Applied Decision Analytics is an international, peer-reviewed journal that serves as a platform for advancing decision sciences at the intersection of data science, artificial intelligence, and applied mathematics. The journal seeks to publish cutting-edge research that not only develops novel decision methodologies but also demonstrates their effectiveness in solving complex, high-impact real-world problems. The primary aims of the journal are to: Provide a forum for theory-driven and data-driven decision analytics, emphasizing the integration of mathematical models with modern data science techniques. Advance the next generation of decision support systems that combine big data analytics, machine learning, optimization, and simulation for actionable insights. Encourage research that addresses uncertainty, vagueness, and dynamic complexity through approaches such as fuzzy sets, probabilistic reasoning, rough sets, and hybrid soft computing. Promote cross-disciplinary collaboration among operations research, computer science, statistics, management, engineering, and social sciences. Highlight the societal and managerial impact of decision analytics in areas such as healthcare, energy, sustainability, climate change, supply chain resilience, smart cities, and digital transformation. The scope of Applied Decision Analytics covers both methodological advances and application-driven studies. Contributions may include but are not limited to: Decision Models and Frameworks: Multi-criteria decision-making (MCDM), multi-objective optimization, Bayesian decision theory, stochastic and dynamic programming, and game-theoretic models. Data Science in Decision Analytics: Integration of machine learning, deep learning, natural language processing, and network analysis for decision support and predictive analytics. Uncertainty and Risk: Modeling imprecision, ambiguity, and incomplete information through fuzzy systems, intuitionistic fuzzy sets, probabilistic graphical models, and scenario analysis. Human-Centered and Cognitive Decision Analytics: Behavioral decision-making, explainable AI in decision processes, and human–machine collaboration in analytics. Applications: Real-world decision problems in healthcare systems, energy management, finance and banking, logistics and transportation, environmental management, policy analysis, digital epidemiology, and Industry 4.0. Unique Contributions: Combining algorithmic advances in data science with decision theory to move beyond descriptive analytics toward prescriptive and cognitive analytics. Bridging the gap between predictive machine learning models and decision optimization frameworks. Demonstrating scalability of decision analytics in big data and real-time environments, with emphasis on computational efficiency and interpretability. What makes Applied Decision Analytics unique is its commitment to showcasing research that not only advances methodology but also provides demonstrable practical value through case studies, decision-support tools, and data-driven applications. The journal positions itself at the frontier of decision intelligence, where data science meets decision theory to inform complex choices under uncertainty. General Journal guidelines for authors Applied Decision Analytics publishes research articles, reviews, short communications, and case studies. Research articles must include: motivation for the work, an adequate overview of the representative work in the field including up-to-date references, a clear statement of the novelty in the presented research, suitable theoretical background, one or more examples to demonstrate and discuss the presented ideas and, finally, conclusions. Short communications are usually 4-7 pages long, research articles and case studies 8-14 pages, while reviews can be longer. Page number limits are not strict and, with appropriate reasoning, the submitted articles can also be longer or shorter. Authors are requested to follow the ADA guidelines and strictly format their manuscripts as per the article template that is available here. If extensions of previously published conference papers are submitted, Editors will check if sufficient new material has been added to fulfill the journal standards and qualify the submission for the review process. The added material must not have been previously published. New results are desired but not necessarily required; however, the submission should contain expansions of key ideas, examples, elaborations, etc., of the conference submission. ADA’s acceptance rate is 24%. In 2025, the median time from submission to acceptance for all articles was 67 days, and 12 days from acceptance to online publication in the ONLINE FIRST section. The ONLINE FIRST section of ADA lists the papers accepted for publication and copy-edited but not yet assigned to an issue. Aims and Scope: The principal aim of the journal is to bring together the latest research and development in various fields of decision science. We would like to highlight that papers should refer to Aims and scope, but they are not limited to. Publication Frequency: One issue per year is published online, but processed and accepted papers, with full bibliographic data, are added to the issue continuously over the whole year. 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 ADA is free of charge for the authors. The Applied Decision Analytics is annually classified by the Ministry of Education, Science and Technological Development of the Republic of Serbia.
