Precise crop type classification and agricultural suitability analysis are critical for optimizing land use, improving productivity, and ensuring food security under climate variability. Traditional models often struggle with overlapping class boundaries, high-dimensional input spaces, and lack of interpretability, limiting their practical deployment in decision-support systems. This study proposes a hybrid fuzzy-machine learning framework that integrates Principal Component Analysis for dimensionality reduction, Fuzzy C-Means for uncertainty-aware clustering, and a Random Forest classifier for robust prediction. The framework was trained and validated on a real-world dataset of soil and climatic parameters (soil pH, Nitrogen, Phosphorus, Potassium, temperature, humidity, and wind speed) covering seven major crops, with class-wise stratified five-fold cross-validation ensuring reliability. Principal Component Analysis reduced redundancy while retaining over 87% of total variance, and Fuzzy C-Means membership values captured uncertainty in overlapping crop suitability patterns. The combined Principal Component Analysis, Fuzzy C-Means clustering, and Random Forest model achieved an average accuracy of 93.8%, precision of 94.3%, recall of 94.1 %, and F1-score of 94.1 %, representing a 6% gain in F1-score over baseline Random Forest. Crops such as Cotton, Sugarcane, and Soybean consistently showed high classification performance, while errors were concentrated in pairs with overlapping requirements ( Potato vs. Soybean and Sugarcane vs. Rice). SHapley Additive Explanations-based analysis revealed that The first principal component and fuzzy membership features contributed most to decision-making, aligning with agronomic knowledge and providing interpretability. These results confirm that the proposed framework improves accuracy, enhances transparency, and offers practical decision-support value for sustainable agricultural planning.

In the theory of the Intuitionistic Fuzzy Sets (IFS) there exist operators of modal type, of level type, and of topological type. The modal operators themselves are from three different types. Initially, an intuitionistic fuzzy analogues of the classical modal operators "necessity" and "possibility" were introduced. Following that, they were generalized to new modal-type operators specific to IFSs. In the present paper is considered the most general (at least until this moment) of these operators. It is shown that with appropriate choice of its parameters, this operator can transform the intuitionistic fuzzy values of an arbitrary IFS to the intuitionistic fuzzy values of another arbitrarily chosen IFS.

This paper presents an Elliptic Intuitionistic Fuzzy Quad (E-IFQ) extension of our prior Intuitionistic Fuzzy (IF) productivity model and its circular IF variant for evaluating habilitated academic staff in STEM higher education. Productivity is assessed across core dimensions (research output, teaching/mentoring load, project leadership, academic service). Unlike the IF and circular IF models, the E-IFQ aggregation yields a centroid (aggregated membership and non-membership degrees) that can differ due to the asymmetric representation of uncertainty. The novelty lies in substituting the symmetric (circular) hesitation zone with an elliptical one, parameterized by a major and a minor axis. These axes capture asymmetric dispersion in expert assessments, allowing disagreement to manifest differently along the membership and non-membership directions. This representation separates the central value (consensus) from directional uncertainty (asymmetry), enriching interpretability and highlighting the nature of evaluator disagreement. A numerical case study illustrates that, under uneven evidence across criteria, the E-IFQ model yields more informative evaluation profiles than the circular IF counterpart, pinpointing whether disagreement concentrates in positive or negative assessments, while also capturing changes in the central score. This geometric refinement supports transparent reporting and nuanced, uncertainty-aware promotion and planning decisions at institutional level.

The rapid development of electric vehicle technology has led to a significant increase in the popularity of fully electric sport utility vehicles (SUVs), which offer environmental benefits and operational efficiency. However, the decision-making process for selecting the most suitable electric SUV model involves multiple criteria and inherent uncertainties. This paper proposes a novel approach for evaluating and ranking fully electric SUV alternatives using the Interval-Valued Proportional Spherical Fuzzy Analytic Hierarchy Process (IVPSF AHP). This method integrates the strengths of proportional fuzzy logic, spherical fuzzy sets, and interval-valued data to better capture expert judgments under uncertainty. A comprehensive set of evaluation criteria—including technical, economic, environmental, functional, safety, and market factors—is considered. Five SUV alternatives with similar purchasing costs are analyzed. The results indicate that brand reliability, purchase price, and driving range are the most significant factors in the selection process. The proposed methodology provides a robust and flexible decision support tool for complex multi-criteria evaluations in the electric vehicle market.

