Journal of Decision Analytics and Intelligent Computing
Journal of Decision Analytics and Intelligent Computing (JDAIC) is a refereed international forum to exchange analytical information, applied research outcomes and knowledge about data-driven decision analytics and intelligent computing involving a variety of innovative analytical methods. Areas that include but are not limited to artificial intelligence, Big data analytics, computational intelligence, cloud computing, cognitive intelligence, decision support systems, expert systems, evolutionary computation, fuzzy set theory, machine learning, mathematical programming, multi-criteria decision making, natural computing, neutrosophic theory, neural networks, optimization, prescriptive analytics, quantum computing, rough set theory, soft computing, support vector machines, simulation, swarm intelligence. The journal will publish papers in all application domains of decision analytics and intelligent computing including accounting, agriculture, bioinformatics, business intelligence, communications, crisis management, defence, energy and environment, engineering, finance, healthcare, human resources management, information systems, intelligent database management, logistics, marketing, manufacturing, materials, network management, power, production and operations management, project management, risk assessment, services management, supply chain, strategic management to name a few. Open Access This is an open-access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission from the publisher or the author. Publication fee: There is no submission charge or publication fee. Publication in JDAIC is free of charge for the authors. Publisher: SCIENTIFIC OASIS PUBLISHER distributes open-access journals subject to rigorous peer review and compliant with the Committee on Publication Ethics (COPE). SCIO promotes academic publishing and strives to maintain the highest publishing standards through journals in various scientific and engineering fields. In addition to scientific journals, SCIO is committed to publishing academic books, monographs, and proceedings of scientific conferences...more info Indexing: ROAD Google Scholar (h5-index=13, i10 index=18, 2024) Dimensions doiSerbia
Regional Association for Security and crisis management and European centre for operational research
An autonomous pharmaceutical supply chain network for resilience and optimization: A multi-agent deep reinforcement learning framework
The pharmaceutical supply chain must sustain high service levels under demand shocks, cold-chain constraints, and data-sharing limitations. However, many studies still separate forecasting from replenishment, rely on static policies, or evaluate resilience without a clearly specified control model. This paper develops an Autonomous Pharmaceutical Supply Chain Network (APSCN) that couples demand forecasting, synthetic time-series generation, and multi-agent deep reinforcement learning for resilient inventory control. The case study represents an anonymized regional United States vaccine distribution network with one central manufacturing hub and ten regional distribution centers operating over a 52-week horizon. A long short-term memory (LSTM) forecaster and an extreme gradient boosting (XGBoost) benchmark are trained on historical demand and time-series generative adversarial network (TimeGAN)-augmented disruption scenarios informed by a susceptible-infected-recovered (SIR) epidemic signal. Inventory decisions are then coordinated by multi-agent deep deterministic policy gradient (MADDPG) agents under explicit shelf-life, service-level, and cost constraints. The paper contributes a formal optimization and reward framework for cold-chain pharmaceutical logistics, a richer case-study description linking geography, network topology, and disruption design, and a comparative evaluation against autoregressive integrated moving average (ARIMA), Prophet, XGBoost, static rules, and single-agent reinforcement learning. In the simulated disruption scenario, the proposed LSTM + MADDPG configuration reduces mean absolute percentage error from 11.7% to 6.0%, lowers total cost from $1.25M to $0.85M, maintains a 99.1% service level, eliminates stockout incidents, and shortens recovery time from 28 to 4 days. The findings indicate that autonomous, decentralized control can materially improve both efficiency and resilience in pharmaceutical distribution networks.
Copyright (c) 2026 Divanshu Mittal https://creativecommons.org/licenses/by/4.0
Forecasting gold prices in India: A time series analysis using Box Jenkins methodology
In India, gold holds significant cultural and economic importance. It serves not only as a financial asset but also as a symbol of tradition and social value. This study forecasts gold prices in the Indian market using the Box–Jenkins (BJ) methodology and analyzes monthly data covering the period from 2004 to 2025. Based on the empirical analysis, the ARIMA (1,1,1) model was selected as the best-fitting model. The model demonstrated strong statistical validity and low forecasting errors, indicating a high level of predictive accuracy. The forecast further predicts a steady rise in gold prices from December 2024 to April 2025. This projected increase is consistent with historical trends and seasonal consumer demand. Consumer behavior plays a significant role in influencing gold prices. This is particularly evident in the increased demand during festival and wedding seasons. Gold is also widely preferred as a hedge against inflation and economic uncertainty, which further strengthens demand. In addition, investment patterns are evolving. A growing number of individuals are adopting gold-backed ETFs and digital gold platforms, reflecting the changing investment preferences of Indian households. Macroeconomic factors also significantly influence gold prices, including currency depreciation and global market uncertainties. Collectively, these factors explain the expected upward movement in gold prices in the Indian market.
