Soft Aggregation Research Articles

The integrating of environmental sustainability assessment by using bipolar complex fuzzy soft Aczel-Alsina aggregation operators with EDAS approach.

The act of responsibly engaging with the world is referred to as environmental sustainability. It entails preserving the world's ecosystems and natural resources for the benefit of present and future generations. Being forward-thinking is essential to environmental sustainability because many decisions have long-term effects on the environment. Now, environmental sustainability is dealing with other fields in their way. The environmental sustainability having up-down situations in two dimensions is worthy of investigation. Therefore, it deals in the environment of bipolar complex fuzzy soft (BCFS) Aczel-Alsina aggregation operators (AAAOs). Here, we want to use some suitable and dominant tools like evaluation based on distance from average solution (EDAS) approach. We will also handle the problem of environmental sustainability and will solve it through AAAOs with the EDAS method. Further, we take some materials of environmental sustainability and will solve it by the EDAS method with associated AOs. After this, we select the best material for its stability and loading capability.

Evaluating renewable energy projects using fuzzy bipolar soft aggregation and entropy weights

Evaluating renewable energy projects using fuzzy bipolar soft aggregation and entropy weights

Multi-perspective feature collaborative perception learning network for non-destructive detection of pavement defects

Pavement defects detection has made significant progress with the development of convolutional neural networks. Due to the topological complexity of pavement defects regions, most existing detection methods are limited to a limited number of types of defects, which results in unsatisfactory detection results for multi-classified defects. Several studies have used enhanced data to address this issue, however, few have considered the inefficiency caused by imbalanced differences between classes from the perspective of the network model itself. In light of this issue, our goal is to fill this gap by better exploring the fusion of different feature information to improve the performance of the network. As a result, we propose a multi-perspective feature collaborative perception learning network (MFCPLNet), which uses the model's contextual information to guide high-quality multi-classified defects detection and that can be adapted to various pavement conditions. Specifically, for adaptive feature extraction, an irregular perceptual extraction network (IPENet) with enhanced edge information is first designed to draw upon hybrid deformable convolution for efficient network block hybridization. Additionally, a multi-balanced collaborative learning mechanism (MCLM) consisting of auxiliary axial information and 3D feature guidance is constructed to enhance deep semantic features, improve the localization of defects in defective regions, and compensate for weak category supervision. Lastly, in the detection head, an iterative soft aggregation module has been designed to enhance scalable spatial interactions. The results of extensive testing were based on publicly available pavement defects datasets. Based on the proposed network, the mAP index reaches 29.6% and 33.8%, respectively, an increase of 8.3% and 4.6% over the baseline, which is qualitatively and quantitatively superior to mainstream methods.

Determination of the most influential robot in the medical field by utilizing the bipolar complex fuzzy soft aggregation operators

From science fiction to the hospital, robots are transforming the medical field. These robots mostly look like R2D2 from Star Wars more than humanoids, yet they are having a substantial impact on the medical field. Robots in the medical field support by releasing medical staff from repetitive chores that distract their consideration to more vital responsibilities and by dropping the danger and expenditure of medical treatment for patients. The robots can also move hazardous materials and execute accurate surgery in small spaces. Considering this influence of robots on the medical field, in this manuscript, we investigate the influence of robots on the medical field and discover which robot is more influential in the medical field. For this, the bipolar complex fuzzy soft set (BCFSS) is a substantial mathematical model that covers genuine decision-making (DM) dilemmas where the obscurity and imprecision contain a negative pole, 2nd dimension, and parameterization at one time. Moreover, in this study, we establish arithmetic and geometric aggregation operators (AOs) in the setting of BCFSS, like bipolar complex fuzzy soft-weighted arithmetic averaging (BCFSWAA) and bipolar complex fuzzy soft-weighted geometric averaging (BCFSWGA) operators. Moreover, for discovering the most influential robot, we invent a technique of DM relying on the diagnosed AOs in the environment of BCFSS, and then, we describe a numerical example (determination of the most influential robot in the medical field). To demonstrate the superiority and condescension of our work we compare it with prevailing work.

Impact of AI Techniques in Electrical Engineering With Spherical Fuzzy Soft Geometric Aggregation Operators

Impact of AI Techniques in Electrical Engineering With Spherical Fuzzy Soft Geometric Aggregation Operators

Identification of Types of Pollution That Mostly Affect the Environment by Using Picture Fuzzy Soft Aggregation Operators

Identification of Types of Pollution That Mostly Affect the Environment by Using Picture Fuzzy Soft Aggregation Operators

T-Spherical fuzzy soft rough aggregation operators and their applications in multi-criteria group decision-making

T-Spherical fuzzy soft rough aggregation operators and their applications in multi-criteria group decision-making

Q-Rung orthopair fuzzy soft Hamacher aggregation operators and their applications in multi-criteria decision making

q-Rung orthopair fuzzy soft Hamacher aggregation operators and their applications in multi-criteria decision making

A new approach to sustainable logistic processes with q-rung orthopair fuzzy soft information aggregation.