Research Center for Business and Decision Analytics
From Multi-Agent Reinforcement Learning to Agentic AI: A Comprehensive Literature Review of Algorithmic Advances and Decision-Analytic Implications (2020-2025)
Agentic artificial intelligence has evolved from a research aspiration to a deployable technology between 2020 and 2025. This evolution rests on two intertwined research trajectories: the maturation of multi-agent reinforcement learning (MARL) for coordinated sequential decision-making, and the emergence of large language model (LLM)-based agent architectures integrating symbolic reasoning, tool use, and natural-language communication into cooperative multi-agent workflows. This literature review synthesizes 57 peer-reviewed and openly archived contributions published since 2019 across journals and reputable venues, organized into a thematic taxonomy spanning value-decomposition algorithms (QMIX, QPLEX, Weighted QMIX, FACMAC), trust-region and sequence-model policy methods (MAPPO, HAPPO, MAT, HARL, UPDeT), communication and role learning (NDQ, I2C, ROMA, RODE), credit assignment (LICA, Difference Rewards Policy Gradients, DOP), game-theoretic equilibrium solvers (Pipeline PSRO, JPSRO, Online Double Oracle), open-ended and mixed-motive learning (Open-Ended Learning Team, CICERO, alliance dilemmas), and LLM-based agentic frameworks (AutoGen, MetaGPT, CAMEL, AgentVerse, ChatDev, Generative Agents, Voyager, ReAct, Reflexion, Tree of Thoughts). We compare benchmark and reproducibility infrastructure (PettingZoo, EPyMARL benchmarking, SMAC variants), examine application domains (autonomous driving, multi-agent pathfinding, software engineering, scientific discovery), and discuss implications for applied decision analytics, including human-in-the-loop arbitration, risk-bounded coordination, and verifiable autonomy. We close with an agenda of open problems including non-stationarity, credit assignment under partial observability, alignment and safety in deceptive agents, evaluation under distribution shift, and integrating symbolic reasoning with reinforcement-learned policies to guide the next phase of agentic AI research.
Centralized training decentralized execution
Copyright (c) 2026 Bharatendra Rai, Milena Popović (Author) https://creativecommons.org/licenses/by/4.0
Application of the Qualitative Multi-Criteria Decision-Making Model DEX in the Selection of Media for Informing Affected Populations in Emergency Situations
Emergency situations represent one of the most prevalent phenomena with a threatening character when societal security is concerned. Prevention occupies the most important position within the system of protection and rescue of people and material assets. The transmission of a signal about an impending danger is of exceptional importance. Therefore, the decision regarding which medium should be used to transmit the signal is crucial, particularly in terms of speed and accessibility. This paper examines the possibilities for selecting media through which endangered populations can be informed in emergency situations, taking into account the required qualitative characteristics of individual media. The DEX method was applied to analyze the applicability of specific media, as it enables the establishment of criteria and the determination of their interdependencies in relation to combinations of criteria. The particular significance of this method lies in the fact that the analysis does not rely on assumed scenarios, but on the knowledge and needs of a real system.
Copyright (c) 2026 Katarina Janković, Milica Mladenović, Nenad Komazec (Author) https://creativecommons.org/licenses/by/4.0
Analytical DEA-MACBETH-OPA Approach for Selecting the Location of a Student Apartment
This paper addresses the problem of selecting the location of a student apartment by applying an integrated multicriteria approach that combines the DEA (Data Envelopment Analysis), MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique), and OPA (Ordinal Priority Approach) methods. The aim of the research is to implement a comprehensive methodological framework that enables objective and rational decision-making under complex conditions in which both quantitative and qualitative criteria are present. The DEA method enables the evaluation of the relative efficiency of alternatives, MACBETH transforms experts’ verbal assessments into numerical attractiveness values, while the OPA method allows the determination of weights based on ordinal preferences. Using the example of student apartment selection in the municipality of Voždovac, the applied integrated model enables the ranking of alternative locations according to multiple criteria such as rental price, distance from faculties, availability of public transportation, apartment size, and housing quality. The research results confirm that the combination of quantitative and qualitative approaches significantly increases the reliability of the decision-making process and contributes to a better understanding of the factors influencing residential location choice among the student population. The proposed model may have broader applications in urban planning, educational institutions, and real estate market analysis.
