Bonferroni Mean Research Articles

Decision methods based on Bonferroni mean operators and EDAS for the classifications of circular pythagorean fuzzy Meta-analysis

<p>Meta-analysis is a statistical technique used to process an overall summary estimation, and the technique of meta-analysis is mostly used in medicine, social science, and psychology. In this manuscript, we aimed to combine the techniques of the Bonferroni mean (BM) operator based on circular Pythagorean fuzzy (CPF) sets, called the CPF Bonferroni mean (CPFBM) operator, and CPF weighted Bonferroni mean (CPFWBM) operator and described their special cases with the help of two parameters, "s" and "t", and some describable properties of them are also proposed. Further, we present the evaluation technique based on distance from average solution (EDAS) technique and the proposed operators. Moreover, we use some examples to show the flexibility and dominance of the proposed operators by comparing the proposed methods with some existing techniques.</p>

A Multi-Criteria Group Decision-Making Approach For Robot Selection Using Interval-Valued Intuitionistic Fuzzy Information And Aczel-Alsina Bonferroni Means

The process of identifying the most appropriate robot for a particular industrial task has grown challenging and more difficult in the fast-paced environment. It is merely driven by the complex evolution and continuous integration of modern characteristics and advanced features by various suppliers. Industrial robots are now widely available in the marketplace, each possessing a distinctive collection of skills, attributes, and requirements. However, the selection of optimal robots is heavily influenced by factors such as the manufacturing environment, product design, production system, and overall cost considerations. These factors directly impact the decision-making process. The ultimate goal for the decision maker is to pinpoint and choose the most suitable robot, capable of delivering the desired output while minimizing costs and catering to the specific requirements of the industry. So, to consider it, in this paper, the hybrid structure of the Aczel-Alsina (AA) and Bonferroni mean (BM) operators for interval-valued intuitionistic fuzzy (IVIF) environment has been proposed, which can show the interrelationship between the multiple criteria and assist the expertise in decision-making (DM) process. Moreover, the algorithm and methodology for the multi-criteria group decision-making (MCGDM) problem have been defined which is further utilized by solving a real-world problem to demonstrate the effectiveness and validity of the proposed method. At last, the comparison analysis between prior and proposed studies has been displayed, and then followed by the conclusion of the result.

Modelling of Green Human Resource Management using Pythagorean Neutrosophic Bonferroni Mean Approach

Green Human Resource Management (GHRM) state a determination of the association using crossing points of employees to stimulate environment performance activity, increase the employee awareness and sustainable activities, consequently, increasing the employee awareness towards environmental challenges. The hotel industry is developing quickly in emerging nations owing to an upsurge in the tourism business; but, conversely, the hotel industry is mainly growing the problem of the environment. As a result, owing to the enormous amount of conservation problems that hotel business has faced, there is a growing potency to pay an accurate response to environmental problems and performing sustainable industry performance like the adoption of GHRM practice provides a win-win situation for its stakeholders and the organization. Accordingly, it indicates the requirement to scrutinize how GHRM performs will augment the environment in the hotel business. This manuscript models the design of GHRM using Pythagorean Neutrosophic Bonferroni Mean (GHRM-PNBM) approach. The presented GHRM-PNBM method objectives are to evaluate the limitation of hotel GRHM. Moreover, the presented technique constructs an expert system analysis technique for assessing the performance of hotel GHRM. Adaptive optimization of hotel GHRM assessment can be done using the PNBM technique, and the parameter selection method can be done using Quasi-Oppositional-Teaching-Learning-Based Optimization (QTLBO) method. The empirical analysis reports that the performance calculation of hotel GHRM has good confidence level and high accuracy

A q-rung orthopair fuzzy decision-making framework considering experts trust relationships and psychological behavior: An application to green supplier selection

