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The difficulty of scheduling jobs or workloads increases due to the stochastic and transient characteristics of the cloud network. As a key prerequisite for establishing QoS, it asserts that effective work scheduling must be developed and executed. Maximum profit is made possible for cloud service providers by proper resource management. The most effective scheduling algorithm considers resources given by providers rather than the task set that users have accumulated. This paper developed a model that works in a two-level hierarchical model comprising global scheduling and local schedules to handle the heterogeneous type of request in real-time. These two levels of scheduling communicate with each other to produce an optimal scheduling scheme. Initially, all the requests are passed to the global scheduler, whose task is to categorize the type of request and pass it to the corresponding queue for assigning it to the related local scheduler using a parabolic intuitionistic fuzzy scheduler. In this work, the heterogeneous types of files are handled by maintaining different queues, in which each queue handles only a specific type of file like text doc, audio, image and video. Once the type of req is initiated by the clients, the global scheduler identifies the type of request and passes it to their relevant queue. In the next level, the local scheduler is assigned to each type of web server cluster. Once the work request is dispatched from the global workload scheduler, it is allocated to the local queue of the local scheduler, which allocates the resources of web servers by adapting the Quantum Honey Badger Algorithm, which searches the best-suited server for completing the assigned work based on the available resource parameters. [ABSTRACT FROM AUTHOR]

Intuitionistic fuzzy twin support vector machines (IFTWSVM) combined the concept of intuitionistic fuzzy sets with twin support vector machines (TWSVM) and showed excellent performance in classification. However, the existing intuitionistic fuzzy number schemes based on the single center and the local neighborhood of the sample are difficult to accurately reflect the location information of the sample, and the L1-norm penalty of the slack variable is not well defined from the point of view of geometric points. In view of the above deficiencies, we design a noval intuitionistic fuzzy number scheme and adopt elastic net to penalize the slack variables, propose Affinity-Based Elastic Net Intuitionistic Fuzzy Twin Support Vector Machines (AENIFTWSVM). It calculates the affinity of different classes of samples according to the Support Vector Data Description (SVDD) model in the kernel space, and considers the contribution of samples to the two classes, and the obtained affinity can be used to identify noise information. A series of experimental outcomes on benchmark datasets and handwritten digit dataset support that the proposed model outperforms some existing models. [ABSTRACT FROM AUTHOR]

Over the last decade, the emergence of various risks and hazards threatening large and infrastructure projects has become a concern for governments and governmentaffliated institutions. Thereby, Build-Operation-Transfer (BOT) contracts have been set up to prevent these risks at the national level, leaving the design, construction, and operation of the projects with all investments to a foreign or private company. Meanwhile, a multi-phase weighting based collective criteria approach and Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology are proposed under Dynamic Intuitionistic Fuzzy (DIF) set environment. In this respect, the weight of each criterion is determined by DIF-collective criteria method and also, the interdependent relation between criteria is computed by developed DIF-DEMATEL methodology to tune the criteria weights. In addition, the weights of experts are determined by the proposed DIFutility degree method to reach a precise solution. Moreover, the proposed approach of this study is extended based on last aggregation concept to reduce data loss. Then, the proposed methodology is implemented on a real case study and the results are compared with recent fndings in the literature to ensure the reliability of the obtained results. Finally, a sensitivity analysis is manipulated to find the most effective parameters that can change the obtained results. [ABSTRACT FROM AUTHOR]

In case of formulating more realistic inventory models, two factors of the problem have been raising attention of the researchers, one being the change in the demand rate and the other being the different types of trade credits. Keeping in view of these, this study includes an exponential demand rate, where demand is proportional to the derivative of inventory level, under the single level trade credit, which attracts more customers, leading to minimization of total cost. Optimum result for the time cycle and minimum total cost corresponding to the inventory model problems in crisp as well as cloudy and intuitionistic fuzzy situations are obtained by taking a suitable numerical example and the obtained results are presented in graphical form. Sensitivity analysis is carried out corresponding to some key parameters involved in the model problem using the Matlab-15 software and finally it ends with conclusion and future scope for research. [ABSTRACT FROM AUTHOR]