Linear Diophantine fuzzy sets (LDFSs) enlarge the admissible representation space for uncertain information through membership and non-membership degrees together with reference parameters. Existing LDFS aggregation studies have mainly considered weighted, ordered, power-based, and parametric operators, while partitioned aggregation operators that model possible interrelationships within criterion groups remain limited. Maclaurin symmetric mean (MSM) operators capture interactions among criteria, and reducible weighted MSM operators have been developed to preserve idempotency and reducibility properties but have not been extended to a partitioned MSM operator. This study develops an MSM-based aggregation framework for LDFSs, including the linear Diophantine fuzzy MSM (LDFMSM), the linear Diophantine fuzzy partitioned MSM (LDFPMSM), a reducible weighted partitioned MSM (RWPMSM) and its extension to LDFSs, and the linear Diophantine fuzzy reducible weighted partitioned MSM (LDFRWPMSM) operators. LDFMSM performs symmetric k-subset aggregation over the complete criterion set, LDFPMSM restricts joint aggregation terms to criteria within the same predefined partition, and LDFRWPMSM incorporates criterion weights while preserving idempotency and reducibility properties. Theoretical properties and special cases are established. The framework is demonstrated through numerical examples and an illustrative multi-attribute decision-making (MADM) application for an AI-driven precision agriculture platform selection. Sensitivity analyses examine the effects of the interaction parameter, criterion weights and partition structure and reveal systematic changes in the ranking results. Comparative analysis shows that the LDFPMSM can distinguish alternatives that remain tied under globally symmetric aggregation, while the LDFRWPMSM identifies a different best alternative from the benchmark operators, reflecting its joint treatment of criterion importance and within-partition interactions.

A brain tumor is one of a clinically significant diseases as it can lead to serious clinical implications can adversely affect survival without timely treatment. Therefore, the accurate and timely diagnosis of brain tumor regions is a major challenge in computational medicine. With the introduction of deep learning in the healthcare sector, improvements have been made in this regard; however, these models still have issues with sensitive areas and boundaries in tumor regions. This problem arises primarily from class imbalance, which leads to reduced performance. In this study, we present a new approach for optimizing medical image segmentation based on the UNet++ architecture. The proposed algorithm focuses on identifying the most informative image regions, which results in more precise segmentation and reliable assessment. We introduce a hybrid loss function, a composition of Binary Cross-Entropy and Focal Tversky loss. This composition helps to improve segmentation accuracy and the class imbalance problem. The Binary Cross-Entropy component ensures consistent and accurate pixel-wise classification, while the Focal Tversky term strengthens the delineation of tumors under severe class imbalance. Collectively, these features allow the model to learn in a way that is not sensitive to the challenges in areas of diagnostics and does not reduce the overall segmentation integrity. In order to further increase the strength and flexibility of the model, we use a hybrid validation approach combining K-fold cross-validation with test-time augmentation, augmented with a new weighted ensemble mechanism. The ensemble combines the predictions of the most competent models, which results in the final segmentation results, which are more consistent and accurate. In general, the experimental framework shows that the complementary benefits of incorporating specific architectural improvements, the design of loss function, and efficient ensemble learning are central to a stable and effective brain-tumor segmentation of medical images.

Increasing apprehension regarding climate change, resource scarcity, and ecological deterioration has propelled the global momentum toward sustainable energy development. In this context, green energy has become an essential part of the shift away from fossil fuels and toward a future with lower carbon emissions. However, choosing the best green energy source for a certain application or specific area is a difficult task with many facets requiring consideration. Green energy solutions necessitate careful evaluation of a wide range of qualitative criteria in contrast to traditional energy sources. We put forward a hybrid fuzzy Multi-Criteria Decision-Making (MCDM) method using interval valued Pythagorean fuzzy numbers in this paper. The possibility degree method is used in the suggested approach to derive the weights of the evaluation criteria. Next, the matrix of decisions is created, and the preferred alternative is selected by entropy theory and cosine similarity theorem. Ultimately, our goal is to develop innovative, reliable techniques using these various theorems. Combining the advantages of each approach improves decision-making proceses as they increase precision and resilience and streamline the ability to handle complicated data in a variety of situations. We utilized the proposed method to evaluate green energy alternatives in Sweden to demonstrate applicability. A sensitivity analysis of the results is conducted to test how changes in input parameters affect the final ranking. Finally, a comparison analysis is provided.

Existing fuzzy inference systems are generally based on ordinary fuzzy sets, which do not let the second and third dimensions of the other fuzzy sets extensions to be employed. This paper suggests a decision-making approach by utilizing the fuzzy inference systems (FIS) based on spherical fuzzy sets (SFS). We prefer spherical fuzzy sets to consider the indecision degree together with membership and non-membership degrees in the proposed FIS. During the defuzzification of SF inference system, the indecision degree is distributed over membership and non-membership degree in balance regarding to indecision degree by using a special transformation function. By applying the proposed approach on FIS, it aims to cover hesitancies and uncertainties caused by insufficient assessments of the decision makers more effectively. The proposed decision-making approach is tested with a real-world application in the field of maintenance work order prioritization for scheduling. Finally, the result of the suggested approach based on SFS is compared with the risk assessment matrix technique (RAM) existing in the literature and Picture Fuzzy Inference Systems (PiFIS). It is observed that the proposed Spherical Fuzzy Inference System (SFIS) is more efficient than RAM and PiFIS methods.