Copyright (c) 2026 Sagnik Maity, Amit Majumder https://creativecommons.org/licenses/by/4.0
Enhancing operational planning at the tactical level: Application of the fuzzy DIBR II – TOPSIS model for selecting a maneuver form in offensive urban operations
Modern military operations are characterized by high dynamism and uncertainty, requiring rapid decision-making that takes into consideration numerous complex factors. Urban offensive operations, in particular, present unique challenges due to dense terrain, the presence of civilians, and the dynamic nature of combat. Selecting the optimal form of maneuver is a critical component in developing a course of action (COA) and requires the balancing of multiple, often conflicting, criteria. This paper presents a hybrid multi-criteria decision-making (MCDM) model that integrates the DIBR II (Defining Interrelationships Between Ranked Criteria II) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methods to support decision-making at the tactical level. DIBR II is used to determine the relative importance of criteria, while TOPSIS identifies the most suitable form of maneuver based on proximity to an ideal solution. The model is applied to a simulated urban operation scenario, evaluating maneuver forms against expert-defined criteria. This approach reduces subjectivity and enhances decision efficiency by providing a transparent and replicable framework. Universal criteria for maneuver selection, as proposed by military experts, are incorporated. Beyond maneuver selection, the model demonstrates potential applicability to other aspects of operational planning, such as risk assessment and target prioritization.
Copyright (c) 2025 Miša Živković, Ilija Stojanović, Aleksandar Aleksić https://creativecommons.org/licenses/by/4.0
Selection of the organizational structure of an agro-food company using an intuitionistic approach
This research examines which organizational structure would yield the best results for the operations of a selected agro-food company. To do this, a decision-making model was developed, including six organizational structures and 19 criteria. The model was evaluated by experts using linguistic assessments. In addition, uncertainty was incorporated into this decision-making process through an intuitionistic approach. The results were obtained using the SiWeC (Simple Weight Calculation) and RAWEC (Ranking of Alternatives with Weights of Criterion) methods. The SiWeC method identified employee satisfaction and decision-making speed as the most important criteria, while the RAWEC method found that a digital organizational structure provides the best results. However, to achieve even better results in this company, it is necessary to upgrade the digital organizational structure to a network structure, leveraging the strengths of both. In this way, by forming a hybrid organizational structure, better effects would be obtained than using a single organizational structure.
Copyright (c) 2025 Miroslav Nedeljković, Adis Puška, Anđelka Štilić, Jurica Bosna https://creativecommons.org/licenses/by/4.0
Application of multi-criteria analysis methods to assess the implications of modern weaponry on the risk level of its use
The paper analyzes modern weaponry through the application of SWOT analysis and multi-criteria decision-making based on the AHP method. Depending on the degree to which modern technological factors are incorporated into weapon production, the possibilities for their use under different conditions are determined. The objective of the research is to identify and rank the strengths, weaknesses, opportunities, and threats that shape the development and application of modern weaponry. By combining qualitative and quantitative approaches, a comprehensive framework was established for assessing the strategic, technical, and security implications of new technologies applied in modern weapon systems. The results indicate that modern weaponry simultaneously enhances combat effectiveness while generating new security risks, thereby confirming the need for a balanced approach to its development and use.
Copyright (c) 2025 Katarina Janković, Nenad Komazec, Milica Mladenović https://creativecommons.org/licenses/by/4.0
Soft union-intersection product and soft symmetric difference-union product of groups
In settings where parametric variability is present, soft set theory has evolved into a robust and versatile mathematical framework for modeling and analyzing uncertainty. Central to this framework are the operations and product constructions on soft sets, which together provide a powerful algebraic infrastructure for addressing complex parameter-dependent problems. Formally, it is shown that under the union operation, the collection of all soft sets defined over a fixed parameter set forms a bounded semilattice, thereby supplying essential algebraic structure and coherence. Through careful analysis, it is further demonstrated that the algebraic system consisting of all soft sets over a fixed parameter set endowed with a group structure, equipped with the union operation and the proposed product, satisfies the axiomatic framework of a hemiring. This structural characterization yields two major theoretical implications: it strengthens the algebraic foundations of soft set theory and lays the groundwork for constructing a soft group theory analogous to its classical counterpart. A new product on soft sets, termed the soft symmetric difference–union product, is then introduced for the case in which the parameter set carries a group structure. This operation is examined in detail from both axiomatic and structural perspectives, with particular attention to its compatibility with soft equality and soft subsethood. The approach presented here makes a substantial contribution to the ongoing algebraic refinement and theoretical advancement of soft set theory, as the formal development of soft algebraic systems fundamentally relies on rigorously defined operations and product constructions.
