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Application of Artificial Intelligence in Decision Making

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D2: Operations Research and Fuzzy Decision Making".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 17024

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Special Issue Editors

Dr. Muhammad Syafrudin

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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

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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

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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

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Published Papers (10 papers)

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Research

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33 pages, 7332 KiB

Open AccessArticle

Synergistic Integration of Edge Computing and 6G Networks for Real-Time IoT Applications

by Ahmed M. Alwakeel

Mathematics 2025, 13(9), 1540; https://doi.org/10.3390/math13091540 - 7 May 2025

Viewed by 315

Abstract

The rapid proliferation of Internet of Things (IoT) applications necessitates real-time data processing and low-latency communication, challenging traditional cloud computing paradigms. This research addresses these challenges by integrating edge computing with emerging 6G networks, proposing the ARMO (Adaptive Resource Management and Offloading) model. The ARMO model leverages intelligent task scheduling, dynamic resource allocation, and energy-efficient strategies to enhance the performance of edge computing environments. Our comprehensive methodology involves collecting and preprocessing data from IoT devices, extracting relevant features, predicting resource demand, optimizing task offloading, and continuously monitoring and adjusting resource allocation using advanced machine learning techniques. The results demonstrate significant improvements, including a 47% reduction in average latency, a 40% decrease in total energy consumption, and a 20% increase in resource utilization. Additionally, the model achieved a 98% task completion rate and consistently higher network throughput compared to previous models. These findings underscore the ARMO model’s potential to support the next generation of real-time IoT applications, providing a robust, efficient, and scalable solution for integrating edge computing with 6G networks. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Decision Making)

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33 pages, 6783 KiB

Open AccessArticle

Adaptive Zero Trust Policy Management Framework in 5G Networks

by Abdulrahman K. Alnaim

Mathematics 2025, 13(9), 1501; https://doi.org/10.3390/math13091501 - 1 May 2025

Viewed by 200

Abstract

The rapid evolution and deployment of 5G networks have introduced complex security challenges due to their reliance on dynamic network slicing, ultra-low latency communication, decentralized architectures, and highly diverse use cases. Traditional perimeter-based security models are no longer sufficient in these highly fluid and distributed environments. In response to these limitations, this study introduces SecureChain-ZT, a novel Adaptive Zero Trust Policy Framework (AZTPF) that addresses emerging threats by integrating intelligent access control, real-time monitoring, and decentralized authentication mechanisms. SecureChain-ZT advances conventional Zero Trust Architecture (ZTA) by leveraging machine learning, reinforcement learning, and blockchain technologies to achieve autonomous policy enforcement and threat mitigation. Unlike static ZT models that depend on predefined rule sets, AZTPF continuously evaluates user and device behavior in real time, detects anomalies through AI-powered traffic analysis, and dynamically updates access policies based on contextual risk assessments. Comprehensive simulations and experiments demonstrate the robustness of the framework. SecureChain-ZT achieves an authentication accuracy of 97.8% and reduces unauthorized access attempts from 17.5% to just 2.2%. Its advanced detection capabilities achieve a threat detection accuracy of 99.3% and block 95.6% of attempted cyber intrusions. The implementation of blockchain-based identity verification reduces spoofing incidents by 97%, while microsegmentation limits lateral movement attacks by 75%. The proposed SecureChain-ZT model achieved an authentication accuracy of 98.6%, reduced false acceptance and rejection rates to 1.2% and 0.2% respectively, and improved policy update time to 180 ms. Compared to traditional models, the overall latency was reduced by 62.6%, and threat detection accuracy increased to 99.3%. These results highlight the model’s effectiveness in both cybersecurity enhancement and real-time service responsiveness. This research contributes to the advancement of Zero Trust security models by presenting a scalable, resilient, and adaptive policy enforcement framework that aligns with the demands of next-generation 5G infrastructures. The proposed SecureChain-ZT model not only enhances cybersecurity but also ensures service reliability and responsiveness in complex and mission-critical environments. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Decision Making)

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29 pages, 5780 KiB

Open AccessArticle

Zero Trust Strategies for Cyber-Physical Systems in 6G Networks

by Abdulrahman K. Alnaim and Ahmed M. Alwakeel

Mathematics 2025, 13(7), 1108; https://doi.org/10.3390/math13071108 - 27 Mar 2025

Cited by 2 | Viewed by 496

Abstract

This study proposes a Zero Trust security framework for 6G-enabled Cyber-Physical Systems (CPS), integrating Adaptive Access Control (AAC), end-to-end encryption, and blockchain to enhance security, scalability, and real-time threat detection. As 6G networks facilitate massive device connectivity and low-latency communication, traditional perimeter-based security models are inadequate against evolving cyber threats such as Man-in-the-Middle (MITM) attacks, Distributed Denial-of-Service (DDoS), and data breaches. Zero Trust security eliminates implicit trust by enforcing continuous authentication, strict access control, and real-time anomaly detection to mitigate potential threats dynamically. The proposed framework leverages blockchain technology to ensure tamper-proof data integrity and decentralized authentication, preventing unauthorized modifications to CPS data. Additionally, AI-driven anomaly detection identifies suspicious behavior in real time, optimizing security response mechanisms and reducing false positives. Experimental evaluations demonstrate a 40% reduction in MITM attack success rates, 5.8% improvement in authentication efficiency, and 63.5% lower latency compared to traditional security methods. The framework also achieves high scalability and energy efficiency, maintaining consistent throughput and response times across large-scale CPS deployments. These findings underscore the transformative potential of Zero Trust security in 6G-enabled CPS, particularly in mission-critical applications such as healthcare, smart infrastructure, and industrial automation. By integrating blockchain-based authentication, AI-powered threat detection, and adaptive access control, this research presents a scalable and resource-efficient solution for securing next-generation CPS architectures. Future work will explore quantum-safe cryptography and federated learning to further enhance security, ensuring long-term resilience in highly dynamic network environments. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Decision Making)

