Vladimir Simic
7005545253
Publications - 42
Tackling energy poverty with renewable energy Projects: Fuzzy decision support system based on virtual and real experts
Publication Name: Renewable Energy
Publication Date: 2026-01-01
Volume: 256
Issue: Unknown
Page Range: Unknown
Description:
Energy poverty is a serious problem that increases economic inequalities, especially because individuals living in low-income areas have difficulty accessing energy. The development of renewable energy projects (REP) plays a critical role in reducing energy poverty. However, there is considerable uncertainty in determining strategies that will increase the effectiveness of REP to solve the problem of energy poverty. The purpose of this paper is to identify significant strategies to improve REP for the effective management of energy poverty problems by establishing a novel model. First, dimension reduction methodology is considered to calculate the importance of decision makers. The second stage includes prioritization of criteria using p,q-Spherical fuzzy (SFS) analytic hierarchy process (AHP). The final stage focuses on ranking of renewable energy investment (REI) alternatives using p,q-SFS weighted aggregated sum product assessment (WASPAS). The contribution of this paper to the literature is the determination of critical indicators that will increase the performance of REI to reduce the energy poverty problem with an original and comprehensive decision-making model. Creating a virtual expert is the main superiority of this proposed model. With the help of this issue, it can be possible to reach a sufficient number of experts. Hence, a more diverse and comprehensive evaluation can be conducted. The findings denote that start-up costs and geographical conditions have the highest significance to improve REP for the aim of minimizing energy poverty problem. Rooftop solar panels and micro wind turbines are also found as the most essential REI strategies.
Open Access: Yes
Driving sustainable hydroelectric investments: Leveraging two-step logarithmic normalization for sustainable investment prioritization
Publication Name: Energy Reports
Publication Date: 2025-12-01
Volume: 14
Issue: Unknown
Page Range: 2110-2122
Description:
Hydroelectric energy investments involve substantial techno-economic risks that can increase costs and undermine economic sustainability if not properly managed. However, the literature lacks comprehensive studies addressing these risks. This study proposes a novel decision-making model to identify and prioritize strategies for effective risk management in hydroelectric projects. The model integrates z-scoring for expert selection, the Criteria Importance Assessment (CIMAS) method for weighting criteria, and the Alternative Ranking using Two-Step Logarithmic Normalization (ARLON) method for ranking EU-15 countries according to their strategies. Pythagorean fuzzy numbers are incorporated to better handle uncertainty and improve evaluation accuracy. Results indicate that challenges in adopting new technologies and grid integration issues are the most influential risk factors. The findings provide actionable insights for policymakers and investors to enhance the sustainability and efficiency of hydroelectric energy investments. Policymakers should implement targeted incentives and regulatory frameworks to accelerate technology adoption and address grid integration challenges in hydroelectric projects. Strategic planning should prioritize infrastructure modernization, cross-border energy cooperation, and capacity-building programs to enhance sector resilience and investment security.
Open Access: Yes
Integration of data-driven T-spherical fuzzy mathematical models for evaluation of electric vehicles: Response to electric vehicle market demands
Publication Name: Renewable and Sustainable Energy Reviews
Publication Date: 2025-11-01
Volume: 223
Issue: Unknown
Page Range: Unknown
Description:
The rapid growth of the electric vehicle (EV) market necessitates advanced multi-criteria decision-making (MCDM) frameworks capable of integrating diverse quantitative and qualitative factors under uncertainty. Traditional MCDM approaches often struggle to capture the complexity and imprecision inherent in EV evaluations, particularly in dynamic contexts like India. To address this gap, this study proposes the T-Spherical Fuzzy (T-SF) MARCOS and T-SF MOORA methods, which utilize T-Spherical Fuzzy Numbers (T-SFNs) to enhance decision precision. T-SFNs extend conventional fuzzy models by independently incorporating degrees of membership, non-membership, and hesitation, enabling a more granular and realistic modeling of expert judgments. In the methodological construction, numerical criteria (e.g., battery capacity, charging time) are directly incorporated, while qualitative criteria (e.g., safety, comfort) are initially evaluated by four domain experts through linguistic assessments, subsequently transformed into T-SFNs for integrated evaluation and accurate criteria weighting. The developed models are then employed to rank ten EV alternatives across 21 comprehensive technical and consumer-centric criteria. Comparative analysis shows that T-SF MARCOS and T-SF MOORA achieve superior ranking accuracy, with a high mutual Pearson correlation of 0.71, while traditional SF methods like SF-WSM and SF-WASPAS exhibit negative correlations of −0.43 and −0.42, respectively. Sensitivity analyses—covering variations in criteria weights and additional criteria integration—confirm the robustness and stability of the frameworks, with rank reversal rates remaining below 10 % across all scenarios. This study presents a technically resilient, uncertainty-aware evaluation framework, offering strategic insights for advancing consumer-centric EV development.
Open Access: Yes
A state-of-the-art review on machine learning techniques for driving behavior analysis: clustering and classification approaches
Publication Name: Complex and Intelligent Systems
Publication Date: 2025-09-01
Volume: 11
Issue: 9
Page Range: Unknown
Description:
Smart mobility has ushered in advanced sensing technologies. These, together with high‑level data analytics, are revolutionizing how we analyze driving behavior. Excellent performance in dealing with real-world, high-technology complexities for machine learning has made wide enthusiasm to utilize them to study driver behavior. This article gives a thorough overview of the important machine learning methods—especially clustering and classification techniques—that help analyze complex driving behaviors, predict fuel and energy use, and improve vehicle safety systems. The review specifically explains unsupervised methods like fuzzy c-means, k-means, and density-based spatial clustering of applications with noise, as well as supervised techniques such as artificial neural networks, k-nearest neighbors, and support vector machines. Also, this review discusses the integration of clustering and classification techniques with hybrid deep learning models, and examines their applications in eco-driving, energy forecasting, and intelligent transport systems while offering novel findings that contribute to more sustainable mobility. Emphasis is placed on how these methods transform vast, heterogeneous driving data into actionable insights that support real-time monitoring and personalized feedback for eco-driving and smart transportation applications. Finally, current benefits and barriers, and future research opportunities and challenges in integrating machine learning into intelligent transportation systems are reviewed. The potential to advance to safer, better, and more sustainable forms of mobility is emphasized.
Open Access: Yes
An insightful multicriteria model for the selection of drilling technique for heat extraction from geothermal reservoirs using a fuzzy-rough approach
Publication Name: Information Sciences
Publication Date: 2025-01-01
Volume: 686
Issue: Unknown
Page Range: Unknown
Description:
Geothermal energy stands out as an exceptional renewable resource for power generation, offering a consistent power production without the intermittency issues. Despite its potential to deliver a consistent supply of electricity on demand, geothermal adoption is hindered due to substantial costs. Utilising the most effective drilling method can alleviate this challenge by boosting efficiency and reducing operational costs. The primary goal of this study is to identify the best drilling method for extracting heat from geothermal reservoirs. This optimised approach facilitates better access to geothermal reservoirs, leading to increased heat recovery rates and improved project viability. Traditional methods often fall short in evaluating optimal drilling alternatives due to uncertainties. To address this, our research introduces an innovative paradigm that integrates novel T-Spherical Hesitant Fuzzy Rough (T−SHFR) set, method for the removal effects of criteria with a geometric mean and ranking alternatives with weights of criterion hybrid Multiple Criteria Decision-Making (MCDM) techniques. By leveraging the novel T−SHFR concept, our approach allows for a comprehensive assessment of various factors. This holistic evaluation ensures an exhaustive comprehension of the decision-making environment. The study reveals that reservoir characteristics play a significant role in selecting a sustainable drilling alternative. Furthermore, directional drilling appears as the most promising method with higher energy yields followed by slim hole drilling. The robustness and credibility of these findings are established through sensitivity and comparative analyses, indicating the potential applicability of this MCDM method to analogous challenges in different contexts. The findings of the ranking techniques were validated using Spearman's rank correlation coefficient, which revealed a positive and notable correlation. This research will empower stakeholders to make informed decisions, thereby enhancing the overall efficiency and sustainability of geothermal energy projects.
