

Data Analysis and Symbolic Regression Models for Predicting CO and NO_x Emissions from Gas Turbines

by Olga Kochueva and Kirill Nikolskii

Computation 2021, 9(12), 139; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9120139 - 13 Dec 2021

Cited by 6 | Viewed by 2859

Abstract

Predictive emission monitoring systems (PEMS) are software solutions for the validation and supplementation of costly continuous emission monitoring systems for natural gas electrical generation turbines. The basis of PEMS is that of predictive models trained on past data to estimate emission components. The gas turbine process dataset from the University of California at Irvine open data repository has initiated a challenge of sorts to investigate the quality of models of various machine learning methods to build a model for predicting CO and NO_x emissions depending on ambient variables and the parameters of the technological process. The novelty and features of this paper are: (i) a contribution to the study of the features of the open dataset on CO and NO_x emissions for gas turbines, which will enable one to more objectively compare different machine learning methods for further research; (ii) for the first time for the CO and NO_x emissions, a model based on symbolic regression and a genetic algorithm is presented—the advantage of this being the transparency of the influence of factors and the interpretability of the model; (iii) a new classification model based on the symbolic regression model and fuzzy inference system is proposed. The coefficients of determination of the developed models are:

R^{2} = 0.83

for NO_x emissions,

R^{2} = 0.89

for CO emissions. Full article

(This article belongs to the Special Issue Explainable Computational Intelligence, Theory, Methods and Applications)

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19 pages, 3108 KiB

Open AccessArticle

Wavelet Power Spectral Domain Functional Principal Component Analysis for Feature Extraction of Epileptic EEGs

by Shengkun Xie

Computation 2021, 9(7), 78; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9070078 - 07 Jul 2021

Cited by 3 | Viewed by 2396

Abstract

Feature extraction plays an important role in machine learning for signal processing, particularly for low-dimensional data visualization and predictive analytics. Data from real-world complex systems are often high-dimensional, multi-scale, and non-stationary. Extracting key features of this type of data is challenging. This work proposes a novel approach to analyze Epileptic EEG signals using both wavelet power spectra and functional principal component analysis. We focus on how the feature extraction method can help improve the separation of signals in a low-dimensional feature subspace. By transforming EEG signals into wavelet power spectra, the functionality of signals is significantly enhanced. Furthermore, the power spectra transformation makes functional principal component analysis suitable for extracting key signal features. Therefore, we refer to this approach as a double feature extraction method since both wavelet transform and functional PCA are feature extractors. To demonstrate the applicability of the proposed method, we have tested it using a set of publicly available epileptic EEGs and patient-specific, multi-channel EEG signals, for both ictal signals and pre-ictal signals. The obtained results demonstrate that combining wavelet power spectra and functional principal component analysis is promising for feature extraction of epileptic EEGs. Therefore, they can be useful in computer-based medical systems for epilepsy diagnosis and epileptic seizure detection problems. Full article

(This article belongs to the Special Issue Explainable Computational Intelligence, Theory, Methods and Applications)

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15 pages, 524 KiB

Open AccessArticle

Multi-Model Approach and Fuzzy Clustering for Mammogram Tumor to Improve Accuracy

by Sarada Ghosh, Guruprasad Samanta and Manuel De la Sen

Computation 2021, 9(5), 59; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9050059 - 18 May 2021

Cited by 3 | Viewed by 2016

Abstract

Breast Cancer is one of the most common diseases among women which seriously affect health and threat to life. Presently, mammography is an uttermost important criterion for diagnosing breast cancer. In this work, image of breast cancer mass detection in mammograms with [...] Read more.

1024 \times 1024

pixels is used as dataset. This work investigates the performance of various approaches on classification techniques. Overall support vector machine (SVM) performs better in terms of log-loss and classification accuracy rate than other underlying models. Therefore, further extensions (i.e., multi-model ensembles method, Fuzzy c-means (FCM) clustering and SVM combination method, and FCM clustering based SVM model) and comparison with SVM have been performed in this work. The segmentation by FCM clustering technique allows one piece of data to belong in two or more clusters. The additional parts are due to the segmented image to enhance the tumor-shape. Simulation provides the accuracy and the area under the ROC curve for mini-MIAS are

91.39 %

and

0.964

respectively which give the confirmation of the effectiveness of the proposed algorithm (FCM-based SVM). This method increases the classification accuracy in the case of a malignant tumor. The simulation is based on R-software. Full article

(This article belongs to the Special Issue Explainable Computational Intelligence, Theory, Methods and Applications)

