In fuzzy artificial intelligent decision support systems, three-way intelligent-decision making (TWIDM) has played a very important role in ranking objects under the double hierarchy linguistic variable (DHLV). The 8 × 8 S-boxes are very important for image encryption in secure communication. Therefore, the aim of the present study is to develop a new approach to artificial intelligent three-way decision making via DHLV and apply it to S-box image encryption. Artificial intelligent based three-way decision-making problems with double hierarchy hesitant linguistic terms are developed. The first and second hierarchy hesitant linguistic term sets make up the double hierarchy hesitant linguistic term set, which allows for more flexible expressions of doubt and fuzziness. First, we define the Einstein operational laws, score function, and Einstein aggregation operators; i.e., double hierarchy hesitant linguistic Einstein weighted averaging and weighted geometric operators. First, the unknown weight vector for decision experts is determined by using aggregation operators and entropy measures for DHLV. Then, we find the weight vector for our criteria by using the distance measure. In TWIDM, conditional probability is determined by using the extended TOPSIS method for evaluating the S-boxes for image encryption. The expected losses are then computed by aggregating the loss functions with the help of Einstein-weighted averaging aggregation operators. Finally, we apply the minimum-loss decision rules for the selection of S-box to image encryption. The proposed decision technique has been compared with existing three-way decisions and the result of proposed three-way decision making for analyzing and ranking the S-box is very good and reliable for decision making.

Network selection in heterogeneous wireless networks (HWNs) is a complex issue that requires a thorough understanding of service features and user preferences. This is because the various wireless access technologies have varying capabilities and limitations, and the best network for a voice, video, and data service depends on a variety of factors. For selecting the optimal network in HWNs, varying factors such as the user’s position, accessible network resources, service quality requirements, and user preferences must be considered. The classical decision making procedure is very difficult and uncertain to select the desirable HWNs for voice, video, and data. Therefore, we develop a novel decision making model based on feed-forward neural networks under the double hierarchy linguistic information for the selection of the best HWNs for voice, video, and data. In this article, we introduce a novel feed-forward double hierarchy linguistic neural network using the Hamacher t-norm and t-conorm. Further, the feed-forward double hierarchy linguistic neural network applies to the decision making model for the selection of the best HWNs for voice, video, and data. In this novel decision making model, we first take the given data about HWNs and use the converting function to convert the given data into a double hierarchy linguistic term set. We calculate the hidden layer and output layer information by using Hamacher aggregation operations. Finally, we use the sigmoid activation function on the output layer information to decide on the best HWNs for voice, video, and data according to ranking. The proposed approach is compared with other existing models of decision making and the results of the comparison show that the proposed technique is applicable and reliable for the decision support model.

In the real life world, the multi-criteria group decision-making (MCGDM) is very important in the decision process. The intelligent decision process for deep learning is playing a very vital role in the selection of deep learning technique for health care. The main aim of the present work is develop an intelligent based three way decision models for analyzing and ranking the deep learning techniques for health care under the double hierarchy linguistic information. The double hierarchy linguistic term set (DHLTS), which enables more adaptable representation of uncertainty and fuzziness. The Yager operational laws for DHLTSs has considered and defined to develop the Yager aggregation operators for DHLTSs. We defined double hierarchy linguistic Yager weighted averaging (DHLYWA) operators, double hierarchy linguistic Yager hybrid aggregation (DHLYHA) operators, double hierarchy linguistic Yager geometric aggregation (DHLYWG) operator, double hierarchy linguistic Yager geometric aggregation (DHLYHG) operator. Next, three way intelligent decision model developed by using the proposed aggregation operators with the help of EDAS decision making. The three way decision making is based on the loose function. The proposed three way intelligent decision making model apply to deep learning techniques to ranking and analyze the K-nearest neighbor, support vector machines, decision tree, A 4: naive Bayes, linear regression, and k-means clustering. The entropy method is used to determine the unknown weights of the decision expert and criterion. Then, we find the conditional probability using the EDAS method. The expected losses are then calculated by combining the loss functions using the double hierarchy linguistic Yager aggregation operators. The proposed models compare with already developed models and also provide the sensitivity analysis of the proposed models.