Knowledge and Decision Systems with Applications
Knowledge and Decision Systems with Applications is a peer-reviewed interdisciplinary journal dedicated to advancing research in data-driven decision-making, artificial intelligence, and knowledge management. The journal serves as a platform for publishing innovative solutions that enhance decision processes, optimize knowledge systems, and address real-world challenges. With a focus on practical applications, the journal appeals to researchers, practitioners, and decision-makers across fields such as healthcare, finance, environmental science, and emerging technologies.
A BIM-Enabled Framework for Design Continuity and Lifecycle Decision-Making
Building Information Modelling (BIM) has evolved into a fundamental platform for lifecycle information management within the Architecture, Engineering, Construction, and Facilities Management (AEC-FM) sector. Despite its effectiveness in supporting technical coordination and information exchange, BIM remains limited in preserving Architectural Design Intent (ADI), particularly the spatial, material, functional, and experiential rationale underpinning design decisions. Consequently, critical design knowledge is often fragmented or lost during transitions between design, construction, and facility management phases, undermining design continuity and lifecycle decision-making. This study investigates how ADI is represented, transferred, and preserved within BIM-enabled workflows and proposes a structured mechanism for maintaining design continuity throughout the building lifecycle. A mixed-methods approach was adopted, combining a systematic literature review, comparative analysis of four documented BIM case studies, and a questionnaire survey involving architects, BIM managers, contractors, and facility managers. The findings reveal that ADI loss occurs predominantly during design-to-construction and construction-to-facility-management transitions. Key causes include inadequate documentation of design rationale, model simplification during handovers, fragmented stakeholder communication, and the absence of structured metadata for capturing qualitative design knowledge. Three principal dimensions of vulnerability were identified: spatial layout, materiality, and functionality. Survey results further demonstrate strong industry recognition of the importance of preserving ADI while highlighting practical implementation barriers, including unclear responsibilities, time constraints, and limited client requirements. To address these challenges, this study proposes a Design Intent Preservation Framework (DIPF) integrating rationale annotation, metadata tagging, design constraint recording, and lifecycle coordination triggers within BIM environments. The framework contributes to lifecycle-oriented BIM research by extending information management beyond technical interoperability toward the preservation of design knowledge, thereby supporting informed decision-making and long-term design continuity.
Building Information Modelling; Facilities Management; Design Framework; Decision-Making
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
A Grey Hybrid Multi-Criteria Decision-Making Framework for Assessing Tax Performance in G7 Economies
In spite of the fact that tax performance constitutes a central dimension of fiscal governance, its comparative measurement across countries has received limited attention within the multi-criteria decision-making literature. Addressing this gap, the present manuscript proposes an integrated hybrid decision-making methodology — combining the Grey Symmetry Point of Criterion (Grey SPC) and Grey Simple Ranking Process (Grey SRP) methods — for the systematic and comparative assessment of tax performance. The introduced model derives objective criterion weights through the Grey SPC approach and generates country rankings through the Grey SRP technique, ensuring full methodological consistency between the two stages while preserving uncertainty throughout the analytical process via grey interval numbers. The developed approach is empirically applied to the G7 economies using ten-year average data for the period 2013–2022 across four tax performance indicators: total tax revenue, income and profits tax, corporate tax, and goods and services tax. The empirical findings reveal goods and services tax as the most influential criterion, while France attains the highest overall tax performance score among the G7 economies. The robustness of the rankings is confirmed through systematic weight sensitivity analyses and comparisons with five widely adopted grey MCDM methods. Overall, the findings provide useful policy insights for improving fiscal performance through structural reforms in tax composition rather than through a sole emphasis on aggregate revenue levels.
Copyright (c) 2026 Berrak Tekgün, Şerife Merve Koşaraoğlu (Author) https://creativecommons.org/licenses/by/4.0
Traffic Accidents Analysis and Hotspots Identification Using GIS and Analytical Hierarchy Process: A Case Study of Lahore
Road traffic accidents are a major health and safety problem in the developing regions. The high rate of casualties annually in Lahore, Pakistan needs to be properly risk-assessed and intervened with safety measures. This paper applies QGIS and Multi-Criteria Decision Making to determine the level of accidents in District Lahore. The Analytical Hierarchy Process uses numerical weights, which represent the frequency, severity, road geometry, population density, and land use patterns, and its consistency ratio is 0.076. GIS classifies the levels of severity between 1 (low) and 5 (severe). Ferozepur Road (4.92), GT Road (4.92), and Canal Road (4.008) become the most dangerous streets since they consistently obtain the highest scores in terms of severity. This has largely been attributed to both high volumes of traffic as well as wide land use along these routes. The results suggest that these places have been causing disproportionately to the accident burden of the district with the consequences being very socio-economic and health-threatening to Lahore. As a way of mitigating these risks, the authorities should immediately join force to roll out specific safety measures, focus on mitigating hotspots that have been identified, and keep track of the results of accidents to ensure their effectiveness. GIS and AHP integration in this study, therefore, offer a strong foundation of evidence-based planning to reduce accidents and casualties.