Healthcare is essential for survival, but poor biomedical waste management (BWM) harms the ecosystem, including plants and animals, and directly influences human health. BWM is vital to maintaining environmental sustainability and public health. There are meaningful challenges in evaluating the efficacy of BWM because of the inherent uncertainties and complexity associated with waste characteristics and disposal methods. This paper proposes a fuzzy multi-criteria decision-making (MCDM) method for an exhaustive appraisal of the biomedical waste management. To successfully manage biomedical waste in the system, we developed a γ -cut area-based fuzzy MCDM method in this study. Also, to measure the performance of the BWM techniques, we invented a γ -cut area ranking method under the generalized heptagonal fuzzy number (GHpFN) environment. In the BWM system, we considered five BWM techniques (Incineration ( A_1 ), Microwaves ( A_2 ), Autoclaving ( A_3 ), Chemical treatment ( A_4 ), and Recycling ( A_5 )) and to assess the BWM techniques based on technological sustainability, health and safety, environmental impact, and cost-effectiveness criteria. The results revealed that autoclaving achieved the highest ranking by the cut area ranking method, indicating its superior efficiency, compliance with regulations, and environmental impact. The ranking order by the proposed method is Ar_3>Ar_2>Ar_4>Ar_5 > Ar_1 . The ranking provides practical insights for policy-makers and healthcare administrators, emphasizing the need to adopt the strategic practices employed by Autoclaving. In the biomedical system, the suggested method promotes sustainable waste management techniques and offers practitioners and policy-makers a valuable tool for improving waste management strategies.

The foundation of a nation’s future lies in the performance of its students. Their academic success shapes not only their personal growth but also the strength and progress of the entire country. High-achieving students go on to become skilled professionals, innovators, and leaders who drive economic development and social change. In this way, student performance plays a vital role in building a knowledgeable and competitive society. Investing in education today ensures a stronger, brighter nation tomorrow. Student performance depends on various factors such as the quality of teaching, availability of learning resources, parental support, and a conducive learning environment. Motivation, mental health, and socio-economic background also significantly influence how well a student performs academically. Although previous studies have applied either clustering techniques or machine learning algorithms independently to evaluate student performance, they often lacked the ability to capture the uncertainty and overlap in student characteristics. This research addresses that gap by combining fuzzy c-means clustering, which allows for soft classification, with a machine learning algorithm. The hybrid model enhances the accuracy and interpretability of performance evaluation by leveraging the strengths of both methods. This integrated approach provides a more nuanced and data-driven understanding of student outcomes. In addition to this, explainable artificial intelligence technique is employed to provide a transparent and interpretable summary of how the proposed hybrid model functions. By integrating this technique, the research not only improves prediction accuracy but also ensures that the decision-making process is understandable to educators and stakeholders. Furthermore, feature distribution graphs and correlation heatmap are drawn to have visual understanding of the related features. At the last, comparison with existing techniques, limitations and future directions are being discussed.

This study focuses on developing a new extension of Analytic Hierarchy Process (AHP) by using Intuitionistic Fuzzy Sets with Ordered Pairs (IFSOP). By leveraging the functionality of two-way questions, positive and negative, the membership degrees are determined, and fuzzy sets are constructed based on expert responses. The research contributes to the field by introducing a hierarchical prioritization using the recently introduced analytic hierarchy process based on Intuitionistic Fuzzy Sets with Ordered Pairs (IFSOP-AHP).

Classical break-even analysis (BEA) is applied by considering linear or non-linear revenue and total cost functions. However, the experts' estimations for these functions are vague and imprecise rather than exact. If the experts have insufficient data, the application of the classical BEA approach will not yield accurate results. Under these conditions, experts prefer to make predictions for parameters by using linguistic expressions. Due to the capability of fuzzy set theory to model such predictions, fuzzy BEA will be developed in this paper using one of the fuzzy sets' extensions in the literature. Picture fuzzy sets, which are one of the most recent extensions of ordinary fuzzy sets, are employed in the development of BEA under fuzziness incorporating experts' thoughts on the positive, neutral, and negative membership degrees of an estimation. Picture fuzzy singular and triple non-linear total cost and revenue functions are considered in the BEA model. The developed picture fuzzy BEA is applied to an excavator rent or buy decision problem.