Copyright (c) 2026 Ilija Ivezić, Nikolina Žajdela Hrustek, Yuhao Su (Author) https://creativecommons.org/licenses/by/4.0
A Novel Multi Criteria Decision Framework for Modeling Trust and Loyalty in Social Media Marketing Ecosystems
This study develops a fuzzy logic framework to quantify customer trust and loyalty in social media marketing, addressing uncertainty in online perceptions beyond traditional engagement metrics. In this study, we used the Mamdani fuzzy inference with centroid defuzzification for modelling interactivity, perceived trust, and SMM intensity variables. Results indicate non-linear relationships between moderate interactivity and high trust with an optimal loyalty score (7.8). It also reflects how higher interactivity and consistent social media engagement improve trust and loyalty outcomes. The framework captures subjective perception ambiguity but requires adaptive neuro-fuzzy optimization for broader generalizability. It offers marketers an interpretable decision-support tool promoting ethical, trust-based consumer relationships, bridging behavioral marketing and computational intelligence.
Copyright (c) 2026 SANDEEP BHATTACHARJEE (Author) https://creativecommons.org/licenses/by/4.0
An MCDM-based Optimization Approach for Identifying Efficient Renewable Energy Sources under Fermatean Fuzzy Environment
In recent decades, the growing global demand for energy and the depletion of conventional fossil fuel resources have intensified the need for sustainable and renewable energy alternatives. Consequently, identifying efficient renewable energy sources has become a critical challenge for policymakers and energy planners worldwide. This study evaluates major renewable energy sources, including solar, wind, tidal, hydropower, geothermal, and biomass energy, with the aim of ranking them according to their technical efficiency. To achieve this objective, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is applied within a Fermatean fuzzy framework to address uncertainty and ambiguity in expert judgments. Furthermore, a comparative analysis under different uncertainty scenarios is conducted to validate the robustness and reliability of the obtained results. The proposed approach provides a systematic decision-support framework for researchers, energy-sector authorities, and policymakers to facilitate sustainable and informed energy planning
Copyright (c) 2026 Prodip Bhaduri, Aditi Biswas, Kamal Hossain Gazi, Sankar Prasad Mondal (Author) https://creativecommons.org/licenses/by/4.0
Bridging Prediction and Prescription: Explainable AI for Managerial Decision Analytics under Uncertainty
The increasing adoption of artificial intelligence in business environments has significantly enhanced predictive capabilities; however, a persistent gap remains between prediction and actionable decision-making. This study addresses this limitation by developing a conceptual framework that integrates predictive analytics, explainable artificial intelligence, and prescriptive decision-making within managerial contexts. The proposed framework is structured around three interconnected layers: predictive, explainability, and decision, where predictive models generate insights, explainability interprets these outputs, and the decision layer translates them into actionable strategies. Explainability is positioned as a central mechanism that bridges predictive outputs and decision logic, enhancing transparency, interpretability, and usability. The study further examines decision-making under uncertainty, emphasizing the role of explainability in improving decision quality, reducing ambiguity, and enabling adaptive decision processes. Theoretical propositions are developed to guide future empirical research, and conceptual insights demonstrate the framework’s value in strengthening decision effectiveness. Overall, the study contributes by offering an integrated perspective that connects prediction and prescription through explainability, with implications for improving managerial decision-making in complex and uncertain environments.
Explainable Artificial Intelligence (XAI)
Copyright (c) 2026 Mohamed Ibrahim Hassan Farag (Author) https://creativecommons.org/licenses/by/4.0
Business Analytics for Ride-Hailing Platforms: Demand Forecasting and Ride-Completion Modeling
Digital ride-hailing platforms operate in dynamic environments characterized by fluctuating demand and variable user behavior. Business analytics plays a key role in supporting data-driven decision-making and operational planning in such systems. This study examines the application of descriptive and predictive analytics to analyze ride demand and ride completion behavior on a digital transportation platform. The empirical analysis is based on a dataset containing ride-level and user-related information. Descriptive analytics is used to preprocess the data, generate summary statistics, and identify temporal demand patterns. Predictive analytics is applied to forecast short-term ride demand using time-series methods and to model ride completion as a binary outcome using logistic regression. The results indicate that ride demand exhibits recurring temporal patterns suitable for short-horizon forecasting, with exponential smoothing achieving improved accuracy compared to a naïve approach. In addition, waiting time is identified as a key factor influencing ride completion probability. The findings demonstrate that business analytics can support proactive demand management, improved resource allocation, and enhanced understanding of user behavior in ride-hailing services.