Copyright (c) 2026 Huzaifa Qadeer, Asim Farooq, Danish Farooq, Summera Fahmi Khan, Shahid Noor Jan, Daniyal Hussain, Atta Ullah khan (Author) https://creativecommons.org/licenses/by/4.0
A Novel Integrated MCDM Framework for ESG Risk Assessment in Manufacturing Firms: The LOGSTA-SPC-MUNRA Hybrid Model
Environmental, social, and governance (ESG)-based risks have become key factors shaping firms’ sustainability levels, competitive strength, and long-term resilience. Particularly in the manufacturing industry, where environmental obligations, energy intensity, operational sensitivities, and governance pressures interact simultaneously, the systematic measurement of these risks has gained growing importance. However, the multidimensional nature of ESG risks makes both inter-firm comparison and holistic performance assessment methodologically challenging. The aim of this paper is to develop an integrated decision-making model to assess firms’ ESG risks and reveal their relative performance structure. For this purpose, an integrated weighting framework based on LOGarithmic normalization and STAndard Deviation (LOGSTA) and Symmetry Point of Criterion (SPC) was constructed to determine the importance levels of the criteria, while the relative ranking of firm performance was carried out through the Multiple Normalization Rating Analysis (MUNRA) approach. To assess the applicability of the developed model, an empirical case study was conducted on 13 manufacturing firms listed in the BIST Sustainability-25 Index. The results of the integrated weighting findings, as derived from the LOGSTA and SPC procedures, indicated that the ESG risk structure of the sampled firms is mainly constructed along the environmental dimensions. In this respect, carbon emission risk was identified as the most important criterion from the viewpoint of firm performance, whereas human capital risk was identified as the criterion that had the lowest relative discriminatory power. The ranking results, as derived from the proposed MUNRA approach, indicated that the top-performing firms were Ülker, Tüpraş, and Anadolu Efes, whereas the firms that had the lowest performance were Oyak Çimento, Çimsa, and Petkim. Finally, the proposed model was compared to other approaches, and it was indicated that the proposed model provides stable, consistent, and reliable results
Copyright (c) 2026 Osman Yavuz Akbulut (Author) https://creativecommons.org/licenses/by/4.0
A Kano-Based Decision Framework for Evaluating Customer Satisfactionin Face-to-Face Banking Services
Increased competition among companies in the banking sector is a reality in the current market and is driven mainly by the increase in the number of competitors and significant changes in the services provided. In this context, customer satisfaction is a highly relevant factor. This study aims to evaluate customer satisfaction with face-to-face banking. To this end, the Kano model was used to understand which aspects of in-person banking service impact customer satisfaction, allowing improvements to be prioritized and efforts that are not being effective to identify. The attributes evaluated were aggregated into 10 dimensions – tangibility, fees, accessibility, reliability, empathy, security, frontline personnel, services provided, communication, and responsiveness–defined based on a literature review. Through data collection and analysis, these attributes were classified as one-dimensional, must-be, indifferent, or reverse. Overall, the results showed that 25 attributes were considered one-dimensional, six were classified as indifferent, two were classified as must-be, 2 as reverse, and none of the attributes were classified as attractive. It is expected that this research will contribute to understanding which attributes impact customer satisfaction with in-person banking services and which directions should be taken to improve service quality, thereby increasing user satisfaction.
Copyright (c) 2026 Cinthia Monteiro de Santana, Aline Amaral Leal Barbosa, Raíssa Souto Maior Corrêa de Carvalho, Fagner José Coutinho de Melo, Denise Dumke de Medeiros (Author) https://creativecommons.org/licenses/by/4.0
A Structural Equation Modeling Approach for Sustainable Institutional Decision Support: A Case Study in a Philippine Countryside University
The Sustainable Development Goals (SDGs) provide a global framework for advancing sustainability across social, economic, environmental, and institutional dimensions. Higher education institutions play a critical role in promoting sustainability through teaching, research, campus operations, and community engagement. This study assessed SDG awareness and sustainability integration among teaching and non-teaching personnel at Romblon State University and examined the relationships among teaching-learning application or office operations, engagement with SDG-related activities, campus environment integration, and research and development. Using a descriptive-quantitative design, data from 140 respondents were analyzed through descriptive statistics and Structural Equation Modeling (SEM). Results revealed that SDG awareness was generally high, with engagement in sustainability-related activities rated very high. SDG 4 (Quality Education) and SDG 5 (Gender Equality) were the most visible goals. However, key barriers included limited awareness, insufficient resources, weak collaboration, and policy implementation challenges. SEM findings indicated that research and development strongly influenced campus sustainability integration. The study highlights the need for stronger sustainability mainstreaming, enhanced research productivity, inclusive participation, and sustained institutional support to improve SDG implementation in higher education.
Sustainable Development Goals; SDG awareness; sustainability integration; higher education; Structural Equation Modeling; Philippines
Copyright (c) 2026 Dave D. Lota, Jayson Reeves C. Mani, Gianinna Elaine M. Labicane, Garry Vanz V. Blancia (Author) https://creativecommons.org/licenses/by/4.0
Data-Driven Evaluation of Bioactive Glass for pH Regulation in Dental Applications
Maintaining physiological pH in the oral environment is essential to prevent demineralization and support natural remineralization processes. Acidic conditions accelerate tissue degradation and increase the risk of dental caries. This study investigates the alkalizing performance of two bioactive glass (BAG) compositions, 45S5 and S53P4, under simulated oral conditions. An integrated experimental and modeling approach was used to evaluate how compositional differences influence pH regulation and to estimate the material dosage required to achieve target pH levels. Experiments were conducted in acidic and neutral media, while a two-stage kinetic model was implemented to simulate ion release and pH evolution. Results show that 45S5 rapidly increases pH from highly acidic conditions to near-neutral levels within 24 hours, whereas S53P4 exhibits slower buffering behavior. Under moderately acidic conditions, both materials improve pH stability, though 45S5 performs more efficiently. The findings highlight the importance of composition-dependent kinetics for optimizing bioactive materials in dental applications.
