With the development of online interactive media platforms, a large amount of short text has appeared on the internet. Determining how to classify these short texts efficiently and accurately is of great significance. Graph neural networks can capture information dependencies in the entire short-text corpus, thereby enhancing feature expression and improving classification accuracy. However, existing works have overlooked the role of entities in these short texts. In this paper, we propose a heterogeneous graph-convolution-network-based short-text classification (SHGCN) method that integrates heterogeneous graph convolutional neural networks of text, entities, and words. Firstly, the model constructs a graph network of the text and extracts entity nodes and word nodes. Secondly, the relationship of the graph nodes in the heterogeneous graphs is determined by the mutual information between the words, the relationship between the documents and words, and the confidence between the words and entities. Then, the feature is represented through a word graph and combined with its BERT embedding, and the word feature is strengthened through BiLstm. Finally, the enhanced word features are combined with the document graph representation features to predict the document categories. To verify the performance of the model, experiments were conducted on the public datasets AGNews, R52, and MR. The classification accuracy of SHGCN reached 88.38%, 93.87%, and 82.87%, respectively, which is superior to that of some existing advanced classification methods.

Rogue nodes in the Internet of vehicles (IoV) bring traffic congestion, vehicle collision accidents and other problems, which will cause great social losses. Therefore, rogue node discovery plays an important role in building secure IoV environments. Existing machine learning-based rogue node detection methods rely too much on historical data, and these methods may lead to long network delay and slow detection speed. Moreover, methods based on Roadside Units (RSUs) have poor performance if the number of RSUs is insufficient. Based on the widespread presence of ground vehicles, we propose a rogue node detection scheme based on the fog network formed by roadside parked vehicles. To achieve efficient rogue node discovery, a fog network composed of stable roadside parked vehicles is dynamically established, in which each fog node firstly collects the information of moving vehicles on the road in its coverage range, and then fog nodes use the U-test method to determine the rogue nodes in parallel, so as to find the bad nodes efficiently. Simulation results show that the proposed algorithm has higher detection accuracy and stability than the other rogue node detection schemes.

Vehicular edge computing (VEC) has greatly enhanced the quality of vehicle service with low latency and high reliability. However, in some areas not covered by roadside infrastructures or in cases when the infrastructures are damaged or fail, the offloaded tasks cannot have the chance to be performed. Even in the areas deployed with infrastructures, when a large number of offloaded tasks are generated, the edge servers may not be capable of processing them in time, owing to their computing resources constraint. Based on the above observations, we proposed the idea of parked vehicle cooperation in VEC, which uses roadside parked vehicles with underutilized computational resources to cooperate with each other to perform the compute-intensive tasks. Our approach aims to overcome the challenge brought by infrastructure lacking or failure and make up for the shortage of computing resources in VEC. In our approach, firstly, the roadside parked vehicles are managed as different parking clusters. Then, the optimal amount of resources required for each offloaded task is analyzed. Furthermore, a task offloading algorithm based on deep reinforcement learning (DRL) is proposed to minimize the total cost, which is composed of the task execution delay and the energy consumption overhead of the parked vehicles for executing the task. A large number of simulation results show that, compared with other algorithms, our approach not only has the highest task completion execution successful rate, but also has the lowest task execution cost.

The Sparrow Search Algorithm (SSA), a metaheuristic renowned for rapid convergence, good stability, and high search accuracy in continuous optimization, faces inherent limitations when applied to discrete multi-variable combinatorial optimization problems like feature selection. To enable effective multi-variable evaluation and discrete feature subset selection using SSA, a novel binary variant, Position Binarization-based Sparrow Search Algorithm (BSSA), is proposed. BSSA employs a sigmoid transformation function to convert the continuous position vectors generated by the standard SSA into binary solutions, representing feature inclusion or exclusion. Recognizing that the inherent exploitation bias of SSA and the complexity of high-dimensional feature spaces can lead to premature convergence and suboptimal solutions, we further enhance BSSA by introducing stochastic Gaussian noise (zero mean) into the sigmoid transformation. This strategic perturbation actively diversifies the search population, improves exploration capability, and bolsters the algorithm’s robustness against local optima stagnation during multi-variable evaluation. The fitness of each candidate feature subset (solution) is evaluated using the classification accuracy of a Support Vector Machine (SVM) classifier. The BSSA algorithm is compared with four high-performance optimization algorithms on 12 diverse benchmark datasets selected from the UCI repository, utilizing multiple performance metrics. Experimental results demonstrate that BSSA achieves superior performance in classification accuracy, computational efficiency, and optimal feature selection, significantly advancing multi-variable evaluation for feature selection tasks.