Copyright (c) 2026 Sara Debeljević, Filip Peovski (Author) https://creativecommons.org/licenses/by/4.0
Governing Fair Algorithmic Hiring Under the EU AI Act: A Multi-Layer Decision Architecture for High-Risk Recruitment Systems
Artificial intelligence is increasingly used to support recruitment decisions, while the European Union’s (EU) Artificial Intelligence (AI) Act treats many employment-related AI systems as high-risk because they may significantly affect career opportunities, livelihoods, and fundamental rights. However, existing debates on fair algorithmic hiring often remain fragmented, treating fairness either as a technical bias-mitigation issue, a legal compliance issue, or a matter of applicant perception. This fragmentation leaves organizations without an integrated governance architecture that connects regulatory obligations, decision accountability, human oversight, data controls, and procedural justice. The purpose of this conceptual paper is to develop a multi-layer decision architecture for governing fair algorithmic hiring under the EU AI Act. The article applies a theory-building methodology based on conceptual synthesis, integrating the AI Act’s requirements for high-risk systems with organizational justice theory, algorithmic fairness research, and human resource management governance literature. The study develops a six-layer governance architecture consisting of strategic accountability, lifecycle risk management, data and model assurance, human oversight, candidate-facing transparency and redress, and continuous monitoring and organizational learning. In addition, the article formulates seven theoretical propositions explaining how governance mechanisms can translate compliance obligations into distributive, procedural, interpersonal, and informational justice in recruitment. The article concludes that fair algorithmic hiring cannot be achieved through isolated audits, bias testing, or disclosure statements alone. Instead, it requires an integrated decision architecture that allocates rights and responsibilities across the recruitment pipeline, preserves meaningful human judgment, enables candidate voice and appeal, and embeds legal and ethical safeguards into ongoing organizational routines.
Copyright (c) 2026 Veranika Khlud (Author) https://creativecommons.org/licenses/by/4.0
A Decision Analytics Approach to Apartment Affordability Analysis Using Power BI
In the contemporary real estate market, the ability of citizens to meet their housing needs represents a significant economic and social challenge. This paper focuses on analyzing the purchasing power of the population in Serbia in the context of buying residential property, using modern tools for data processing and visualization. The research relies on data covering average prices of newly built apartments, average net salaries, interest rates, and credit conditions. The aim of this study is to provide an objective insight into the financial affordability of housing for an individual with an average income in Serbia, based on the available data. The Stata software package was used for data preparation and processing, while analysis, metric calculations, and result visualization were carried out in Microsoft Power BI. The analysis reveals pronounced regional differences, with particularly noticeable challenges in urban areas such as Belgrade and Novi Sad. Furthermore, while both average salaries and apartment prices have shown steady growth over the years, the increase in property prices significantly outpaces salary growth, further reducing housing affordability. Ultimately, the results of this study may serve as a basis for designing housing policy measures and for the further application of BI tools in economic analysis.
Copyright (c) 2026 Aleksandra Simic (Author) https://creativecommons.org/licenses/by/4.0
Grounding Systems of Urban and Shipboard Power Networks
The analysis of ground potential distribution in the grounding system of an extended urban area during a single line-to-ground fault requires detailed modelling of its individual components. This includes the grounding systems of high-voltage power stations and transmission lines, as well as the grounding of the associated distribution network. In city transformer stations (TS 10/0.4 kV), the star point on the secondary side is often connected to ground (TNS, TNCS, and TT systems), while in industrial facilities, hospital operating theatres, and on ships, the star point of the secondary coil is isolated from ground (IT system).
Copyright (c) 2026 Tatjana Stanivuk, Miroslav Dujmović, Nikola Muslim, Marin Dujmović (Author) https://creativecommons.org/licenses/by/4.0
Comparative Analysis of MS Excel and Power BI Tools for Business Reporting
The modern business environment is characterized by the need to make decisions based on data, and this is precisely the basis for achieving and maintaining a competitive advantage. The increasing amount and complexity of data impose the need for advanced business analytics tools that enable efficient processing, transformation, and interpretation of data. Among the most used tools are Power BI, Tableau, Qlik Sense, IBM Cognos Analytics, MS Excel, and others. Comparative analyses of the most widespread solutions in this area are often found in the literature. Despite their widespread use, direct comparative analyses of Power BI and Excel, as two Microsoft tools, are rare in the literature. The goal of this paper is to identify the advantages and disadvantages of both solutions based on a review of relevant sources and practical research; in order to determine in which business contexts their application can contribute to more effective decision-making and the improvement of business intelligence.