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21 pages, 4854 KiB

Open AccessArticle

Integrated Multi-Model Face Shape and Eye Attributes Identification for Hair Style and Eyelashes Recommendation

by Theiab Alzahrani, Waleed Al-Nuaimy and Baidaa Al-Bander

Computation 2021, 9(5), 54; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9050054 - 27 Apr 2021

Cited by 9 | Viewed by 9275

Abstract

Identifying human face shape and eye attributes is the first and most vital process before applying for the right hairstyle and eyelashes extension. The aim of this research work includes the development of a decision support program to constitute an aid system that analyses eye and face features automatically based on the image taken from a user. The system suggests a suitable recommendation of eyelashes type and hairstyle based on the automatic reported users’ eye and face features. To achieve the aim, we develop a multi-model system comprising three separate models; each model targeted a different task, including; face shape classification, eye attribute identification and gender detection model. Face shape classification system has been designed based on the development of a hybrid framework of handcrafting and learned feature. Eye attributes have been identified by exploiting the geometrical eye measurements using the detected eye landmarks. Gender identification system has been realised and designed by implementing a deep learning-based approach. The outputs of three developed models are merged to design a decision support system for haircut and eyelash extension recommendation. The obtained detection results demonstrate that the proposed method effectively identifies the face shape and eye attributes. Developing such computer-aided systems is suitable and beneficial for the user and would be beneficial to the beauty industrial. Full article

(This article belongs to the Special Issue Explainable Computational Intelligence, Theory, Methods and Applications)

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15 pages, 424 KiB

Open AccessArticle

Classification of Categorical Data Based on the Chi-Square Dissimilarity and t-SNE

by Luis Ariosto Serna Cardona, Hernán Darío Vargas-Cardona, Piedad Navarro González, David Augusto Cardenas Peña and Álvaro Ángel Orozco Gutiérrez

Computation 2020, 8(4), 104; https://0-doi-org.brum.beds.ac.uk/10.3390/computation8040104 - 04 Dec 2020

Cited by 11 | Viewed by 2808

Abstract

The recurrent use of databases with categorical variables in different applications demands new alternatives to identify relevant patterns. Classification is an interesting approach for the recognition of this type of data. However, there are a few amount of methods for this purpose in the literature. Also, those techniques are specifically focused only on kernels, having accuracy problems and high computational cost. For this reason, we propose an identification approach for categorical variables using conventional classifiers (LDC-QDC-KNN-SVM) and different mapping techniques to increase the separability of classes. Specifically, we map the initial features (categorical attributes) to another space, using the Chi-square (C-S) as a measure of dissimilarity. Then, we employ the (t-SNE) for reducing dimensionality of data to two or three features, allowing a significant reduction of computational times in learning methods. We evaluate the performance of proposed approach in terms of accuracy for several experimental configurations and public categorical datasets downloaded from the UCI repository, and we compare with relevant state of the art methods. Results show that C-S mapping and t-SNE considerably diminish the computational times in recognitions tasks, while the accuracy is preserved. Also, when we apply only the C-S mapping to the datasets, the separability of classes is enhanced, thus, the performance of learning algorithms is clearly increased. Full article

(This article belongs to the Special Issue Explainable Computational Intelligence, Theory, Methods and Applications)

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16 pages, 977 KiB

Open AccessArticle

Modelling Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based Highway via Artificial Neural Networks

by Shengkun Xie, Anna T. Lawniczak and Junlin Hao

Computation 2020, 8(3), 64; https://0-doi-org.brum.beds.ac.uk/10.3390/computation8030064 - 08 Jul 2020

Cited by 1 | Viewed by 2415

Abstract

A lot of effort has been devoted to mathematical modelling and simulation of complex systems for a better understanding of their dynamics and control. Modelling and analysis of computer simulations outcomes are also important aspects of studying the behaviour of complex systems. It often involves the use of both traditional and modern statistical approaches, including multiple linear regression, generalized linear model and non-linear regression models such as artificial neural networks. In this work, we first conduct a simulation study of the agents’ decisions learning to cross a cellular automaton based highway and then, we model the simulation data using artificial neural networks. Our research shows that artificial neural networks are capable of capturing the functional relationships between input and output variables of our simulation experiments, and they outperform the classical modelling approaches. The variable importance measure techniques can consistently identify the most dominant factors that affect the response variables, which help us to better understand how the decision-making by the autonomous agents is affected by the input factors. The significance of this work is in extending the investigations of complex systems from mathematical modelling and computer simulations to the analysis and modelling of the data obtained from the simulations using advanced statistical models. Full article

(This article belongs to the Special Issue Explainable Computational Intelligence, Theory, Methods and Applications)

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