An analytical framework for enhancing hospital pharmacy supply chain performance using fuzzy rough set theory

Efficiency of decision rule sets in fuzzy rough set theory

To find reasonable solutions for complex issues, multi-attribute group decision-making is an essential method that considers relevant attributes. For this purpose, the Schweizer–Sklar t-norms and t-conorms offer flexible and effective aggregation operators. Meanwhile, prioritized aggregation operators integrate critical information from available data to further enhance decision-making. To address uncertainty and imprecision in decision-making, in this script, we explore the spherical fuzzy rough set theory. Motivated by the utility of the Schweizer–Sklar t-norms and t-conorms, we propose a range of novel aggregation operators specifically designed for spherical fuzzy rough values, including the spherical fuzzy rough Schweizer–Sklar weighted averaging and spherical fuzzy rough Schweizer–Sklar weighted geometric operators. We examine the fundamental properties of the proposed operators in detail and demonstrate how multi-attribute group decision-making can benefit from them. A numerical example in agricultural management systems is provided to illustrate how to select the best alternative based on the given criteria. Finally, we compare the outcomes obtained using these newly postulated operators with those derived from existing studies in the literature to validate the effectiveness and practicality of the designed approaches.

This study proposes a novel methodology for solving constrained bi-matrix games with payoffs represented by fuzzy rough numbers, addressing uncertainties common in real-world decision-making. By integrating fuzzy rough set theory with α-cut techniques, the method establishes the existence of an fuzzy rough equilibrium value. Five linear programming models are developed to compute the mean equilibrium and its lower-lower, lower-upper, upper-lower, and upper-upper bounds, based on 0-cut and 1-cut representations. For any confidence level α, corresponding equilibrium bounds are determined through α-cut-based optimization. The methodology is validated through a case study on corporate environmental behavior, demonstrating its effectiveness and practical relevance.

This study investigates the determinants of superior long-term business performance in Thai digital entrepreneurship through an innovative mixed-method approach combining Rough Set Fuzzy Theory and Second-order Confirmatory Factor Analysis. This research addresses a significant gap in the existing literature by incorporating business strategies, product innovation, social media adoption, and entrepreneurial orientation into a comprehensive framework, extending beyond traditional Technology–Organization–Environment (TOE) models. This study analyzes seven key factors that influence digital business success: technology, organization, external environment, social media adoption, business strategy, product innovation, and entrepreneurial orientation. The methodological approach employed for this study utilized expert consensus validation and model verification techniques to develop a novel integrated model specifically tailored for Thailand’s digital SME context. The findings reveal that business strategy and entrepreneurial orientation are primary drivers of business success. This research provides valuable insights for practitioners in the Thai digital entrepreneurship ecosystem, offering a structured approach to achieving sustainable long-term business success.

Existing feature selection methods mainly target single-label learning and multi-label learning, and only a few algorithms are optimized for label distribution learning. In label distribution learning, the associated labels of each sample have different levels of importance. Therefore, multi-label feature selection algorithms cannot be directly applied to label distribution learning. Discretizing label distribution data into multi-label data will cause part of the supervision information to be lost. In most practical applications of label distribution learning, the feature space is undefined, and the features are in the form of flow features. To solve this problem, this paper applies fuzzy rough set theory and applies the flow feature framework to propose a dynamic label distribution feature selection algorithm that handles flow features. Experimental results show that the proposed method is more effective than six state-of-the-art feature selection algorithms on 12 datasets with respect to six representative evaluation metrics.