Open Access: Yes
Prioritization of AI-based material handling approaches for smart logistics in sustainable warehouses: A q-rung orthopair fuzzy CoCoSo methodology with consensus reaching
Publication Name: Environment Development and Sustainability
Publication Date: 2025-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
This study aims to address the artificial intelligence-based material handling approach selection problem under circular economy to contribute the smart and sustainable business management in logistics systems. The "consensus-reaching process" for experts is not emphasized in the current decision-making procedures with q-rung orthopair fuzzy data. Experts working on group decision-making challenges may hold views that are very dissimilar from one another as a result of their knowledge and experiences. In order for experts to increase the amount of consensus, a consensus-building process is needed. Besides, the ranking results provided by "combined compromise for ideal solution" do not change dramatically in line with the changing weight distributions of characteristics. So, q-rung orthopair fuzzy-based combined compromise for ideal solution methodology with consensus reaching is introduced for solving the addressed emerging problem of logistics companies. This robust and logical decision-making method can comprehensively analyze the advantages, disadvantages, and potential barriers to the acceptance of artificial intelligence-based material handling approaches. The real-life study is offered for a logistics company that plans to invest in robotic solutions based on artificial intelligence. The findings show that autonomous mobile robots represent the best artificial intelligence-based material handling approach. Recommendations for adopting alternative solutions are provided to assist in the efficient completion of smart logistics activities.
Open Access: Yes
Enhancing decision-making with linear diophantine multi-fuzzy set: application of novel information measures in medical and engineering fields
Publication Name: Scientific Reports
Publication Date: 2024-12-01
Volume: 14
Issue: 1
Page Range: Unknown
Description:
This study offers a comprehensive analysis of novel information for linear diophantine multi-fuzzy sets and illustrates its applications in practical scenarios. We introduce innovative similarity metrics tailored for linear diophantine multi-fuzzy sets, including Cosine similarity, Jaccard similarity, and Exponential similarity. Additionally, we propose Entropy, Inclusion, and Distance measures, providing a robust theoretical foundation supported by developed theorems that explain the interactions between these metrics. The practical implications of these theoretical advancements are demonstrated through various case studies. Specifically, we apply the similarity measures to predict preeclampsia, a severe condition affecting pregnant women, showcasing their potential in medical diagnostics. The entropy measure is used to identify the optimal materials manufacturing method for medical surgical robots, underscoring its importance in ensuring patient safety and the effectiveness of medical procedures. Furthermore, the inclusion measure is employed in pattern recognition tasks, highlighting its utility in complex data analysis. The comparative and superiority analysis shows the effectiveness of our research. The novel aspect of this study is the implementation of information metrics for LDMFS. These efforts aim to enhance the impact and practical applicability of linear diophantine multi-fuzzy sets, fostering innovation and improving outcomes across multiple fields.
Open Access: Yes
Application of the new simple weight calculation (SIWEC) method in the case study in the sales channels of agricultural products
Publication Name: Methodsx
Publication Date: 2024-12-01
Volume: 13
Issue: Unknown
Page Range: Unknown
Description:
In this research is presented a new method for determining the weights of criteria called simple weight calculation (SIWEC) method. The steps of this method are presented in the practical example of determining the importance of criteria for the needs of sales of agricultural products in the Semberija region. During the presentation of this method two methods are elaborated the simple SIWEC method which includes numerical ratings and the fuzzy SIWEC method which includes ratings in the form of linguistic value. In the selected example is presented how to use this method in order to determine the importance of criteria and in both cases the criterion of sales reliability is given the greatest weight. The contribution SIWEC method is reflected in its simplicity, which facilitates decision-making. • The method presented in this research apart from others is that it uses the evaluation of the criteria by decision makers, so the criteria should not be ranked and compared, but simply evaluated. • Unlike similar methods, the presented method uses the adjusted steps of the method for ranking the alternatives, and decision makers are given a different importance in the decision-making.
Open Access: Yes
Technology adaptation in sugarcane supply chain based on a novel p, q Quasirung Orthopair Fuzzy decision making framework
Publication Name: Scientific Reports
Publication Date: 2024-12-01
Volume: 14
Issue: 1
Page Range: Unknown
Description:
The present paper contributes to the literature in two ways. First, it develops a novel p, q Quasirung Orthopair Fuzzy (p, q QOF) based group decision making framework to modify a recently developed multi-criteria decision making (MCDM) model such as Comparisons between Ranked Criteria (COBRAC). Second, the paper ruminates on the Strength-Weakness-Opportunity-Threat (SWOT) of the sugarcane supply chain (SSC) in India vis-à-vis adaptation of the advanced technologies featuring Industry 4.0. To set the sub-factors of various dimensions of SWOT, the theoretical ground of Technology-Organization-Environment (TOE) framework has been used. The sub-factors of SWOT have been derived through an informal in-depth discussion with the experts of the sugar industry. Then using a Likert five-point linguistic scale the experts rated the sub-factors based on their relative importance. To determine the weights the modified COBRAC method has been applied. In subsequent stages the reliability of the model has been tested and sensitivity analysis has been carried out to check the stability of the result. The analysis reveals that while experience, by-product utilization and high demand provides strength and create opportunities for SSC, the areas of concern are lack of variety, fragmented nature of supply chains, shortage of next-gen talent and inadequate infrastructure. However, there are enough promises for SSC. The paper shall provide impetus to strategic decision makers for the sugar industry and puts forth a new decision-making framework for the analysts.
Open Access: Yes
Cloud spot instance price forecasting multi-headed models tuned using modified PSO
Publication Name: Journal of King Saud University Science
Publication Date: 2024-12-01
Volume: 36
Issue: 11
Page Range: Unknown
Description:
The increasing dependence and demands on cloud infrastructure have brought to light challenges associated with cloud instance pricing. The often unpredictable nature of demand as well as changing costs of supplying a reliable instance can leave companies struggling to appropriately budget to support a healthy cash flow while maintaining operating costs. This work explores the potential of multi-headed recurrent architectures to forecast cloud instance prices based on historical and instance data. Two architectures are explored, long short-term memory (LSTM) and gated recurrent unit (GRU) networks. A modified optimizer is introduced and tested on a publicly available Amazon elastic compute cloud dataset. The GRU model, enhanced by the proposed modified approach, had the most impressive outcomes with an MAE score of 0.000801. Results have undergone meticulous statistical validation with the best-performing models further analyzed using explainable artificial intelligence techniques to provide further insight into model reasoning and information on feature importance.
Open Access: Yes
Biogeography-Based Optimization of Machine Learning Models for Accurate Penetration Rate Prediction Using Rock Texture Coefficient
Publication Name: International Journal of Computational Intelligence Systems
Publication Date: 2025-12-01
Volume: 18
Issue: 1
Page Range: Unknown
Description:
Predicting drill penetration rate (PR) in rock environments remains a significant challenge due to the complex interplay between rock texture, drilling fluid properties, and operational parameters. Traditional empirical models often lack generalizability and are based on inconsistent datasets, limiting their reliability. To address these limitations, this study develops a comprehensive experimental dataset using rock samples collected from various mines in Iran, tested under controlled laboratory conditions with different drilling fluids, bit loads, and rotational speeds. Texture coefficient (TC), electrical conductivity (EC), load on bit (LOB), and bit rotational velocity (BRV) were selected as input features. Four machine learning models—support vector regression (SVR), stochastic gradient descent (SGD), K-nearest neighbors (KNN), and decision tree (DT)—were trained to predict PR. A biogeography-based optimization (BBO) algorithm was employed to fine-tune hyperparameters and enhance model accuracy. Additionally, a novel hybrid error index (HEI) was introduced to comprehensively evaluate model performance. Among all models, the DT achieved the best accuracy with an HEI of 0.3753, followed by KNN, SVR, and SGD. These findings demonstrate the potential of the DT model, combined with optimized learning and a robust dataset, to reliably predict penetration rate in rock-based engineering projects.
