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Fake news and its significance carried the significance of affecting diverse aspects of diverse entities, ranging from a city lifestyle to a country global relativity, various methods are available to collect and determine fake news. The recently developed machine learning (ML) models can be employed for the detection and classification of fake news. This study designs a novel Chaotic Ant Swarm with Weighted Extreme Learning Machine (CAS-WELM) for Cybersecurity Fake News Detection and Classification. The goal of the CAS-WELM technique is to discriminate news into fake and real. The CAS-WELM technique initially pre-processes the input data and Glove technique is used for word embedding process. Then, N-gram based feature extraction technique is derived to generate feature vectors. Lastly, WELM model is applied for the detection and classification of fake news, in which the weight value of the WELM model can be optimally adjusted by the use of CAS algorithm. The performance validation of the CAS-WELM technique is carried out using the benchmark dataset and the results are inspected under several dimensions. The experimental results reported the enhanced outcomes of the CAS-WELM technique over the recent approaches.

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Wireless sensor network (WSN) plays a vital part in real time tracking and data collection applications. WSN incorporates a set of numerous sensor nodes (SNs) commonly utilized to observe the target region. The SNs operate using an inbuilt battery and it is not easier to replace or charge it. Therefore, proper utilization of available energy in the SNs is essential to prolong the lifetime of the WSN. In this study, an effective Type-II Fuzzy Logic with Butterfly Optimization Based Route Selection (TFL-BOARS) has been developed for clustered WSN. The TFL-BOARS technique intends to optimally select the cluster heads (CHs) and routes in the clustered WSN. Besides, the TFL-BOARS technique incorporates Type-II Fuzzy Logic (T2FL) technique with distinct input parameters namely residual energy (RE), link quality (LKQ), trust level (TRL), inter-cluster distance (ICD) and node degree (NDE) to select CHs and construct clusters. Also, the butterfly optimization algorithm based route selection (BOARS) technique is derived to select optimal set of routes in the WSN. In addition, the BOARS technique has computed a fitness function using three parameters such as communication cost, distance and delay. In order to demonstrate the improved energy effectiveness and prolonged lifetime of the WSN, a wide-ranging simulation analysis was implemented and the experimental results reported the supremacy of the TFL-BOARS technique.

The internet of agents (IoA) is an emergent model, which mainly intends to resolve the limitations of the internet of things (IoT) devices with respect to reasoning and social competencies for improving proactivity, intelligence, and interoperability. This article presents a novel grasshopper optimization with deep learning enabled multi‐agent system for age and gender classification (GOADL‐MASAGC) model for IoA. The proposed GOADL‐MASAGC technique intends to categorize age as well as gender. The proposed GOADL‐MASAGC technique applies a multi‐agent system, which incorporates distinct processes for age and gender classification like pre‐processing, feature extraction, and classification. Besides, the GOADL‐MASAGC technique enables to performance concurrent process of classification and regression to identify age and gender respectively. In addition, the GOA with Capsule Network (CapsNet) model was executed for deriving a suitable group of feature vectors and the GOA is employed as a hyperparameter optimizer. Finally, wavelet kernel extreme learning machine (WKELM) was employed as a classifier for gender classification and deep belief network (DBN) is used as a regression approach for age recognition. For demonstrating the improved performance of the GOADL‐MASAGC model, a series of simulations were executed and the outcomes are examined in various aspects. The extensive comparative analysis reported the enhanced outcomes of the GOADL‐MASAGC approach over the existing methods.