Drug discovery and development is a challenging and time-consuming process. Laboratory experiments conducted on Vidarabine showed IC50 6.97 µg∕mL, 25.78 µg∕mL, and ˃ 100 µg∕mL against non-small Lung cancer (A-549), Human Melanoma (A-375), and Human epidermoid Skin carcinoma (skin/epidermis) (A-431) respectively. To address these challenges, this paper presents an Artificial Intelligence (AI) model that combines the capabilities of Deep Learning (DL) to identify potential new drug candidates, Fuzzy Rough Set (FRS) theory to determine the most important chemical compound features, Explainable Artificial Intelligence (XAI) to explain the features’ importance in the last layer, and medicinal chemistry to rediscover anticancer drugs based on natural products like Vidarabine. The proposed model aims to identify potential new drug candidates. By analyzing the results from laboratory experiments on Vidarabine, the model identifies Sulfur and magnesium oxide (MgO) as new potential anticancer agents. The proposed model selected Sulfur and MgO based on Interpreting their promising features, and further laboratory experiments were conducted to validate the model’s predictions. The results demonstrated that, while Vidarabine was inactive against the A-431 cell line (IC50 ˃ 100 µg∕mL), Sulfur and MgO exhibited significant anticancer activity (IC50 4.55 and 17.29 µg/ml respectively). Sulfur displayed strong activity against A-549 and A-375 cell lines (IC50 3.06 and 1.86 µg/ml respectively) better than Vidarabine (IC50 6.97 and 25.78 µg/ml respectively). However, MgO showed weaker activity against these two cell lines. This paper emphasizes the importance of uncovering hidden chemical features that may not be discernible without the assistance of AI. This highlights the ability of AI to discover novel compounds with therapeutic potential, which can significantly impact the field of drug discovery. The promising anticancer activity exhibited by Sulfur and MgO warrants further preclinical studies.

A New Modeling to Feature Selection Based on the Fuzzy Rough Set Theory in Normal and Optimistic States on Hybrid Information Systems

Analysing climate change is challenging due to climate data’s intricate and dynamic nature. The primary issue is starting with high dimensionality. High dimensionality impacts the model’s performance, computation time, cost, and accuracy. Feature selection can be employed as a strategy to address the issue of dimensionality reduction, resulting in more precise insights and the identification of more explicit patterns. Various techniques are used for feature selection. Still, there is scope for progress in this field. This study uses fuzzy rough set theory (FRST) to perform feature selection in the analysis of climatic data. The dataset in the present study, obtained from Kaggle, is an authentic climate change dataset in the real world. FRST effectively addresses uncertainty and vagueness in climate data by identifying the most relevant temperature parameters and treating them as the deciding attribute. We identified 25 reducts from the original dataset using FRST. Compared to the original dataset, the best reducts had good classification accuracy. It indicates that FRST reducts preserve the essential features of the original climate data, assuring the reduced dataset’s integrity and relevance. FRST was more accurate than usual climate data analysis methods, proving its efficacy.

FRRI: A novel algorithm for fuzzy-rough rule induction

This study uses fuzzy–rough analysis to investigate the influence of Environmental, Social, and Governance (ESG) ratings, along with critical financial and growth ratios, on the stock returns of blue-chip companies in Taiwan. The growing importance of ESG factors in investment decisions underscores the need to understand their impact on stock performance. By integrating the fuzzy–rough set theory, which accommodates uncertainty and imprecision in data, we analyze the complex relationships between ESG ratings, traditional financial metrics (such as ROE, return on equity), and stock returns. Our findings provide insights into how ESG considerations, alongside financial indicators, drive the returns of Taiwan’s blue-chip stocks. Three public-listed companies were evaluated using this approach, and the results are consistent with the actual stock performance. This research contributes to the field by offering a robust methodological approach to assess the nuanced effects of ESG factors on financial performance, thus aiding investors and management teams in making informed decisions.

As the complexity of power systems increases, accurate load forecasting becomes crucial. This paper proposes a method for short-term electrical load forecasting that integrates fuzzy rough set (FRS) theory and multi-kernel extreme learning machine (MKELM) to improve both the accuracy and reliability of load predictions. First, we introduce the FRS theory for pre-selecting features. Next, we use correlation analysis (CA) to get rid of redundant features and choose the most important ones as prediction targets. Second, we introduce a novel prediction model based on the multi-kernel extreme learning machine (MKELM), utilizing an enhanced differential evolution algorithm (DEA) to optimize the kernel function’s parameters and the model’s weights. This approach allows for effective adaptation to various feature subsets. Experimental results on actual power load data demonstrate that our approach achieves high accuracy and reliability in short-term load forecasting. Moreover, comparative evaluations reveal that the proposed method outperforms alternative prediction models on key metrics. ANOVA and multiple comparisons further validate the statistical significance and superiority of the proposed method.