Open Access: Yes
Computational Assessment of Energy Supply Sustainability Using Picture Fuzzy Choquet Integral Decision Support System
Publication Name: Computers Materials and Continua
Publication Date: 2025-01-01
Volume: 85
Issue: 1
Page Range: 1311-1337
Description:
For any country, the availability of electricity is crucial to the development of the national economy and society. As a result, decision-makers and policy-makers can improve the sustainability and security of the energy supply by implementing a variety of actions by using the evaluation of these factors as an early warning system. This research aims to provide a multi-criterion decision-making (MCDM) method for assessing the sustainability and security of the electrical supply. The weights of criteria, which indicate their relative relevance in the assessment of the sustainability and security of the energy supply, the MCDM method allow users to express their opinions. To overcome the impact of uncertainty and vagueness of expert opinion, we explore the notion of picture fuzzy theory, which is a more efficient and dominant mathematical model. Recently, the theory of Aczel-Alsina operations has attained a lot of attraction and has an extensive capability to acquire smooth approximated results during the aggregation process. However, Choquet integral operators are more flexible and are used to express correlation among different attributes. This article diagnoses an innovative theory of picture fuzzy set to derive robust mathematical methodologies of picture fuzzy Choquet Integral Aczel-Alsina aggregation operators. To prove the intensity and validity of invented approaches, some dominant properties and special cases are also discussed. An intelligent decision algorithm for the MCDM problem is designed to resolve complicated real-life applications under multiple conflicting criteria. Additionally, we discussed a numerical example to investigate a suitable electric transformer under consideration of different beneficial key criteria. A comparative study is established to capture the superiority and effectiveness of pioneered mathematical approaches with existing methodologies.
Open Access: Yes
Optimizing industrial robot selection using novel trigonometric Pythagorean fuzzy normal aggregation operators
Publication Name: Complex and Intelligent Systems
Publication Date: 2025-10-01
Volume: 11
Issue: 10
Page Range: Unknown
Description:
The modern world uses an increasing number of robots, notably service robots. Robots will be able to easily manipulate everyday objects in the future, but only if they are paired with planning and decision-making procedures that allow them to comprehend how to complete a task. This research presents new techniques to handling multi-attribute problem solving with trigonometric Pythagorean normal fuzzy numbers. The sine trigonometric Pythagorean fuzzy sets combine the concept of Pythagorean fuzzy sets with sine trigonometric functions to represent uncertainty in decision-making. It is feasible to combine trigonometric Pythagorean fuzzy numbers and normal fuzzy numbers to get trigonometric Pythagorean fuzzy normal numbers. In addition to the fundamental interaction aggregation operators, we define the trigonometric Pythagorean fuzzy normal numbers. The trigonometric Pythagorean fuzzy normal numbers satisfy the following properties: associative, distributive, idempotent, bounded, commutative and monotonicity. Four novel approaches are introduced such as weighted averaging, weighted geometric, generalized weighted averaging and generalized weighted geometric. These operators can be used in the development of a multi-attribute decision-making algorithm. We demonstrate how improved Euclidean and Hamming distances are used in practical situations. For industrial robots, the two most crucial elements are computer science and machine tool technology. The four criteria of weights, orientations, speeds and accuracy may be used to assess robotic systems. They are also more practical, easier to understand, and more adept at identifying the best answer more quickly. The effectiveness and accuracy of the models we are looking at are demonstrated by comparing many existing models with those that have been developed.
Open Access: Yes
Food safety risk analysis utilising K-lexicographic-max product of neutrosophic graph
Publication Name: Ain Shams Engineering Journal
Publication Date: 2025-12-01
Volume: 16
Issue: 12
Page Range: Unknown
Description:
In this study, we introduce the concept of the K-Lexicographic Max Product (K−LMP) of neutrosophic graphs and explore its associated degree structure to enhance decision-making frameworks in food safety applications related to risk assessment, including freshness, contamination, and spoilage. Neutrosophic graphs, capable of handling indeterminacy, inconsistency, and incompleteness, provide a flexible mathematical foundation for modelling complex systems. By incorporating the K−LMP into neutrosophic graphs, we offer a novel approach to comparing and ranking food safety scenarios where multiple attributes and uncertain information coexist. We present example graphs and theorems related to K−LMP and further define the K-Lexicographic degree to quantify node significance within the context of neutrosophic graphs. To validate the practical utility of this approach, a food safety analysis is implemented, demonstrating how the model identifies critical control points and supports more robust, transparent decision-making under uncertainty. This work contributes to the advancement of neutrosophic graph theory and its interdisciplinary application in food quality and safety management.
Open Access: Yes
Data-driven decision-making framework for the evaluation of the traders in the stock market using cosine trigonometric single-valued neutrosophic approach
Publication Name: Journal of Mathematics and Computer Science
Publication Date: 2026-01-01
Volume: 41
Issue: 2
Page Range: 222-243
Description:
The cosine trigonometric single valued neutrosophic number (CT-SVNN) is a suitable expansion of the standard neutrosophic number. Single-valued neutrosophic sets (SVNSs) may effectively overcome three components: degree of truth, indeterminacy, and falsity. In recent years, the aggregation operator (AO) and its applications have undergone development. This study introduces a few new AOs for multi-attribute decision-making (MADM). We introduce a novel approach for cosine trigonometric SVNS (CT-SVNS) and CT-SVNS with normal (CT-SVNNS), which are SVNS extensions. It is also required to discuss the CT-SVNNS method fundamental features in this communication, such as idempotency, boundedness, commutativity and monotonicity. There are numerous CT-SVNNS operators that have been proposed, including CT-SVN normal weighted averaging (CT-SVNNWA), CT-SVN normal weighted geometric (CT-SVNNWG), generalized CT-SVNNWA (GCT-SVNNWA) and generalized CT-SVNNWG. A powerful strategy for solving the MADM problem is provided that makes use of new developed generalized operators. Through a case study, the value of the suggested MADM approach is demonstrated. The new strategy is shown using a market share problem, and the outcomes are contrasted and examined against an existing method. This combination of generalized AO was rated successful based on expert preferences. As a result, a varied collection of experts may be accepted.
Open Access: Yes
Enhancing confidence level in decision-making frameworks using fermatean fuzzy rough sets: Application in industry 4.0
Publication Name: Applied Soft Computing
Publication Date: 2026-01-01
Volume: 186
Issue: Unknown
Page Range: Unknown
Description:
Multidimensional decision-making has substituted traditional decision-making due to the increased risk and complexity involved in the decision processes and cognitive behaviors. Moreover, uncertainty management is necessary in the decision-making processes that involve the degree of confidence of the experts. Conflict assessment and resolution are paramount to the smooth functioning of the industrial ecosystem in such an automated dynamic environment. This research aims to create a multi-attribute decision-making (MADM) model in a hybrid fuzzy frame to evaluate and resolve conflicts in Industry 4.0. The MADM model dwells on three primary points, i.e., (i) how to efficiently manage ambiguity and interrelationships in MADM issues; (ii) how to encompass the mindset of the decision maker in all areas concerned; and (iii) how to demonstrate results in terms of acceptance and rejection rather than ranking issues when more than one factor is involved. The test data of a fermatean fuzzy set (FFS) with rough relations, which addresses upper and lower approximations, demonstrates the possible uncertainty of the information. A fermatean fuzzy rough set (FFRS) is initially defined within the model. Subsequently, an FFRS incorporating the operator's confidence level is delineated. This demonstrates the importance of FFRS in MADM contexts and suggests that they require further examination of their data processing regulations. Furthermore, we evaluate the accuracy and validity of the results by employing mean absolute errors, cosine similarity of the operators, and Spearman rank correlation. To illustrate the accuracy and validity of our method in the MADM context, we performed a comparative analysis. Finally, a practical illustration of the selection of Industry 4.0 technologies within the healthcare sector exemplifies the efficacy and potential of this innovative approach for future applications of MADM. The intricate multi-stakeholder conflicts and data uncertainties presented by Industry 4.0 environments, especially regarding healthcare technology implementation, will be examined using the research framework illustrated in Fig. 1.