Bioactive Glass; pH Regulation; Decision Modeling; Kinetic Modeling; Dental Materials; Ion Release;Optimization; Simulation
Copyright (c) 2026 Aslıhan Yelkenci (Author) https://creativecommons.org/licenses/by/4.0
Advancing Environmental Sustainability through Artificial Intelligence: A Fuzzy SWOT–LOGSTA-Based Strategic Analysis
Sustainability has become a major global priority, highlighting goals such as achieving net-zero emissions by 2050, reducing waste, and promoting the use of recycled materials. In response, companies are working to lower carbon emissions and improve resource efficiency. Artificial intelligence (AI) offers strong potential to address these challenges. This study adopted a fuzzy based strategic approach to assess the strengths, weaknesses, opportunities, and threats (SWOT) related to AI role for environmental sustainability. First, 12 SWOT factors are identified based on experts’ opinions and literature review. Then, a fuzzy logarithm normalization and standard deviation (F-LOGSTA) is applied to determine the weight of SWOT factors. The findings indicated that governance, energy use, and economic risks are the most influential factors for promoting environmental sustainability through AI. This study advances understanding of AI in sustainability by offering insights for researchers, practitioners, and policymakers, supporting further work at the nexus of AI and sustainable development.
Artificial intelligence; environmental sustainability; SWOT analysis; LOGSTA approach.
Copyright (c) 2026 Mouhamed Bayane Bouraima, Ibrahim Badi (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence and Strategic Decision-Making for Sustainable Governance
The increasing use of artificial intelligence (AI) in global governance systems could influence how sustainable strategies are developed by governments and other organizations; however, issues related to accountability, ethical alignment, and inclusivity remain challenges for AI analytical precision and prediction capabilities. This study addresses this question about how AI can be systematically integrated into strategic decision-making processes that enhance transparency, adaptability, and long-term sustainability through the development of a theoretical framework connecting artificial intelligence-driven analysis with structures in ethics and institutions to support sustainable governance via data-based evidence from multidisciplinary literature on AI management, sustainability studies, and governance theory using conceptual analytical methods for synthesizing ideas. The main findings are that good AI governance needs clear accountability mechanisms, open data ecosystem transparency, and the inclusion of sustainability metrics within decision algorithms; however, when governed responsibly, artificial intelligence can shift decisions to strategy instead of administration so institutions can maintain innovation while achieving balance between equity and long-term societal value.
Copyright (c) 2026 Mohamed Ibrahim Hassan Farag (Author) https://creativecommons.org/licenses/by/4.0
Physics-Informed Machine Learning for Predicting Thermomechanical Stress in Rotating Carbon-Aramid Hybrid Composites
This work presents a robust analytical and computational framework to investigate the thermomechanical behavior of rotating hybrid composites, specifically PAN-based carbon fiber and aramid subjected to high-speed rotation and extreme thermal gradients. By employing plane strain theory, we modeled the structural response of multilayered cylinders to evaluate how angular velocities and temperature fluctuations interact to influence structural integrity. A distinctive feature of this research is the validation of closed-form analytical solutions against machine learning (ML) predictive models at operational speeds of 100 and 200 rad/s, utilizing the Von Mises yield criterion to delineate elastic-plastic boundaries. The results reveal a significant correlation between rotational-thermal synergies and radial displacement, highlighting the unique load-bearing advantages of the carbon-aramid hybrid. While the PAN-carbon phase provides high specific stiffness, the aramid layer enhances toughness and impact resistance. The integration of physics-based modeling with ML validation confirms the framework’s high predictive accuracy and thermal stability. This hybrid approach offers a scalable, intelligent decision-support tool for aerospace and automotive engineering, where lightweight, resilient structural components are paramount for mission-critical performance.
Hybrid Composite Structures; Machine Learning; Predictive Modeling; Thermomechanical Stress Analysis; Von Mises Yield Criterion
Copyright (c) 2026 Firat Kayiran (Author) https://creativecommons.org/licenses/by/4.0
Generalizing Pascal’s Triangle through Mathematical Modeling: Meta Triangles and Cross-Domain Applications in Energy, Education, and Systems Analysis
This paper introduces an innovative mathematical construct known as the Guelph Expansion, which enables the generalization of Pascal’s Triangle into a family of Meta Triangles. Two efficient algorithms, the Embedded Pascal Triangles (EPTs) method and the Staircase Horizontal–Vertical (SHV) method, are developed to systematically generate these structures while enhancing numerical efficiency and scalability. In addition, the paper utilizes the Guelph expansion to formulate a novel Inhour Polynomial and a corresponding Coefficient-Based Model (CBM) for nuclear reactor kinetics, contributing to robust, and reliable reactor kinetics modeling. Beyond energy applications, the proposed framework demonstrates broad interdisciplinary relevance, including computational approaches for surface generation in computer graphics, genome tagging and DNA sequencing, fault tree analysis, and probabilistic modeling. By unifying diverse applications within a single mathematical tool, the study supports knowledge integration, and decision making across scientific disciplines.