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29 pages, 3674 KiB

Open AccessArticle

Advanced Tax Fraud Detection: A Soft-Voting Ensemble Based on GAN and Encoder Architecture

by Masad A. Alrasheedi, Samia Ijaz, Ayed M. Alrashdi and Seung-Won Lee

Mathematics 2025, 13(4), 642; https://doi.org/10.3390/math13040642 - 16 Feb 2025

Viewed by 922

Abstract

The world prevalence of the two types of authorized and fraudulent transactions makes it difficult to distinguish between the two operations. The small percentage of fraudulent transactions, in turn, gives rise to the class imbalance problem. Hence, an adequately robust fraud detection mechanism must exist for tax systems to avoid their collapse. It has become significantly difficult to obtain any dataset, specifically a tax return dataset, because of the rising importance of privacy in a society where people generally feel squeamish about sharing personal information. Because of this, we arrive at the decision to synthesize our dataset by employing publicly available data, as well as enhance them through Correlational Generative Adversarial Networks (CGANs) and the Synthetic Minority Oversampling Technique (SMOTE). The proposed method includes a preprocessing stage to denoise the data and identify anomalies, outliers, and dimensionality reduction. Then the data have undergone enhancement using the SMOTE and the proposed CGAN techniques. A unique encoder design has been proposed, which serves the purpose of exposing the hidden patterns among legitimate and fraudulent records. This research found anomalous deductions, income inconsistencies, recurrent transaction manipulations, and irregular filing practices that distinguish fraudulent from valid tax records. These patterns are identified by encoder-based feature extraction and synthetic data augmentation. Several machine learning classifiers, along with a voting ensemble technique, have been used both with and without data augmentation. Experimental results have shown that the proposed Soft-Voting technique outperformed the original without an ensemble method. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Decision Making)

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15 pages, 772 KiB

Open AccessArticle

MFAN: Multi-Feature Attention Network for Breast Cancer Classification

by Inzamam Mashood Nasir, Masad A. Alrasheedi and Nasser Aedh Alreshidi

Mathematics 2024, 12(23), 3639; https://doi.org/10.3390/math12233639 - 21 Nov 2024

Cited by 8 | Viewed by 1033

Abstract

Cancer-related diseases are some of the major health hazards affecting individuals globally, especially breast cancer. Cases of breast cancer among women persist, and the early indicators of the diseases go unnoticed in many cases. Breast cancer can therefore be treated effectively if the detection is correctly conducted, and the cancer is classified at the preliminary stages. Yet, direct mammogram and ultrasound image diagnosis is a very intricate, time-consuming process, which can be best accomplished with the help of a professional. Manual diagnosis based on mammogram images can be cumbersome, and this often requires the input of professionals. Despite various AI-based strategies in the literature, similarity in cancer and non-cancer regions, irrelevant feature extraction, and poorly trained models are persistent problems. This paper presents a new Multi-Feature Attention Network (MFAN) for breast cancer classification that works well for small lesions and similar contexts. MFAN has two important modules: the McSCAM and the GLAM for Feature Fusion. During channel fusion, McSCAM can preserve the spatial characteristics and extract high-order statistical information, while the GLAM helps reduce the scale differences among the fused features. The global and local attention branches also help the network to effectively identify small lesion regions by obtaining global and local information. Based on the experimental results, the proposed MFAN is a powerful classification model that can classify breast cancer subtypes while providing a solution to the current problems in breast cancer diagnosis on two public datasets. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Decision Making)

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21 pages, 1133 KiB

Open AccessArticle

A Stacking Ensemble Based on Lexicon and Machine Learning Methods for the Sentiment Analysis of Tweets

by Sharaf J. Malebary and Anas W. Abulfaraj

Mathematics 2024, 12(21), 3405; https://doi.org/10.3390/math12213405 - 31 Oct 2024

Viewed by 1768

Abstract

Sentiment is employed in various fields, such as collecting web-based opinions for the formulation of governmental policies, measuring employee and customer satisfaction levels in business organizations, and measuring the sentiment of the public in political and security matters. The field has recently faced new challenges since algorithms must operate with highly unstructured sentiment data from social media. In this study, the authors present a new stacking ensemble method that combines the lexicon-based approach with machine learning algorithms to improve the sentiment analysis of tweets. Due to the complexity of the text with very ill-defined syntactic and grammatical patterns, using lexicon-based techniques to extract sentiment from the content is proposed. On the same note, the contextual and nuanced aspects of sentiment are inferred through machine learning algorithms. A sophisticated bat algorithm that uses an Elman network as a meta-classifier is then employed to classify the extracted features accurately. Substantial evidence from three datasets that are readily available for public analysis re-affirms the improvements this innovative approach brings to sentiment classification. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Decision Making)

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26 pages, 1232 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 805

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 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

Open AccessArticle

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

Cited by 4 | Viewed by 1736

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 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

Open AccessArticle

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 2 | Viewed by 3004

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, 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

Open AccessReview

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 17 | Viewed by 4875

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 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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