The selection of an optimal supplier is a critical and open challenge in supply chain management. While experts' assessments significantly influence the supplier selection process, their subjective interactions can introduce unpredictable uncertainty. Existing methods have limitations in effectively representing and handling this uncertainty. The paper aims to address these challenges by proposing a novel approach that leverages q-rung orthopair fuzzy sets (q-ROFSs). The novelty of the proposed approach lies in its ability to accurately capture experts' preferences through the use of q-ROFSs, which offer membership and non-membership degrees, providing a broader expression space compared to conventional fuzzy sets. Additionally, it incorporates social network analysis (SNA) to effectively consider the trust relationships among experts. The proposed approach is divided into three stages. The first stage, presents a novel method to determine experts' weights by combining SNA, the Bayesian formula, and the maximum entropy principle. This approach allows for a more precise representation of varying levels of expertise and influence among experts, addressing the uncertainty arising from subjective interactions. The second stage introduces a hybrid weight determination method to determine criteria weights within the context of q-ROFSs. Entropy plays a crucial role in capturing fuzziness and uncertainty in q-ROFSs, while the projection measure simultaneously provides information about the distance and angle between alternatives. By employing both objective weights estimated using entropy and normalized projection measure and subjective weights derived using an aggregation operator and a score function, the presented approach achieves a comprehensive assessment of criteria importance. To incorporate both subjective and objective weights effectively, game theory is applied which allows us to align decision-making with both quantitative and qualitative aspects, making the method more versatile and applicable. The third stage redefines the traditional Combined Compromise Solution (CoCoSo) method using Bonferroni mean operators which captures interrelationships among arguments to be aggregated. Furthermore, in recognition of the importance of an expert risk preferences and psychological behaviors, we apply regret theory, ensuring that the chosen solutions align more effectively with their underlying preferences and aspirations. The applicability and effectiveness of the proposed approach are demonstrated through a numerical example of green supplier selection. The comparative analysis illustrates the practicality and real-world relevance while the sensitivity analysis, confirms the stability and robustness of the proposed approach.

Corrections to “Some T-Spherical Hesitant Fuzzy Shapley Bonferroni Mean Operators and Their Applications”

Corrections to “Some T-Spherical Hesitant Fuzzy Shapley Bonferroni Mean Operators and Their Applications”

Some T-Spherical Hesitant Fuzzy Shapley Bonferroni Mean Operators and Their Applications

Some T-Spherical Hesitant Fuzzy Shapley Bonferroni Mean Operators and Their Applications

Innovative Bonferroni Mean Product and Linear Diophantine Fuzzy Bipartite Decision Graphs With Application to Sustainable Development

Innovative Bonferroni Mean Product and Linear Diophantine Fuzzy Bipartite Decision Graphs With Application to Sustainable Development

Enhancing Digital Social Innovation Ecosystems: A Pythagorean Neutrosophic Bonferroni Mean (PNBM) -DEMATEL Analysis of Barriers Factors for Young Entrepreneurs

This study employs a Pythagorean Neutrosophic Bonferroni Mean (PNBM) - Decision Making Trial and Evaluation Laboratory (DEMATEL) approach to analyze barriers faced by young entrepreneurs in Digital Social Innovation (DSI). Pythagorean Neutrosophic Set (PNS) enriches the analysis, accommodating uncertainties in the complex socio-economic context. Limited Access to Funding emerges as the most influential barrier, showcasing its pivotal role in impacting other DSI challenges. Regulatory and Compliance Challenges are identified as interdependent, emphasizing their interconnected nature with broader barriers. Neutrosophic elements elucidate the uncertainties surrounding financial constraints, regulatory frameworks, and mentorship dynamics. The study contributes to a nuanced understanding of DSI challenges and pioneers the application of neutrosophic logic in socio-economic research. It advocates for a more inclusive decision-making methodology, fostering adaptability in addressing the indeterminate nature of barriers faced by young entrepreneurs navigating the digital social innovation landscape. The findings aim to enhance support systems, fostering a conducive environment for DSI initiatives and encouraging future research in neutrosophic decision-making methodologies.

An intelligent decision methodology for triangular Pythagorean fuzzy MADM and applications to college English teaching quality evaluation

Abstract The development of contemporary college English teaching methods and practice should respect the subjective will of college students; fully tap their wisdom and potential; constantly innovate college English teaching methods, teaching practice activities, classroom teaching, and teaching evaluation models; create a good teaching atmosphere; and establish the concept of developmental evaluation. Through the innovation of teaching methods, college students’ actual language communication ability is cultivated, the teaching objectives and teaching methods of college English reading are innovated, and interactive, body language, role play, situational teaching, communicative teaching, and other teaching methods are used. The College English teaching quality evaluation is a classical multiple attribute decision making (MADM). In this article, the triangular Pythagorean fuzzy sets (TPFSs) are introduced, and the MADM problem is investigated under TPFSs. Based on the traditional dual generalized weighted Bonferroni mean operator and power average operator, the triangular Pythagorean fuzzy dual generalized power Bonferroni mean (TPFDGPBM) operator is proposed. Accordingly, the TPFDGPBM operator is employed to construct the triangular Pythagorean fuzzy MADM method. Finally, a numerical example for College English teaching quality evaluation is constructed to verify the TPFDGPBM technique.