Many agile projects use expert judgment-based methods for estimating effort. Commonly, the judgments made during estimating project features are consensual. However, this will hardly be achieved when a conflict arises between estimators. Besides, estimate depends on the experience and skills of the estimator and could be threatened by his uncertainty to make reliable and accurate assessments. To fill these gaps, an intuitionistic fuzzy expert judgment method is proposed. The latter allows making fuzzified assessments and integrates estimators' priorities according to a set of human factors. As well, it provides consensual estimates either by the end of the estimation rounds or automatically using an iterative algorithm. On the other hand, an initial empirical study has been conducted on an agile project in which user stories have been estimated by students and experts. The first findings have revealed that the proposal is more suitable for inexperienced estimators or in the first sprints of the project where disagreement is still significant. Nevertheless, when group agreement is increased during the estimating process, the proposal maintains a null bias toward the overestimation or the underestimation of the user stories. [ABSTRACT FROM AUTHOR]

With increasing concerns about climate change and resource-environmental limitations, the green development of the mining industry has become mainstream and gained much support. Driven by the concept of sustainable and green development, China has made the advancement of green mine construction a crucial part of establishing an eco-society and has put forward the overall goal of green mines. An important future strategy is to evaluate a large number of mines. However, developing scientific, reliable, and comprehensive index systems and evaluation methods is extremely difficult because of the objective complexity of green mine evaluation and the fuzziness of some indicators. The kernel method and intuitionistic fuzzy set can effectively handle these problems. This study proposed a comprehensive evaluation index system and a hybrid evaluation method based on the kernel distance measure and intuitionistic fuzzy TOPSIS method. The index system contains 22 indicators considering six aspects: mining area environment, resource development mode, comprehensive utilization of resources, energy saving and emission reduction, technical innovation, and corporation management. The hybrid evaluation method was applied to the practical assessment of ten green mines in Panxi, China. Comparative analyses were carried out to demonstrate its applicability and sensitivity. The results verify that the hybrid method can fully depict the construction achievements of green mines in all aspects with strong reliability and stability. This approach is a valuable reference for evaluators and decision-makers in government departments. [ABSTRACT FROM AUTHOR]

This study aims to propose an integrated approach for supplier selection based on the lean, agile, resilience, green, and sustainable (LARGS) paradigm. This approach was validated using structural equation modelling (SEM) and the intuitionistic fuzzy TOPSIS method. A comprehensive literature review was conducted, identifying twenty-six criteria, which were then consolidated into five main criteria. A questionnaire was distributed to 237 individuals from manufacturing companies listed on the Tehran Stock Exchange, and the collected data were analyzed using third-order factor analysis and the partial least squares method. Subsequently, the proposed integrated approach was applied to evaluate four suppliers in an intuitionistic fuzzy environment, utilizing expert opinions and a case study on the automotive industry. The results demonstrate the effectiveness and practicality of the proposed approach in terms of prioritizing and selecting suitable suppliers according to LARGS criteria. In conclusion, this study contributes to the existing literature by proposing an integrated approach that addresses the decision-making challenges in supplier selection. This approach offers a practical tool for managers seeking to enhance sustainable supply chain performance from the LARGS perspective. [ABSTRACT FROM AUTHOR]

The multi-energy complementary ecosystem is an important form of the modern energy system. However, standardized evaluation criteria and the corresponding method framework have not yet been formed, resulting in unclear standards and irregular processes of its construction. To cope with this issue, a novel comprehensive evaluation framework for multi-energy complementary ecosystems is proposed in this study. First, a 5D comprehensive evaluation criteria system, including environment, economy, technology, safety and systematicness, is constructed. Then, a novel multicriteria decision-making model integrating an analytic network process, entropy and preference-ranking organization method for enrichment evaluation under an intuitional fuzzy environment is proposed. Finally, four practical cases are used for model testing and empirical analysis. The results of the research show that the unit cost of the energy supply and the internal rate of return indexes have the highest weights of 0.142 and 0.010, respectively. It means that they are the focus in the construction of a multi-energy complementary ecosystem. The net flows of four cases are 0.015, 0.123, –0.132 and –0.005, indicating that cases with a variety of energy supply forms and using intelligent management and control platforms to achieve cold, heat and electrical coupling have more advantages. [ABSTRACT FROM AUTHOR]

