卷 23, 编号 2 (2024)

The article is focused on a single-channel preemptive queuing system. Two stationary Poisson flows of customers are incoming to the system. The first flow has an absolute priority over the second one: a new high-priority customer from the first flow displaces a low-priority one from the service channel and takes its place. The capacity of the system is limited to k customers. There is a probabilistic push-out mechanism in the system: if a new high-priority customer finds that all the places in the queue are occupied, then it has the right to displace one low-priority customer from the queue with probability a. Both types of customers have the same exponentially distributed service times. Customers who failed to enter the system due to the limited size of the queue, as well as those expelled from the queue or service channel when the push-out mechanism is triggered, are not lost immediately, but they are sent to a special part of the system called the orbit and designed to store repeated customers. In orbit, there are two separate unlimited queues, consisting of low-priority and high-priority repeated customers, respectively. If there are no free places in the system, new customers with a probability q are added to the corresponding orbital queue. The waiting time of repeated customers in orbit is distributed according to an exponential law. The parameter of this law may differ for different types of customers. After waiting in orbit, secondary customers try to re-enter the system. The probabilistic characteristics of the described queuing system are calculated by the method of generating functions, previously proposed by the authors for calculating a similar system without repeated customers. This method allows finding the main probabilistic characteristics of distributions for both types of customers. Particular attention is paid to the study of the dependence of the loss probabilities for both types of customers on the parameters of the system, primarily on the push-out probability a, the capacity of the system k, and the probability of repeated circulation (probability of persistence) q. It is shown that the effect of blocking the system and the effect of the linear law of customers’ losses, previously identified in similar problems without repeated customers, remain valid even in the presence of secondary repeated customers. The theoretical results are proved by numerical calculations. The blocking area for the second type of customers was calculated along with the area of linear loss law for both types of customers. We studied the influence of the probability of repeated circulation q on the shape of these areas and on the dependence of the loss probabilities for both types of customers on the push-out probability a.

In today’s world, the Internet of Things has become an integral part of our lives. The increasing number of intelligent devices and their pervasiveness has made it challenging for developers and system architects to plan and implement systems of Internet of Things and Industrial Internet of Things effectively. The primary objective of this work is to automate the design process of Industrial Internet of Things systems while optimizing the quality of service parameters, battery life, and cost. To achieve this goal, a general four-layer fog-computing model based on mathematical sets, constraints, and objective functions is introduced. This model takes into consideration the various parameters that affect the performance of the system, such as network latency, bandwidth, and power consumption. The Non-dominated Sorting Genetic Algorithm II is employed to find Pareto optimal solutions, while the Technique for Order of Preference by Similarity to Ideal Solution is used to identify compromise solutions on the Pareto front. The optimal solutions generated by this approach represent servers, communication links, and gateways whose information is stored in a database. These resources are chosen based on their ability to enhance the overall performance of the system. The proposed strategy follows a three-stage approach to minimize the dimensionality and reduce dependencies while exploring the search space. Additionally, the convergence of optimization algorithms is improved by using a biased initial population that exploits existing knowledge about how the solution should look. The algorithms used to generate this initial biased population are described in detail. To illustrate the effectiveness of this automated design strategy, an example of its application is presented.

Despite being extensively used in numerous uses, precise and effective human activity identification continues to be an interesting research issue in the area of vision for computers. Currently, a lot of investigation is being done on themes like pedestrian activity recognition and ways to recognize people's movements employing depth data, 3D skeletal data, still picture data, or strategies that utilize spatiotemporal interest points. This study aims to investigate and evaluate DL approaches for detecting human activity in video. The focus has been on multiple structures for detecting human activities that use DL as their primary strategy. Based on the application, including identifying faces, emotion identification, action identification, and anomaly identification, the human occurrence forecasts are divided into four different subcategories. The literature has been carried several research based on these recognitions for predicting human behavior and activity for video surveillance applications. The state of the art of four different applications' DL techniques is contrasted. This paper also presents the application areas, scientific issues, and potential goals in the field of DL-based human behavior and activity recognition/detection.

Hydrocephalus is a central nervous system disorder which most commonly affects infants and toddlers. It starts as an abnormal build-up of cerebrospinal fluid in the ventricular system of the brain. Hence, early diagnosis becomes vital, which may be performed by Computed Tomography (CT), one of the most effective diagnostic methods for diagnosing Hydrocephalus (CT), where the enlarged ventricular system becomes apparent. However, most disease progression assessments rely on the radiologist's evaluation and physical measures, which are subjective, time-consuming, and inaccurate. This paper develops an automatic prediction utilizing the H-detect framework for enhanced accurate hydrocephalus prediction. This paper uses a pre-processing step to normalize the input image and remove unwanted noises, which can help extract valuable features easily. The feature extraction is done by segmenting the image based on edge detection using triangular fuzzy rules. Thereby, the exact information on the nature of CSF inside the brain is highlighted. These segmented images are saved and again given to the CatBoost algorithm. The Categorical feature processing allows for quicker training. When necessary, the overfitting detector will stop model training and thus efficiently predicts Hydrocephalus. The outcomes demonstrate that the new H-detect strategy outperforms the traditional approaches.

Information security risk assessment is a crucial component of industrial management techniques that aids in identifying, quantifying, and evaluating risks in comparison to criteria for risk acceptance and organizationally pertinent objectives. Due to its capacity to combine several parameters to determine an overall risk, the traditional fuzzy-rule-based risk assessment technique has been used in numerous industries. The technique has a drawback because it is used in situations where there are several parameters that need to be evaluated, and each parameter is expressed by a different set of linguistic phrases. In this paper, fuzzy set theory and an artificial neural network (ANN) risk prediction model that can solve the issue at hand are provided. Also developed is an algorithm that may change the risk-related factors and the overall risk level from a fuzzy property to a crisp-valued attribute is developed. The system was trained by using twelve samples representing 70%, 15%, and 15% of the dataset for training, testing, and validation, respectively. In addition, a stepwise regression model has also been designed, and its results are compared with the results of ANN. In terms of overall efficiency, the ANN model (R2= 0.99981, RMSE=0.00288, and MSE=0.00001,) performed better, though both models are satisfactory enough. It is concluded that a risk-predicting ANN model can produce accurate results as long as the training data accounts for all conceivable conditions.