Nanomaterials are a key component of nanoelectronics and selecting the most suitable nanomaterial for nanoelectronics remains a significant challenge for companies. The classical decision making process is often difficult and uncertain when identifying the ideal nanomaterial. To address this, we develop a novel decision making model based on a fuzzy credibility neural network with Hamacher aggregation operators. We apply the proposed model to select the most suitable nanomaterial for nanoelectronics. For this purpose, we collect information matrices from three experts regarding various nanomaterials. To analyze these matrices, we use entropy to calculate the weights of each criterion. After determining the weights, we apply fuzzy credibility Hamacher weighted aggregation operators to combine the input signals and their corresponding weights in order to compute the hidden layer information for the nanomaterials. To ensure accurate and reliable results, we apply the fuzzy credibility Hamacher weighted aggregation operator once again to the hidden layer information, aggregating it with the appropriate weights to generate the output layer information. Next, we use a score function based on fuzzy credibility numbers to calculate the score values of the output information. After this, we apply three activation functions to compute the final output of the proposed model. Based on the results, graphene is identified as the best nanomaterial for nanoelectronics. Furthermore, we perform a sensitivity analysis of the proposed model by varying the Hamacher parameter. To confirm the effectiveness and accuracy of the proposed approach, we finally validate the results using three well-known MCDM methods.

A mission to explore a distant planet is being planned by the Pakistan Space and Upper Atmosphere Research Commission. For this mission to be successful, t

The demand for third-party logistics (3PL) providers becomes an increasingly important issue for corporations seeking improved customer service and cost reduction. Currently, there is no way to select the appropriate method for selecting 3PL. Therefore, this paper develops a new extended multi-objective optimization ratio analysis plus full multiplicative form (MULTIMOORA) method under double hierarchy linguistic single-valued neutrosophic sets (DHLSVNSs). For this, we first develop a new mathematical tool, i.e., DHLSVNSs, by studying single-valued neutrosophic set (SVN) and double hierarchy linguistic term set (DHLTSs), which is very effective for solving uncertainty in decision-making problems. A list of Einstein aggregation operators and their fundamental aspects for DHLSVNSs are presented based on Einstein's norms, as aggregation operators play an essential role in decision-making. A step-by-step algorithm of the Extended DHLSVN-MULTIMOORA approach is designed to tackle ambiguous and uncertain data during decision-making problems. The algorithm developed for the suggested technique is illustrated with a numerical example relevant to 3PL. A comparison of the proposed methods with various existing methodologies is carried out to demonstrate the superiority of the suggested algorithms.