In this work, an interval Type-3 fuzzy logic system (IT3FLS) with the principal objective of enhancing the whale optimization algorithm (WOA) in function optimization is offered. The effectiveness of the IT3FLS is validated with a set of experiments. The IT3FLS helps in finding the r⟶1 and r⟶2 parameter values to achieve the best efficiency of the original WOA. The IT3FLS, in conjunction with WOA, shows good ability in finding minimum values. It is noted that IT3FLS allows convergence stabilization to minimum values for mathematical function optimization. A comparison with the FWOA-generalized Type-2 FLS (GT2FLS) and original WOA is presented, demonstrating that the FWOA-IT3FLS approach is better at minimizing the results obtained with respect to 7 mathematical functions out of the 10 used in the case study, which is based on the experimentation that was undertaken. Good execution performance of the Type-3 variant of WOA was shown in a set of benchmark functions, as there is an increase in execution time, but the complexity of computing the Type-3 fuzzy systems is still linear.
The fast development of artificial intelligence (AI) and robotics has altered the current logistics and service industry, where sustainability, reliability, and efficiency have become critical objectives. Autonomous systems in such dynamic environments are required to make important decisions in uncertain, imprecise, and conflicting situations. To address such decision-making problems, this study proposes a novel multicriteria decision-making (MCDM) framework based on linguistic Z-intuitionistic fuzzy sets (LZIFSs). The framework introduces aggregation operators and operational laws based on algebraic norms to handle linguistic assessments and the reliability of expert judgment. Its applicability is illustrated by selecting a sustainable, AI-driven robotic food delivery system and evaluating it against various criteria. The results demonstrate the advantages of the proposed LZIFS-based model in producing robust, transparent, and reliable decision outcomes and in processing vague and uncertain information. A comparative analysis using the WASPAS method validates the obtained rankings for consistency and effectiveness. The results confirmed that the best-ranked alternative is Θ1, which replaces the actual best-ranked alternative, thus validating the efficiency and feasibility of the proposed framework for sustainable robotic mobility and intelligent delivery services.
Nowadays, Z-numbers are known as the most powerful tool for describing the uncertainty process concerning the degree of confidence. In this matter, regression based on Z-numbers is called an essential issue because it establishes a functional relationship among Z-number variables. More importantly, quantile regression is an efficient statistical method for estimating and inferring conditional quantile functions. Thus, this paper aims to develop quantile regression under three models. The first model encompasses explanatory variables, response variable, and Z-number parameters. The second one includes explanatory variables, a Z-number response variable, and crisp parameters. Finally, the third one includes crisp explanatory variables, response variable, and Z-number parameters. The idea of the method for the first model is to employ the mean squared error (MSE) method. On the other hand, the idea of the second model is to utilize a new ranking function. The concept of the third model is to propose a loss function between numbers based on Z-numbers, which can render some quantiles based on Z-numbers. Then, a quantile regression model is fitted based on the proposed loss function. Furthermore, two indices are defined to assess the goodness of fit of the optimal quantile regression models based on Z-numbers. Meanwhile, several examples are solved to assess the efficiency of the proposed models. Eventually, the applicability of the proposed approach is surveyed in heart disease prediction modeling based on a realistically simulated dataset, and future validation on real patient data is recommended. The obtained results reveal that the proposed method outperforms in every condition where regression based on Z-numbers fails and also provides in-depth results.
Wireless body area networks (WBANs) play a vital role in saving lives by accurately measuring physiological information in a cost-effective manner. However, reliability and energy consumption remain major constraints in WBANs. Many researchers have claimed that transmission through relay nodes can mitigate these constraints. Therefore, this work proposes a fuzzy-based relay MAC protocol based on the IEEE 802.15.6 model. Fuzzy rules have been used to select the relay node, considering three attributes: residual energy, received signal strength indicator (RSSI) and data rate. The combination of predefined and fuzzy-based opportunistic relay nodes has significantly reduced data relaying failure rates, leading to an increased packet delivery ratio. Through simulation, the performance of IEEE 802.15.6, DMTM-MAC, ADT-MAC and the proposed adaptive fuzzy-based MAC (AFBM) has been compared and analysed. The results indicate that AFBM effectively reduces packet delivery delay and energy consumption while improving data relaying and packet delivery ratios.
A neutrosophic distance measure using average functions is defined, and the metric axioms are verified by discussing its properties on a neutrosophic structure. Further, the similarity measure’s axioms are also derived for the complement of the proposed distance measure. This research paper presents a novel methodology for identifying suitable attributes of corresponding alternatives in decision-making problems using neutrosophic distance measure values. As an application, a numerical model is offered for the neutrosophic evaluation of smartphone selection by customers. Moreover, it is perceived that the decision of the proposed distance measure is consistent with the results derived from other existing distance measures.