Edge detection is a vital aspect of medical image processing, playing a key role in delineating borders and contours within images. This capability is instrumental for various applications, including segmentation, feature extraction, and diagnostic procedures in the realm of medical imaging. COVID-19 is a deadly disease affecting people in most of countries in the world. COVID-19 is due to the coronavirus which belongs to the family of RNA viruses and causes various symptoms such as pneumonia, fever, breathing difficulty, and lung infection. ROI extraction plays a vital role in disease diagnosis and therapeutic treatment. CT scans can help detect abnormalities in the lungs that are characteristic of COVID-19, such as ground-glass opacities and consolidation. This research work proposes an Intuitionistic fuzzy (IF) edge detector for the segmentation of COVID-19 CT images. Intuitionistic fuzzy sets go beyond conventional fuzzy sets by incorporating an additional parameter, referred to as the hesitation degree or non-membership degree. This extra parameter enhances the ability to represent uncertainty more intricately in expressing the degree to which an element may or may not belong to a set. The IF edge detector generates proficient results, when compared with the traditional edge detection algorithms and is validated in terms of performance metrics for benchmark images. Intuitionistic fuzzy edge detection has been shown to be effective in handling uncertainty and imprecision in edge detection.

The development of a country cannot be realized only through the amount of energy it produces and its industrialization. In a country where its people are left homeless and poor, and its cultural and natural riches are destroyed, the electricity produced is not a measure of development on its own. Development and progress must be considered from a holistic perspective that includes the country's geographical structure, all its living creatures, culture, urban and social structure as a whole. In this respect, the transition to renewable energy is imperative. One of the most widely used renewable energies in the Netherlands is solar and wind energy. For these power plants, site selection is an important factor in reducing the installation cost of the wind and solar power plant and achieving maximum efficiency during operation. This paves the way for the study of a site selection problem. In this study, we first investigate possible locations for solar-wind power plant installation for 12 regions of the Netherlands, namely Noord Holland, Gelderland, Friesland, North Brabant, Drenthe, Groningen, Zeeland (Middelburg), Utrecht, Zuid Holland, Limburg, Over Ijssel and Flevoland, using GIS as a mapping method, and then apply a Intuitionistic fuzzy-based approach to the problem to obtain the optimal locations for both solar and wind energy. Furthermore, the results of two methods (GIS and Intuitionistic fuzzy-based approach) are compared to obtain more accurate results. The results show that 35317.2 km2 is suitable for solar power plant and 34844.5 km2 is suitable for wind turbine, but only 34875.8 km2 is suitable for solar-wind power plan installation.

The decision-making process is characterized by some doubt or hesitation due to the existence of uncertainty among some objectives or criteria. In this sense, it is quite difficult for decision maker(s) to reach the precise/exact solutions for these objectives. In this study, a novel approach based on integrating the technique for order preference by similarity to ideal solution (TOPSIS) with the intuitionistic fuzzy set (IFS), named TOPSIS-IFS, for solving a multi-criterion optimization problem (MCOP) is proposed. In this context, the TOPSIS-IFS operates with two phases to reach the best compromise solution (BCS). First, the TOPSIS approach aims to characterize the conflicting natures among objectives by reducing these objectives into only two objectives. Second, IFS is incorporated to obtain the solution model under the concept of indeterminacy degree by defining two membership functions for each objective (i.e., satisfaction degree, dissatisfaction degree). The IFS can provide an effective framework that reflects the reality contained in any decision-making process. The proposed TOPSIS-IFS approach is validated by carrying out an illustrative example. The obtained solution by the approach is superior to those existing in the literature. Also, the TOPSIS-IFS approach has been investigated through solving the multi-objective transportation problem (MOTP) as a practical problem. Furthermore, impacts of IFS parameters are analyzed based on Taguchi method to demonstrate their effects on the BCS. Finally, this integration depicts a new philosophy in the mathematical programming field due to its interesting principles.

