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Q-Rung Orthopair Fuzzy Sets-Enhanced FMEA for COVID-19 Risk Assessment

Informatica (Slovenia) 2025年第1期49卷 81-92页

作者： Abdullah, Lazim Awang, Noor Azzah Qiyas, Muhammad Special Interest Group of Modelling and Data Analytics Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Kuala Nerus21030 Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Department of Mathematics Riphah International University Failsabad Campus Pakistan

Failure Modes and Effects Analysis (FMEA) is a widely used tool for risk analysis, primarily to identify risk factors affecting system quality. Due to the limitations of the traditional FMEA model, several recent models incorporating advanced fuzzy set extensions have been developed to enhance the reliability of risk assessment outcomes. However, most of these models limit expert flexibility in expressing preferences and often overlook the impact of unequal expert weights and the stability of risk ranking results. This study introduces a new FMEA model based on Q-Rung Orthopair Fuzzy Sets (Q-ROFSs), termed Q-ROFSs-FMEA. Q-ROFSs, an extension of intuitionistic fuzzy sets, introduce a new linguistic term. The Q-ROFSs-FMEA model considers the unequal weights of experts, enabling a dynamic representation of expert preferences. These weights and the linguistic evaluation of risk factors are integrated through an aggregation operator, facilitating consensus among experts. The model is applied to a case study on COVID-19 risk factors, revealing that ‘older age’ (risk priority number 0.000012) is the highest risk factor, while ‘gender’ (risk priority number -0.0037) is the lowest. It is found that the ranking of risk factors determined by the Q-ROFSs-FMEA model is obtained as FM1 FM3 FM6 FM4 FM5 FM7 FM8 FM2 . Furthermore, a comparative analysis indicates consistent ranking results across different models, demonstrating the reliability of the proposed model. The case study and comparative analysis validate the effectiveness and applicability of the Q-ROFSsbased risk assessment model. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Failure modes

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Characterizing Zagreb Index Bounds in Trees with Specified Metric Dimension

arXiv

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arXiv 2024年

作者： Ali, Waqar Husin, Mohamad Nazri Bin Nadeem, Muhammad Faisal Special Interest Group on Modeling and Data Analytics Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Terengganu Kuala Nerus21030 Malaysia Department of Mathematics COMSATS University Islamabad Lahore Campus Lahore54000 Pakistan

Consider a simple graph G = (V, E), where V are the vertices and E are the edges. The first Zagreb index, M1(G) = Pv∈V ψG(v)2. The second Zagreb index, M2(G) = Puv∈E ψG(u)ψG(v). The metric dimension of a graph refers to the smallest subset of vertices in a resolving set such that the distances from these vertices to all others in the graph uniquely identify each vertex. In this paper, we characterize bounds for the Zagreb indices of trees, based on the order of the tree and its metric dimension. Furthermore, we identify the trees that achieve these extremal bounds, offering valuable insights into how the metric dimension influences the behavior of the Zagreb indices in tree structures. © 2024, CC BY.

关键词： Trees (mathematics)

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Extremal Values of the Atom-Bond Connectivity Index for Trees with Given Roman Domination Numbers

arXiv

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arXiv 2024年

Consider that G = (X, Y) is a simple, connected graph with X as the vertex set and Y as the edge set. The atom-bond connectivity (ABC) index is a novel topological index that Estrada introduced in Estrada et al. (1998). It is defined as ABC(G) = xy∈XY (G)s ζx +ζxζζyy− 2 where ζx and ζx represent the degrees of the vertices x and y, respectively. In this work, we explore the behavior of the ABC index for tree graphs. We establish both lower and upper bounds for the ABC index, expressed in terms of the graph’s order and its Roman domination number. Additionally, we characterize the tree structures that correspond to these extremal values, offering a deeper understanding of how the Roman domination number (RDN) influences the ABC index in tree graphs. © 2024, CC BY.

关键词： Trees (mathematics)

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ANN-based methods for solving partial differential equations: a survey

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Arab Journal of Basic and Applied Sciences 2022年第1期29卷 233-248页

作者： Pratama, Danang A. Bakar, Maharani A. Ismail, N.B. Mashuri, M. Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Terengganu Malaysia Special Interest Group Modelling and Data Analytics Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Terengganu Malaysia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Jenderal Soedirman Purowkerto Indonesia

Traditionally, partial differential equation (PDE) problems are solved numerically through a discretization process. Iterative methods are then used to determine the algebraic system generated by this process. Recently, scientists have emerged artificial neural networks (ANNs), which solve PDE problems without a discretization process. Therefore, in view of the interest in developing ANN in solving PDEs, scientists investigated the variations of ANN which perform better than the classical discretization approaches. In this study, we discussed three methods for solving PDEs effectively, namely Pydens, NeuroDiffEq and Nangs methods. Pydens is the modified Deep Galerkin method (DGM) on the part of the approximate functions of PDEs. Then, NeuroDiffEq is the ANN model based on the trial analytical solution (TAS). Lastly, Nangs is the ANN-based method which uses the grid points for the training data. We compared the numerical results by solving the PDEs in terms of the accuracy and efficiency of the three methods. The results showed that NeuroDiffeq and Nangs have better performance in solving high-dimensional PDEs than the Pydens, while Pydens is only suitable for low-dimensional problems. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis group on behalf of the University of Bahrain.