In recent years, as corporate consciousness of environmental preservation and sustainable growth has increased, the importance of sustainability marketing in the logistic process has grown. Both academics and business have increased their focus on sustainable logistics procedures. As the body of literature expands, expanding the field's knowledge requires establishing new avenues by analyzing past research critically and identifying future prospects. The concept of "q-rung orthopair fuzzy soft set" (q-ROFSS) is a new hybrid model of a q-rung orthopair fuzzy set (q-ROFS) and soft set (SS). A q-ROFSS is a novel approach to address uncertain information in terms of generalized membership grades in a broader space. The basic alluring characteristic of q-ROFS is that they provide a broader space for membership and non-membership grades whereas SS is a robust approach to address uncertain information. These models play a vital role in various fields such as decision analysis, information analysis, computational intelligence, and artificial intelligence. The main objective of this article is to construct new aggregation operators (AOs) named "q-rung orthopair fuzzy soft prioritized weighted averaging" (q-ROFSPWA) operator and "q-rung orthopair fuzzy soft prioritized weighted geometric" (q-ROFSPWG) operator for the fusion of a group of q-rung orthopair fuzzy soft numbers and to tackle complexities and difficulties in existing operators. These AOs provide more effective information fusion tools for uncertain multi-attribute decision-making problems. Additionally, it was shown that the proposed AOs have a higher power of discriminating and are less sensitive to noise when it comes to evaluating the performances of sustainable logistic providers.

Picture fuzzy soft Bonferroni mean aggregation operators and their applications

Due to more advanced features of the picture fuzzy soft set and valuable characteristics of aggregation operators, in this article, we present the notion of picture fuzzy soft Bonferroni mean aggregation operators and weighted picture fuzzy soft Bonferroni mean aggregation operators. Moreover, some basic properties of these introduced aggregation operators have been given. As cancer is one of the most rapidly increasing diseases globally. But due to different kinds of cancer diseases, it is very difficult to say which type of cancer disease is increasing rapidly. So to reduce this difficulty in the medical field, we have applied our work to medical diagnosis problems to ensure that fuzzy ideas can also help in the medical field. Also, an algorithm along with a descriptive example is established that conforms to the authenticity of initiated work. Furthermore, a comparative analysis of the introduced work has been given to show how this work is more efficient and dominant than other existing theories.

A novel study of spherical fuzzy soft Dombi aggregation operators and their applications to multicriteria decision making

In many decision-making situations, we are not restricted to two kinds of aspects, such as membership degree or nonmembership degree, and sometimes we need to include the abstinence degree (AD). However, many fuzzy set theories fail to cover issues, such as an intuitionistic fuzzy soft set, Pythagorean fuzzy soft set and q-rung orthopair fuzzy soft set. All the above notions can only consider membership degree and a nonmembership degree in their structures. The spherical fuzzy soft set compensates for these drawbacks in its structure. Moreover, the Dombi t-norm and Dombi t-conorm are the fundamental apparatuses to generalize the basic operational laws of sum and product. Therefore, in this article, based on the dominant features of spherical fuzzy soft sets and valuable features of the Dombi t-norm and Dombi t-conorm, we initially developed the basic Dombi operational laws for spherical fuzzy soft numbers. Moreover, based on these newly developed operational laws, we introduced aggregation operators called spherical fuzzy soft Dombi average (weighted, ordered weighted, hybrid) aggregation operators. We discussed the basic properties of these aggregation operators. Additionally, we have developed a multiple criteria decision making (MCDM) approach using an explanatory example via our approach to show its effective utilization. Furthermore, a comparative study of our approach shows the superiority of our introduced notions.

A model for emergency supply management under extended EDAS method and spherical hesitant fuzzy soft aggregation information

Due to the frequent occurrence of numerous emergency events that have significantly damaged society and the economy, the need for emergency decision-making has been manifest recently. It assumes a controllable function when it is critical to limit property and personal catastrophes and lessen their negative consequences on the natural and social course of events. In emergency decision-making problems, the aggregation method is crucial, especially when there are more competing criteria. Based on these factors, we first introduced some basic concepts about SHFSS, and then we introduced some new aggregation operators such as the spherical hesitant fuzzy soft weighted average, spherical hesitant fuzzy soft ordered weighted average, spherical hesitant fuzzy weighted geometric aggregation, spherical hesitant fuzzy soft ordered weighted geometric aggregation, spherical hesitant fuzzy soft hybrid average, and spherical hesitant fuzzy soft hybrid geometric aggregation operator. The characteristics of these operators are also thoroughly covered. Also, an algorithm is developed within the spherical hesitant fuzzy soft environment. Furthermore, we extend our investigation to the Evaluation based on the Distance from Average Solution method in multiple attribute group decision-making with spherical hesitant fuzzy soft averaging operators. And a numerical illustration for “supply of emergency aid in post-flooding the situation” is given to show the accuracy of the mentioned work. Then a comparison between these operators and the EDAS method is also established in order to further highlight the superiority of the established work.