Data sharing to the multiple organizations are essential for analysis in many situations. The shared data contains the individual's private and sensitive information and results in privacy breach. To overcome the privacy challenges, privacy preserving data mining (PPDM) has progressed as a solution. This work addresses the problem of PPDM by proposing statistical transformation with intuitionistic fuzzy (STIF) algorithm for data perturbation. The STIF algorithm contains statistical methods weight of evidence, information value and intuitionistic fuzzy Gaussian membership function. The STIF algorithm is applied on three benchmark datasets adult income, bank marketing and lung cancer. The classifier models decision tree, random forest, extreme gradient boost and support vector machines are used for accuracy and performance analysis. The results show that the STIF algorithm achieves 99% of accuracy for adult income dataset and 100% accuracy for both bank marketing and lung cancer datasets. Further, the results highlights that the STIF algorithm outperforms in data perturbation capacity and privacy preserving capacity than the state-of-art algorithms without any information loss on both numerical and categorical data. [ABSTRACT FROM AUTHOR]

In the field of multi-attribute group decision-making (MAGDM), "decision-making trial and evaluation laboratory" (DEMATEL) is a popular method that has been deployed to discover hidden relationships among many inter-twined factors of numerous real-world problems. This paper discusses several limitations of the DEMATEL method in mining the causal relationships and ranking the factors. Also, in this paper, we develop a novel decision-making framework that will be able to overcome the issues found in the DEMATEL method and tackle both formal and informal decisions. This model, which we call "Causal Relationship and Ranking Technique (CRRT)", uses a data-deterministic expert weighing setup rather than assigning equal weights or pre-defined weights to each expert. The model proposes a better technique to mine factor inter-relations and proposes two new types of digraphs portraying the causal inter-relationships among the factors, and also proposes a new two-step factor ranking method based on the derived causal relationships. The intuitionistic fuzzy setting is considered in this work in order to capture the uncertainty of expert opinions. Furthermore, our proposed framework is applied to a real-life case study on discovering various factors of a high school administration, that have a significant influence on students' performance. Comparison with standard methods such as TOPSIS and VIKOR for student performance data and also some other datasets such as supplier selection data, and sustainability data proves that the model is able to rank the factors better than the existing models. [ABSTRACT FROM AUTHOR]

The decision-making process is characterized by some doubt or hesitation due to the existence of uncertainty among some objectives or criteria. In this sense, it is quite difficult for decision maker(s) to reach the precise/exact solutions for these objectives. In this study, a novel approach based on integrating the technique for order preference by similarity to ideal solution (TOPSIS) with the intuitionistic fuzzy set (IFS), named TOPSIS-IFS, for solving a multi-criterion optimization problem (MCOP) is proposed. In this context, the TOPSIS-IFS operates with two phases to reach the best compromise solution (BCS). First, the TOPSIS approach aims to characterize the conflicting natures among objectives by reducing these objectives into only two objectives. Second, IFS is incorporated to obtain the solution model under the concept of indeterminacy degree by defining two membership functions for each objective (i.e., satisfaction degree, dissatisfaction degree). The IFS can provide an effective framework that reflects the reality contained in any decision-making process. The proposed TOPSIS-IFS approach is validated by carrying out an illustrative example. The obtained solution by the approach is superior to those existing in the literature. Also, the TOPSIS-IFS approach has been investigated through solving the multi-objective transportation problem (MOTP) as a practical problem. Furthermore, impacts of IFS parameters are analyzed based on Taguchi method to demonstrate their effects on the BCS. Finally, this integration depicts a new philosophy in the mathematical programming field due to its interesting principles. [ABSTRACT FROM AUTHOR]

