Abstract Interval-valued q-rung orthopair fuzzy set (IVq-ROFS) is a powerful tool for dealing with uncertainty. In this paper, we first propose new method aggregating multiple IVq-ROFSs, which easier to understand and implement in the multi-attribute group decision making process compared current aggregation operators. Secondly, paper introduces entropy parameters based on IVq-ROFS, highly flexible due its adjustable parameters. Based this, IVq-ROFS-based attribute weight calculation...

Q-rung orthopair hesitant fuzzy set (q-ROHFS) is a potent and effective technique for dealing with more general complex uncertainty. Multiple attribute decision-making (MADM) under uncertainty has been key research issue. However in the existing MADM approaches, entropies involve much higher hesitancy degree loss measure of attributes can not be determined objectively. Also these methods have high data redundancy low computational efficiency. In order to solve problems, this paper proposes...

Interval-valued Fermatean fuzzy sets (IVFFSs) were introduced as a more effective mathematical tool for handling uncertain information in 2021. In this paper, firstly, novel score function (SCF) is proposed based on IVFFNs that can distinguish between any two IVFFNs. And then, the SCF and hybrid weighted measure used to construct new multi-attribute decision-making (MADM) method. Besides, three cases are demonstrate our method overcome disadvantages existing approaches cannot obtain...

There has been a rapid growth of interest in developing approaches that are capable dealing with imprecision and uncertainty. To this end, an interval-valued fuzzy soft set (IVFSS) combines theory proposed to handle uncertainty applications such as decision-making problems. However, there little focus on parameter reduction the sets, which is significant In paper, we introduce four different definitions sets satisfy needs decision makers. We propose heuristic algorithms reduction. Finally,...

The ineffectiveness of information retrieval systems often caused by the inaccurate use keywords in a query. In order to solve problem systems, many solutions have been proposed over years. most common techniques are revolving around query modification such as expansion, refinement, etc. Due high similarity these techniques, people confused about their differences. However, few existing survey papers compare Hence, this paper, we first briefly discuss basic technique suggestion and then make...

In blockchain, the consensus algorithm is a core component that governs trust among participants in blockchain activities. However, exiting algorithms suffer from performance bottleneck such as low throughput, high delay, unstable performance, sustainability issues and vulnerability to targeted attacks. this paper, we propose new consortium algorithm, referred Weighted Byzantine Fault Tolerance (WBFT) improves system throughput delay. We introduce dynamic weighting mechanism for nodes, which...

This study was to develop a low-cost N-doped porous biocarbon adsorbent that can directly adsorb CO2 in high-temperature flue gas from fossil fuel combustion. The prepared by nitrogen doping and nitrogen-oxygen codoping through K2CO3 activation. Results showed these samples exhibited high specific surface area of 1209-2307 m2/g with pore volume 0.492-0.868 cm3/g content 0.41-3.3 wt %. optimized sample CNNK-1 adsorption capacity 1.30 0.27 mmol/g the simulated (14.4 vol % + 85.6 N2) CO2/N2...

In a typical formulation of decision-making under uncertainty, decision-maker must choose single-optimal option among many possible options. However, the problem selecting unique and optimal choice has remained significant challenge to solve. this article, we propose new interval-valued intuitionistic fuzzy soft set (IVIFSS) based approach address problem. The proposed is on value score membership/nonmembership degrees. Furthermore, three parameter reduction algorithms are proposed. We apply...

Abstract Hesitant Fermatean fuzzy sets (HFFS) can characterize the membership degree (MD) and non-membership (NMD) of hesitant elements in a broader range, which offers superior data processing capabilities for addressing complex uncertainty issues. In this research, first, we present definition Bonferroni mean operator (HFFBM). Further, with basic operations HFFS Einstein t-norms, derivation process (HFFEBM) are given. addition, considering how weights affect decision-making outcomes,...

Water ecological civilization construction (WECC) is regarded as the core and cornerstone of construction. However, a lot uncertainty involved in assessing WECC level, which presents serious intricate difficulties for related multiple- attribute decision-making (MADM) processes. The interval-valued hesitant Fermatean fuzzy set (IVHFFS) powerful tool handling MADM issues. existing approaches, weight calculation involves high data redundancy low computational efficiency. aggregation operators...

The research on incomplete soft sets is an integral part of the and has been initiated recently. However, existing approach for dealing with only applicable to decision making low forecasting accuracy. In order solve these problems, in this paper we propose a novel data filling sets. missing are filled terms association degree between parameters when stronger exists or distribution other available objects no parameters. Data converts set into complete set, which makes not but also areas....

Kong et al. introduced the concept of normal parameter reduction in fuzzy soft sets. However, due to entries sets belonging unit interval [0, 1], it is nearly impossible obtain real applications. At same time, this method involves a great amount computation. In order solve these problems, paper, we propose distance-based set, which has much higher applicability and less computation compared with Two case studies twenty synthetic generated datasets show our contributions.

The soft set theory is a completely new mathematical tool for modeling vagueness and uncertainty, which can be applied to decision making. However, in the process of making decision, there are some unnecessary superfluous information should reduced. Normal parameter reduction good way reduce information, keeps entire ability. algorithm has low redundant degree, involves great amount computation. It not certain that normal solution, is, it success rate finding reduction. Parameterization...