Copyright (c) 2025 Aslıhan Sezgin , Zeynep Ay, İbrahim Durak https://creativecommons.org/licenses/by/4.0
Fuzzy score and fuzzy cost SuperHyperFunction
A HyperFunction associates each input with a set of admissible outputs, extending conventional functions by allowing multi-valued rather than single-valued mappings. A SuperHyperFunction further generalizes this concept by employing iterated powersets for both its domain and codomain, enabling the representation of hierarchical, multi-level output structures and HyperStructural multi-valued behavior within complex systems. Although HyperFunctions and SuperHyperFunctions offer expressive tools for modeling hierarchical functional relationships, their study in the existing literature remains relatively limited. This paper extends the fuzzy score function and fuzzy cost function within the frameworks of HyperFunctions and SuperHyperFunctions and provides a concise theoretical analysis of their essential properties.
Copyright (c) 2025 Takaaki Fujita, Arif Mehmood https://creativecommons.org/licenses/by/4.0
Application of the DIBR II-fuzzy MABAC model for shooting stance selection
The paper introduces a hybrid multi-criteria decision-making (MCDM) model that combines DIBR II and fuzzy MABAC methods to select an optimal shooting stance. This study focuses on choosing the optimal combat shooting stance during tactical operations, based on expert-defined criteria that affect target engagement effectiveness. The objective is to develop a decision-support model based on MCDM techniques to assist either the shooter in selecting the most effective stance or the commander in issuing an appropriate order. The model aims to reduce the time required to adopt the stance before engagement and lower the risk of poor decisions that could harm operational effectiveness. These methods were combined for several reasons. The DIBR II method is used to assign weights to the criteria because it offers a simple way to calculate them. It can be easily integrated into the ranking process, providing a more objective evaluation and reducing the subjectivity seen in traditional methods. It is also user-friendly for experts solving problems. When uncertainty is low, crisp (real) numbers are used; when uncertainty is higher, fuzzy numbers are applied, which is relevant here. Many factors contribute to uncertainty, including weapon type, shooter training, the external environment, exposure to enemy action, and the area the shooter can see and target. Therefore, using fuzzy numbers, especially simple triangular ones, is justified. The fuzzy MABAC method is then used to identify the most suitable alternative among six possible shooting stances for small-arms combat operations. The MABAC method was chosen because it is a modern approach with a limited number of steps and because it identifies the alternative closest to the ideal solution within the upper or lower approximation boundary. Sensitivity analysis was conducted by varying the criterion with the highest weighting coefficient, and the ranking's validity was confirmed through scenario testing using the Spearman correlation coefficient.
Copyright (c) 2025 Aleksandar Aleksić, Željko Jokić, Miša Živković, Marko Radovanović, Stefan Tošić https://creativecommons.org/licenses/by/4.0
Double framed T-bipolar soft sets and their applications in decision making
Handling symmetric information that involves both positive and negative aspects, while avoiding data loss and a two-dimensional nature, has always been a problem for researchers. So far, no structure is available to address this issue. In addition to bipolarity, symmetry, and avoiding data loss, the notion of a T-bipolar soft set is more advanced. However, to address the issue of two dimensions simultaneously, we have developed the concept of double-framed T-bipolar soft sets in this manuscript. Moreover, we have defined the ideas of “AND” product, “OR” product, extended union, extended intersection, restricted union, and restricted intersection for double-framed T-bipolar soft sets. To utilize the introduced approach, we have developed an algorithm and provided an example to demonstrate its benefits and usefulness. We have utilized our work in the prioritization of advanced techniques in Digital Signal Processing (DSP). The comparative analysis of the delivered work shows the superiority of the introduced work.
Copyright (c) 2025 Jabbar Ahmmad, Tahir Mahmood https://creativecommons.org/licenses/by/4.0
DriDrowsy: An enhanced framework for drowsiness detection
Road accidents remain one of the leading causes of non-natural deaths worldwide, and driver distraction is a major contributor. Among the various forms of distraction, drowsiness is particularly dangerous as it greatly increases the likelihood of traffic collisions. Detecting drowsiness early and issuing timely alerts can help prevent such incidents by encouraging drivers to take necessary breaks. This paper presents an image-based drowsiness detection method that utilizes the Eye Aspect Ratio (EAR) and Mouth Opening Ratio (MOR) in combination with a modified Haar Cascade model. The system detects facial indicators of drowsiness that include yawning, closed eyelids, and partially closed eyes through thresholding and expression analysis. Experimental results show that the method achieves an accuracy of over 96.75% in identifying drowsy states. This proposed method is experimentally tested on three different benchmarked data sets. By consistently monitoring driver alertness, this approach offers a promising solution for reducing fatigue-related road accidents and enhancing overall driving safety.