Copyright (c) 2026 Wajdi Mohamed Ratemi (Author) https://creativecommons.org/licenses/by/4.0
Comparative Analysis of Fuzzy Analytic Hierarchy Process and Interval Analytic Hierarchy Process for Multi-Stakeholder Evaluation of Public Transport Supply Quality
Evaluating public transport service quality requires balancing diverse stakeholder perspectives, a complex task essential for sustainable urban mobility. The efficiency of Multi-Criteria Decision-Making (MCDM) techniques, such as the Fuzzy Analytic Hierarchy Process (FAHP), in fostering consensus among diverse groups is still up for debate. This study compares FAHP and Interval AHP (IAHP) to determine which method better achieves stakeholder agreement in public transport quality assessment. Using a three-level criteria hierarchy, data were collected from regular passengers, potential passengers, and government officials in Mersin, Turkey. Both FAHP and IAHP derived priority weights, with consensus measured by Kendall’s coefficient of concordance (W). Results revealed that consensus was strongest for strategic goals and weakened for operational details. Notably, IAHP consistently outperformed FAHP, producing higher concordance across all levels. The findings demonstrate that IAHP is a more effective tool for reconciling divergent preferences, offering transport planners a superior method for fostering legitimate, consensus-driven policies and improvements.
Multi-Criteria Decision Making (MCDM), Fuzzy Analytic Hierarchy Process (FAHP), Interval Analytic Hierarchy Process (IAHP), Stakeholder Analysis, Public Transport Quality, Kendall’s Coefficient of Concordance (W), Urban Mobility
Copyright (c) 2026 Hiba Solieman, Szabolcs Duleba (Author) https://creativecommons.org/licenses/by/4.0
The Evolution of Disclosure Narratives in Audit Firms: Evidence from Dynamic Community Detection of Transparency Reports
Transparency reporting has emerged as a critical regulatory mechanism for enhancing accountability in the audit profession following prominent corporate scandals. Nevertheless, the literature contests the extent to which these mandatory disclosures provide substantive information rather than serving as symbolic compliance tools. The present study aims to address the existing gap in understanding how audit firm disclosure narratives evolve and how firms strategically reposition themselves within the disclosure landscape. The objective of this research is to examine the longitudinal dynamics of disclosure strategies in Turkish audit firm transparency reports from 2013 to 2024, investigating the emergence of narrative communities and firms' strategic repositioning behaviours. A novel methodological framework is employed, integrating computational text analysis with dynamic network science. Specifically, term frequency-inverse document frequency and cosine similarity measures are utilized to assess narrative proximity between transparency reports. The Leiden algorithm for dynamic community detection is then applied to identify and track disclosure communities across 230 firm-year observations from 89 distinct audit firms. The results reveal a fundamental transformation in the disclosure landscape: an initially homogeneous narrative structure dominated by two generalist communities in early years evolved into a fragmented system comprising eight specialized disclosure communities by 2024. Furthermore, audit firms engage in systematic narrative repositioning, with a pronounced gravitation toward compliance-oriented communities during periods of maximum fragmentation. The results show that mandatory transparency rules can have unintended effects. For example, when narratives become more complex, companies tend to use symbolic, procedure-focused disclosure strategies instead of substantive differentiation. The study offers critical implications for regulators seeking to design disclosure frameworks that foster genuine transparency rather than superficial compliance.
Copyright (c) 2026 Abdullah Kürşat Merter, Murat Özcan, Yavuz Selim Balcıoğlu, Mehmet Günlük (Author) https://creativecommons.org/licenses/by/4.0
A Comprehensive Study on Fault Detection Techniques for Wheel Bearings in Rotating Machinery: Assessment of the LBF-MABAC model Based on Power Strategies
The wheel bearing is a very important part of the car, because they help or supports rotation and reduces friction among moving parts. A detailed investigation and comparison of the many models used to detect faults or failures in wheel bearings, which are critical and complex techniques in rotating machines such as vehicles, turbines, industrial equi⋕ent, and motors. Four main faults are noticed in the wheel bearing, such as: outer race defects, cage defects, ball/roller defects, and inner race defects, but the most important are preventing catastrophic failures, reducing downtime and repair costs with enables predictive maintenance. The main theme of this study is to choose or develop a technique for engineers’ implementation of condition monitoring systems; therefore, first, we design the model of the linguistic bipolar fuzzy technique, then we evaluate the models of “power averaging technique” and “power geometric technique” for linguistic bipolar fuzzy models. Additionally, we also construct the model of the “multi-attribute border approximation area comparison” technique, which is used for the assessment of the fault detection techniques for wheel bearing in rotating machinery. Finally, we illustrate numerical examples to describe the comparative analysis between our ranking values and the ranking values of old models, to mention the advantages and disadvantages of all approaches.
Bipolar fuzzy logic; Decision-making analysis; Fault detection; Linguistic term sets; Wheel bearing in rotating machinery.
Application of Cost-Effectiveness and Durability of Cement Treated Base in Road Construction
Cement Treated Base (CTB) is a type of stabilized base layer widely used in modern road construction to improve pavement strength and durability. It involves mixing a predetermined amount of cement with aggregate and water, resulting in a bound layer with enhanced load-bearing capacity and resistance to environmental conditions. The expense of producing the base layer of road and material used in pavements, including as drilling, blasting, crushing, and transporting, makes it an expensive choice. However, when subjected to high traffic and/or strong weather, this material does not hold up. The purpose of the research is, to increase the load bearing capacity of the road using CTB at feasible cost. The methods used are the Unconfined compressive strength, Modified proctor test for the cement-treated base. Results showed the optimal strength at the 3.5 % cement ratio, indicating its suitability for high-load-bearing applications. While 4% cement ratio demonstrated the least amount of rutting, making it ideal for high-traffic roads and areas prone to heavy loads. Cracking behaviors was influenced by both the cement content and curing conditions. The CTB methods saved the most money on construction costs of road, focusing on its benefits and inconveniences. In light of the writing survey and contextual investigations, the paper reveals that CTB can be a practical and robust solution for roads, development, especially in regions with frail subgrades or high traffic volumes.
