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Theory and Applications of Machine Learning and Artificial Intelligence
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Theory and Applications of Machine Learning and Artificial Intelligence

A special issue of Sci (ISSN 2413-4155). This special issue belongs to the section "Computer Sciences, Mathematics and AI".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 9815

Special Issue Editors

Prof. Dr. Asok Ray

E-Mail Website
Guest Editor
Department of Mechanical Engineering and Mathematics, Pennsylvania State University, University Park, PA 16802, USA
Interests: machine learning and pattern recognition; decision and control; diagnostics and prognostics; structural health monitoring
Dr. Chandrachur Bhattacharya

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Pennsylvania State University, University Park, PA 16802, USA
Interests: data-driven modeling; time-series analysis; deep learning; Gaussian processes; thermofluidic modeling

Special Issue Information

Dear Colleagues,

Machine learning and artificial intelligence are the next steps forward in making the world safer, better, and more sustainable. To this end, intelligent learners are being used to create optimal designs of decision and control systems that can extract the maximum efficiency from embedded sensor arrays to trigger warnings or shutdowns in addition to automated maintenance and failure analyses. At its crux, machine learning relies on having relevant input data, as well as their processing and fusion, to learn how to map into the desired outputs using numerical models. More often than not these inputs are signals from sensors or observations coming from their analyses, which entail some preprocessing of the observed data. Thus, signal processing plays an important role in many machine learning and decision making applications, and their combined analyses are of great importance to researchers. Therefore, the research topic of machine learning would also include several associated tasks, such as the following: 1) signal processing of multiple sensor data; 2) fusion of the resulting information; and 3) decision making based on the fused information.

Prof. Dr. Asok Ray
Dr. Chandrachur Bhattacharya
Guest Editors

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. Sci is an international peer-reviewed open access quarterly 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 1200 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.

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Published Papers (3 papers)