Modified EDAS method for MCDM in robotic agrifarming with picture fuzzy soft Dombi aggregation operators

Modified EDAS method for MCDM in robotic agrifarming with picture fuzzy soft Dombi aggregation operators

Prioritization of thermal energy techniques by employing picture fuzzy soft power average and geometric aggregation operators

Energy storage is a way of storing energy to reduce imbalances between demand and energy production. The ability to store electricity and use it later is one of the keys to reaching large quantities of renewable energy on the grid. There are several methods to store energy such as mechanical, electrical, chemical, electrochemical, and thermal energy. Regarding their operation, storage, and cost, the choice of these energy storage techniques appears to be interesting. This issue becomes very serious when there involves uncertainty. To consider this kind of uncertain information, a picture fuzzy soft set is found to be a more appropriate parameterization tool to deal with imprecise data. Based on the advanced structure of picture fuzzy soft set, here in this article, firstly, we have developed the notions of basic operational laws for picture fuzzy soft numbers. Then based on these developed operational laws, we have established the notions of picture fuzzy soft power average left({mathrm{PFS}}_{mathrm{ft}}mathrm{PA}right), weighted picture fuzzy soft power average left({mathrm{WPFS}}_{mathrm{ft}}mathrm{PA}right) and ordered weighted picture fuzzy soft power average left({mathrm{OWPFS}}_{mathrm{ft}}mathrm{PA}right) aggregation operators. Moreover, we have introduced the notions for picture fuzzy soft power geometric left({mathrm{PFS}}_{mathrm{ft}}mathrm{PG}right), weighted picture fuzzy soft power geometric left({mathrm{WPFS}}_{mathrm{ft}}mathrm{PG}right) and ordered weighted picture fuzzy soft power geometric left({mathrm{OWPFS}}_{mathrm{ft}}mathrm{PG}right) aggregation operators. Furthermore, we have established the application of picture fuzzy soft power aggregation operators for the selection of thermal energy storage techniques. For this, we have developed a decision-making approach along with an explanatory example to show the effective use of the developed theory. Furthermore, a comparative analysis of the introduced work shows the advancement of developed notions.

New group-based generalized interval-valued q-rung orthopair fuzzy soft aggregation operators and their applications in sports decision-making problems

New group-based generalized interval-valued q-rung orthopair fuzzy soft aggregation operators and their applications in sports decision-making problems

A Low-Rank Approximation for MDPs via Moment Coupling

Markov Decision Process Tayloring for Approximation Design Optimal control problems are difficult to solve for problems on large state spaces, calling for the development of approximate solution methods. In “A Low-rank Approximation for MDPs via Moment Coupling,” Zhang and Gurvich introduce a novel framework to approximate Markov decision processes (MDPs) that stands on two pillars: (i) state aggregation, as the algorithmic infrastructure, and (ii) central-limit-theorem-type approximations, as the mathematical underpinning. The theoretical guarantees are grounded in the approximation of the Bellman equation by a partial differential equation (PDE) where, in the spirit of the central limit theorem, the transition matrix of the controlled Markov chain is reduced to its local first and second moments. Instead of solving the PDE, the algorithm introduced in the paper constructs a “sister”' (controlled) Markov chain whose two local transition moments are approximately identical with those of the focal chain. Because of this moment matching, the original chain and its sister are coupled through the PDE, facilitating optimality guarantees. Embedded into standard soft aggregation, moment matching provides a disciplined mechanism to tune the aggregation and disaggregation probabilities.

Applications of complex picture fuzzy soft power aggregation operators in multi-attribute decision making

The major theme of this analysis is to suggest a new theory in the form of complex picture fuzzy soft (CPFS) information and to initiate their major algebraic laws, score value, and accuracy values. The mathematical form of the CPFS set includes three main functions, called supporting, abstinence, and supporting against terms with a prominent characteristic that the sum of the triplet will lie in the unit interval. Further, in the consideration of the power aggregation operator using generalized t-norm and t-conorm and CPFS information, we diagnosed the mathematical concept of CPFS power averaging (CPFSPA), CPFS weighted power averaging (CPFSWPA), CPFS ordered weighted power averaging (CPFSOWPA), CPFS power geometric (CPFSPG), CPFS weighted power geometric (CPFSWPG), CPFS ordered weighted power geometric (CPFSOWPG). Moreover, the major results and their particular investigation of the invented approaches are also deliberated. Additionally, in the consideration of diagnosed operators using CPFS information, we illustrated a MADM (“multi-attribute decision-making”) tool to find the best option from the family of decisions. Finally, we have shown the supremacy and feasibility of the diagnosed operators with the help of sensitive analysis and geometrical representations.

