ABSTRACT An emerging component of smart cities is vehicle‐to‐grid (V2G) technology, which provides a novel approach to scheduling and energy storage. Security threats currently impede V2G's normal operations. V2G security faces two challenges. Current V2G security schemes only consider the static security approach, which is insufficient to handle the problem of advanced persistent attacks and high dynamics in V2G. However, the lack of a unified information modeling technique in present V2G causes problems with security and communication. The aim is to propose a novel technique in task allocation and security analysis based on smart energy management using a machine learning model in V2G architecture. Here, the smart energy management and task allocation are carried out using a hybrid fuel cell model with a deep vector Q‐gradient model. Then, the security analysis of the V2G network is carried out using a multilayer blockchain smart contract‐based federated LSTM model. Experimental analysis is carried out in terms of QoS, energy efficiency, network efficiency, data integrity, and training accuracy. Simulation results are conducted to prove the effectiveness of this proposed method.

One of the most critical activities of revealing terrorism-related information is classifying online documents.The internet provides consumers with a variety of useful knowledge, and the volume of web material is increasingly growing. This makes finding potentially hazardous records incredibly difficult. To define the contents, merely extracting keywords from records is inadequate. Many methods have been studied so far to develop automatic document classification systems, they are mainly computational and knowledge-based approaches. due to the complexities of natural languages, these approaches do not provide sufficient results. To fix this shortcoming, we given approach of structure dependent on the WordNet hierarchy and the frequency of n-gram data that employs word similarity. Using four different queries terms from four different regions, this approach was checked for the NY Times articles that were sampled. Our suggested approach successfully removes background words and phrases from the document recognizes connected to terrorism texts, according to experimental findings.