Copyright (c) 2025 Soma Datta, Moumita Shee https://creativecommons.org/licenses/by/4.0
Multimodal analysis and prediction of risk-related disclosures in financial reports
The rapid changes in the business environment have made it increasingly challenging for experts to accurately analyze and classify risk-related statements in security reports, which often contain large volumes of unstructured information. Over time, several methods have been developed; however, these approaches still encounter difficulties when processing diverse risk expressions. This study utilizes a large dataset of economic and financial statements to explore the relationship between financial risks and the sentiment associated with them. A multimodal approach is proposed, integrating both supervised and unsupervised techniques such as Bag of Words, Term Frequency-Inverse Document Frequency (TF-IDF), Word Embeddings, and topic modeling methods like Latent Dirichlet Allocation (LDA), to develop a model capable of efficiently and accurately predicting a company's risk structure from its security reports. Sentiment analysis is performed on the texts, where negative sentiment is indicative of risk. Various feature sets are then combined, and the resulting model is tested using four classifiers, achieving a highest accuracy of 80.9%. The findings suggest that the model can be effectively developed for risk analysis and identification within financial data and other relevant sectors.
Copyright (c) 2025 Moumita Chatterjee, Dhrubasish Sarkar https://creativecommons.org/licenses/by/4.0
Dynamic panel and nonlinear causality analytics of defense and debt in fragile Africa
This research integrates several analytical models to examine how rising military spending in fragile African states relates to external indebtedness between 2000 and 2023. A combination of dynamic panel methods—including System-GMM to address endogeneity, Prais-Winsten regression to correct for serial correlation, and panel Granger causality tests—is employed to develop a robust framework for exploring the fiscal-security nexus. The findings reveal a significant nonlinear dynamic: while moderate military spending appears fiscally neutral, surpassing a critical threshold transforms it into a major driver of debt accumulation. Beyond the effects of scale, increased defense spending alone does not reliably reduce conflict. Instead, strong governance, effective regulation, and sustained economic growth emerge as the most decisive factors in building lasting peace. The analysis also identifies a self-reinforcing feedback loop—a “fiscal–security trap”—in which rising debt and defense expenditures mutually reinforce one another over time. By advancing dynamic modeling of fiscal risks in fragile contexts, this research contributes to the broader objective of promoting sustainable debt management and durable peace.
Copyright (c) 2025 Nardos Aschalew, Minyahil Alemu https://creativecommons.org/licenses/by/4.0
Optimizing healthcare pricing strategies: A data-driven approach to enhance margins and customer value
The healthcare sector is facing various challenges, such as high costs, misaligned incentives, and a lack of transparency and accountability in services. Traditional pricing models, which are largely static and cost-based, are becoming ineffective, failing to capture the true value of care or adapt to market dynamics, resulting in decreased profitability. This paper addresses a value-based approach by exploring the transition from old pricing systems to a value pricing system using machine learning and data-driven strategies. The focus of this work is to investigate three distinct pricing strategies: traditional static pricing, dynamic pricing, and value-based pricing. Based on field and literature surveys, efforts have been made to generate a dataset that builds a predictive framework for each strategy using machine learning models such as XGBoost, Random Forest, and Neural Networks. This analysis moves beyond abstract theory to create quantifiable models that translate clinical outcomes, operational efficiency, and patient-specific factors into concrete price points. The results demonstrate a clear hierarchy of performance: while dynamic pricing offers a significant margin improvement over static models by aligning prices with anticipated costs, value-based pricing emerges as the superior strategy. It not only yields the highest potential margin but also realigns financial incentives with patient outcomes, rewarding high-quality, efficient care. This paper provides a practical roadmap for healthcare organizations to navigate the complex pricing landscape, enhance financial sustainability, and, most importantly, deliver greater value to the customers they serve.
Copyright (c) 2025 Divanshu Mittal, Meenakshi, Dilbagh Panchal https://creativecommons.org/licenses/by/4.0
Exploring blockchain adoption for supply chain transformation in industry 4.0
By observing the last decade of supply chain transformation among different industries, a pattern of adopting cutting-edge technologies can be observed. Industrial leaders are showcasing how any supply chain can be transformed with the use of new technologies in a new, digital-focused way that is better suited for all of the challenges and opportunities that Industry 4.0 brings. The intent of this paper is to examine the role blockchain has in the transformation process of supply chains, specifically how this technology is used in various supply chain processes. The research methods used include desk analysis, a comparative analysis of the literature, and an examination of relevant case studies. The findings show that blockchain can be adopted in an assortment of ways within supply chains and that adopting blockchain technology enhances supply chain transformations. Practical examples of the use of blockchain are highlighted as well.