Cement; Cement Treated Base; Cost estimation, durability, high traffic
Ranking of AI-Driven Strategies for Optimizing the Health Tourism Supply Chain Using Stratified BWM
Health tourism, as an emerging and strategic sector of the tourism industry, plays a significant role in economic growth, the development of international cooperation, and the enhancement of healthcare service quality. By integrating medical needs with recreational services, this sector holds great potential for attracting international patients. In this context, artificial intelligence technologies—including machine learning, data mining, natural language processing, and recommender systems—offer innovative opportunities to improve the efficiency of the health tourism supply chain. From enhancing patient data management to facilitating travel planning and personalizing services, AI can play a pivotal role in optimizing industry processes. This study aims to identify and rank the most effective AI-driven strategies for optimizing the health tourism supply chain. First, a comprehensive literature review and analysis of domain-specific sources were conducted to extract a set of key strategies. Subsequently, these strategies were evaluated and prioritized using the multi-criteria decision-making method known as the Stratified Best-Worst Method (Stratified BWM). The results revealed that the top three strategies were: “Patient Data Management and Analysis” (A1), “Healthcare supply chain optimization” (A2), and “Customer Experience and Digital Services” (A3). These strategies—by leveraging intelligent data usage, optimal resource allocation, and improved digital engagement with patients—significantly contribute to higher efficiency, customer satisfaction, and service quality. The findings of this research provide valuable insights for policymakers, healthcare managers, and tourism stakeholders in designing targeted programs and promoting the sustainable development of the health tourism supply chain.
Health Tourism; Supply Chain Optimization; Artificial Intelligence, Patient Data Analytics, Stratified BWM
The Utilization of Artificial Intelligence in Healthcare: Review, Challenges, and Future Research
Integrating Artificial Intelligence (AI) into the healthcare system has made major progress for diagnosing disease, patient monitoring, and medical imaging, creating a highly interconnected ecosystem for improved medical decision-making. AI-driven disease diagnosis utilizes machine learning models to analyze vast medical datasets, enabling quick and precise identification of diseases. This diagnostic capability is further enhanced by AI-powered medical imaging, where deep learning techniques, including convolutional neural networks (CNNs), refine image analysis, segmentation, and classification, providing critical support for precise diagnosis. Alongside these deep learning techniques, support vector machines (SVMs) offer strong classification powers that work especially well in situations requiring high-dimensional data processing with sparse training data. By combining CNNs for obtaining features and SVMs for categorizing, the advantages of both methods are combined to increase computational efficiency and diagnostic certainty. These AI-based insights are then reinforced through patient monitoring, where wearable sensors and IoT devices continuously track patient health, feeding real-time data into AI models that detect anomalies and predict disease progression. The synergy between these three areas ensures a continuous flow of medical information, enhancing predictive analytics and personalized treatment strategies. This review examines how powered system AI unifies disease diagnosis and patient monitoring using medical imaging into an integrated healthcare system, discussing current challenges such as data security, interpretability, and clinical adoption. The findings highlight AI's role in bridging diagnostic precision, real-time monitoring, and advanced imaging, paving the way for a more proactive and efficient healthcare framework.
Artificial Intelligence; machine learning; disease diagnosis; patient monitoring; medical imaging; IOT Devices; CNN; SVM.
Arabic Text Diacritization Using Deep Neural Networks and Transformer-Based Architectures
This study investigates the application of deep learning architectures for automatic Arabic text diacritization, with a particular focus on character-level neural networks. Four architectures were implemented: a Transformer encoder-decoder, a BiGRU model, a baseline stacked BiLSTM, and a CBHG model. Diacritic Error Rate (DER) and Word Error Rate (WER) were used as evaluation metrics, with training and evaluation conducted on the Tashkeela corpus. The results show that the CBHG model achieved faster inference times while slightly outperforming the Transformer encoder-decoder in diacritic accuracy. However, the findings also suggest that the Transformer model may yield better performance with larger datasets, improved parameter tuning, and increased model capacity.
Arabic Diacritization; Natural Language Processing (NLP); Sequence-to-Sequence Models; Transformer Architecture
A Hybrid Multi-Criteria Decision-Making Model for Performance Assessment in the Banking Industry
This work presents a novel hybrid multi-criteria decision-making (MCDM) framework designed to assess the financial and non-financial Environmental, Social, and Governance (ESG) performance of the banking sector. To demonstrate its applicability, a longitudinal case study was conducted on a major Turkish bank using annual data from 2012 to 2022. The proposed framework integrates two complementary methodologies: Maximum of Criterion (MAXC) and Range of Value (ROV). The MAXC method was utilized to objectively compute the weights of financial and non-financial performance indicators, while the ROV procedure enabled the ranking of annual performance across both dimensions. The evaluation followed a two-stage structure. In the first phase, MAXC revealed that the most influential financial indicators were return on average assets, return on average equity, and the ratio of non-performing loans to total loans. For the non-financial dimension, community engagement, governance quality, and human rights emerged as the most impactful criteria. In the second phase, the ROV method was employed to calculate composite performance scores and derive annual rankings. The results indicate that 2022, 2012, and 2014 marked the strongest years in terms of financial performance, while 2021, 2022, and 2020 demonstrated the highest ESG-related performance. Finally, a series of sensitivity analyses confirmed the robustness and reliability of the proposed hybrid model, thereby validating its suitability for comprehensive performance evaluation in the banking sector.