Q-Rung orthopair fuzzy soft aggregation operators based on Dombi t-norm and t-conorm with their applications in decision making

Recently, some improvement has been made in the dominant notion of fuzzy set that is Yager investigated the generalized concept of fuzzy set, Intuitionistic fuzzy set (IFS) and Pythagorean fuzzy set (PFS) and called it q-rung orthopair fuzzy (q-ROF) set (q-ROFS). The aim of this manuscript is to present the concept of q-ROF soft (q-ROFSt) set (q-ROFStS) based on the Dombi operations. Since Dombi operational parameter possess natural flexibility with the resilience of variability. Some new operational laws are defined based on hybrid study of soft sets and q-ROFS. The advantage of Dombi operational parameter is very important to express the experts’ attitude in decision making. In this paper, we present q-ROFSt Dombi average (q-ROFSt DA) aggregation operators including q-ROFSt Dombi weighted average (q-ROFSt DWA), q-ROFSt Dombi ordered weighted average (q-ROFSt DOWA) and q-ROFSt Dombi hybrid average (q-ROFSt DHA) operators. Moreover, we investigate q-ROFSt Dombi geometric (q-ROFSt DG) aggregation operators including q-ROFSt Dombi weighted geometric (q-ROFSt DWG), q-ROFSt Dombi ordered weighted geometric (q-ROFSt DOWG), and q-ROFSt Dombi hybrid geometric (q-ROFSt DHG) operators. The basic properties of these operators are presented with detail such us Idempotency, Boundedness, Monotonicity, Shift invariance, and Homogeneity. Thus from the analysis and advantages of proposed model, it is clear that the investigated q-ROFSt DWA operator is the generalized form of IF St DWA, PFSt DWA and q-ROFDWA operators. Similarly, the investigated q-ROFSt DWG operator is the generalized form of IF St DWG, PFSt DWG and q-ROFDWG operators. By applying the develop approach, this manuscript contains the technique and algorithm for multicriteria decision making (MCDM). Further a numerical example is developed to illustrate the flexibility and applicability of the developed operators.

New MCDM Algorithms with Linear Diophantine Fuzzy Soft TOPSIS, VIKOR and Aggregation Operators

In this paper, we focus on several ideas associated with linear Diophantine fuzzy soft sets (LDFSSs) along with its algebraic structure. We provide operations on LDFSSs and their specific features, elaborating them with real-world examples and statistical depictions to construct an inflow of linguistic variables based on linear Diophantine fuzzy soft (LDFSS) information. We offer a study of LDFSSs to the multi-criteria decision-making (MCDM) process of university determination, together with new algorithms and flowcharts. We construct LDFSS-TOPSIS, LDFSS-VIKOR and the LDFSS-AO techniques as robust extensions of TOPSIS (a technique for order preferences through the ideal solution), VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) and AO (aggregation operator). We use the LDFSS-TOPSIS, LDFSS-VIKOR and LDFSS-AO techniques to solve a real-world agricultural problem. Moreover, we present a small-sized robotic agri-farming to support the proposed technique. A comparison analysis is also performed to examine the symmetry of optimal decision and to analyze the efficiency of the suggested algorithms.

Video segmentation via target objectness constraint and multi-head soft aggregation

Objectness is a notion of how a generic object looks like. Recently, matching-based methods have yielded impressive video object segmentation results. However, these methods suffer from mis-segmentation of distractors that are distant from the targets due to the lack of objectness constraint. The segmentation mask has a very low objectness when both the correct object and the distant distractor are considered as a target. Position information is also ignored in matching-based methods. In this paper, a video object segmentation method based on target objectness constraint and multi-head soft aggregation is proposed. The target objectness constraint is introduced by forcing the network to learn to predict object center and pixel-to-object boundary distances. The problem of mis-segmentation of objects or regions with similar appearance at a distance is alleviated. Besides, the target position consistency constraint is strengthened by introducing the target position information in the feature matching of adjacent frames. Furthermore, an instance attention mechanism is implemented using multi-head soft aggregation to re-calibrate channel importance. Multi-head soft aggregation enables better utilization of target feature information than global average pooling. Comprehensive experiments demonstrate that the proposed method obtains a competitive performance, surpassing many other existing methods.