Copyright (c) 2025 Miljana Luković, Biljana Cvetić, Dragan Vasiljević, Miloš Danilović https://creativecommons.org/licenses/by/4.0
Education indicators and decision analytics: A basis for using renewable energy sources and knowledge management
Knowledge about decision analytics and knowledge management is essential when it comes to using renewable energy sources. Strategic energy management and education indicators play an important role in the application of modern technologies. Knowledge management can provide significant long-term energy benefits while requiring the same level of financial investment. The use of renewable energy sources necessitates a high level of knowledge management, particularly during the installation phase. Importing modern energy plants in the energy sector is possible, but it is not always the best solution. This paper discusses the importance of knowledge management and having educated personnel. Engineers need to know how to perform tasks related to the installation and maintenance of alternative energy plants. Education indicators are essential for avoiding problems and ensuring effective strategic management. Knowledge is a key resource that provides a competitive advantage in the European market. One of the most crucial requirements for achieving the energy policy goals is the Energy Sector Development Strategy of the Republic of Serbia up to 2025, with projections up to 2030. Effective education indicators and adequate decision-making in the field of applying renewable energy sources are also crucial for the maintenance of installed equipment. A lack of knowledge management during the implementation phase can lead to shorter exploitation periods and technical problems.
Copyright (c) 2025 Jelena Malenovic-Nikolic, Bojana Zlatkovic, Lidija Milosevic https://creativecommons.org/licenses/by/4.0
MCDM model for the selection of network planning techniques in the army for the purposes of performing engineering works when overcoming water obstacles
Overcoming water obstacles is a demanding combat action that requires serious planning under conditions of uncertainty and risk. Choosing a planning method for the implementation of engineering works in the army, as well as how to choose them, has always been a challenge that engineering officers have faced. In this paper, for the selection of the network planning technique, the use of the multi-criteria decision-making (MCDM) model, which contains the methods Logarithm Methodology of Additive Weights (LMAW) and Grey Operational Competitiveness Rating (OCRA), as well as the Einstein weighted arithmetic average (EWAA) operator for aggregating expert opinions, is presented. The LMAW method was used to define the weights of the criteria, while the Grey OCRA method was used to select the optimal planning technique. The Event Chain Methodology (ECM) was identified as the most suitable method for planning the engineering works in question, while the Critical Path Method (CPM) and Precedence Diagramming Method (PDM) are also suitable. In order to check the consistency and validation of the obtained results, a sensitivity analysis to changes in criteria weights and a comparative analysis were performed, where the results were compared with four other MCDM methods in a grey environment. The results of the analyses indicate that the model provides consistent and valid results.
Copyright (c) 2025 Duško Tesić, Darko Božanić, Dragan Pamučar, Boža Miljković, Adis Puška https://creativecommons.org/licenses/by/4.0
PCA - Enhanced regression approach for predicting internet use based on formal education
The regular use of digital devices and Internet applications in modern society is significantly influenced by the user's educational attainment. In educational institutions, particularly at the primary and secondary levels, digital technologies (DT) are recognized as essential to the teaching process. Current educational programs involve active use of DT in class and thus improve digital skills necessary for active engagement in the digital world. A hybrid machine learning model was built to analyze the impact of varying educational degrees on Internet usage and innovation investments across the European Union (EU) population. Principal component analysis (PCA) was employed for detecting the primary indicators of education and innovation, resulting in the reduction of the initial variables to two factors, one including four indicators and the other containing two indicators. Linear regression (LR) was applied to the PCA factor loadings derived from the primary factor, which exhibited a statistically significant relationship with the percentage of Internet usage (r = 0.767). The results demonstrate that formal education, supported by investments in innovations within the education system, is an essential precondition for the continued development of the digital society.
Copyright (c) 2025 Ivana Petkovski, Petar Vranić https://creativecommons.org/licenses/by/4.0
Drivers of AI adoption in SMEs: Missing skills and other firm-level factors
This paper empirically examines factors and firm-level determinants of artificial intelligence (AI) adoption. It is particularly focused on factors related to missing skills in European small and medium-sized enterprises (SMEs). It uses micro-level data from Flash Eurobarometer 537, which includes more than 19,000 firms from 27 EU and 9 non-EU countries. The aim was to identify the key characteristics associated with the likelihood of AI adoption and the anticipation of significant skill transformations related to AI adoption. Results of probit and bivariate probit regressions reveal that firms with younger employees, growing turnover, and membership in industry clusters are more likely to adopt AI. Moreover, firms that have problems with shortages of skills in specific areas (such as IT, R&D, marketing, and customer care) are more likely to adopt AI. These firms also expect that AI adoption will significantly change their skill demands in the future. Interestingly, community size and difficulties with recruiting are closely related to the adoption of AI but have a limited effect on firms' expectations of its impact on skills demanded in the future. Furthermore, the analysis revealed significant differences across industries. These findings contribute to the emerging literature on digital transformation and expand the knowledge on the decision-making process for AI introduction by clarifying the drivers of AI adoption and its perceived skill implications.