Financial Performance; Non-Financial Performance; Banking Sector; Multi-Criteria Decision-Making; Maximum of Criterion; Range of Value
Determining the Optimal Selection Criteria for Unmanned Surface Vehicles in Port Security Using Fuzzy AHP and the Best-Worst Method
The primary objective of this study is to evaluate the selection criteria for unmanned maritime vehicles in the context of port security. The selection criteria were obtained through a comprehensive review of the extant literature and consultation with experts in the field. The objective was to ascertain the relative weights of these criteria using the fuzzy AHP and Best Worst Method. The evaluations of five decision makers were utilised in the implementation phase. Following a thorough evaluation of the available data, it was determined that both methods yielded analogous results. The most significant criteria was determined to be "Endurance," while "Maintenance Cost" and "Investment Cost" were found to be of lesser importance. The study provides port operators with a unique perspective on the use of unmanned maritime vehicles for port security, emphasising the criteria that should be given greater attention.
Unmanned Surface Vehicle; Port Security; Fuzzy AHP; Best-Worst Method; Maritime Security; Multi-criteria Decision Making.
Numerical Technique for Solving Non-Linear Deterministic Hepatitis B Model
Epidemic models classify the process of infectious disease into a series of discrete stages, with transition from susceptible to different states. In this paper, a non-linear epidemic model is proposed to control and understand the dynamics of hepatitis B disease. Epidemic model describes a set of equations where computational tools are used to study the spread of transmissible pathogens in host populations. This model considers the effect of vaccination during the formulation of proposed model. In the work, the numerical solution of the model is further attained through Homotopy Analysis Method, which provides an effective and flexible way for controlling and adjusting the convergence region of the infinite series solution by means of an auxiliary parameter. The outcomes of this paper display that the hepatitis B disease has the inclination over a period of time but it is under control after vaccination.
Hepatitis B; SEIVR model; Initial value problem; Homotopy Analysis method
Evaluating the Electronic Waste Management System by Using Technique for Order Performance by Similarity to Ideal Solution
The waste created after the useful life of electronic or electrical products is labeled as electronic waste. Introduction of better and upgraded options into market increase the rate of disposal of obsolete discarded products from the consumer side. Electronic waste contains many toxic ingredients and major toxicants are heavy metal ions that may find the way to environment matrix after improper disposal of products. In recent years due to rapid technological development and industrialization; electronic waste management has become a major challenge throughout the world and receiving an attention from researchers, governments, and public. Poor management of waste may adversely affect the environment and human health. Thus, it is necessary to segregate all e-waste materials at the point of generation, for appropriate treatment/disposal. The assessment of the most suitable and appropriate framework has been a subject of extreme research interest as it involves many conflicting factors such as cost, type of waste, energy consumption, environmental impact of waste residue, rate of waste generation, treatment efficiency, and practical feasibility, consumer awareness, occupational hazards etc. There are numerous constraints and knowledge gaps to plan a feasible strategy in realistic scenario. The exact amount of disposed of e-waste is very difficult to predict because of many reasons including the unavailability of collection units for e-waste as well lack of awareness to consumer regarding its proper way of disposal. Various approaches that have been proposed needs to be analyzed with respect to every individual factor. To address this problem, we have proposed multi-criteria decision-making(MCDM) based Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to develop framework that determine the interdependency among different factors and obtained the priority value of each e-waste treatment method for appropriate selection and successful implementation in real scenario. From the results of the given information gathered from 3 sources, it is observed that re-use method is the preferred over recycle and landfill methods. The findings of this study will support the organizations and stakeholders to take suitable steps for the disposal of electronic waste strategies.
Electronic waste; Waste management; Multi criteria decision making; Environment; Health effects; Solid Waste
Optimizing Cluster Head Selection for Reliable and Efficient WSNs under Uncertainty
WSNs are often deployed in harsh and challenging environments where nodes may have limited energy and be prone to failure, reliability is an essential component of these networks. Cluster reliability is one of the approaches that can lead WSNs to reliable communication and energy efficiency at the highest degree of order. Cluster reliability is defined as the ability of a cluster to retain its structure and functionality over time, especially in the event of node failures or other unfavourable circumstances. The majority of current clustering protocol-based research focuses on (i) cluster Head Role (ii) cluster formation, (iii) data accuracy, and (iv TDMA schedule creation, all of which entirely ignore the importance of appropriate cluster head selections, which have a significant impact on WSN energy efficiency. The network has been divided into optimum clusters in this study employing a hierarchical clustering agglomerative approach after the optimal number of clusters has been established and confirmed by beta CV cluster validity Indexing. Each node's value is determined by the fitness function proposed in this study using parameters such as (i) residual energy, (ii) distance from sink, (iii) SNR (signal to noise ratio), and (iv) average distance. The weight parameter is determined using the entropy-weighted technique. It has been noted that our suggested method, which is shown in Figure 5, has 20% and 10% longer network lifetimes in comparison to the LEACH and LEACH-FC protocols. Finally, the obtained results have been validated by the testing of statistical hypotheses.