关键词： 68-XX 68W25 Artificial neural networks discretization Nangs module Neuroiffeq method partial differential equations Pydens method

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Investigation of Zagreb indices and Zagreb coindices of line graphs implementing subdivision process

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AIP Conference Proceedings 2024年第1期2905卷

作者： Mohamad Nazri Husin Nur Hafiza Azwani Mohd Saudi Special Interest Group on Modelling Data Analytics (SIGMDA) Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu 21030 Kuala Nerus Kuala Terengganu Malaysia

A topological index is a numerical measure characterizing a whole structure of a particularly given graph. Moreover, in chemical graph theory, the Zagreb indices idea, which is based on the number of vertex degrees, was proposed. More Zagreb index variations were later created, and some of these indexes were recently produced. This paper outlines the Zagreb indices and Zagreb coindices with respect to the line graphs of Polyphenylene dendrimer subdivision graphs. Numerous creative families of Zagreb indices were also numerically simulated, comparing the two types of indices that can be relevant with respect to the quantitative structure-activity relationship (QSAR) as well as quantitative structure-property relationships (QSPR) research.

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Embedded Legendre expansion layer of physics-informed neural network with restarting strategy in solving partial differential equation in fluid dynamics

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Results in Engineering 2025年 26卷

作者： Danang A. Pratama Maharani A. Bakar Ruwaidiah Idris Norizan Mohamed Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Kuala Nerus Terengganu 21030 Malaysia Special Interest Group Modelling and Data Analytics Universiti Malaysia Terengganu Kuala Nerus Terengganu 21030 Malaysia

Partial differential equations (PDEs) have long been used to describe and solve problems in fluid dynamics. The advent of Artificial Neural Networks (ANNs) has revolutionized the ways of tackling many problems, including the landscape of scientific computing. The Physics-Informed Neural Network (PINN) is a type of ANN used for unsupervised learning tasks, designed to solve problems by incorporating PDEs. On the other hand, single-layer Functional Link Artificial Neural Network (FLANN) has been a topic of interest in the field of artificial intelligence due to its unique architecture and computational efficiency. Leveraging the optimal structural complexity of FLANN and the effective design of PINN for solving PDEs under Legendre Expansion Layer (LEL) has the potential to tackle complex problems more effectively. This novel approach will be simulated using both the restarting and non-restarting strategies, resulting in LEL-rPINN and LEL-PINN. Three different fluid dynamics PDEs—the Allen-Cahn (AC) equation, the flow mixing (FM) equation, and the Navier-Stokes (NS) system of equations—are employed to demonstrate the improvements and potential drawbacks of incorporating LEL into both PINN and rPINN to solve PDEs. LEL-based methods demonstrate improved agreement with both analytical and ground-truth solutions, outperforming basic PINN and rPINN, as shown in the figures and tables. These results demonstrate that the involvement of LEL shows promising performance across a diverse set of problems, particularly in the field of fluid dynamics.

关键词： Partial differential equations Fluid dynamics FLANN PINN LEL Restarting strategy

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Women's Health Diary: Tracker System for Menstruation, Intermenstrual, Ovulation, and Postnatal Bleeding Using Rules-Based Technique

Women's Health Diary: Tracker System for Menstruation, Inter...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Nursyuhada binti Abdullah Azwa bin Abdul Aziz Wan Azani Mustafa Muntather Almusawi Abbas Hameed Abdul Hussein Faculty of Informatics & Computing Universiti Sultan Zainal Abidin (UniSZA) Besut Campus Terengganu Malaysia Data Science & Analytics (DASA) Special Interest Group (SIG) Universiti Sultan Zainal Abidin (UniSZA) Terengganu Malaysia Centre of Excellence Advanced Computing (CoE AdvComp) Universiti Malaysia Perlis Arau Perlis Malaysia Faculty of Engineering The Islamic University Najaf Iraq College of Pharmacy Ahl Al Bayt University Karbala Iraq

Menstruation, a natural physiological process in women, entails regular vaginal bleeding as part of the monthly menstrual cycle. While existing systems facilitate menstrual cycle tracking, they often lack the capability to differentiate between various types of vaginal bleeding, such as menstruation (haid), intermenstrual bleeding (istihadah), or postnatal bleeding (nifas). To address this limitation, the Women's Health Diary: Tracker System for Menstruation, Intermenstrual, Ovulation, and Postnatal Bleeding) was developed using a rule-based technique. This system employs an iterative method comprising eight phases: initial planning, planning, requirements, analysis and design, implementation, testing, evaluation, and deployment. By ensuring the optimal development and performance of this system, women can effectively manage their menstrual blood, a vital aspect of their overall health. Moreover, this system holds particular significance for every women, as it facilitates tracking and identification of regular and irregular cycles, enabling them to uphold the legitimacy of their worship.