Open Access: Yes
MABAC model based on linguistic (p, q)-rung orthopair fuzzy Z-number and their application in green supply chain management
Publication Name: International Journal of Cognitive Computing in Engineering
Publication Date: 2026-12-01
Volume: 7
Issue: Unknown
Page Range: 247-267
Description:
The problem and complication arise from the growing environmental inefficiencies and concerns in traditional supply chains, for instance, poor accountability, excessive waste, and lack of transparency. The green supply chain practices aim to reduce or minimize the environmental impact of supply chain activities, but these efforts often face problems, for example, difficulty in monitoring sustainability performance, data manipulation, and limited traceability across numerous stakeholders. The main problem is that without effective techniques to verify and track eco-friendly practices, enterprises struggle to utilize and enforce green initiatives reliably. The blockchain technique is being derived as a solution because of its capability to give decentralized, transparent, and immutable records of processes and transactions. By integrating the blockchain into green supply chain practices, we aim to design the model of linguistic (p, q)-rung orthopair fuzzy Z-number sets with algebraic and Sugeno-Weber operational laws for the construction of the power weighted averaging operator and power weighted geometric operator. These operators can be used in the utilization of the multi-attributive border approximation area comparison model, which is also explained step-by-step with the help of examples to simplify the supremacy and validity of the invented model by comparing their ranking values with the ranking values of the existing approaches.
Open Access: Yes
Data-driven Floyd’s algorithm with AirQo monitoring device for optimizing transportation routes in an uncertain environment
Publication Name: Engineering Applications of Artificial Intelligence
Publication Date: 2026-01-01
Volume: 163
Issue: Unknown
Page Range: Unknown
Description:
This manuscript presents a novel All-pair shortest path algorithm that enhances Floyd’s method by integrating a soft computing-based decision model tailored for transportation routing in an uncertain environment. The routing problem is formulated as a graph, where the edges are aggregated into a single representative weight from multiple influencing factors using an aggregation operator and the score function. These weights represent pollution levels based on air quality data collected by the AirQo monitoring device along different route segments. By integrating decision making method, the enhanced Floyd’s algorithm is then used to compute the most effective route between a defined source and destination. The proposed method supports healthier travel choices by identifying routes with comparatively cleaner air. Preliminary simulations indicate that the suggested technique facilitates more informed route selection compared to conventional approaches. The uniqueness of this method lies in its integration of classical graph theory with decision-making for real-time environmental sensing, offering reduced exposure to pollutants and supporting cleaner, safer mobility in urban environments.
Open Access: Yes
Entity-relation enhanced bidirectional information fusion for relational triples extraction
Publication Name: Engineering Applications of Artificial Intelligence
Publication Date: 2026-01-01
Volume: 163
Issue: Unknown
Page Range: Unknown
Description:
Relational triple extraction refers to identifying triples consisting of entities and relations form unstructured texts. The existing studies usually adopt an unidirectional extraction strategy, which fails to fully explore the semantic information related to entities and relations. And they rely heavily on initial extraction results when conducting multi-step extraction. To address this issue, we propose a novel Entity-Relation Enhanced Bidirectional Information Fusion approach (ER-EBIF). Specifically, we adopt a bidirectional extraction strategy of ”entity-to-relation” and ”relation-to-entity” to identify triples. One branch extracts potential relations, then extracts entities associated with those relations. The other branch initially extracts the potential subjects and objects as well as subsequently extracts relations between pairs of entities consisting of subjects and objects. Moreover, the contextual information is enhanced with a self-attention mechanism by integrating the information of potential relations and potential entities to better exploit the semantic information of entities and relations. Extensive experimental results on various datasets show that ER-EBIF exhibits better performance than other baselines and effectiveness in addressing the issue of dependency on initial results in multi-step extraction.
Open Access: Yes
Scenario-driven decision models for rare element waste management by integrating koch snowflake fuzzy sets and euclidean expert weighting
Publication Name: Sustainable Futures
Publication Date: 2025-12-01
Volume: 10
Issue: Unknown
Page Range: Unknown
Description:
The most critical factors must be determined to effectively manage environmental wastes generated during the extraction of rare elements. Otherwise, businesses may not be able to effectively manage their limited financial and human resources. This situation negatively affects the financial performance of the projects. The limited number of existing studies in the literature causes environmental risks to be insufficiently managed and recycling processes to be unoptimized. This study aims to determine priority strategies to increase the effectiveness of rare element waste management processes. Comprehensive and original decision-making models are created under three different scenarios. Koch Snowflake fuzzy sets, Euclidean based expert weighting and cognitive information modelling and analysis system (CIMAS) approaches are integrated in this model. The main contribution of this study is that a new type of fuzzy numbers called Koch Snowflake fuzzy sets is developed by considering the concept of fractal numbers. Fractal geometry is a powerful tool for modelling complex and dynamic systems. Hence, these new sets provide more flexible and more detailed uncertainty modelling. Moreover, considering different scenarios dynamic strategies can be developed that can adapt to changing conditions, such as pandemics or trade wars. The findings denote that technological developments are determined as the most critical factor under normal conditions. In the scenario where trade wars occur, it is revealed that political and regulatory measures should be addressed as a priority. In the event of a new epidemic disease such as COVID-19, it is concluded that more importance should be given to long-term storage strategies.
Open Access: Yes
Reliable power management and predictive analysis of domestic appliances with insights of XAI
Publication Name: Energy Reports
Publication Date: 2025-12-01
Volume: 14
Issue: Unknown
Page Range: 3704-3718
Description:
The unanimous focus of the sustainable technological development is energy conservation and environmental friendly production. Power management is an essential aspect of sustainable development. It not only support energy production and conservation, but also increases the life time of domestic appliances and thereby reducing the global electronic wastage. The existing systems involving Artificial Intelligence (AI) were mere prediction models, without the evidence on the detailing behind the prediction. Traditional AI systems have focused on predictive analysis but often lack transparency in decision-making and limiting consumer trust. This study proposes a solution combining remote power monitoring with the ZigBee module and Explainable Artificial Intelligence (XAI) to offer both predictive accuracy and interpretability. XAI models are more consumer oriented in every area of application, similar to the problem discussed, which tells about the impact of various parameters in power management in domestic appliances. Local Interpretable Model Agonistic Explainer(LIME) and SHAP explainer are used in the proposed work, providing explainability in the local and global surrogates. The proposed work applies various regression models such as Decision Tree (DT), Random Forest(RF), Support Vector Regressor (SVR), Gradient Boost Regressor (GBR) and Extreme Graident Boost Regressor (XGBR). The RF provides the best R2-Score of 94.71% , which is 1.5%–3.0% more than the rest of the models, and also with variance score of 68.82% , had been chosen for explainability. This study demonstrates how XAI can improve transparency and reliability in AI-powered domestic energy systems, offering actionable insights for more sustainable power consumption.