Copyright (c) 2026 Ksenija Ćosić, Mónika Imreh-Tóth (Author) https://creativecommons.org/licenses/by/4.0
A Review of the Application of MCDM Methods in Business Analytics
Multi-Criteria Decision-Making (MCDM) techniques have become quite important in solving complex business decision-making problems that involve numerous conflicting criteria and uncertainty. This review provides a full discussion of theoretical backgrounds, methodological development, and practical implementation of MCDM within the framework of business analytics in marketing, finance and supply chain management, operations, human resource management, and strategic management. Classical methods such as AHP, ANP, TOPSIS, VIKOR, DEMATEL, COPRAS, Entropy, WASPAS, and MOORA have proved to be effective in prioritization, performance evaluation, and risk assessment. Hybrid structures that combine fuzzy logic, DEMATEL, ANP, and AI-based predictive analytics are additionally used to gain robustness, interpretability, and real-time decision support. The analysis identifies major methodological trends, gaps in the research, and uncharted areas and offers a systematic roadmap on which future research can be directed. These results emphasize the strategic importance of MCDM as a decision-support facilitator that incorporates ordered multi-criteria reasoning with data-driven evaluations that encourage transparency, resilience, and sustainability of managerial choices.
Copyright (c) 2026 Arkyadeep Sarkar, Shankha Shubhra Goswami (Author) https://creativecommons.org/licenses/by/4.0
Numerical Simulation of Mathematical Model of Fractional Order Partial Differential Equation by Asymptotic Homotopy Perturbatin Method
In the proposed manuscript, a model of the fractional-order partial Cauchy reaction-diffusion equation (CRDE) is solved by a tested and a recent technique, Asymptotic Homotopy Perturbation Method. This technique is a powerful tool for the numerical treatment of various mathematical models of order fractional of linear and non-linear order. CRDE of order-fractional is one of these mathematical models is employed across various fields such as physics, ecology, biology and engineering to model spatial effects and dynamic processes involving both diffusion and chemical reactions. The Caputo order-fractional derivative can also be used for this purpose. Further solution of the concerned model can be provided in the form of a series. The computational work is done by MATLAB software.
Copyright (c) 2025 Zakir Ullah, Motasim Billah (Author) https://creativecommons.org/licenses/by/4.0
A Multi-period Assessment of Firm Efficiency in the Stock Market using the DEA Approach
Structural issues and cyclical fluctuations owing to various macroeconomic events affect stock market performance. This paper aims to evaluate the efficiency of leading firms listed on the National Stock Exchange (NSE) in India over a five-year period. We apply an input-oriented super-efficiency Data Envelopment Analysis (DEA) approach with Variable Returns to Scale (VRS) and also account for Constant Returns to Scale (CRS), with input variables namely Beta, the price-earnings (PE) ratio, and price-to-book, and output variables namely return on equity (ROE), earnings per share (EPS), and market capitalization, to reveal a strong foundation of best-in-class companies. Power Grid Corporation, ONGC, Hindustan Unilever, Cipla, and Dr. Reddy's Laboratories are the companies that reach efficiency in all five years of our analysis, i.e., Tier 1 efficiency. This establishes that underlying valuation multiples, alongside strong underlying fundamental outputs, reveal underlying structural efficiency.
Copyright (c) 2026 Vinay Kumar Shaw, Banhi Guha, Sanjib Biswas (Author) https://creativecommons.org/licenses/by/4.0
Railway Infrastructure as a Factor of Economic Development: An Empirical Analysis of Central European Countries (2010-2021)
This paper investigates the impact of investments in railway infrastructure on economic growth across six Central European countries (Croatia, Slovenia, Poland, Czech Republic, Slovakia, and Hungary) between 2010 and 2021. The study applies a panel regression model to test both the direct effects of these investments and their indirect effects through passenger and freight rail transport. Contrary to theoretical expectations, the findings reveal no statistically significant correlation between railway infrastructure investment and GDP growth in the observed countries. These results highlight the importance of institutional efficiency, intermodal integration, and alignment of transport policy as preconditions for unlocking the growth potential associated with railway investments.