In the present era, the most delicate environmental issue is global warming, and because of this, countries across the globe are trying to manage the most hazardous emissions by making certain investments in projects to promote green industrial practices. The proposed study creates sustainable deteriorating inventory models in both crisp and fuzzy environments, with both cloudy and intuitionistic fuzzy considerations, where the demand is taken to be time-dependent. In the current study, the emission of CO2 from transportation is controlled by the optimum investments in green technology (GT). This work develops the previous research that has worked on a sustainable inventory system with controllable greenhouse facilities through green investment. The present research includes an optimum GT investment in an inventory system with two warehouses to restrict carbon emissions due to the transportation of goods from owned to rented warehouses and then to customers. To have control over the total cost, this work considers two warehouses to manage the stock-out conditions and represents models with shortages for crisp, cloudy fuzzy (CF), and intuitionistic fuzzy (IF) environments. A multiple prepayment option for the purchasing cost involving an installment is provided to the retailers. In the present research, we develop non-linear crisp and fuzzy deteriorating inventory models and suggest an algorithm for the solution process. Model problems are illustrated through numerical examples and validated through sensitivity analysis. A comparative inquiry is conducted for the optimum results obtained in all three cases. [ABSTRACT FROM AUTHOR]

Intuitionistic fuzzy (IF) theory and evidence reasoning (ER) are two commonly used tools in the decision-making process. They have their own advantages and disadvantages in information expression and information aggregation. How to use these two methods' strengths to make up for each other's shortcomings in the decision-making process is a very interesting topic. We refine IF expressions of decision attributes and develop a conversion formula about membership degree and belief degree, which unifies IF theory and ER. Then, we propose a double-level multi-attribute group decision-making method based on IF theory and ER. In the new method, we study the setting and aggregation of weights under double-level attributes, and optimize the process of information aggregation. The proposed method is verified by a detailed case study, which shows its validity and stability. It is expected that this method, which combines IF theory and ER, will become a useful tool for multi-attribute group decision-making. [ABSTRACT FROM AUTHOR]

The paper aims to obtain a computational algorithm to solve a geometric Programming Problem by weighted sum method with equal priority in imprecise condition i.e. in Fuzzy, Intuitionistic Fuzzy and Neutrosophic field. A contrasting study of optimal solution among three has been prescribed to show the efficiency of this method. Numerical example and an application Gravel Box Design Problem is presented to compare different designs. Proposed method is determined by maximizing the truth and indeterminacy membership degree and minimizing the negative membership degree.

One of the challenging tasks of requirement engineering is to choose which among the set of requirements are more advantageous than others and ought to be viewed as first for execution. Years of research has established it is vital to capture, analyze and prioritize requirements, which to a great extent relies on acknowledgment acceptance of stakeholder’s requirements, concerns and criteria’s. Proposed method provides interactive support for eliciting stakeholders and developer’s initial ranking decision in collaborative manner. Intuitionistic Fuzzy Approach (IFS) is used to support stakeholders view; whereas analysis of inter relationships, requirement slicing and backtracking are used to support developers view using weighted page rank algorithm. Finally a ranking is generated in a collaborative manner to support requirement prioritization. The feasibility of the approach is illustrated through an experimental proof of concept by comparing results of proposed approach with state of art, Analytic Hierarchy Process (AHP) and Interactive Genetic Algorithm (IGA) prioritization techniques. Results of experimentation conclude that presented approach is capable of producing accurate and comparable results by handling technical constraints of dependency, tradeoffs of collaboration and scope of inclusion of initial multi-criteria preferences expressed as priority values to support reliability and robustness to errors.