Copyright (c) 2025 Jan Hunady https://creativecommons.org/licenses/by/4.0
Refurbishment of object-oriented code: A methodical approach to minimize program complexity
In order to determine software testing competence, test coverage analysis techniques are commonly implemented. The uncovered areas or statements of a program are identified using a technique that employs a set of test cases best suited for the program. Depending on the current approach or the complexity of the program, the concept of test coverage is especially useful in identifying and eliminating test cases that do not contribute significantly to achieving the desired output. In other words, this technique helps in making a program more robust and error-free. It is also useful for identifying sections of a requirement that are not covered by existing test cases. The technique is helpful in creating new test cases to enhance the coverage of a program. As a rigorous quality control practice, it supports the development of experimental test coverage metrics. Therefore, ineffective test scenarios that do not contribute to increased coverage should be tracked and eliminated, thereby reducing the overall complexity of the program. Our approach in this paper aims to create a Control Flow Graph (CFG) for randomly generated object-oriented programs in Java and assess the cyclomatic complexity for each by analyzing the nodes and edges using the traditional formula from the software engineering paradigm. We then identify the best approach to write the program by applying prioritization techniques to the calculated complexity values of the object-oriented programs. This enables us to minimize the complexity of the program during the code development process.
Copyright (c) 2025 Santosh Kumar Kar, Brojo Kishore Mishra, Sanjit Kumar Acharya https://creativecommons.org/licenses/by/4.0
Improving decision making under uncertainty with data analytics: Bayesian networks, reinforcement learning, and risk perception feedback for disaster management
Decision-making under uncertainty is one of the most critical challenges in disaster management, where human risk perception is the most important factor that shapes responses to crises. This paper introduces a hybrid risk-based decision model that integrates behavioral science, probabilistic risk assessment, and machine learning in an analysis of how individuals and organizations make high-stakes decisions under uncertainty. Our model fuses cognitive biases with dynamic risk evaluation to capture real-time fluctuations in decision-making behavior during disasters. While conventional models rely on the basis of rational decision-making, we introduce bounded rationality, emotional triggers, and social influence factors to make it more realistic. Bayesian networks and agent-based simulations are applied in the proposed framework to forecast adaptive decision pathways and gain insight into the evolution of uncertainty perception over time. The model is tested by case studies on natural disasters, pandemics, and industrial accidents and uncovers the key patterns of delays in response, overconfidence, and risk-averse attitudes. Extensive results show how decision inertia, misinformation, and cognitive overload under stress violate many axioms of traditional risk models. We propose a new "risk-perception feedback loop" by integrating real-time information with AI-driven adaptive decision-making tools, aiming at enhanced resilience within disaster scenarios. The study blends computational modelling with psychological insight, yielding a revolutionary approach to both understanding and better decision-making in the face of extreme uncertainty, with a strong possibility of profoundly smarter disaster response systems and significantly more effective risk communication strategies.
Copyright (c) 2025 Safiye Turgay, Abdulkadir Aydin https://creativecommons.org/licenses/by/4.0
Examining regional economic differences in Europe: The power of ANFIS analysis
The paper analyzes the interrelation between labour markets, demographic factors, and economic performance in Northern and Southeastern European countries during the period 2012–2023, using advanced ANFIS methodology. The aim of the study was to identify key differences between the developed countries of Northern Europe (Sweden, Norway, Finland) and the less developed countries of Southeastern Europe (Croatia, Bulgaria, Romania) and to pinpoint factors contributing to sustainable economic development. The research findings reveal significant differences in labour market structures, the impact of demographic factors, and resilience to macroeconomic shocks between the regions studied. Developed Northern European countries demonstrate high productivity levels, labour market stability, and resistance to inflation, while less developed Southeastern European countries are characterized by greater sensitivity to inflation and a higher dependency on low wages. The analysis highlights the need for structural reforms and increased investment in human capital and technology in less developed countries to reduce their reliance on volatile factors and foster sustainable growth. This paper contributes to understanding the complex relationships between labour markets, demographic factors, and economic performance and provides guidance for formulating effective public policies within the European Union.
Copyright (c) 2025 Jurica Bosna https://creativecommons.org/licenses/by/4.0
A comparative study on the financial inclusion status of G20 countries
The G20 forum serves as a platform for the convergence of the world's most significant and systemically crucial economies. However, recent occurrences have given rise to a state of economic upheaval on a global scale. It's essential for determining where each country stands economically. Gaining access to economic growth can be facilitated by expanding financial inclusion. This paper aims to fill this gap by thoroughly analyzing financial inclusion in the G20 nations. This study creates a financial inclusion index based on three dimensions: banking penetration, disbursement, and services. And distributed countries as per their index score in three levels: high, middle, and low, where 1 indicates complete inclusion, and 0 indicates no inclusion. The research reveals that among the 20 countries analyzed, 11 exhibited significant advancements in financial inclusion, while six were categorized as moderately progressing, and three were classified as having low levels of progress. Japan obtained a comparatively elevated score on the index, whereas Mexico registered the lowest score. The present research aims to assist policymakers and regulators in developing a structural framework for countries with low levels of inclusion. The findings indicate that enhancing the accessibility and adoption of official financial services among underprivileged segments of the population could be advantageous in maximizing the overall welfare of society.