A Multi-Criteria Decision-Making Model for Prioritizing the Causes of Inflation in Libya Using the Best-Worst Method and Analytic Hierarchy Process
Inflation remains one of the most persistent and destabilizing economic challenges facing Libya, affecting purchasing power, investment, and overall economic stability. Understanding its root causes is essential for designing effective and targeted policy interventions. This study investigates the main drivers of inflation in Libya by applying a hybrid Multi-Criteria Decision-Making (MCDM) approach that combines the Best-Worst Method (BWM) and the Analytic Hierarchy Process (AHP). Eight critical inflation-related factors were identified through expert consultations and economic literature, including structural, monetary, and institutional elements. The BWM was first used to determine initial weight coefficients by comparing the most and least influential factors, followed by the AHP to conduct comprehensive pairwise comparisons. The final weights were derived by aggregating the results from both methods to ensure robustness and consistency. The findings indicate that weak domestic production and political instability are the most significant contributors to inflation in Libya, followed by currency devaluation and monetary expansion. These results emphasize the need for integrated policy responses that not only address monetary dynamics but also confront deeper structural constraints. The study offers a transparent and replicable framework for inflation analysis and contributes to more informed and context-specific economic policymaking in fragile and transitional economies.
Multi-Criteria Decision-Making; s the Best- Worst Method; Analytic Hierarchy Process; Inflation; policymaking
A Decade of Picture Fuzzy Sets in Multi-Criteria Decision-Making: A Comprehensive Review of Trends, Gaps, and Future Directions
Picture fuzzy sets (PFSs) are extensively utilized in medical diagnostics and multi-criteria decision-making (MCDM) due to their enhanced flexibility, distinguishing capability, and applicability in handling uncertainty and hesitation, particularly in complex domains such as healthcare, transportation, environmental decision-making, artificial intelligence (AI), and machine learning. Guided by four research questions, this review employed descriptive statistics to analyse the extent of research focused on PFSs. An intensive literature search was performed across leading publishers, including IEEE Xplore, SpringerLink, ScienceDirect, the Association for Computing Machinery (ACM), and the Multidisciplinary Digital Publishing Institute (MDPI). Findings revealed significant scholarly efforts to adopt PFSs in healthcare (22.6%), transportation (24.5%), environmental decision-making (7.5%), AI and machine learning (5.7%), and other domains (39.6%). Research from 2013 to 2024 demonstrated notable advancements in mathematical operations and extensions (25%), aggregation operators and similarity measures (30%), hybrid approaches and MCDM applications (20%), domain-specific implementations (15%), and theoretical developments (10%). MCDM emerged as a prominent tool for enhancing decision-making across diverse fields. The study highlights the need to explore additional areas of application for PFSs, particularly in refining distance measures to further enhance their utility.
Picture Fuzzy Sets; Multi-Criteria Decision-Making; Uncertainty Management; Aggregation Operators and Artificial Intelligence
An Innovative Decision Support Model for the Financial Performance Assessment: A Study of BIST Cement Firms
The regularly assessment of the financial performance of firms in the real sector is highly essential, both in terms of increasing the level of operational efficiency and in terms of effectively managing potential risk factors and obtaining sustainable competitive advantage. This paper introduces a new decision algorithm for the assessment of firm performance. This decision algorithm consists of the integration of Logarithmic Percentage Change-driven Objective Weighting (LOPCOW), Modified Standard Deviation (MSD), Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS), Multi-Objective Optimization on the Basis of Simple Ratio Analysis (MOOSRA), Multi Atributive Ideal-Real Comparative Analysis (MAIRCA) and Borda Count methodologies. In order to test the presented decision-making procedure, a real-time case study was applied as part of the study. This case study is focused on analyzing the financial performance of 13 cement industry firms whose shares are listed on the Borsa Istanbul (BIST) for the year 2023. In the process of assessing the performance of these companies, 10 performance indicators were selected with the help of previous literature. LOPCOW and MSD algorithms were applied to determine the final importance weights of these indicators, while CRADIS, MOOSRA, MAIRCA and Borda Count algorithms were employed to rank the firms. The findings of the final weighting algorithm indicate that the three most important performance indicators are total debt to total equity ratio, total debt to total assets ratio and cash ratio. Moreover, according to the final ranking results obtained based on the CRADIS, MOOSRA, MAIRCA and Borda Count algorithms, BUCIM is the most financially successful firm in 2023. Additionally, the findings of the robustness analyses also support the conclusion that the results obtained from the presented model are reliable and applicable.
Financial Performance; BIST Cement Sector Firms; MCDM; LOPCOW; MSD; CRADIS, MOOSRA; MAIRCA; Borda Count.
A Cooperative Heterogeneous Multi-Agent System Leveraging Deep Reinforcement Learning
The agent plays a pivotal role in the multi-agent environment. Previous studies have primarily focused on homogeneous agent collaboration, but the approaches used in these studies cannot be directly applied to heterogeneous agent collaboration. There is a need for a model that supports collaboration among heterogeneous agents, as real-world systems consist of agents with diverse shapes, functions, and tasks. This study proposes a framework that uses visual and vector observations to facilitate collaboration among heterogeneous agents. The seaport environment is simulated for this study. Visual observations and vector observations are employed to interpret signals from other agents and calculate distance between the agents in the environment. Two types of agents are used: the crane agent, which lifts and lowers heavy materials between the ship and the designated location, and the lifter agent, which transports bulky materials from the seaport to the storeroom. These agents are heterogeneous due to differences in size, capabilities, and tasks. Separate neural networks are trained for each agent. The results show that the proposed model outperforms the base model in terms of mean reward, extrinsic reward, episode length, and value estimation. In terms of value loss and policy loss, the proposed model performs similarly to the base model.