Smart algorithmic solutions for neutrosophic multiple-attribute decision-making and applications to chair furniture comfort design evaluation

The activities in public places are becoming increasingly frequent, and public seats have become an important component of public space and an “auxiliary tool” for work, life, and leisure. The demand for such furniture in China is gradually increasing, but the design and research work of public seats is lagging behind the development status and speed of other civil and office furniture. Usually, public seats can be divided into two parts based on their usage: public indoor seats and public outdoor seats. How to understand the role and impact of public seats on the indoor environment, and how to establish a new design method for public seats in the context of the overall environmental system, are currently urgent issues to be solved. The chair furniture comfort design evaluation is a classical MADM issues. In such paper, the generalized weighted Bonferroni mean (WBM) operator and power average (PA) is constructed for MADM with single-valued neutrosophic sets (SVNSs). Then, the generalized single-valued neutrosophic number power WBM (GSVNNPWBM) operator is built and then the MADM decision methods are proposed based on the GSVNNPWBM operator. Finally, an example about chair furniture comfort design evaluation and some comparative analysis were given to demonstrate the GSVNNPWBM method.

Interval-valued q-rung orthopair fuzzy Aczel–Alsina operations-based Bonferroni mean aggregation operators and their applications

Interval-valued q-rung orthopair fuzzy Aczel–Alsina operations-based Bonferroni mean aggregation operators and their applications

Application of fermatean fuzzy weight operators and MCDM model DIBR-DIBR II-NWBM-BM for efficiency-based selection of a complex combat system

The use of complex automated combat systems is a basic feature of every modern combat operation, and all modern armies strive to implement such systems in their units. This paper investigates the choice of a complex combat system based on the efficiency criteria that condition it. The Defining Interrelationships Between Ranked criteria (DIBR) and the Defining Interrelationships Between Ranked criteria II (DIBR II) methods were used to determine the weight coefficients of the criteria, which proved to be effective in assessing the importance of each criterion in the context of making complex decisions. Aggregation of expert opinions, for each of the methods, was performed using the Normalized Weighted Bonferroni Mean (NWBM) operator. After the weight coefficients for both methods were obtained, the aggregation of the obtained values was performed using the Bonferroni Mean (BM) operator, which resulted in the final values of the weight coefficients of the criteria. In order to choose the optimal alternative, the Fermatean Fuzzy Weight Operators (FFWO) and BM operator were used. These operators contributed to the precise evaluation and ranking of alternatives, taking into account their characteristics and specificities. Furthermore, the paper analyzed the sensitivity of the output results to changes in the weight coefficients of the criteria. This is important in order to assess the stability of the Multiple Criteria Decision Making (MCDM) model and ensure its reliability in different scenarios. This work represents a significant contribution to the field of decision-making in this context and provides a useful framework for the selection of complex combat systems, and the combination of different methods and operators enables a comprehensive analysis and optimization of the research problem.

Dual Hesitant q-Rung Orthopair Fuzzy Interaction Partitioned Bonferroni Mean Operators and Their Applications

The purpose of this paper is to introduce interaction partitioned Bonferroni mean operators under dual hesitant q-rung orthopair fuzzy environment. Motivated by the idea of q-rung orthopair fuzzy interaction operational laws, partitioned Bonferroni mean, and dual hesitant q-rung orthopair fuzzy sets, for dual hesitant q-rung orthopair fuzzy numbers, we present dual hesitant q-rung orthopair fuzzy interaction operational rules and propose several dual hesitant q-rung orthopair fuzzy interaction partitioned Bonferroni mean aggregation operators, including the interaction partitioned Bonferroni mean operator for dual hesitant q-rung orthopair fuzzy numbers, the weighted interaction partitioned Bonferroni mean operator for dual hesitant q-rung orthopair fuzzy numbers, the interaction partitioned geometric Bonferroni mean operator for dual hesitant q-rung orthopair fuzzy numbers, and the weighted interaction partitioned geometric Bonferroni mean operator for dual hesitant q-rung orthopair fuzzy numbers. Moreover, some properties and special cases associated with these proposed operators are also analyzed. For dual hesitant q-rung orthopair fuzzy numbers, based on the proposed operators, a multicriteria group decision-making method is proposed. Finally, an example for missile purchase problem is illustrated to demonstrate the superiority and feasibility by comparing with other existing multicriteria group decision-making methods.

