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Mathematics
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Application of Artificial Intelligence in Decision Making
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Application of Artificial Intelligence in Decision Making

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Fuzzy Sets, Systems and Decision Making".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 4837

Special Issue Editors

Dr. Muhammad Syafrudin

E-Mail Website
Guest Editor
Department of Artificial Intelligence, Sejong University, Seoul 05006, Republic of Korea
Interests: machine learning; artificial intelligence; information system; IoT; health informatics
Special Issues, Collections and Topics in MDPI journals
Dr. Norma Latif Fitriyani

E-Mail Website
Guest Editor
Department of Data Science, Sejong University, Seoul, Republic of Korea
Interests: data mining and analysis; machine learning; image processing; artificial intelligence; health informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In today's digitally connected world, advanced artificial intelligence (AI) techniques are being employed in various applications ranging from image processing to social network analysis, and from computer-generated images to routing algorithms. The impact of AI on the world is expected to be immense, transforming everything from corporate to domestic applications. It is predicted that AI will contribute more to the global economy than China and India combined, and almost every successful industry or corporation will use some form of AI within the next decade.

Applied artificial intelligence has the potential to revolutionize decision-making in various sectors such as science, engineering, industry, medical, robotics, manufacturing, entertainment, optimization, and business. This Special Issue aims to showcase the latest research and breakthroughs in AI and highlight their practical applications in various fields.

The topics of interest for this Special Issue include, but are not limited to, the application of AI in the Internet of Things (IoT), cyber–physical systems (CPS), intelligent transportation systems (ITS), and smart vehicles. Additionally, this Special Issue addresses topics such as big data analysis, deep learning, neural networks, fuzzy systems, distributed AI systems, decision-support systems, knowledge representation, expert systems, image processing, pattern recognition, speech recognition, and fault detection, analysis, diagnostics, and monitoring.

The aim of this Special Issue is to encourage researchers and practitioners to submit high-quality original research or review articles on these subjects in order to disseminate the latest developments and practical applications of AI in various industries. This Special Issue will also feature case studies and benchmarking in order to showcase the practical applications of AI in industry.

Dr. Muhammad Syafrudin
Dr. Norma Latif Fitriyani
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • advanced artificial intelligence
  • image processing
  • social network analysis
  • routing algorithms
  • decision-making
  • optimization
  • Internet of Things (IoT)
  • cyber–physical systems (CPS)
  • intelligent transportation systems (ITS)
  • big data analysis
  • deep learning
  • neural networks
  • fuzzy systems
  • distributed AI systems
  • decision-support systems
  • expert systems
  • pattern recognition
  • speech recognition
  • fault detection

Published Papers (4 papers)