Multi-Agents; Deep Reinforcement Learning; Heterogeneous-Agents
Evaluating Financial Performance with SPC-LOPCOW-MARCOS Hybrid Methodology: A Case Study for Firms Listed in BIST Sustainability Index
Periodic analysis of financial performance is instrumental in enabling manufacturing firms to enhance their operational efficiency, manage risks effectively, make strategic decisions, maintain a competitive advantage, ensure sustainability, and promote good corporate governance. This research introduces an innovative decision-making methodology for evaluating and ranking the financial performance of firms by utilizing financial ratio metrics. To this end, the present study puts forward a novel decision-making methodology for evaluating the performance of a corporation, integrating the Symmetry Point Criterion (SPC), the Logarithmic Percentage Change Based Objective Weighting (LOPCOW), and the Measurement of Alternatives and Ranking by Consensus Solution (MARCOS). The feasibility of the proposed decision approach in the existing work is evaluated through a real-time case study. The case analysis concentrates on the financial performance assessment of 16 real sector firms whose shares are traded in the Borsa Istanbul (BIST) Sustainability 25 Index (XSD25). In order to examine the financial performance of firms in the real sector, ten financial performance indicators are selected based on earlier literature. The SPC and LOPCOW procedures were applied to ascertain the weight values of the assessment indicators, and the MARCOS procedure was employed to rank the firms' financial performance. The findings of the weighting process indicate that the three most influential criteria are the ratio of total debt to total equity, the return on equity, and the average price-earnings ratio. According to the MARCOS ranking procedure, ENKAI was the company with the highest financial performance compared to its peers during the analysis period.
Financial Performance; Real Sector Firms; SPC; LOPCOW;MARCOS
Fuzzy Multi-Criteria Decision-Making Based Security Management: Risk Assessment and Countermeasure Selection in Smart Cities
The aim of this study is to develop a fuzzy MCDM based framework for security management in smart cities. The study focuses on the assessment of security risks and the selection of appropriate security measures. To address the uncertainties and complexities in the assessment of security risks and measures, this research employs two different fuzzy methods: weighting the risk factors using the Fuzzy weight by envelope and slope (F-WENSLO) method and selecting the most appropriate security measures using the Fuzzy Ranking Alternatives with Weights of Criterion (F-RAWEC) method. The proposed approach is applied to a case study on identifying and assessing potential security risks in a smart city infrastructure. The risk factors used in the study are identified as flash floods, traffic accidents and transportation security, robbery and public safety threats, cybersecurity threats, health crises and pandemics, and energy infrastructure and outages. Security systems are defined as physical security systems, emergency response and crisis management systems, software and cybersecurity systems, monitoring and surveillance systems, and sensor-based early warning systems. The results show that physical security measures play a critical role in preventing and responding to incidents, while emergency response and crisis management systems perform less well. It is also stated that software and cybersecurity systems provide an effective solution against digital threats, but their integration with other systems is important. The results show that the fuzzy-based model significantly increases the accuracy in prioritizing risks and provides a systematic method for selecting the most effective security measures. This study contributes to the literature by presenting an innovative decision-making tool for security management in smart cities.
Fuzzy MCDM; Smart Cities; Security Management; Risk Assessment.
Analysis of the Corporate Financial Performance Based on Grey PSI and Grey MARCOS Model in Turkish Insurance Sector
The use of MCDM approaches in the insurance sector provides important information to the top management or decision maker’s authorities of insurance companies on issues such as accurate performance measurement, identification of strengths and weaknesses, optimisation of operations and improvement of strategic decisions. This, in turn, helps senior management to balance profitability and risk and contributes to the establishment of a sustainable business model. The present study aims to measure and rank corporate financial performance in the non-life insurance sector by using the Grey PSI and Grey MARCOS integrated model. For the purpose of this study, the proposed decision-making approach is applied through a case study. The case study focuses on the corporate financial performance assessment process of nine companies operating in the Turkish non-life insurance sector and consistently ranked in the top ten in terms of premium production in the period 2021-2023.In the current study, twelve financial performance indicators were identified for corporate financial performance analysis, taking into account previous studies in the literature. The importance weights of these performance criteria are calculated using the Grey PSI procedure. The Grey MARCOS method was then employed to determine the ranking of the decision alternatives. The results obtained from the Grey PSI method indicate that the net profit-to-total equity, net premiums received-to-gross premiums received and total operating expenses-to-gross premiums received are the three most significant criteria affecting the corporate financial performance of the relevant non-life insurance companies. The application of the grey MARCOS ranking method shows that Türkiye Sigorta has superior corporate financial performance for the period 2021-2023 compared to other insurance companies analysed in the study. The assessment and evaluation of corporate financial performance in the insurance sector is of critical importance for sector managers, policyholders, regulatory authorities, policymakers and other stakeholders, due to the significant benefits insurance companies provide at both micro and macroeconomic levels.
Corporate Financial Performance; Insurance Sector; MCDM; Grey System Theory; Grey PSI; Grey MARCOS