Open Access: Yes
A data-driven approach to tackling academic stress-coping and mental health issues in college students using spherical fuzzy MARCOS methodology
Publication Name: Applied Soft Computing
Publication Date: 2025-12-01
Volume: 185
Issue: Unknown
Page Range: Unknown
Description:
The drastically developing nature of the knowledge economy and the rising need for top-notch expertise have placed tremendous pressure on college students. As higher education becomes more accessible, masses of students are enrolling in colleges, which puts additional pressure on colleges and institutions; as a result, they cannot provide adequate resources to the students. As the class size increases, many students require mental health assistance, academic guidance, and financial aid, which then puts pressure on the teachers and the facilities. This flood of students overloads the facilities, resulting in it becoming more challenging to provide attention and concern, leading many students to feel overlooked and affecting their mental health. Due to not getting timely support, students may find it challenging to handle their academic responsibilities. Moreover, the students face a heavy workload, unclear guidance, and limited resource access. The objective of this study is to develop a structured, data-driven decision-making framework for systematically evaluating and improving student mental health and academic stress-coping strategies in a college setting. To address this, a comprehensive decision-making structure, measurement of alternatives, and ranking according to the compromised solution (MARCOS) within the spherical fuzzy (SF) environment, has been applied, which evaluates the key factors causing mental health issues by comparing the ideal and anti-ideal alternatives. The novelty of the proposed approach lies in leveraging the SF framework's explicit ability to model hesitation (abstinence) alongside truth and falsity degrees, enabling more accurate representation of subjective psychological assessments compared to traditional fuzzy models. Furthermore, the method calculates utility functions corresponding to each alternative (coping technique), prioritizes the strategies, and selects the most effective intervention. The results reveal that personalized mental health plans emerged as the top-ranked coping strategy, highlighting the importance of tailored support in culturally and contextually diverse academic environments.
Open Access: Yes
Decision Support System for Financial and Accounting Performance Assessment in Manufacturing Industries
Publication Name: International Journal of Computational Intelligence Systems
Publication Date: 2025-12-01
Volume: 18
Issue: 1
Page Range: Unknown
Description:
The increasing intricacy of financial and accounting decisions within manufacturing sectors necessitates comprehensive, data-driven assessment tools. This study examines the assessment of financial and accounting performance in manufacturing firms amidst uncertainty, emphasizing the importance of reliable and transparent decision support. A novel decision support system is proposed, integrating advanced multi-criteria decision-making techniques. Picture fuzzy sets are utilized to represent uncertainty and hesitation in expert evaluations by depicting positive, neutral, and negative assessments with different levels of indeterminacy. A dual weighting approach is utilized, employing the logarithmic percentage change-driven objective weighting method to quantify the dispersion and relevance of criterion data, while the ranking comparison method systematically integrates expert preferences. The MARCOS method is employed to assess alternatives and rank firms according to compromise solutions. A case study of manufacturing firms demonstrates the model’s applicability, revealing that profitability, liquidity, and efficiency of costs are the primary financial and accounting measures. The automotive part supplier has been recognized as the best option due to its emphasis on liquidity ratios and efficiency in operations, enabling it to fulfill supply commitments and mitigate risks related to profit margin limitations and quality compliance costs. The sensitivity and comparative analyses illustrate the system’s endurance and adaptability under different circumstances and stakeholder perspectives.
Open Access: Yes
An expert-driven digital platform for decision support in sustainable building retrofitting
Publication Name: Energy and Buildings
Publication Date: 2026-02-01
Volume: 352
Issue: Unknown
Page Range: Unknown
Description:
This study introduces an expert-guided decision-support platform developed to improve the selection of building retrofit measures for energy efficiency. The platform addresses a key gap in existing tools by combining expert input with systematic decision logic, offering a more transparent and adaptable approach to retrofit planning. Unlike simulation-based or policy-orientated systems, this platform focuses on supporting real-world decisions at the property level. It allows for both general (global) and building-specific configurations, giving users the flexibility to define and adjust decision criteria based on retrofit needs. A three-phase analytical workflow supports users via assigning expert importance, classifying assessment criteria, and deciding on a ranking method. The strategy combines data-driven and expert-based weighing methodologies, resulting in balanced and context-aware outputs. The system includes an explainable AI module that generates editable reasons for final recommendations, allowing stakeholders to better understand and discuss decisions. The platform’s efficacy was demonstrated through a case study of a mid-terrace house, showing strong potential for supporting consistent, stakeholder-informed, and auditable retrofit decisions. This work contributes a flexible and scalable solution of practical value to planners, housing authorities, and retrofit consultants.
Open Access: Yes
Airline performance assessment using an improved neutral cross-efficiency method: principal component analysis through Q-methodology
Publication Name: Transportation Research Interdisciplinary Perspectives
Publication Date: 2025-11-01
Volume: 34
Issue: Unknown
Page Range: Unknown
Description:
Assessing the performance of airlines is vital in the aviation industry, as it affects multiple stakeholders, including airlines, travelers, regulatory authorities, and investors. It is known as a key driver of growth and sustainability in the aviation sector. Hence, the main aim of the current study is to utilize the Principal Component Analysis (PCA) through Q-methodology within the Neutral Cross Efficiency Method (referred to as QNCEM) as an innovative technique to provide an assessment framework for airlines. QNCEM offers policymakers numerous advantages as it permits the elimination of irrelevant perspectives during the assessment process, enables the determination of each Decision-Maker’s (DM) contribution, and plays a crucial role in achieving consensus by leveraging factor analysis to extract perspectives that are representative of the group’s opinions. In this research, the efficiency of 17 Iranian airlines is assessed using QNCEM, considering both desirable and undesirable outputs, such as flight delays, demonstrating its practicality and effectiveness. The selection of a loading factor of 0.626 allowed QNCEM to encompass a comprehensive range of viewpoints from 17 DMs. This deliberate choice ensures the inclusion of a diverse set of perspectives, maximizing the richness of the analysis and explaining a cumulative variance of more than 96%.
Open Access: Yes
Enhancing urban solar photovoltaic system performance evaluation through a disc spherical fuzzy aggregation framework
Publication Name: Journal of Computational Science
Publication Date: 2026-01-01
Volume: 93
Issue: Unknown
Page Range: Unknown
Description:
The integration of solar photovoltaic (PV) systems in urban environments promises great potential for sustainable energy applications. However, the unique characteristics of cities, the varieties of weather that occur at the place, and technology inefficiency make performance evaluation difficult. This paper sought to address the pressing need for a robust performance evaluation framework for urban solar PV systems by developing a disc spherical fuzzy aggregation framework. It develops basic algebraic aggregation operations in the framework of the disc spherical fuzzy set (D-SFSs), proving their completeness and describing their essential characteristics. These new operators conceived to operate on D-SFSs furnish theoretical robustness and provide the foundation for decisions made. A shining novel disc spherical fuzzy method is developed namely combinative distance-based assessment (CODAS) in D-SFS. A case study regarding the application of this model in the assessment of performance by urban solar PV systems is being conducted, thus proving the application aspect. Results come out positive in interpreting the decision-making dilemma and differences among several experts. This would, therefore, encourage various sectors to expand the use of D-SFSs in decision support systems and similar areas by showing how useful they can be in actual situations.
Open Access: Yes
Cluster analysis selecting tools using quadri partitioned Pythagorean neutrosophic normal interval-valued set with an aggregation operators
Publication Name: Journal of Mathematics and Computer Science
Publication Date: 2025-01-01
Volume: 41
Issue: 4
Page Range: 487-518
Description:
The goal of a quadri partitioned Pythagorean neutrosophic normal interval-valued fuzzy set (QPPNNIVFS) is to provide the neutrosophic sets a more comprehensive mathematical foundation. QPPNNIVFS divides the indeterminacy component into unknown and contradiction classes. The several aggregating operations that have been understood thus far are discussed here. The fuzzy weighted QPPNNIVFW averaging (QPPNNIVFWA), QPPNNIVFW geometric (QPPNNIVFWG), generalized QPPNNIVFW averaging (GQPPNNIVFWA) and generalized QPPNNIVFW geometric (GQPPNNIVFWG) are considered as a novel concept. We show that algebraic structures like associative, distributive, idempotent, bounded, commutative, and monotonic characteristics are satisfied by QPPNNIVFSs. We illustrate the practical applications of increased Euclidean distance, Hamming distance, score, and accuracy values. Unless there is a mathematical justification for selecting one cluster technique over another, the clustering strategy must be selected empirically. An algorithm that performs well on one set of data will not perform well on another. There are several approaches of conducting cluster analysis. These include social network analysis, distribution-based, density-based, centroid-based and hierarchical. Therefore, it is clear that the natural number θ has a big impact on the models. To illustrate the comparison analysis, sensitivity analysis and the validity of our suggested methodologies are also conducted. The outcomes will be very helpful to decision makers in handling uncertain and conflicting data effectively.