Copyright (c) 2026 Jurica Bosna, Ugo Udović, Adis Puška (Author) https://creativecommons.org/licenses/by/4.0
Interpretable Extreme Gradient Boosting Models for Multi-Class Stock Movement Prediction Using Technical Indicators
Accurately forecasting short-horizon stock price movements remains a challenging problem in financial markets, as classical financial theory suggests that prices in liquid markets quickly incorporate available information. Nevertheless, empirical evidence continues to document short-term patterns related to momentum, volatility, and regime shifts, motivating the use of technical indicators within modern machine learning frameworks. While recent studies report promising predictive performance, many emphasize aggregate accuracy, apply limited hyperparameter tuning, or provide little interpretability, leaving uncertainty about the robustness and economic meaning of their results. The purpose of this study is to evaluate whether Extreme Gradient Boosting models built exclusively on technical indicators can reliably and transparently classify short-term stock movements into down, neutral, and up regimes. Using daily data for three large-cap U.S. equities—Tesla, Apple, and Nvidia—we construct a feature set of 36 price- and volume-based technical indicators. A two-stage hyperparameter optimization strategy combining adaptive cross-validation with refined grid search is employed, followed by strict out-of-sample evaluation using a chronological train–test split. Model interpretability is examined through global feature importance and local, instance-level explanations. The results show consistently higher sensitivity for directional movements than for neutral regimes across all assets, with momentum-based indicators, particularly Rate of Change, emerging as the dominant predictors. Local explanations reveal that strong alignment among momentum and oscillator signals underpins confident directional predictions, whereas mixed signals lead to neutral classifications. Overall, the findings demonstrate that well-tuned and interpretable models can extract economically meaningful structure from technical indicators, while also clarifying the limitations of predicting low-signal market regimes.
Copyright (c) 2025 Bharatendra Rai (Author) https://creativecommons.org/licenses/by/4.0
Machine Learning–Driven Sentiment Analysis for Tourism Insights: The Case of North-East Indian States
This study examines publicly available online reviews from Sikkim, Meghalaya, Arunachal Pradesh, Assam, Nagaland, Mizoram, and Manipur to evaluate the effectiveness of sentiment analysis for improving tourism decision-making and addressing the constraints of traditional surveys in capturing emotional and informal experiential insights. Open-access datasets from TripAdvisor, Reddit, and travel blogs (GitHub) were methodically tokenized, lemmatized, and stop-word-free to ensure clean and analysable textual input. NRC Emotion Lexicon determined emotional tone, VADER and TextBlob estimated sentiment polarity, and a state-wise sentiment index was generated. Emotion and experience themes were clustered by K-Means. The findings suggest a considerably favourable attitude in Sikkim and Meghalaya due to natural beauty and hospitality, whereas Assam and Nagaland have mixed sentiments due to infrastructure constraints. Joy, trust, and surprise are dominant feelings, while sadness and contempt are related to service issues. The study concluded that AI-driven sentiment analytics enhances destination branding, service improvement, and policy planning, providing a strong foundation for data-informed tourist growth.
Copyright (c) 2025 SANDEEP BHATTACHARJEE (Author) https://creativecommons.org/licenses/by/4.0
Unsupervised Phenotyping of Forest Fires Using Clustering of Fire Weather Index and Meteorological Variables
Due to climate change and human negligence, forest fire problems have become a hot topic in recent years and it is of great importance to be able to prevent them. The aim of this research is to propose a classification model using unsupervised machine learning on the UCI forest fire dataset to identify different fire phenotypes based on the components of the Fire Weather Index (FWI), meteorological variables, weather coding, and burned area characteristics. The model uses K-Means, Gaussian mixture models, DBSCAN (density-based clustering algorithm), and HDBSCAN (Hierarchical Density-Based Spatial Clustering) to optimize the number of clusters. The results obtained show that clustering provides a powerful framework for characterizing forest fire behavior, thereby improving the possibilities for further development in order to suppress and prevent the outbreak of new fires.