Modeling methods for deep fuzzy inference systems based on feature selection

Imbalanced class distributions are common in real-world scenarios, including datasets with multiple labels. One widely acknowledged approach to addressing imbalanced distributions is through oversampling, a technique that both balances the class distribution and improves the effectiveness of classification models. However, when generating synthetic data for multi-label datasets, complexities arise due to the presence of multiple-label sets, which require careful placement and labeling. We propose MLCSMOTE-FRST, an algorithm for synthetic data generation based on label-specific clustering and fuzzy rough set theory. Generation ratios and dependency samples are provided by clusters specific to each label, with a focus on the overall label distribution and the distribution within each cluster. The labels are supported by intra-cluster positive samples, determined using fuzzy rough set theory, which helps to capture the consensus label set. Experimental results on multi-label datasets using four classifiers demonstrate the effectiveness of the proposed method in terms of macro-F1 and micro-F1 scores.

Consistency approximation: Incremental feature selection based on fuzzy rough set theory

In recent days researchers have tried to handle the maximum information and use those techniques and methods in which there is no chance of data loss or loss of information is minimum. The structure like fuzzy set and complex fussy set cannot discuss the upper and lower approximations. Moreover, we can observe that a fuzzy rough set cannot discuss the second dimension and in this case, there is a chance of data loss. To cover all these issues in previous ideas, the notion of a complex fuzzy rough set in Cartesian form is the demand of the day because this structure can discuss the second dimension as well as upper and lower approximations. For this purpose, in this manuscript, we have developed the theory of complex fuzzy relation and complex fuzzy rough set in Cartesian form. Moreover, we have initiated the fundamental laws for complex fuzzy rough numbers based on Frank t-norm and t-conorm. The fundamental tools that can convert the overall input into a single output are called aggregation operators (AOs). So based on the characteristics of AOs, we have defined the notion of complex fuzzy rough Frank average and complex fuzzy rough Frank geometric AOs. The utilization of the developed theory is necessary to show the importance and validity of the delivered approach. So based on developed notions, we have defined an algorithm for this purpose along with an illustrative example. We have utilized the introduced structure for the classification of AI tools for civil engineering. Moreover, the comparative analysis of the delivered approach shows the advancement of the introduced structure as compared to existing notions.

Assessing indoor positioning system: A q-spherical fuzzy rough TOPSIS analysis

A novel intuitionistic fuzzy rough instance selection and attribute reduction with kernelized intuitionistic fuzzy C-means clustering to handle imbalanced datasets

This study investigates the fluctuation in product scores of new energy vehicles (NEVs) using a combination of fuzzy rough set theory and cellular automaton. By integrating these two methodologies, we aim to provide a comprehensive understanding of how NEV product scores evolve over time. Firstly, the fuzzy rough set theory is employed to handle the uncertainty and imprecision inherent in NEV product evaluation, optimizing the selection of influential factors. Subsequently, a cellular automaton model is utilized to simulate the dynamic changes in NEV product scores, incorporating factors identified through fuzzy rough set theory. Through this combined approach, we can continuously monitor and analyze the variations in NEV product scores, enabling stakeholders to make informed decisions for improving product competitiveness and market performance. This study contributes to the advancement of methodologies for evaluating NEV products and offers insights into the dynamic nature of their competitive landscape.

Fuzzy rough entropy established in the notion of fuzzy rough set theory, which has been effectively and efficiently applied for feature selection to handle the uncertainty in real-valued datasets. Further, Fuzzy rough mutual information has been presented by integrating information entropy with fuzzy rough set to measure the importance of features. However, none of the methods till date can handle noise, uncertainty and vagueness simultaneously due to both judgement and identification, which lead to degrade the overall performances of the learning algorithms with the increment in the number of mixed valued conditional features. In the current study, these issues are tackled by presenting a novel intuitionistic fuzzy (IF) assisted mutual information concept along with IF granular structure. Initially, a hybrid IF similarity relation is introduced. Based on this relation, an IF granular structure is introduced. Then, IF rough conditional and joint entropies are established. Further, mutual information based on these concepts are discussed. Next, mathematical theorems are proved to demonstrate the validity of the given notions. Thereafter, significance of the features subset is computed by using this mutual information, and corresponding feature selection is suggested to delete the irrelevant and redundant features. The current approach effectively handles noise and subsequent uncertainty in both nominal and mixed data (including both nominal and category variables). Moreover, comprehensive experimental performances are evaluated on real-valued benchmark datasets to demonstrate the practical validation and effectiveness of the addressed technique. Finally, an application of the proposed method is exhibited to improve the prediction of phospholipidosis positive molecules. RF(h2o) produces the most effective results till date based on our proposed methodology with sensitivity, accuracy, specificity, MCC, and AUC of 86.7%, 90.1%, 93.0% , 0.808, and 0.922 respectively.