In this work, several types of intuitionistic fuzzy sets, inspired by Type-2 and Type-3 concepts, are introduced. In particular, the newly proposed interval Type-3 intuitionistic sets are very interesting as they extend the opportunities of both interval Type-3 fuzzy sets and intuitionistic sets when capturing specific forms of uncertainty. A comparative study of interval Type-3 with respect to intuitionistic fuzzy is presented. Based on this comparison, a novel concept of an interval Type-3 intuitionistic fuzzy set is put forward to enhance the capabilities of modeling uncertainty with respect to the individual Type-3 and intuitionistic concepts. Basically, the secondary and tertiary membership functions are added to the intuitionistic fuzzy sets to build an interval Type-3 intuitionistic fuzzy set. An illustrative example dealing with controlling the imaging system of televisions is provided to envision the potential applicability and advantages of interval Type-3 intuitionistic fuzzy sets in real problems. For this application, the intuitionistic Type-3 approach shows the potential to outperform previous approaches in controlling this system. [ABSTRACT FROM AUTHOR]

The classification of imbalanced datasets is one of the main challenges in machine learning techniques. Support vector machine (SVM), which generates a model biased within the majority class, usually has bad performance on the minority class because this class may be considered incorrectly as noises. Moreover, datasets often include noises and outliers, and SVM cannot effectively deal with those datasets. In this paper, to defeat the aforementioned challenges, we propose intuitionistic fuzzy twin support vector machines for imbalanced data (IFTSVM-ID). The proposed method can easily handle imbalanced datasets in the presence of noises and outliers. A reasonable weighting strategy is offered to deal with imbalanced classes, and a margin-based technique is assigned to reduce the impact of noise and outliers. We formulate the linear and non-linear kernel functions to find two non-parallel hyperplanes. One real-world and thirty-two imbalanced datasets are selected to validate the performance of IFTSVM-ID. The Friedman test and the bootstrap technique with 95% confidence interval are applied to quantify the results statistically. The experimental results show that our proposed method has much better performance in comparison with other similar techniques. [ABSTRACT FROM AUTHOR]

We define weighted mean summability method of double sequences in intuitionistic fuzzy normed spaces(IFNS), and obtain necessary and sufficient Tauberian conditions under which convergence of double sequences in IFNS follows from their weighted mean summability. This study reveals also Tauberian results for some known summation methods in the special cases.

Gas sensor drift has consistently been a bottleneck in the progression of electronic noses (E-noses) systems. In contrast to target-domain-adaptive anti-drift classification algorithms, target-domain-free methods exhibit independence from target domain information, thereby possessing broader applicability. Support vector machine (SVM), as a popular target-domain-free classifier, is widely employed to solve the E-noses drift problem. However, SVM-based methods have some inherent flaws in drift resistance performance: (1) The objective of maximizing minimum margin, rendering it highly sensitive to disturbances; (2) The utilization of hinge loss, resulting in poor robustness; (3) The absence of sample reliability measurement, contributing to noise susceptibility. To tackle these problems, we present a novel twin-center intuitionistic fuzzy large margin classifier with unified pinball loss (TC-IFUPLMC), which effectively improves the anti-drift performance in E-noses system. Specifically, the model achieves strong disturbance resistance by adopting marginal mean and variance as the optimization objective. Furthermore, the unified pinball loss is utilized to measure the distance between two categories of sample sets, which essentially further enhances the robustness. Additionally, a novel twin-center based IF function is used to obtain the confidence level of each sample, which further weakens the noise susceptibility. Comparative experiments based on different sensor drift datasets demonstrate the effectiveness of the proposed model in improving the anti-drift performance. The high stability of TC-IFUPLMC is also substantiated by the parameter sensitivity analysis experiments. • An effective target-domain-free TC-IFUPLMC model is proposed. • TC-IFUPLMC is the first LDM-based model to solve electronic noses drift. • TC-IFUPLMC sensibly enhances the anti-drift performance in electronic noses system. • TC-IFUPLMC has superior disturbance resistance, robustness, and noise resistance. [ABSTRACT FROM AUTHOR]