Copyright (c) 2025 Nataša Milosavljević, Sretenka Srdić (Author) https://creativecommons.org/licenses/by/4.0
Decision Analytics Applications of the Relationship Between TreeSoft Graphs and ForestSoft Graphs
Various concepts for handling uncertainty, such as Fuzzy Sets, Neutrosophic Sets, Rough Sets, and Plithogenic Sets, are well-known in the literature. Among them, this paper focuses on the Soft Set framework. Soft sets provide a robust foundation for decision-making by mapping parameters to subsets of a universal set, effectively addressing uncertainty and vagueness. As a generalization of soft sets, the concept of Soft Graphs has been introduced. Additionally, TreeSoft Sets and their corresponding TreeSoft Graphs are known from recent studies. In this paper, we introduce a novel concept called the ForestSoft Graph, which is defined as the graphical counterpart of the ForestSoft Set.
Copyright (c) 2025 Takaaki Fujita, Ajoy Kanti Das, Arif Mehmood, Suman Das, Volkan Duran (Author) https://creativecommons.org/licenses/by/4.0
Ethical Transformational Leadership and Governance: Restoring Trust in Zimbabwe’s State-Owned Transport Enterprises
This study utilises Zimbabwe's crisis-ridden transport sector as a case study to investigate the role of transformational leadership in revitalising corporate governance practices within state-owned enterprises (SOEs). Focusing on systemic inefficiencies, corruption, and operational stagnation, the research provides actionable strategies to align SOEs with national development goals, empower ethical leadership, and restore eroding public trust in critical infrastructure entities such as the National Railways of Zimbabwe (NRZ), Air Zimbabwe (AZ), and Zimbabwe United Passenger Company (ZUPCO). A mixed-methods, cross-sectional design engaged 148 participants from four SOEs, combining qualitative insights from 12 in-depth executive interviews with quantitative surveys of 136 employees. Methodological rigour was ensured through data triangulation, a high response rate (87. 5%), and validated instruments (Cronbach's α > 0.7). Qualitative themes were derived via iterative coding, while quantitative analysis employed descriptive statistics and regression models (R² = 0.64, p < 0.01) to test leadership-governance correlations. Transformational leadership traits such as idealised influence, inspirational motivation, intellectual stimulation, and individualised consideration demonstrated significant positive impacts on governance outcomes. Qualitative data exposed systemic communication gaps reported by 82% of interviewees and disengaged frontline staff. At the same time, quantitative results revealed strong correlations between leadership practices and ethical governance (r = 0.78, p < 0.01). Strategic interventions include leadership development programs, participatory decision-making platforms, and blockchain-enabled accountability frameworks to combat procurement fraud. While sector-specific focus and self-reported data constrain generalisability, the findings offer critical insights for Africa's transportation sector. Future studies should adopt longitudinal designs across multi-sector SOEs and private firms to assess the durability and scalability of leadership interventions in diverse developing contexts. This is a pioneering Zimbabwe-centric study linking transformational leadership to SOEs governance. It bridges theoretical frameworks with context-specific solutions for resource-constrained economies. It advances practical strategies to mitigate corruption by leveraging Fourth Industrial Revolution technologies while providing a governance blueprint for similar Global South nations.
Copyright (c) 2025 Leo Mataruka, Simona Činčalová, Stanley Sitole, Christopher Zishiri (Author) https://creativecommons.org/licenses/by/4.0
Machine Learning for Analyzing Effects of Various Factors on Business Economic
In the last few decades, machine learning has become very useful in many areas of human life, such as medicine, farming, the military, transportation systems, and more. Machine learning is a great way to look at and guess how different things will affect the corporate economy. Businesses can use regression, decision trees, support vector machines, neural networks, clustering algorithms, and time series models to simulate and anticipate how changes in the economy, society, politics, and technology will affect them. These methods help organizations make better choices in many areas, such as risk analysis, demand forecasting, pricing management, policy effect simulation, and resource management. They also help firms come up with better plans in the ever-changing and complicated world of business. We will use machine learning methods to look at several things that have an effect on the corporate economy in this study.