Copyright (c) 2025 Sagnik Maity, Amit Majumder https://creativecommons.org/licenses/by/4.0
Application of analytical hierarchy process for emergency managers selection
The selection of the most qualified staff for the performance of responsible duties in the field of emergency management is an extremely sensitive task. By excluding some external factors and focusing exclusively on the professional domain, the application of certain mathematical tools significantly facilitates the work of decision-makers. One of the most commonly used tools for solving multi-criteria decision-making problems in the analytical decision-making approach is the Analytical Hierarchy Process. Through the paper, the theoretical basis of this method is explained in more detail through the basic steps, and the mathematical basis is described for easier understanding, emphasizing the advantages and disadvantages of the method. The application of the method is practically presented on the example of the selection of the best candidate for a management position in the emergency management sector.
Copyright (c) 2024 Goran Maksimović https://creativecommons.org/licenses/by/4.0
A hybrid MCDM model for personnel selection based on a novel Gray AHP, Gray MOORA and Gray MAUT methods in terms of business management: An application in the tourism sector
Personnel selection and high performance of personnel are crucial in businesses operating in the tourism sector. In response to this important issue, an application was conducted in a local hotel operating in Bartın, Turkey. The study analyzed the performance of service personnel working in the restaurant section of the hotel. The Gray AHP, Gray MOORA, and Gray MAUT methods were used in the analyses. The weights of the evaluation criteria used to measure the performance of service employees were determined using the Gray AHP method. The study evaluated three main criteria and eleven sub-criteria. As a result of the analysis, it was concluded that the most effective criteria in evaluating the service personnel working in the hotel restaurant were “Diction - effective and eloquent speaking ability” and “Effective Communication - the ability to effectively use verbal and non-verbal communication skills” under the main criterion of “Personal Characteristics,” and “Experience - knowledge and experience gained by working in a business operating in the tourism sector” under the main criterion of “Professional Characteristics.” The performance of five personnel working in the hotel restaurant was ranked separately using Gray MOORA and Gray MAUT, with identical rankings obtained in both methods. The study is considered beneficial both for the hotel management where the application was conducted and for businesses engaged in local hotel management. This application also introduced a hybrid model to the literature, utilizing Gray AHP-based Gray MOORA and Gray MAUT methods together.
Copyright (c) 2024 Emine Genç, Murat Kemal Keleş, Aşkın Özdağoğlu https://creativecommons.org/licenses/by/4.0
Evaluation of banks in terms of customer preferences with fuzzy SWARA and fuzzy MOORA integrated approach
This study examines the factors influencing customer preferences in retail and private banking in Turkey, utilizing the fuzzy SWARA methodology. In response to global challenges and advancements in technology, the banking sector has undergone significant transformations over the past two decades, leveraging technology to enhance services and efficiency. However, customer preferences may vary across countries and cultures, with Turkish banking customers showing a mix of traditional and digital banking preferences. Against the backdrop of a dramatic financial crisis in 2001, trust emerges as a paramount consideration for Turkish banking customers, alongside factors such as mobile services, customer service quality, and pricing compatibility. Through an analysis of nine banks with the fuzzy MOORA method, both private and state-owned, this study aims to provide valuable insights into the factors driving customer preferences in the Turkish banking sector. The findings contribute to the existing literature and offer guidance for banks seeking to meet the evolving needs and expectations of their customers in Turkey's dynamic banking environment.
Copyright (c) 2024 Aşkın Özdağoğlu, Murat Kemal Keleş, Mehrzad Şenefe https://creativecommons.org/licenses/by/4.0
Ranking of risk using the application of the AHP method in the risk assessment process on the Piraeus-Belgrade-Budapest railway corridor
The paper examines the application of the AHP method in risk assessment for the Piraeus-Belgrade-Budapest railway corridor, which is part of the "Belt and Road Initiative". The AHP method is employed to identify, analyze, and evaluate risks in five key areas: security, operational, economic, environmental, and political risks. The analysis includes the assessment of potential threats to infrastructure security, operational efficiency, economic sustainability, and environmental protection. The results are presented in a tabular format, providing insight into risk prioritization and their relative importance. This study contributes to the understanding of the complex challenges accompanying the implementation of major international infrastructure projects, offering a systematic approach to risk management and supporting decision-making within the comprehensive logistics chain connecting Asia and Europe.