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Research

14 pages, 1635 KiB  
Article
Multimodal and Multidomain Feature Fusion for Emotion Classification Based on Electrocardiogram and Galvanic Skin Response Signals
by Amita Dessai and Hassanali Virani
Sci 2024, 6(1), 10; https://doi.org/10.3390/sci6010010 - 4 Feb 2024
Viewed by 2028
Abstract
Emotion classification using physiological signals is a promising approach that is likely to become the most prevalent method. Bio-signals such as those derived from Electrocardiograms (ECGs) and the Galvanic Skin Response (GSR) are more reliable than facial and voice recognition signals because they [...] Read more.
Emotion classification using physiological signals is a promising approach that is likely to become the most prevalent method. Bio-signals such as those derived from Electrocardiograms (ECGs) and the Galvanic Skin Response (GSR) are more reliable than facial and voice recognition signals because they are not influenced by the participant’s subjective perception. However, the precision of emotion classification with ECG and GSR signals is not satisfactory, and new methods need to be developed to improve it. In addition, the fusion of the time and frequency features of ECG and GSR signals should be explored to increase classification accuracy. Therefore, we propose a novel technique for emotion classification that exploits the early fusion of ECG and GSR features extracted from data in the AMIGOS database. To validate the performance of the model, we used various machine learning classifiers, such as Support Vector Machine (SVM), Decision Tree, Random Forest (RF), and K-Nearest Neighbor (KNN) classifiers. The KNN classifier gives the highest accuracy for Valence and Arousal, with 69% and 70% for ECG and 96% and 94% for GSR, respectively. The mutual information technique of feature selection and KNN for classification outperformed the performance of other classifiers. Interestingly, the classification accuracy for the GSR was higher than for the ECG, indicating that the GSR is the preferred modality for emotion detection. Moreover, the fusion of features significantly enhances the accuracy of classification in comparison to the ECG. Overall, our findings demonstrate that the proposed model based on the multiple modalities is suitable for classifying emotions. Full article
(This article belongs to the Special Issue Theory and Applications of Machine Learning and Artificial Intelligence)
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28 pages, 5698 KiB  
Article
A Dual Multimodal Biometric Authentication System Based on WOA-ANN and SSA-DBN Techniques
by Sandeep Pratap Singh and Shamik Tiwari
Sci 2023, 5(1), 10; https://doi.org/10.3390/sci5010010 - 1 Mar 2023
Cited by 4 | Viewed by 3202
Abstract
Identity management describes a problem by providing the authorized owners with safe and simple access to information and solutions for specific identification processes. The shortcomings of the unimodal systems have been addressed by the introduction of multimodal biometric systems. The use of multimodal [...] Read more.
Identity management describes a problem by providing the authorized owners with safe and simple access to information and solutions for specific identification processes. The shortcomings of the unimodal systems have been addressed by the introduction of multimodal biometric systems. The use of multimodal systems has increased the biometric system’s overall recognition rate. A new degree of fusion, known as an intelligent Dual Multimodal Biometric Authentication Scheme, is established in this study. In the proposed work, two multimodal biometric systems are developed by combining three unimodal biometric systems. ECG, sclera, and fingerprint are the unimodal systems selected for this work. The sequential model biometric system is developed using a decision-level fusion based on WOA-ANN. The parallel model biometric system is developed using a score-level fusion based on SSA-DBN. The biometric authentication performs preprocessing, feature extraction, matching, and scoring for each unimodal system. On each biometric attribute, matching scores and individual accuracy are cyphered independently. A matcher performance-based fusion procedure is demonstrated for the three biometric qualities because the matchers on these three traits produce varying values. The two-level fusion technique (score and feature) is implemented separately, and their results with the current scheme are compared to exhibit the optimum model. The suggested plan makes use of the highest TPR, FPR, and accuracy rates. Full article
(This article belongs to the Special Issue Theory and Applications of Machine Learning and Artificial Intelligence)
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28 pages, 598 KiB  
Article
A Concise Tutorial on Functional Analysis for Applications to Signal Processing
by Najah F. Ghalyan, Asok Ray and William Kenneth Jenkins
Sci 2022, 4(4), 40; https://doi.org/10.3390/sci4040040 - 21 Oct 2022
Cited by 1 | Viewed by 3726
Abstract
Functional analysis is a well-developed field in the discipline of Mathematics, which provides unifying frameworks for solving many problems in applied sciences and engineering. In particular, several important topics (e.g., spectrum estimation, linear prediction, and wavelet analysis) in signal processing had been initiated [...] Read more.
Functional analysis is a well-developed field in the discipline of Mathematics, which provides unifying frameworks for solving many problems in applied sciences and engineering. In particular, several important topics (e.g., spectrum estimation, linear prediction, and wavelet analysis) in signal processing had been initiated and developed through collaborative efforts of engineers and mathematicians who used results from Hilbert spaces, Hardy spaces, weak topology, and other topics of functional analysis to establish essential analytical structures for many subfields in signal processing. This paper presents a concise tutorial for understanding the theoretical concepts of the essential elements in functional analysis, which form a mathematical framework and backbone for central topics in signal processing, specifically statistical and adaptive signal processing. The applications of these concepts for formulating and analyzing signal processing problems may often be difficult for researchers in applied sciences and engineering, who are not adequately familiar with the terminology and concepts of functional analysis. Moreover, these concepts are not often explained in sufficient details in the signal processing literature; on the other hand, they are well-studied in textbooks on functional analysis, yet without emphasizing the perspectives of signal processing applications. Therefore, the process of assimilating the ensemble of pertinent information on functional analysis and explaining their relevance to signal processing applications should have significant importance and utility to the professional communities of applied sciences and engineering. The information, presented in this paper, is intended to provide an adequate mathematical background with a unifying concept for apparently diverse topics in signal processing. The main objectives of this paper from the above perspectives are summarized below: (1) Assimilation of the essential information from different sources of functional analysis literature, which are relevant to developing the theory and applications of signal processing. (2) Description of the underlying concepts in a way that is accessible to non-specialists in functional analysis (e.g., those with bachelor-level or first-year graduate-level training in signal processing and mathematics). (3) Signal-processing-based interpretation of functional-analytic concepts and their concise presentation in a tutorial format. Full article
(This article belongs to the Special Issue Theory and Applications of Machine Learning and Artificial Intelligence)
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