The flow maximization problem being a leading problem in network optimization is widely studied with many applications. In this work, we find the maximum flow pulled out from the source and sent to the sink using the uncertainty theory. We develop a flow function to get maximum flow for the network having uncertain arc capacities and sufficient storage capacities at the intermediate vertices. The centiles-method is used to find the maximum flow value and an algorithm is proposed to get non-conservative maximum flow. The result is also illustrated by an example. The flow values are compared graphically to see the dominance of the non-conservative maximum flow over the conservative maximum flow.

The issue of regulation of cross-border data flows is one of the difficulties in digital trade negotiations among countries. This paper considers three cross-border data flow regulation scenarios under technological progress: namely, unilateral, bilateral and trilateral cross-border data flow regulation. By constructing a differential game model, the game equilibrium strategies under the three scenarios are solved and analyzed. The results show that in the unilateral scenario, the higher the digital technology level of the data importing country, the lower the actual cross-border data restriction index of the data exporting country. In the bilateral scenario, when a country’s digital technology is relatively low, its government regulation is high and it tends to adopt a “defensive” cross-border data flow policy. In the trilateral case, due to the existence of double digital trade barriers and other effects, the actual cross-border data restriction index of a country in the middle of the digital technology level is much higher than that of a country at the bottom of the digital technology level; a country at the bottom of the digital technology level is more restrictive than a country in the middle of the digital technology level than a country at the top of the digital technology level.

The trade subjects of digital services have constructed and strictly implemented a more targeted system of trade barriers. Information Communications Technology (ICT) services, as an important trade subject in the digital services field, are more prominently affected by the digital services trade barriers. To investigate the trade effects of digital services trade barriers on ICT services exports, this paper conducts a series of empirical tests using trade panel data of 18 exporting countries and 38 importing countries during the period of 2014–2020. Our results in the benchmark regression show that digital services trade barriers significantly inhibit the expansion of ICT services exports. By the industry and country heterogeneity analysis, we first find computer services are most significantly affected by the negative effect of the digital services trade barriers; then it reveals that the degree of trade inhibition of high ICT countries exporting to low ICT countries is extremely significant, followed by low ICT inter-country exports.

With the rapid development of the economy, the fourth party logistics (4PL) has become an indispensable part of economic development, and the path optimization is an important topic in the research of 4PL routing problem. This paper studies the fourth party logistics routing problem with random demand and uncertain transportation time and transfer time by considering the randomness and uncertainty of the environment. In order to solve the path optimization strategy that minimizes the total transportation cost, this paper establishes an uncertain random programming model under the constraints of the total transportation time, the carrying capacity of the logistics provider, the transfer capacity of the transfer station, and the demand of the distributor. Subsequently, this paper transforms the uncertain random programming model into an equivalent deterministic programming model based on the distribution functions of random demand and uncertain time, and verifies the effectiveness of the model through two examples. The results show that the uncertain random programming model can give a reasonable path optimization strategy.

The postman problem is one of the optimization problems that has many applications in today’s world. With the progress of science, such optimization problems are used more. The postman’s problem is to find a route that has the minimum cost. The postman starts the route from one point and returns to the same point and must travel all the routes. If the round-trip costs are symmetric, in other words, the round-trip cost is the same, the problem is called the Chinese postman problem, and if the costs are asymmetric, that is, the round-trip cost is different from the return cost, the problem is called the windy postman problem (WPP). Here we describe two models of the WPP, which include the WPP and the rural WPP. The model for these problems in the real world needs to apply uncertainty theory. Liu expressed the theory of uncertainty. Here is a model of the windy postman uncertainty problem and a model of the windy rural postman uncertainty problem. So far, there has been no such uncertainty analysis on these models. To solve these problems, we use the Lagrangian heuristic method after turning it into a deterministic model. To solve these problems, after turning the problem into a deterministic problem, the heuristic Lagrangian method was used, and for the wind postman problem, we stated the corresponding Lagrangian algorithm and for the rural wind postman, the Lagrangian algorithm. Examples of these are problems and uncertainties. We expressed them and finally solved them by direct and Lagrangian methods.

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), [Formula: see text]-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.

World Health Organization (W.H.O) has coined the word “Infodemic” to refer to the dissemination of fake news during this pandemic, which is considered to be as harmful as the virus itself. Verifying the information available on the internet is a prerequisite to ensuring the ecosystem is maintained which is the driving force behind this work. The primary goal of this study is to address the problem of time-consuming automatically voluminous fake news detection of certain data and consider the uncertainty of data from causal relations using a rich feature set. This research produces significant feature reduction for reduced time and improved accuracy by filtering out significant features using recursive feature selection (RFE). The retained features by the RFE algorithm are also compared to a standard statistical measure of Pearson’s correlation to ensure no information loss while reducing features. The suggested methodology has also defined appropriate class output assurance levels and accurate prediction ambiguity for the fake identification jobs. Comparative analysis for existing methods for feature selection is performed. The result of experimentation testifies the improvement of a 6% increase in precision and a 97% reduction in execution time.

Through Advanced Persistent Threats (APTs), which can reveal data alteration, destruction, or Denial of Service attacks through the examples of exposed hardware and software, the information technology model advances. Moving Target (MTD) is a promising risk-reduction strategy that primarily relies on APTs by utilizing dynamic and randomization techniques on properties that are collaborated. Although there are various MTD approaches to implement the blind random mutation, it still produces better performance overhead as well as poor defense utility. Additionally, APT is a unique assault strategy that was typically developed by hacking groups to steal data or deactivate systems for enormous originalities and uniform countries. APT is a multi-stage, long-term representative, and it is difficult to identify attacks effectively using an outmoded approach. In this paper, Conditional Dingo Optimization Algorithm Deep Residual Network (CDOA-based DRN) is devised for APT detection. Moreover, correlation Tversky index-based similarity is designed for performing feature fusion. The hybrid optimization algorithm effectively increases the performance and reduces various real-world issues. Testing accuracy, True Positive Rate, and False Positive Rate of the newly developed CDOA-based DRN are 95.43%, 96.34%, and 91.43%, respectively, for better performance.

Uncertain time series models have been proposed to forecast future data based on imprecise known data. As an uncertain time series model, the uncertain threshold autoregressive model was proposed to deal with nonlinear data. The previous study estimated the unknown parameters in the uncertain threshold autoregressive model with the least-squares estimation. However, for nonlinear time series models, the least-squares estimation may cause overfitting of the model. To deal with this problem, this paper adds the least absolute shrinkage and selection operator penalty to the uncertain threshold autoregressive model to alleviate the level of overfitting. In addition, this paper uses the sum of square errors criterion to select the optimum order of the model. Finally, a real stock price example is provided to compare two different parameter estimation methods.