In today's rapidly evolving and digitalizing world. reliability engineering is facing new challenges. Every product's design, testing, and efficient production must include an effective reliability programme. In this research, we use intuitionistic fuzzy set theory to provide a new approach for analyzing the fuzzy system reliability of a series and parallel system. The paper examines the reliability using various types of intuitionisue fuzzy numbers. The paper describes a method for determining the reliability of a skim-milk powder- production system. With the set of three intuitionistic Triangular Fuzzy Number (TIFN), an averaging-operator with equal weights is utilized. For demonstration purposes. a mathematical example is solved. [ABSTRACT FROM AUTHOR]

The paper aims to obtain a computational algorithm to solve a geometric Programming Problem by weighted sum method with equal priority in imprecise condition i.e. in Fuzzy, Intuitionistic Fuzzy and Neutrosophic field. A contrasting study of optimal solution among three has been prescribed to show the efficiency of this method. Numerical example and an application Gravel Box Design Problem is presented to compare different designs. Proposed method is determined by maximizing the truth and indeterminacy membership degree and minimizing the negative membership degree. [ABSTRACT FROM AUTHOR]

Regarding the high importance of university in the growth and development of a country, the efficiency of educational and research groups in universities is a vital consideration. The black box Data Envelopment Analysis (DEA) model is mathematical programming for measuring the relative efficiency of a set of Decision-Making Units (DMUs) without considering the operations of the component processes that may have misleading results. To overcome this problem, network models are recommended. This paper intends to propose a hybrid Intuitionistic Fuzzy Analytic Network Process (IFANP) and Network DEA (NDEA) technique to evaluate the efficiency of the Faculty of Basic Sciences of Islamic Azad University. IFANP was used to evaluate the overall weights among all the criteria and sub-criteria and the weights were in turn used in the NDEA model to measure the relative efficiency. A hypothetical example showed that the efficiency of all DMUs was equal to 1 by using the DEA and there was no ranking among the DMUs. The results of the IFANP-NDEA could be more meaningful with full ranking of the DMUs considering the component process operations. Finally, the model could prioritize the efficient DMUs and determine the efficiencies of the DMUs' functions. This model enables managers to identify the areas of weakness in the subject under their study. [ABSTRACT FROM AUTHOR]

We recall and improve our 2019 and 2020 concepts of n-SuperHyperGraph, Plithogenic nSuperHyperGraph, n-Power Set of a Set, and we present some application from the real world. The nSuperHyperGraph is the most general form of graph today and it is able to describe the complex reality we live in, by using n-SuperVertices (groups of groups of groups etc.) and nSuperHyperEdges (edges connecting groups of groups of groups etc.).

One of the challenging tasks of requirement engineering is to choose which among the set of requirements are more advantageous than others and ought to be viewed as first for execution. Years of research has established it is vital to capture, analyze and prioritize requirements, which to a great extent relies on acknowledgment acceptance of stakeholder's requirements, concerns and criteria's. Proposed method provides interactive support for eliciting stakeholders and developer's initial ranking decision in collaborative manner. Intuitionistic Fuzzy Approach (IFS) is used to support stakeholders view; whereas analysis of inter relationships, requirement slicing and backtracking are used to support developers view using weighted page rank algorithm. Finally a ranking is generated in a collaborative manner to support requirement prioritization. The feasibility of the approach is illustrated through an experimental proof of concept by comparing results of proposed approach with state of art, Analytic Hierarchy Process (AHP) and Interactive Genetic Algorithm (IGA) prioritization techniques. Results of experimentation conclude that presented approach is capable of producing accurate and comparable results by handling technical constraints of dependency, tradeoffs of collaboration and scope of inclusion of initial multi-criteria preferences expressed as priority values to support reliability and robustness to errors. [ABSTRACT FROM AUTHOR]

