Economic Management Data Envelopes Based on the Clustering of Incomplete Data

In this paper, the economic management data envelope is analyzed by an algorithm for clustering incomplete data, a local search method based on reference vectors is designed in the algorithm to improve the accuracy of the algorithm, and a final solution selection method based on integrated clustering is proposed to obtain the final clustering results from the last generation of the solution set. The proposed algorithm and various aspects of it are tested in comparison using benchmark datasets and other comparison algorithms. A time-series domain partitioning method based on fuzzy mean clustering and information granulation is proposed, and a time series prediction method is proposed based on the domain partitioning results. Firstly, the fuzzy mean clustering method is applied to initially divide the theoretical domain of the time series, and then, the optimization algorithm of the theoretical domain division based on information granulation is proposed. It combines the clustering algorithm and the information granulation method to divide the theoretical domain and improves the accuracy and interpretability of sample data division. This article builds an overview of data warehouse, data integration, and rule engine. It introduces the business data integration of the economic management information system data warehouse and the data warehouse model design, taking tax as an example. The fuzzy prediction method of time series is given for the results of the theoretical domain division after the granulation of time-series information, which transforms the precise time-series data into a time series composed of semantic values conforming to human cognitive forms. It describes the dynamic evolution process of time series by constructing the fuzzy logical relations to these semantic values to obtain their fuzzy change rules and make predictions, which improves the comprehensibility of prediction results. Finally, the prediction experiments are conducted on the weighted stock price index dataset, and the experimental results show that applying the proposed time-series information granulation method for time series prediction can improve the accuracy of the prediction results.

SSE Path Routing Protocol for WSN IOT Using Fuzzy Mean Weighted Code and Enthalpy ANN-GOA

Wireless Sensor Networks (WSNs) play a key role in the Internet of Things (IoT) by delivering cost-effective solutions for specific IoT applications. The amount of energy used to transport data from a sensor node to its destination is a significant consideration when developing wireless sensor network routing strategies. A variety of routing protocols are utilised in WSN to improve network performance. When a node's mode transitions from active to sleep, the efficiency declines because data packets must wait at the starting point where they were transmitted, which increases packets' waiting time and end-to-end latency, resulting in an increase in energy consumption. This study proposes the SSE Path routing protocol as a Scalable, Secure, and Energy Efficient Routing Protocol for WSN IoT. Our proposed protocol, first selecting the nodes using a single-hop clustering algorithm. Second, FMWC (Fuzzy Mean Weighted Code) based packet coded operation for the security of packet. Finally, Scalable Enthalpy Artificial Neural-Network Based Grasshopper Optimization Algorithm (EANN-GOA) algorithm for selecting the congestion-free path for transit the data packets. The experimental results revelations the dominance of presented protocol comparing with the existing protocols.

Martingale convergence theorem for a conditional intuitionistic fuzzy mean value

The aim of this contribution is to show a representation of a conditional intuitionistic fuzzy mean value of intuitionistic fuzzy observables by a conditional mean value of random variables. We formulate a martingale convergence theorem for a conditional intuitionistic fuzzy mean value, too.

Conditional Intuitionistic Fuzzy Mean Value

The conditional mean value has applications in regression analysis and in financial mathematics, because they are used in it. We can find papers from recent years that use the conditional mean value in fuzzy cases. As the intuitionstic fuzzy sets are an extension of fuzzy sets, we will try to define a conditional mean value for the intuitionistic fuzzy case. The conditional mean value in crisp intuitionistic fuzzy events was first studied by V. Valenčáková in 2009. She used Gödel connectives. Her approach can only be used for special cases of intuitionistic fuzzy events, therefore, we want to define a conditional mean value for all elements of a family of intuitionistic fuzzy events. In this paper, we define the conditional mean value for intuitionistic fuzzy events using Lukasiewicz connectives. We use a Kolmogorov approach and the notions from a classical probability theory for construction. B. Riečan formulated a conditional intuitionistic fuzzy probability for intuitionistic fuzzy events using an intuitionistic fuzzy state in 2012. In classical cases, there exists a connection between the conditional probability and the conditional mean value, therefore we show a connection between the conditional intuitionistic fuzzy probability induced by the intuitionistic fuzzy state and the conditional intuitionistic fuzzy mean value.