Open Access: Yes
Promoting transition towards sustainable air transport systems: A hybrid decision support system for effective national-level performance evaluation
Publication Name: Journal of Air Transport Management
Publication Date: 2026-05-01
Volume: 133
Issue: Unknown
Page Range: Unknown
Description:
Air transport plays a pivotal role in enhancing economic development by supporting trade, tourism, and regional competitiveness. The growing environmental concerns and social expectations have necessitated the transition towards sustainable air transport systems. Sustainable air transport refers to aviation activities that balance environmental, economic, and social objectives, aiming to minimize carbon emissions, promote renewable energy usage, and enhance socio-economic welfare. In this study, a novel multi-criteria decision-making (MCDM)-based decision support system (DSS) is proposed to evaluate the sustainable air transport performance of the European countries. The main objective of this research is to develop a comprehensive and integrative framework for measuring and ranking the sustainable air transport performance of nations. A hybrid method, termed fractional fuzzy–ranking comparison-response to criteria weighting (RANCOM)–response to criteria weighting (RECA)–ranking technique by geometric mean of similarity ratio to optimal solution (RATGOS), is introduced. DSS consists of five main stages: expert-based subjective weighting using fractional fuzzy RANCOM, objective weighting via RECA, aggregation of weights, and final performance ranking through the RATGOS method. The results indicate that Germany ranks highest, while Cyprus has the lowest sustainable air transport performance among the evaluated countries. The criterion “commercial aircraft fleet by age of aircraft” is determined to have the highest importance among the sustainable air transport performance indicators. The study provides a comprehensive, replicable framework for policymakers and stakeholders aiming to monitor and improve sustainable aviation systems. It contributes to the literature by addressing the gap in national-level sustainable air transport performance evaluation.
Open Access: Yes
Hospital Admission Classification of Cardiac Patients Utilizing Metaheuristics-Optimized Two Tier Framework
Publication Name: International Journal of Computational Intelligence Systems
Publication Date: 2026-12-01
Volume: 19
Issue: 1
Page Range: Unknown
Description:
Accurate evaluation of a cardiac patient’s risk at the point of hospital entry is critical for efficient triage and ensuring timely, suitable medical intervention. This study aims to forecast a range of clinical outcomes by leveraging admission data from a cardiac care unit, utilizing a refined and optimized machine learning approach. This research introduces a hybrid architecture that integrates convolutional neural networks (CNNs) with advanced machine learning classifiers, namely light gradient boosting machine (LightGBM) and categorical boosting (CatBoost), further enhanced through metaheuristic optimization techniques to maximize their performance. The proposed two-tiered design organizes feature extraction and final decision modeling into a coherent pipeline tailored for multi-class hospital admission classification. A comprehensive evaluation using a real-world hospital admission dataset demonstrates the framework’s effectiveness on a real-world, publicly available hospital admission dataset, supporting its utility for multi-class cardiac outcome prediction. Three experiments were conducted using publicly available datasets, where the best-performing models achieved a peak classification accuracy of 99.79%. Furthermore, explainable AI techniques were employed to interpret model predictions, offering actionable insights that can guide future data acquisition and strengthen the accurate classification of cardiac patients.
Open Access: Yes
A hybrid data-driven approach for the viable supplier selection problem: a case study of the oil and gas industry
Publication Name: Environment Development and Sustainability
Publication Date: 2026-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
The Supplier Selection Problem (SSP) plays a significant role in Supply Chain Management (SCM) in today’s competitive world. With respect to this, the literature reveals that incorporating the viability concept in the SSP for the Oil and Gas (O&G) industry has not been adequately addressed in prior studies. Hence, the current study focuses on the SSP for the energy sector by considering the viability pillars. To do so, a data-driven decision-making model is developed that calculates the weights of indicators executing the Fuzzy Best-Worst Method (FBWM) and then evaluates the performance of the supplier by integrating Data Envelopment Analysis (DEA), Support Vector Machine (SVM), and Random Forest (RF) techniques. Overall, the main contribution of this research is to develop an effective data-driven model to examine the viable SSP for the O&G industry. According to the results obtained, among the potential indicators, cost, quality, responsiveness, manufacturing flexibility, robustness, restorative capacity, pollution control, Waste Management (WM), technical capability, and smart factory are selected as the most significant indicators in their corresponding aspects. Moreover, the comparison results against the classic methods demonstrate the robustness, applicability, and validity of the developed data-driven decision framework. Finally, theoretical and managerial implications are presented.
Open Access: Yes
Evaluating blockchain-based waste management investments in smart cities using a multi-criteria decision support framework
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
With growing urbanization, there are increasing demands on waste management systems that can be performed in an environmentally friendly way as well as efficiently. Current approaches to managing waste often have issues with efficiency, transparency, and engaging with the public. Blockchain technology has been identified as one potential solution to these problems because it offers several benefits including decentralization, security, and transparency. The selection of the best blockchain-based waste management (BBWM) system is very difficult due to the many different evaluation criteria that may conflict with each other. Therefore this research uses a multi-criteria decision making (MCDM) approach using CIMAS (Criteria Importance Assessment), for determining weights based upon subjective input, and LOPCOW (Logarithmic Percentage Change-Driven Objective Weighing), for determining weights based upon objective data within the MCDM framework. To rank alternatives effectively, an Alternative Ranking Order Method Accounting for Two-Step Normalization (AROMAN) technique is applied, ensuring a precise evaluation process. The use of T-Spherical Fuzzy Sets (T-SFS) captures all three (membership, non-membership, hesitation degree) and is used to address the variability that exists when making an expert judgment. Some of the key factors include; Technological Feasibility, Operational Costs, Scalability, Data Security, Regulatory Compliance, Environmental Impact. Based on the evaluation criteria, it appears that the Blockchain Enabled Waste Tracking System is the most appropriate alternative due to its high potential for Transparency, Regulatory Compliance and Fraud Prevention. In addition, this research will provide Policymakers, Urban Planners and Investors with a methodical way of making Data Driven Decisions on BBWM Investments.
Open Access: Yes
Selection of underground hydrogen storage systems using a novel fuzzy model
Publication Name: Energy Conversion and Management
Publication Date: 2026-03-15
Volume: 352
Issue: Unknown
Page Range: Unknown
Description:
Storing hydrogen resources underground can accelerate the transition to renewable energy, facilitate energy supply security, and the adoption and expansion of hydrogen energy, a clean energy source. The selection of sustainable underground hydrogen storage systems is a critical research topic for addressing environmental issues caused using fossil fuels. However, decision-makers still lack a consensus-based and sustainability-oriented framework that can comparatively evaluate alternative underground hydrogen storage geological formations under economic, environmental, social, and technical uncertainties, which constitutes a critical barrier to large-scale hydrogen deployment. This issue has become more prominent as fossil-based fuel reserves are gradually decreasing worldwide. In contrast, researchers and practitioners lack a consensus on which underground storage method is most suitable for economical, safe, and efficient hydrogen storage. If this problem is not addressed correctly and reasonable solutions are not obtained, continued dependence on fossil fuels may persist. Alternatively, other renewable energy sources with relatively lower efficiency and performance may be adopted. In both cases, significant delays in achieving the global sustainability goal are likely to occur. We propose an integrated fuzzy decision-making framework (F-WENSLO & Dombi-Bonferroni & F-ARTASI) to address this selection problem under uncertainty. The proposed framework integrates fuzzy WENSLO (Weights by ENvelope and SLOpe) for robust sustainability-based criteria weighting, the Dombi–Bonferroni aggregation operator to model interdependencies among criteria explicitly, and the fuzzy ARTASI (Alternative Ranking Technique based on Adaptive Standardized Intervals) method to provide flexible and stable ranking of geological alternatives beyond rigid distance-based approaches. Key advantages of the proposed model include producing reliable and consistent solutions that accurately reflect real-world conditions for selecting sustainable underground hydrogen storage systems. The results revealed that C14 (job creation and employment opportunities) (0.0603) is the most influential criterion in selecting the most suitable storage system. In addition, salt caverns with an Ωi of 10,5167 have achieved the highest score, placing them in the first position, and it is the most suitable and advantageous underground hydrogen storage option. The suggested decision-making tool can yield reliable and robust solutions in real-world conditions, enabling the planning of infrastructure design for hydrogen energy systems that incorporate sustainability dimensions. In that regard, the developed model possesses the characteristics of an efficient and practical roadmap that can guide policymakers and decision-makers in transitioning from fossil-based energy sources to renewable energy sources. It has been implemented to evaluate underground geological formations that could facilitate the storage of hydrogen energy underground, serving as a case study. The reliability and robustness of this tool have been verified through extensive validation tests.