Concise representations and limiting cases of q-rung orthopair fuzzy Hamacher–Bonferroni mean aggregations

Concise representations and limiting cases of q-rung orthopair fuzzy Hamacher–Bonferroni mean aggregations

Classification of epileptic EEG signals with the utilization of Bonferroni mean based fuzzy pattern tree

The Electroencephalogram (EEG) is an essential tool used to detect and investigate multiple neurological disorders within the human brain. However, examination and visualization of any such abnormalities in the brain through inspecting these EEG signals is a time-consuming and repetitive task for a neurologist. Hence, there is a need to develop a model that can automatically classify brain disorders based on the EEG signals. In this study, a novel fuzzy classification model is proposed for classifying the EEG signals. First, the discrete wavelet transform (DWT) technique is employed to decompose the signal into time–frequency sub-bands. Then, by utilizing these sub-bands, seven meaningful statistical features are extracted effectively for additional investigation purposes. Then, the retrieved features are provided to the fuzzy pattern tree classifier to obtain meaningful inferences from the data. Here, a distinctive structure of a fuzzy pattern tree is proposed by utilizing the interrelationship handling aggregation operators. More precisely, we utilize the Bonferroni mean (BM) aggregation operator for the enlargement or expansion of the tree while considering the concatenation between the parent and the slave tree. To the best of our knowledge, this is the first work that presents such an interrelationship handling fuzzy logic-based classifier model for EEG signal classification. The proposed algorithm is evaluated using two publicly available datasets, namely the Bonn University (Bonn) epileptic datasets and the Temple University Hospital (TUH) EEG subcorpus of abnormal (TUAB) and epilepsy (TUEP) datasets. This study considers different frames of the binary problem (i.e., healthy vs. seizure detection) and multi-class problem (i.e., healthy vs. seizure free vs. seizure) over the Bonn dataset and achieves more than 99% accuracy over the binary model by testing over all possible combinations of the available experimental dataset and 97.8% accuracy in multi-class model. In the case of TUAB and TUEP categories, the model achieves an accuracy of 88% in predicting normal to abnormal activities and 86% in detecting epilepsy and non-epilepsy within the signals. The outcomes derived from the proposed model outperform some existing studies available in the literature for EEG signal classification. Also, the model is efficient as it considers the uncertainty and association among the features while framing the classifier model.

Evaluation of autonomous vehicle applications in smart airports using Dombi Bonferroni mean operator based CIVL-BWM-TODIM decision making methodology

Recent advancements on autonomous vehicles (AVs) have revolutionized the aviation industry, providing great potential to enhance sustainability for this fuel-intensive industry. Diverse operation types of AVs at airports have emerged (e.g., for baggage/passenger movement). This study aims to provide a guideline on how to switch from the traditional human-driving vehicle operations to AV operations, to help improve the smartness and sustainability of airport operations. Five alternative operation types are evaluated under ten criteria. We propose a new CIVL-BWM-TODIM with Dombi Bonferroni mean operator (DBM) decision framework. The combination of the Continuous Interval-Valued Linguistic tool (CIVL) and the Best Worst Method (BWM) improves the criterial evaluation consistency, and the integration of CIVL and TODIM increases the evaluation accuracy on human language and psychological feelings. Besides, the DBM operator helps effectively integrate information from different experts. Results suggest that applying AVs for both functional activities and scheduled people delivery is the best alternative to build smart and sustainable airports. Sensitivity analyses are conducted to examine the impacts of various parameters on decision making. Moreover, comparative analysis over existing decision-making methods are carried out to validate the merits of the proposed approach.

