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In the Internet of Things (IoT) era, information is collected by sensor devices, resulting in data loss or uncertain data and other consequences. We need to represent the uncertain data collected using probabilities to extract the useful information for production and application from a huge indeterminate data warehouse. The data in the database has a particular order in time or space, so the High-Utility Probability Sequential Pattern Mining (HUPSPM) has become a new investigation and analysis topic in data processing. After the progress of timestamp, many efficient algorithms for sequential mining have been developed. However, these algorithms have a limitation: they can only be executed in a stand-alone environment and are only suitable for small datasets. Therefore, introducing an advanced graph framework for processing large datasets addresses the shortcomings of the existing methods. The proposed algorithm can avoid repeated database searching, splitting the database, and improve the parallel computing capability. The initial database is pruned according to the existing pruning strategy to effectively reduce the number of candidate sets effectively. Experiments show that the algorithm presented in this paper has excellent advantages in mining high-utility probability sequences in large datasets.

This paper focuses on the problem of robust H_∞ filtering for a class of nonlinear uncertain singular systems with time-varying delay. First of all, the definition of robust H_∞ filter is given. Considering the nonlinear disturbance link to uncertain singular systems with time-varying delay effects, the design idea of full-order robust H_∞ filter based on the Lyapunov stability theory is presented. Under the condition that nonlinear uncertain functions satisfy Lipschitz condition, the sufficient condition under which nonlinear uncertain delayed filtering error singular systems are asymptotically stable and satisfy the robust H_∞ performance is obtained by Lyapunov stability theory and linear matrix inequality (LMI) methods. Finally, two numerical examples are given to show the applicability of the proposed method.

The Internet of Things (IoT) incorporates software-based physical devices with sensors which require interconnectivity to communicate through the internet and be able to exchange data. IoT traffic can be thought of as the aggregate of packets created by various devices in various contexts, such as smart homes or smart cities. These settings contain several sensors, each of which is dedicated to a certain duty, such as monitoring schemes or gathering cyber-physical information. Thus, unlike internet traffic, which has some human-centric features, IoT traffic and sensor devices consistently perform different tasks and make uncertain quantities of data on a regular basis. The proposed method is based on calculating the entropy values of various uncertain traffic aspects. We describe a novel scheme for identifying IoT devices based on uncertain traffic entropy in this paper. The testbed consists of five popular Smart Home IoT devices with a Raspberry Pi acting as a gateway. Machine learning (ML) algorithms such as support vector machine (SVM), $k$-nearest neighbor (KNN) and relevance vector machines (RVM) classifier are used to classify the devices which produce IoT traffic based on uncertain entropy significance. The learning rate of the classifier model is improved by using the flower pollination optimization (FPO) technique to increase the classifier performance. The suggested approach minimizes the amount of time spent on optimization operations while maintaining the predictive performance of the induced uncertain models. We work out the entropy standards of traffic characteristics and categorize the uncertain traffic using ML techniques. Our technique successfully identifies devices in a variety of uncertain network situations, with consistent performance in all scenarios. Our technique is also resistant to unpredictability in network behavior, with abnormalities or uncertainties propagating throughout the network.

The recognition and measurement of uncertain accounting matters needs to use the accounting professional judgment to identify. The results directly affect the quality of accounting information. The bounded rationality of accountants in cognition and behavior is the key ingredient affecting the behavior of professional judgment, it may easily cause the consequences of misjudgment or intentional abuse. The treatment of bounded rationality must be based on their root causes, improving the quality of accounting personnel, and strengthening internal and external oversight in company, thus contributing to the accounting professional judgment behavior rationalization.

Exponential stability for switched systems with uncertain parameters and time-varying delay is considered in this paper. The parametric uncertainties are assumed to be time-varying and norm-bounded. By introducing a novel piecewise time-varying Lyapunov function and using Razumikhin techniques, some linear matrix inequalities (LMIs) stability criteria are derived to guarantee the exponential stability of the switched delay systems. A numerical example is presented to demonstrate the effectiveness of the proposed method.