Reliability centered maintenance (RCM) is a methodology formaintenance optimization developed within the aviation industry and adapted to other industries such as Oil & Gas refining. One of the biggest problems is the lack of quantitative data during criticality analysis and therefore a suitable method is needed to process verbal expressions and the uncertainties encountered. This paper presents a novel application of Fuzzy Inference Systems (FIS) based on Picture Fuzzy Sets (PFS) for determining the criticality of refinery assets to utilize in RCM methodology. Unlike traditional methods, this research uses the Mamdani approach in Picture Fuzzy Inference Systems (PFIS) to best address the uncertainties and lack of quantitative values inherent in the analysis process. The unique application of PFS, rarely combined with fuzzy inference systems, offers distinct advantages in dealing with imprecision and ambiguity in maintenance data. The results of PFIS are compared with those of the matrix method, demonstrating the effectiveness of the proposed approach in refining the decision-making process of criticality determination and maintenance prioritization in refinery operations.

Human-centric and sustainable cities have become one of the most popular research areas today. They are based on a holistic approach that aims to both improve people’s quality of life and ensure environmental sustainability. The weighting of numerous tangible and intangible criteria used in the performance evaluation of these cities has emerged as an important problem. The symmetrical representation of vague and imprecise data using fuzzy set theory is an absolute necessity for a successful weighting process. In this study, symmetrical representation of especially intangible criteria is carried out with proportional spherical fuzzy sets. Proportional spherical fuzzy sets provide significant convenience to the expert in determining the membership, non-membership and hesitancy degrees and ensure that the assigned values are more accurate and consistent. In the study, five main human centric and sustainable city (HCSC) criteria and 26 HCSC sub-criteria determined from the literature were weighted. In addition, criteria weights were obtained by classical spherical fuzzy analytic hierarchy process (AHP) for comparison purpose. The first four most important HCSC criteria were determined as water management and conservation, employment/unemployment rate, carbon emission reduction strategies and economic sustainability, respectively.

COmbinative Distance-based Assessment (CODAS) is one of the significant MCDM methods. Since human opinions generally contain indefiniteness, this indefiniteness should be considered in decision-making evaluations. In this paper, spherical fuzzy sets (SFSs) are used to represent human thoughts through truthiness, indecision and falsity degrees which are independent values among themselves. Proportional SFSs are the one of the extensions of SFSs, which have been shown by Kahraman [2]. In this paper, CODAS is extended with interval-valued proportional SFSs, and its steps are presented.

In order to store and oversee inventory, raw materials, or finished goods in different Konya regions, warehouse building management is a challenging task for the authorities that calls for creative and efficient distribution operations. Choosing the ideal site for warehouse facility is typically a difficult procedure, particularly when taking into account a number of parameters.Due to the costs of purchasing the property and associated improvements, the location selection problem is a long-term investment decision; as such, it should be carefully considered using a reliable method to avoid the negative consequences that follow bad decisions. To handle the inherent complexity and numerous ambiguities in real-world circumstances, the problems underlying warehouse management require the application of appropriate multi-criteria decision-making techniques. The goal of the present study is to develop an integrated spherical fuzzy rough number based -Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method. Through the spherical fuzzy rough ideal and anti-ideal solutions, the spherical fuzzy rough-MARCOS technique yields the reference points and allows for a more accurate assessment of alternatives via utility degree. The study’s emphasis on appropriately evaluating the criterion weights utilizing the spherical fuzzy rough -Stepwise Weight Assessment Ratio Analysis (SWARA) algorithm to ascertain the viability of the decisions and reduce the decision-makers’ personal impact. To address ambiguity and imprecision in expert assessment, the spherical fuzzy rough numbers are employed throughout the examination process. To demonstrate how the recommended decision-making methodology may be applied to select the ideal location for warehouses, a case study of Konya is resolved. Our proposed method is compared with two current multi-criteria decision-making techniques, namely the spherical fuzzy rough-TOPSIS method and spherical fuzzy rough-WASPAS, in order to confirm the consistency of results. Sensitivity analysis is applied to verify if the recommended method’s computations are appropriate.

The rapid evolution of artificial intelligence (AI) has introduced novel opportunities and challenges in various fields. In this study, we present a pioneering approach known as Continuous Intuitionistic Fuzzy (CINFU) Evaluation based on Distance from Average Solution (EDAS), an innovative extension of the EDAS method tailored to Continuous Intuitionistic Fuzzy Sets. This methodology is designed to compare the performance of AI tools. The capabilities of AI bots have been examined through their success rates in various tasks and uncertainty levels in decision-making processes. The study aims to evaluate the effectiveness of different models in decision-making processes by analyzing the performances of AI bots such as Chat Generative Pre-trained Transformer (ChatGPT), Bard, and Claude based on both objective measurements and fuzzy evaluation criteria. The comparison focuses on key performance criteria such as Bots Triggered, User Engagement, Message Click-Through Rate, Chat Handoff, User Retention, Bounce Rate & Dwell Time, Leads Captured, and Customer Satisfaction Score. Ultimately, the validity and robustness of the approach have been tested with sensitivity analysis.