Open Access: Yes
Hybrid Pythagorean Fuzzy Decision-Making Framework for Sustainable Urban Planning under Uncertainty
Publication Name: CMES Computer Modeling in Engineering and Sciences
Publication Date: 2026-01-01
Volume: 146
Issue: 1
Page Range: Unknown
Description:
Environmental problems are intensifying due to the rapid growth of the population, industry, and urban infrastructure. This expansion has resulted in increased air and water pollution, intensified urban heat island effects, and greater runoff from parks and other green spaces. Addressing these challenges requires prioritizing green infrastructure and other sustainable urban development strategies. This study introduces a novel Integrated Decision Support System that combines Pythagorean Fuzzy Sets with the Advanced Alternative Ranking Order Method allowing for Two-Step Normalization (AAROM-TN), enhanced by a dual weighting strategy. The weighting approach integrates the Criteria Importance Through Intercriteria Correlation (CRITIC) method with the Criteria Importance through Means and Standard Deviation (CIMAS) technique. The originality of the proposed framework lies in its ability to objectively quantify criteria importance using CRITIC, incorporate decision-makers' preferences through CIMAS, and capture the uncertainty and hesitation inherent in human judgment via Pythagorean Fuzzy Sets. A case study evaluating green infrastructure alternatives in metropolitan regions demonstrates the applicability and effectiveness of the framework. A sensitivity analysis is conducted to examine how variations in criteria weights affect the rankings and to evaluate the robustness of the results. Furthermore, a comparative analysis highlights the practical and financial implications of each alternative by assessing their respective strengths and weaknesses.
Open Access: Yes
Measuring promotional video performance through eye-tracking and cognitive evaluation: A Fermatean fuzzy decision analytics approach
Publication Name: Engineering Applications of Artificial Intelligence
Publication Date: 2026-04-15
Volume: 170
Issue: Unknown
Page Range: Unknown
Description:
Neuromarketing techniques are increasingly employed to measure emotional responses through brainwaves, eye movements, and facial expressions. The primary motivation of this study is to develop a decision support system capable of evaluating promotional video performance levels based on both eye-tracking data and cognitive assessments. The core objective is to propose a hybrid method that simultaneously integrates neuromarketing insights and cognitive evaluations. To this end, a Fermatean fuzzy (FF)−Hamacher−simple weight calculation (SIWEC)−method based on the removal effects of criteria (MEREC)−alternative ranking using two-step logarithmic normalization (ARLON) decision analytics model is developed and implemented. The FF−SIWEC method is employed to determine subjective criterion weights based on expert judgments, while the FF−MEREC method is used to compute objective weights by analyzing both eye-tracking and qualitative evaluations from viewers. Additionally, the Fermatean fuzzy Hamacher weighted aggregation operator ensures precise aggregation of audience evaluations. The FF−ARLON method is applied to obtain final rankings of promotional videos. A real-world case study is conducted to test the applicability of the proposed method, involving six automobile brands and 10 audiences. Eye-tracking analyses are conducted while audiences view the promotional videos, followed by expert and audience evaluations of seven qualitative and six eye-tracking criteria. Among qualitative criteria, “level of emotional impact” is found to be the most significant, while “saccadic direction” emerges as the most important eye-tracking criterion. The promotional video for the Mercedes brand demonstrates the highest overall performance. This study contributes to the literature by proposing a reliable and consistent hybrid model for evaluating promotional video performance.
Open Access: Yes
Analysis and management of climate change incidents spread within the environment under coastal lives: Modeling and chaos control
Publication Name: Results in Control and Optimization
Publication Date: 2026-03-01
Volume: 22
Issue: Unknown
Page Range: Unknown
Description:
Examining the model of climate change by analyzing how changes in climate-related incidents spread within the environment, particularly in coastal areas, as a result of predictions, is the main goal of this study. Following some measurements of impact rates for various variables, a mathematical model is developed using the hypothesis of a healthy environment to investigate the rates of climate change affecting coastal communities. In addition to studying the model equilibrium points, the next generation method is used to determine the models reproductive number to climate incidents spread within the environment. To determine the most sensitive factors and look at how changes in the pace of change under various conditions affect coastal life, a sensitivity analysis was created. Both qualitative and quantitative analyses are performed on a proposed model, with particular focus on existence, boundedness, positivity, and unique solutions, which are key characteristics of the developed model. At endemic sites, the model's local stability is confirmed both theoretically and statistically. The Lyapunov derivative by endemic point of the model is used to investigate the worldwide stability of the model. Chaos control is also used to observe the chaotic behavior of the climate change. A two-step method, Lagrange polynomials, is applied in numerical simulations to investigate the effect of the fractional operator on the generalized form of the power law kernel for ongoing surveillance of climate change under coastal lives. The simulations show how different parameters affect the changes in climate incidents spread within the environment under coastal lives. Simulations have been developed to simulate the effects and behavior of climate change brought on by both natural and human activity, as well as to implement various environmental health initiatives. This type of research will be helpful in figuring out how climate change spreads and in developing future management plans for coastal lives, based on our verified results for various strategies.
Open Access: Yes
Positive Impact of Waste Management Strategies and Decision Analysis with Intuitionistic Fuzzy Sugeno-Weber Aggregation Operators
Publication Name: Boletim Da Sociedade Paranaense De Matematica
Publication Date: 2025-08-13
Volume: 43
Issue: 3
Page Range: Unknown
Description:
Waste management is a crucial and significant subject that has gained much attention globally because it has several environmental, social, financial and economic implications. Solid waste management is a very challenging task for clean urban and rural societies. We studied some reliable strategies for handling the waste materials and garbage produced by people. To serve this purpose, an intuitionistic fuzzy set (IFS) is a well-known model used for modeling and processing unpredictable information and providing accurate approximated results in the decision-making process. Power average operators allow the interrelationship of the input arguments and deal with uncertain information in complicated situations. This article expresses Sugeno-weber triangular norms under intuitionistic fuzzy (IF) information. We developed a class of new aggregation operators, including intuitionistic fuzzy Sugeno-Weber power-weighted average (IFSWPWA) and intuitionistic fuzzy Sugeno-Weber power-weighted geometric (IFSWPWG) operators. It is observed that both the newly proposed operators satisfy the properties of aggregation. The multi-criteria decision-making (MCDM) problem is proposed to evaluate real-life applications and numerical examples. An experimental case study under the system of waste materials is considered in the article to reveal the intensity and applicability of derived approaches. The comparison analysis and sensitivity analysis show the significance of our proposed work.