As a new method of granular computing, the three-way decision (3WD) approach has unique advantages in handling uncertain and imprecise problems. Based on decision-theoretic rough sets (DTRSs) and Bayesian minimum risk theory, conditional probability and loss function are the key research issues in 3WD. Many approaches for handling deterministic and complete information have been developed. However, few studies have focused on the construction of an intuitionistic fuzzy three-way decision (IF3WD) model for an intuitionistic fuzzy incomplete information system (IFIIS). In this paper, an IF3WD model based on an IFIIS is proposed to improve the ability to process complex fuzzy incomplete information systems, which extends the application range of the traditional 3WD. Concretely, we first propose a calculation method to measure the degree of information retention of missing data and describe it in two dimensions: coarse-grained and fine-grained. Next, an intuitionistic fuzzy number approximation (IFNA) strategy for missing data is presented. Then, a loss function with three states is given. Furthermore, combined with the Choquet integral, the interaction and influence between acceptance, rejection, and delay decision costs are investigated, and the corresponding IF3WD rules are induced. Finally, the rationality and effectiveness of our proposed model are verified through case analysis and are compared with those of existing methods. [ABSTRACT FROM AUTHOR]

Generalized eigenvalue proximal support vector machine (GEPSVM) has attracted widespread attention due to its simple architecture, rapid execution, and commendable performance. GEPSVM gives equal significance to all samples, thereby diminishing its robustness and efficacy when confronted with real-world datasets containing noise and outliers. In order to reduce the impact of noises and outliers, we propose a novel intuitionistic fuzzy generalized eigenvalue proximal support vector machine (IF-GEPSVM). The proposed IF-GEPSVM assigns the intuitionistic fuzzy score to each training sample based on its location and surroundings in the high-dimensional feature space by using a kernel function. The solution of the IF-GEPSVM optimization problem is obtained by solving a generalized eigenvalue problem. Further, we propose an intuitionistic fuzzy improved generalized eigenvalue proximal support vector machine (IF-IGEPSVM) by solving standard eigenvalue decomposition resulting in simpler optimization problems with less computation cost which leads to an efficient intuitionistic fuzzy-based model. We conduct a comprehensive evaluation of the proposed IF-GEPSVM and IF-IGEPSVM models on UCI and KEEL benchmark datasets. Moreover, to evaluate the robustness of the proposed IF-GEPSVM and IF-IGEPSVM models, label noise is introduced into some UCI and KEEL datasets. The experimental findings showcase the superior generalization performance of the proposed IF-GEPSVM and IF-IGEPSVM models when compared to the existing baseline models, both with and without label noise. Our experimental results, supported by rigorous statistical analyses, confirm the superior generalization abilities of the proposed IF-GEPSVM and IF-IGEPSVM models over the baseline models. Furthermore, we implement the proposed IF-GEPSVM and IF-IGEPSVM models on the USPS recognition dataset, yielding promising results that underscore the models' effectiveness in practical and real-world applications. The source code of the proposed IF-GEPSVM and IF-IGEPSVM models are available at https://github.com/mtanveer1/IF-GEPSVM. [ABSTRACT FROM AUTHOR]

A shortage of chemical cargo on tanker ships refers to a situation where the declared cargo amount by the shipper is lower than the declared terminal cargo amount, or the amount specified in the Bill of Lading. As the chemical cargoes are high purity and expensive products, any shortage claims may cause high commercial loss even in small amounts. Despite the chemical transportation industry conducting some technical researches on shortage prevention, no systematic, academic and methodological study has yet been proposed which is capable of comprehensively revealing the underlying causes, their contributions to the risk of shortage. In this study, contribution of all human factor-related structural, technical and operational root causes to risk of shortage are identified. Their failure probabilities are determined by utilising Intuitionistic Fuzzy Integrated Fault Tree Analysis (IFFTA). Accordingly, the probabilistic risk of chemical cargo shortage is obtained and found to be 0.0731 and this number is validated with two different methods. It is found that direct or indirect human factors lead to cargo shortages, accounting for approximately 84% of all factors. It is believed that the proposed model provides an effective solution to mitigate the related risk by systematically revealing notable underlying human related causes. • 86% of the top 50 MCSs are found to be directly or indirectly related to human factor. • Contribution of MCSs caused by human factor to chemical cargo shortage incident is 0.842. • Continuous training and evaluation system will be the most cost-effective solution to reduce the risk of cargo shortage. [ABSTRACT FROM AUTHOR]