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Winning the carbon race: Game-based strategies for emission reduction in maritime industry

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Research in Transportation Business and Management 2025年 61卷

作者： Ruslan, Siti Marsila Mhd Osman, Mohd Tarmizi Mansor, Wan Nurdiyana Wan Mokhtar, Kasypi Saidi, Norjipin Roslan, Ummu Atiqah Mohd Hassim, Muhamad Fairus Noor Mclellan, Benjamin Craig Faculty of Maritime Studies Universiti Malaysia Terengganu Malaysia Security Safety & Environmental Division Marine Department Malaysia Faculty of Ocean Engineering Technology Universiti Malaysia Terengganu Malaysia Special Interest Group on Modelling and Data Analytics Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Malaysia Special Interest Group on Modelling and Data Analytics Faculty Science and Marine Environment Universiti Malaysia Terengganu Malaysia Energy Economics Research Group Graduate School of Energy Science Department of Socio-Environmental Energy Science Kyoto University Japan

The maritime industry contributes to global greenhouse gas (GHG) emissions, accounting for approximately 2.89 % of global emissions. With the International Maritime Organization (IMO) setting ambitious goals to reduce carbon emissions by 50 % by 2050, there is a pressing need for effective strategies in the maritime industry. This study examines the strategic interactions between governments, port authorities, and shipping companies by utilizing game theory to explore dynamic and evolving behaviors in carbon emission reduction efforts. By establishing a trilateral game model, the research provides insights into the complex interplay of incentives and deterrents that influence stakeholder actions. Key scenarios analyzed include the net incremental benefits of adopting specific strategies by port authorities and the additional costs incurred by shipping companies when implementing green practices. The findings indicate that port authorities can achieve significant benefits by adopting collaborative strategies, supported by government incentives to offset initial high costs, while for shipping companies, the analysis underscores the importance of weighing additional costs against potential operational efficiencies and market advantages. The study offers targeted policy recommendations to promote the development of a low-carbon maritime industry, highlighting the potential of green fuels and advanced technologies in achieving sustainable maritime operations. This research fills a critical gap in understanding the strategic behaviors driving emission reductions in maritime industry, contributing to broader efforts in combating global climate change. © 2025 Elsevier Ltd

关键词： Game theory GHG emission Government IMO Port Shipping

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Implementation of Artificial Intelligence In Engineering to Achieve Sustainable Development Goals (SDGs)

Implementation of Artificial Intelligence In Engineering to ...

引用

Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Muhammad D. Zakaria Azwa Abdul Aziz Azrul Amri Jamal Wan Azani Mustafa Laith H. Alzubaidi A.H. A. Hussein Faculty of Informatics & Computing Universiti Sultan Zainal Abidin (UniSZA) Tembila Campus Terengganu Malaysia Data Science & Analytics (DASA) Special Interest Group (SIG) Universiti Sultan Zainal Abidin (UniSZA) Gong Badak Terengganu Malaysia Centre of Excellence Advanced Computing (CoE AdvComp) Universiti Malaysia Perlis Arau Perlis Malaysia College of Pharmacy Ahl Al Bayt University Karbala Iraq Faculty of Engineering The Islamic University Najaf Iraq

Artificial Intelligence (AI) is implicit to revolutionize the engineering industry by automating tasks, minimizing costs, improving quality, and creating new opportunities for innovation. The article explores the implementation of AI in engineering as a means to achieve the Sustainable Development Goals (SDGs) set forth by the United Nations. AI technologies hold significant potential in optimizing resource utilization, enabling data-driven decision-making, and fostering innovation in various engineering domains. This study provides an overview of AI applications highlighting their alignment with specific SDGs. It examines the advantages, challenges, and ethical considerations connected to AI integration in engineering practices. Moreover, the article provides recommendations for policymakers, researchers, and practitioners to ensure responsible and inclusive AI development. By harnessing the transformative power of AI in engineering, we can increase the process towards SDGs, creating a more sustainable and prosperous future for all.

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Generalized TODIM method and its application in material selection process

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AIP Conference Proceedings 2023年第1期2484卷

作者： N. N. N. Che Rosli B. Yusoff 1Special Interest Group of Modelling and Data Analytics (SIGMDA) Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Kuala Nerus Terengganu 21030 Malaysia.

TODIM (an acronym in Portuguese for Interactive and Multi-criteria Decision Making) is one of the methods developed to deal with complex decision-making processes. The main feature of TODIM is its effectiveness in modelling the psychological behaviour of experts. The method is based on the Prospect Theory (PT), where the S-shaped growth curve represents its value function. Numerous studies have been conducted on improving the TODIM method to address the multi-criteria problem and the multi-expert problem in decision-making under risk and uncertainty. Nevertheless, the method has suffered from several shortcomings, which may produce counter-intuitive results and rank reversal problems. The generalized TODIM has been proposed recently to overcome these shortcomings. In this work, two paradoxes: weight consistency and weight monotonicity in the TODIM method, are examined. Then, we show that the generalized TODIM method satisfies these two properties compared to the TODIM method. Finally, a numerical example of the material selection process is applied to analyse and validate these results.

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