Open Access: Yes
DOI: 10.5269/bspm.79085
Multi robot task assignment with decision analysis and circular q-Rung orthopair fuzzy Schweizer-Sklar T-norms
Publication Name: Journal of Umm Al Qura University for Applied Sciences
Publication Date: 2026-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
A multi-robotic task assignment and decision analysis system refers to an advanced framework in robotics and artificial intelligence where multiple robots are coordinated to perform a set of tasks efficiently and intelligently. This article designs innovative approaches to fix uncertainty during task allocation in a multi-robotic system under a hybrid fuzzy framework and decision-making models. To achieve this goal, we expose a modified theory of circular q-rung orthopair fuzzy set (Crq-ROFS), which is a broader framework of intuitionistic fuzzy sets and q-rung orthopair fuzzy sets. We formulated feasible operations of Schweizer-Sklar t-norm and t-conorm in light of circular information about q-rug orthopair fuzzy (Crq-ROF). We also delved into a family of mathematical approaches of Schweizer-Sklar t-norm and t-conorm, namely, Crq-ROF Schweizer-Sklar weighted average (Crq-ROFSSWA) and Crq-ROF Schweizer-Sklar weighted geometric (Crq-ROFSSWG) operators with dominant propositions. The theory of the multi-attribute decision-making (MADM) problem offers authentic and reliable solutions by aggregating human judgment. An experimental case study discussed evaluating an ideal solution under consideration of multi-criteria or attribute information. A comparison method is conducted to showcase the reliability and effectiveness of the pioneering approaches with existing approaches.
Open Access: Yes
Complex intuitionistic fuzzy distance measures with hesitance value and their applications in decision making
Publication Name: Physica Scripta
Publication Date: 2026-01-16
Volume: 101
Issue: 2
Page Range: Unknown
Description:
In applications requiring uncertain, imprecise, and multi-dimensional data, where traditional distance measures frequently fall short of capturing the full complexity of interactions among elements, a distance measure for complex intuitionistic fuzzy sets (DMCIFSs) becomes essential. Although DMCIFSs have been developed, most of them do not account for the hesitation degree, which is crucial for capturing ambiguity and uncertainty in human reasoning. As extensions of the normalized Hamming and Euclidean distance measures, this work proposes two new measures namely the Hesitance DMCIFSs (HDMCIFSs) and the Euclidean Hesitance DMCIFSs (EHDMCIFSs). These newly proposed measures provide a more comprehensive framework for modeling uncertainty by explicitly incorporating the hesitancy component. In addition to the proposed measures, several fundamental procedures and theoretical results are also presented. Furthermore, a novel decision-making method utilizing these distance measures is developed and applied to multi-criteria decision-making (MCDM) problems. The effectiveness of the proposed methods is demonstrated through a comparative study, highlighting their potential for improved sensitivity and accuracy in practical decision-making scenarios.
Open Access: Yes
A novel hybrid neutrosophic-fuzzy-uncertain data envelopment analysis model for assessment of wind farm locations
Publication Name: Energy Nexus
Publication Date: 2026-06-01
Volume: 22
Issue: Unknown
Page Range: Unknown
Description:
Renewable energy resources have got much attention in recent years. Because of climate change in the world, the countries try to develop renewable energy resources. Wind farms are renewable energy resources that can produce electricity with no negative effect on climate change. In this study, as an important topic, the location selection problem of wind farms is considered as a real case study. For the first time, a multi-criteria wind farm location selection problem with neutrosophic fuzzy and uncertain criteria at the same time is developed. As the set of criteria consists of both input and output criteria, we develop a novel hybrid neutrosophic-fuzzy-uncertain scheme of BCC DEA model for the first time to solve the problem. As solution approach, a chance-constrained programming approach based on possibility measure of the neutrosophic fuzzy constraints of the model also for the first time is proposed in this study. An extensive computational study on the case study by the proposed approach is performed. The candidate locations of the case study are prioritized where the location of Birjand city with score of 1.1656 is selected as the best location. A sensitivity analysis on the confidence levels of the chance-constrained programming approach is performed and also the obtained results are compared to the approaches of the literature.
Open Access: Yes
Creating digital transformation roadmaps for independent audit firms: An interval-valued q-rung orthopair model
Publication Name: Engineering Applications of Artificial Intelligence
Publication Date: 2026-02-15
Volume: 166
Issue: Unknown
Page Range: Unknown
Description:
The primary objective of this study is to develop a structured digital transformation strategy roadmap that independent audit firms can utilize to manage digital transformation processes effectively. Digital transformation extends beyond integrating Industry 4.0 and advanced technologies into business operations. It necessitates restructuring business models, decision-making frameworks, and stakeholder communication mechanisms. Its implications are critical across all industries. In independent auditing, ensuring data accuracy, enhancing audit process transparency, and meeting speed and quality requirements are becoming increasingly vital. Digital transformation addresses these needs and provides independent audit firms with a sustainable competitive advantage. A review of the existing literature reveals a significant research gap in the identification and prioritization of digital transformation strategies, as well as a lack of comprehensive theoretical studies examining the digital transformation practices of enterprises. This study proposes an integrated decision-making model to address these research and theoretical shortcomings. According to the study results, "providing in-depth analysis with big data analytics and artificial intelligence solutions" is the most essential strategy for managing digital transformation processes. Regarding the applicability of this strategy, "agility" is defined as the most critical and practical criterion. Robustness checks confirm the model's validity and consistency.
Open Access: Yes
A Modified Metaheuristic Optimization Approach for Forecasting the Lifecycle of Rechargeable Lithium-Ion Batteries
Publication Name: Smart Grids and Sustainable Energy
Publication Date: 2026-08-01
Volume: 11
Issue: 2
Page Range: Unknown
Description:
The global shift toward renewable energy is driven by the dual imperatives of rising energy demand and the need to reduce environmental harm caused by fossil fuels. However, renewables like wind and solar power pose unique challenges, particularly due to their intermittent generation and current limitations in energy storage technologies. Battery banks, commonly used to store surplus energy, degrade over time, making accurate forecasting of their remaining usable lifecycles critical for maintaining system reliability and efficiency. This study proposes a novel approach for forecasting battery health using an optimized long short-term memory (LSTM) network. To address the complexity of deep learning hyperparameter selection, a modified metaheuristic optimization algorithm is developed and integrated into a broader optimization framework aimed at improving model performance while minimizing overfitting. The method is benchmarked against several state-of-the-art optimizers, with results validated through comprehensive simulations and statistical analysis. This work contributes a scalable forecasting methodology, an effective optimization strategy, and interpretable results to support sustainable energy storage solutions.
Open Access: Yes
A Spherical Fuzzy ELECTRE III-Based Framework for Evaluating Flood Risk Management Strategies in Vulnerable Watersheds
Publication Name: Boletim Da Sociedade Paranaense De Matematica
Publication Date: 2025-12-29
Volume: 44
Issue: Unknown
Page Range: 1-14
Description:
Flooding is one of the most widespread and damaging natural hazards worldwide, causing significant economic losses, environmental degradation, and risks to human life, particularly in vulnerable watersheds. The multi-criteria decision-making dilemma of managing flood risks in prone watersheds is associated with conflicting economic, social, and environmental objectives. To assess and rank the flood risk management options, this research suggests a single model that should be developed using a mix of the fuzzy analytic hierarchy process and ELECTRE III approaches. The fuzzy analytic hierarchy process is used to capture the uncertainty and subjectivity of the pairwise comparison of decision-makers. Alternative management strategies are ranked using the ELECTRE III technique. The suggested approach is applied to an empirically vulnerable watershed, demonstrating its viability. The suggested fuzzy framework aids decision-makers in selecting the best course of action even before a flood occurs. Watershed managers can use the findings as a scientifically validated tool for resource allocation in flood risk reduction, as they provide a clear and sound hierarchy of strategies that include both structural and non-structural measures.
Open Access: Yes
DOI: 10.5269/bspm.79345