Intuitionistic fuzzy normalized weighted geometric Bonferroni means with reducibility and boundedness and application in decision making1

Bonferroni mean (BM) is an important aggregation operator in decision making. The desirable characteristic of the BM is that it can capture the interrelationship between the aggregation arguments or the individual attributes. The optimized weighted geometric Bonferroni mean (OWGBM) and the generalized optimized weighted geometric Bonferroni mean (GOWGBM) proposed by Jin et al in 2016 are the extensions of the BM. However, the OWGBM and the GOWGBM have neither the reducibility nor the boundedness, which will lead to the illogical and unreasonable aggregation results and might make the wrong decision. To overcome these existing drawbacks, based on the normalized weighted Bonferroni mean (NWBM) and the GOWGBM, we propose the normalized weighted geometric Bonferroni mean (NWGBM) and the generalized normalized weighted geometric Bonferroni mean (GNWGBM), which can not only capture the interrelationship between the aggregation arguments, but also have the reducibility and the boundedness. Further, we extend the NWGBM and the GNWGBM to the intuitionistic fuzzy decision environment respectively, and develop the intuitionistic fuzzy normalized weighted geometric Bonferroni mean (IFNWGBM) and the generalized intuitionistic fuzzy normalized weighted geometric Bonferroni mean (GIFNWGBM). Subsequently, we prove some properties of these operators. Moreover, we present a new intuitionistic fuzzy decision method based on the IFNWGBM and the GIFNWGBM. Two application examples and comparisons with other existing methods are used to verify the validity of the proposed method.

Generalized Orthopair Fuzzy Weighted Power Bonferroni Mean Operator and Its Application in Decision Making

The generalized orthopair fuzzy set is more favored by decision-makers and extensively utilized in areas like supply chain management, risk investment, and pattern recognition because it offers a broader decision information boundary than the intuitionistic fuzzy set and Pythagorean fuzzy set. This enables it to express fuzzy information more comprehensively and accurately in multi-attribute decision-making problems. To this end, this paper combines the ability of the power average (PA) operator to eliminate the impact of extreme values and the advantage of the Bonferroni mean (BMs,t) operator in reflecting the relationships between variables, then incorporates weight indicators for different attributes to define the generalized orthopair fuzzy weighted power Bonferroni mean operator. The effectiveness of this operator is demonstrated through aggregation laws for generalized orthopair fuzzy information. Subsequently, the desirable properties of this operator are discussed. Based on these findings, a novel generalized orthopair fuzzy multi-attribute decision-making method, with a correlation between attributes, is proposed. Lastly, an investment decision-making example illustrates the feasibility and superiority of this method.

An extended intelligent technique for multiple-attribute decision-making under single-valued neutrosophic sets and applications to sustainable development evaluation of rural ecotourism

With the development of society and the times, the socialist modernization cause in China is constantly developing, and the improvement of the economy has also driven the development of rural ecotourism industry in China. The unique natural terrain and cultural customs in rural areas have promoted the continuous development of local ecotourism industry, which has had a positive impact on the economic development of rural areas and not only solved the employment problem of the majority of the population in rural areas. It can also bring a continuous stream of economic benefits and continuously improve the living standards of local residents. Rural ecotourism refers to a rural tourism activity that takes place in rural areas, supported by the agricultural industry, and attracted by the rural environment and typical rural ecotourism resources, with the concept of ecotourism. Rural ecotourism mainly takes the form of rural scenery, agricultural participation, and folk experience, integrating sightseeing experience, cognition, and tourism activities. The main principles of rural tourism development are agricultural support, people-oriented, ecological protection, community participation, income feedback, and ecological home construction. The sustainable development evaluation of rural ecotourism is a classical multiple-attribute decision-making (MADM) issue. In such paper, the generalized weighted Bonferroni mean (GWBM) operator and power average (PA) is constructed for MADM with single-valued neutrosophic sets (SVNSs). Then, the single-valued neutrosophic number power generalized BM (SVNNPGBM) operator is built and then the MADM decision methods are proposed based on the SVNNPGBM operator. Finally, an example for sustainable development evaluation of rural ecotourism and some comparative analysis were given to demonstrate the SVNNPGBM method.

Retracted: Generalized Intuitionistic Fuzzy Normalized Weighted Optimized Geometric Bonferroni Mean and Their Application to MADM

Retracted: Generalized Intuitionistic Fuzzy Normalized Weighted Optimized Geometric Bonferroni Mean and Their Application to MADM