Copyright (c) 2024 Nenad Komazec, Katarina Janković, Milica Mladenović, Maja Mijatović, Zoran Lapčević https://creativecommons.org/licenses/by/4.0
The presentation of the application of game theory to historical events
The modern world is characterized by a multitude of problems that contain conflicts of different interests. The importance and frequency of the problem caused the need to develop techniques for managing conflict situations. Accordingly, game theory has developed as a special direction and methodology used in the study, analysis, and resolution of conflict situations. This theoretical direction deals with the phenomenon of making rational decisions in conflict situations and is considered a specific type of mathematical analysis of conflicts. It is a mathematical discipline that deals with the formalization of the decision-making process in situations where multiple decision-making subjects participate, who have conflicting interests. Each of the opposing sides has several strategies at their disposal. The goal is to determine the strategy that is best for each side in a conflict situation. The use of game theory provides a clearer representation of possible alternative solutions to a conflict situation. The paper shows the application of game theory to certain historical events. Through the petition payment matrix, solutions to the Battle of the Bismarck Sea, the Cuban Missile Crisis, and the arms race are possible. It should be noted that, for the analysis of a mathematical model, game theory requires quantifying and measuring something that is immeasurable, such as motives, outcomes, preferences, etc., when it comes to different conflict situations. During the selection of the strategy, only the most important decision-making factors are included in the consideration, and the others are neglected, which can lead to the absence of a wider picture that could influence the final choice of strategy. Game theory is widely applicable, but the high degree of abstraction greatly limits its practical value.
Copyright (c) 2024 Zorana Ivetić, Jovanka Tošić https://creativecommons.org/licenses/by/4.0
Neutrosophic fuzzy decision-making framework for site selection
Today, the area of interest based on canteen location selection in the university campus is a prominent topic. This research work directly affects students' convenience, accessibility, and the total campus experience. An ideal canteen site not only provides good food to the students and staff but also takes care of their health by providing a healthy environment. In this paper, we choose various criteria and sub-criteria to calculate the best site for a canteen in the university campus. Location, space and layout, infrastructure and utilities, safety and compliance, and environmental factors as criteria with their corresponding sub-criteria are chosen for this work. Here, the data sets used are provided by decision makers in linguistic words and then converted into crisp numbers. The criteria and sub-criteria weights are considered by a popular multi-criteria decision-making (MCDM) method named Criteria Importance Through Intercriteria Correlation, i.e., CRITIC method. Therefore, the ideal location for the canteen in the university campus was determined by applying the complex proportional assessment method (COPRAS), which is basically a ranking method of MCDM. And finally, sensitivity analysis was conducted to make sure the outcomes were flexible and stable.
Copyright (c) 2024 Aditi Biswas, Kamal Hossain Gazi, Prodip Bhaduri, Sankar Prasad Mondal https://creativecommons.org/licenses/by/4.0
Fuzzy DIBR II - MABAC model for flood prevention: A case study of the river Veliki Rzav
This paper presents a hybrid multicriteria decision-making (MCDM) model that integrates the fuzzy DIBR II (Defining Interrelationships Between Ranked Criteria II) method with the MABAC (Multi-Attributive Border Approximation Area Comparison). The proposed model addresses the problem of selecting an appropriate flood protection method for Arilje, Republic of Serbia. Flooding in this region results from the overflow of the Veliki Rzav River, which lacks constructed water structures for flood protection. The study considers three alternative flood protection solutions: sand-filled bags, mobile freestanding plastic systems, and mobile freestanding metal systems. The fuzzy DIBR II method was used to define the weighting coefficients of the criteria within a group decision-making framework. Next, the MABAC method was applied to rank the proposed alternatives. Finally, the results were validated through sensitivity analysis and comparative analysis. The validation confirmed that the developed hybrid model produces stable and reliable results.
Copyright (c) 2024 Darko Božanić, Merita Borota, Anđelka Štilić, Adis Puška, Aleksandar Milić https://creativecommons.org/licenses/by/4.0
Solution of second order linear homogeneous fuzzy difference equation with constant coefficients by geometric approach
In this article, we have solved an initial valued second-order linear homogeneous fuzzy difference equation with constant coefficients using a geometric approach. General fuzzy solution structures for the three cases are established depending on the auxiliary roots of the corresponding homogeneous difference equation. Finally, we have taken the numerical examples and solved them using the theoretical results, and depicted the graphical scenarios to realize the deviation of the uncertain solution from the exact solution as well as the vagueness of the initial values.
Copyright (c) 2024 Abdul Alamin, Kamal Hossain Gazi, Sankar Prasad Mondal https://creativecommons.org/licenses/by/4.0