Copyright (c) 2025 Mojtaba Barati Bagherabad, Elaheh Rivandi, Mahsa Javid Mehr (Author) https://creativecommons.org/licenses/by/4.0
Evolution of Holocene Floodplain of Jaldhaka River in Cooch Behar Plain of West Bengal, India
This study evaluates how the river networks and floodplain geomorphology of the Jaldhaka River have changed within a dynamic Himalayan foreland basin environment over an 88-year period (1934–2022). Floodplain geomorphological maps were prepared using high-resolution satellite images and historical maps. Field validation contributed to improving the accuracy of landform mapping, while sedimentary log analysis allowed the recognition of floodplain types and fluvial architectural elements. Channel oscillation attributes were interpreted based on indices such as channel length, channel width, and sinuosity, as well as bankline migration and erosion–accretion areas. The findings suggest that progressive channel migration and meander shifting have shaped various floodplain landforms in the recent past, such as cut-offs, palaeochannels, backswamps, abandoned channels, and meander scroll bars on a Holocene river plain. These features are well developed in the middle and lower floodplains. Quantitative analysis indicated that the channel sinuosity of the middle and lower segments of the Jaldhaka River fluctuated (1.15, 1.24, 1.42, 1.18, and 1.31 for the years 1934, 1955, 1983, 2003, and 2023, respectively), implying channel lengthening or shortening due to meander development and cut-off formation processes taking place over time. The results also demonstrate spatiotemporal variations in channel width and an increasing trend in erosion–accretion areas during the period 1983–2023. Both natural processes (such as variable discharge, frequent flooding, active tectonics, meander migration, and channel shifting) and human interventions (including channelization, mining, construction of cross-sectional structures, and land-use changes) have contributed to the evolution and morphological modification of the Jaldhaka River system and its floodplains in the Cooch Behar Plain, West Bengal.
Copyright (c) 2025 Mantu Das, Snehasish Saha (Author) https://creativecommons.org/licenses/by/4.0
Balancing Efficiency and Risk in Public Sector Artificial Intelligence with Data Envelopment Analysis and Portfolio Approaches
The integration of Artificial Intelligence (AI) in the public sector offers potential for improved efficiency but faces challenges related to readiness, infrastructure, and strategic planning. This study evaluates AI adoption across 151 countries using a novel framework that combines Data Envelopment Analysis (DEA), Fuzzy Logic, and Modern Portfolio Theory (MPT). DEA assesses efficiency levels, identifying top-performing nations and highlighting areas for improvement. Window analysis captures dynamic efficiency trends, providing a temporal perspective on AI readiness. Fuzzy Logic evaluates risks associated with AI implementation, focusing on key aspects such as governance, technology, and infrastructure. MPT optimizes resource allocation by balancing efficiency and risk, offering tailored investment strategies. The results identify Data Representativeness and Adaptability as critical dimensions for successful AI adoption, with significant disparities across nations. This approach provides actionable insights for policymakers to improve AI integration, reduce risks, and maximize benefits in public sector operations. The study underscores the need for dynamic, multi-dimensional strategies to address evolving challenges in AI implementation globally.
Copyright (c) 2025 Doroteja Mitrović, Gülay Demir, Ibrahim Badi, Mouhamed Bayane Bouraima (Author) https://creativecommons.org/licenses/by/4.0
Hyperfuzzy OffGraphs: A Unified Graph-Based Theoretical Decision Framework for Hierarchical Under Off-Uncertainty
Uncertainty-handling frameworks, such as fuzzy sets, rough sets, intuitionistic fuzzy sets, hesitant fuzzy sets, spherical fuzzy sets, neutrosophic sets, and plithogenic sets, have been extensively investigated. These models have further been generalized into HyperUncertain and SuperHyperUncertain sets to represent more complex forms of uncertainty. Related concepts have also been examined in graph theory, where fuzzy offsets with freely varying interval values have been explored. While research on fuzzy graphs continues to be significant, extensions of fuzzy offgraphs remain relatively underdeveloped. This paper introduces the notion of the hyperfuzzy offgraph, a novel structure that extends fuzzy offgraphs by incorporating hierarchical, over-uncertainty, and under-uncertainty behaviors on both vertices and edges. We provide rigorous definitions, establish key properties, and present illustrative examples to demonstrate expressive real-world applications.
Copyright (c) 2025 Takaaki Fujita, Arif Mehmood, Arkan A. Ghaib (Author) https://creativecommons.org/licenses/by/4.0