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Research

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26 pages, 1232 KiB  
Article
Effort-Aware Fault-Proneness Prediction Using Non-API-Based Package-Modularization Metrics
by Mohsin Shaikh, Irfan Tunio, Jawad Khan and Younhyun Jung
Mathematics 2024, 12(14), 2201; https://doi.org/10.3390/math12142201 - 13 Jul 2024
Viewed by 310
Abstract
Source code complexity of legacy object-oriented (OO) software has a trickle-down effect over the key activities of software development and maintenance. Package-based OO design is widely believed to be an effective modularization. Recently, theories and methodologies have been proposed to assess the complementary [...] Read more.
Source code complexity of legacy object-oriented (OO) software has a trickle-down effect over the key activities of software development and maintenance. Package-based OO design is widely believed to be an effective modularization. Recently, theories and methodologies have been proposed to assess the complementary aspects of legacy OO systems through package-modularization metrics. These package-modularization metrics basically address non-API-based object-oriented principles, like encapsulation, commonality-of-goal, changeability, maintainability, and analyzability. Despite their ability to characterize package organization, their application towards cost-effective fault-proneness prediction is yet to be determined. In this paper, we present theoretical illustration and empirical perspective of non-API-based package-modularization metrics towards effort-aware fault-proneness prediction. First, we employ correlation analysis to evaluate the relationship between faults and package-level metrics. Second, we use multivariate logistic regression with effort-aware performance indicators (ranking and classification) to investigate the practical application of proposed metrics. Our experimental analysis over open-source Java software systems provides statistical evidence for fault-proneness prediction and relatively better explanatory power than traditional metrics. Consequently, these results guide developers for reliable and modular package-based software design. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Decision Making)
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38 pages, 2043 KiB  
Article
Boosting Institutional Identity on X Using NLP and Sentiment Analysis: King Faisal University as a Case Study
by Khalied M. Albarrak and Shaymaa E. Sorour
Mathematics 2024, 12(12), 1806; https://doi.org/10.3390/math12121806 - 11 Jun 2024
Viewed by 641
Abstract
Universities increasingly leverage social media platforms, especially Twitter, for news dissemination, audience engagement, and feedback collection. King Faisal University (KFU) is dedicated to enhancing its institutional identity (ID), grounded in environmental sustainability and food security, encompassing nine critical areas. This study aims to [...] Read more.
Universities increasingly leverage social media platforms, especially Twitter, for news dissemination, audience engagement, and feedback collection. King Faisal University (KFU) is dedicated to enhancing its institutional identity (ID), grounded in environmental sustainability and food security, encompassing nine critical areas. This study aims to assess the impact of KFU’s Twitter interactions on public awareness of its institutional identity using systematic analysis and machine learning (ML) methods. The objectives are to: (1) Determine the influence of KFU’s Twitter presence on ID awareness; (2) create a dedicated dataset for real-time public interaction analysis with KFU’s Twitter content; (3) investigate Twitter’s role in promoting KFU’s institutional identity across 9-ID domains and its changing impact over time; (4) utilize k-means clustering and sentiment analysis (TFIDF and Word2vec) to classify data and assess similarities among the identity domains; and (5) apply the categorization method to process and categorize tweets, facilitating the assessment of word meanings and similarities of the 9-ID domains. The study also employs four ML models, including Logistic Regression (LR) and Support Vector Machine (SVM), with the Random Forest (RF) model combined with Word2vec achieving the highest accuracy of 100%. The findings underscore the value of KFU’s Twitter data analysis in deepening the understanding of its ID and guiding the development of effective communication strategies. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Decision Making)
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17 pages, 3474 KiB  
Article
A Fault Detection System for Wiring Harness Manufacturing Using Artificial Intelligence
by Jinwoo Song, Prashant Kumar, Yonghawn Kim and Heung Soo Kim
Mathematics 2024, 12(4), 537; https://doi.org/10.3390/math12040537 - 8 Feb 2024
Cited by 1 | Viewed by 1280
Abstract
Due to its simplicity, accuracy, and adaptability, Crimp Force Monitoring (CFM) has long been the standard for fault detection in wiring harness manufacturing. However, it necessitates frequent reconfigurations based on the variability in materials, dependency on operator skill, and high costs of implementation, [...] Read more.
Due to its simplicity, accuracy, and adaptability, Crimp Force Monitoring (CFM) has long been the standard for fault detection in wiring harness manufacturing. However, it necessitates frequent reconfigurations based on the variability in materials, dependency on operator skill, and high costs of implementation, and thus reconfiguration presents significant challenges. To solve these problems, this paper introduces a fault detection system that employs an Artificial Intelligence (AI) classification model to enhance the performance and cost-efficiency of the quality control process of wiring harness manufacturing. Since there are no labeled data to train the classification model at the onset of manufacturing, a small number of normal data from each production run are manually extracted to train the model. To address the constraint of the limited available data, the system generates synthetic data from normal data, simulating potential defects by using Regional Selective Data Scaling (RSDS). This innovative method performs upscaling or downscaling on specific regions of the original data to produce synthetic abnormal data, which enables the fault detection system to efficiently train its classification model with a dataset consisting solely of normal operation data. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Decision Making)
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Review

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42 pages, 9098 KiB  
Review
Consequential Advancements of Self-Supervised Learning (SSL) in Deep Learning Contexts
by Mohammed Majid Abdulrazzaq, Nehad T. A. Ramaha, Alaa Ali Hameed, Mohammad Salman, Dong Keon Yon, Norma Latif Fitriyani, Muhammad Syafrudin and Seung Won Lee
Mathematics 2024, 12(5), 758; https://doi.org/10.3390/math12050758 - 3 Mar 2024
Cited by 1 | Viewed by 1766
Abstract
Self-supervised learning (SSL) is a potential deep learning (DL) technique that uses massive volumes of unlabeled data to train neural networks. SSL techniques have evolved in response to the poor classification performance of conventional and even modern machine learning (ML) and DL models [...] Read more.
Self-supervised learning (SSL) is a potential deep learning (DL) technique that uses massive volumes of unlabeled data to train neural networks. SSL techniques have evolved in response to the poor classification performance of conventional and even modern machine learning (ML) and DL models of enormous unlabeled data produced periodically in different disciplines. However, the literature does not fully address SSL’s practicalities and workabilities necessary for industrial engineering and medicine. Accordingly, this thorough review is administered to identify these prominent possibilities for prediction, focusing on industrial and medical fields. This extensive survey, with its pivotal outcomes, could support industrial engineers and medical personnel in efficiently predicting machinery faults and patients’ ailments without referring to traditional numerical models that require massive computational budgets, time, storage, and effort for data annotation. Additionally, the review’s numerous addressed ideas could encourage industry and healthcare actors to take SSL principles into an agile application to achieve precise maintenance prognostics and illness diagnosis with remarkable levels of accuracy and feasibility, simulating functional human thinking and cognition without compromising prediction efficacy. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Decision Making)
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