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The World Health Organization declared the novel coronavirus disease 2019 a pandemic on March 11, 2020. Along with the coronavirus pandemic, a new crisis has emerged, characterized by widespread fear and panic caused by a lack of information or, in some cases, outright fake messages. In these circumstances, Twitter is one of the most eminent and trusted social media platforms. Fake tweets, on the other hand, are challenging to detect and differentiate. The primary goal of this paper is to educate society about the importance of accurate information and prevent the spread of fake information. This paper has investigated COVID-19 fake data from various social media platforms such as Twitter, Facebook, and Instagram. The objective of this paper is to categorize given tweets as either fake or real news. The authors have tested various deep learning models on the COVID-19 fake dataset. Finally, the CT-BERT and RoBERTa deep learning models outperformed other deep learning models like BERT, BERTweet, AlBERT, and DistlBERT. The proposed ensemble deep learning architecture outperformed CT-BERT and RoBERTa on the COVID-19 fake news dataset using the multiplicative fusion technique. The proposed model’s performance in this technique was determined by the multiplicative product of the final predictive values of CT-BERT and RoBERTa. This technique overcomes the disadvantage of these CT-BERT and RoBERTa models’ incorrect predictive nature. The proposed architecture outperforms both well-known ML and DL models, with 98.88% accuracy and a 98.93% F1-score.

Summary From the last decade, every developing nations are focusing on the smart cities. Smart city will provide all necessary services, ie, water management, waste management, traffic management, and health services, to its citizens effectively. A new paradigm in the smart vehicles is able to form a network called vehicular ad hoc network (VANET). There is a wireless enabled roadside unit (RSU) through which the vehicle drivers can effectively exchange the important traffic data and take proper driving decisions. Each roadside unit is capable of managing some vehicles, which can form a group. There are many schemes available for data transmission in VANET group but they are less secured. In this paper, we propose an intelligent conditional privacy preserving scheme for vehicular ad hoc networks using elliptic curve cryptography. The security analysis showed that the proposed scheme is intelligent, efficient, and easily deployable.

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In the last 2 years, medical researchers and clinical scientists have paid close attention to the problem of respiratory sound classification to classify COVID-19 disease symptoms. In the physical world, very few AI-based (Artificial Intelligence) techniques are often used to detect COVID-19/SARS-CoV-2 respiratory disease symptoms from the human respiratory system-generated acoustic sounds such as acoustic voice sound, breathing (inhale and exhale) sounds, and cough sound. We propose a light-weight Convolutional Neural Network (CNN) with Modified-Mel-frequency Cepstral Coefficient (M-MFCC) using different depths and kernel sizes to classify COVID-19 and other respiratory sound disease symptoms such as Asthma, Pertussis, and Bronchitis. The proposed network outperforms conventional feature extraction models and existing Deep Learning (DL) models for COVID-19/SARS-CoV-2 classification accuracy in the range of 4–10%. The model’s performance is compared with the COVID-19 crowdsourced benchmark dataset and gives a competitive performance. We applied different receptive fields and depths in the proposed model to get different contextual information that should aid in classification. And our experiments suggested 1 12 receptive fields and a depth of 5-Layer for the light-weight CNN to extract and identify the features from respiratory sound data. The model is also trained and tested with different modalities of data to showcase its effectiveness in classification.

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The expansion of wireless sensor networks in the underwater environment resulted in underwater wireless sensor networks. It has dramatically impacted the research arena because of its widespread and real-time applications. But successful implementation of underwater wireless sensor networks faces many issues. The primary concern in the underwater sensor network is sensor nodes' energy depletion problem. In this paper, to improve the lifetime of the underwater wireless sensor network, an Energy-Aware Multi-level Clustering Scheme is proposed. The underwater network region is considered 3D concentric cylinders with multiple levels. Further, each level is divided into various blocks, representing one cluster. The proposed algorithm follows vertical communication mode from the sea bed to the surface area in a bottom-up fashion. Multiple levels with varying heights overcome the communication issues due to high water pressure towards the sea bed. Simulations are carried out to show the efficiency of the proposed algorithm, which performs better in terms of a prolonged network lifetime and average residual energy. The simulation result shows significant improvement in the network lifetime compared with current algorithms.

Summary Ciphertext‐policy attribute‐based encryption (CP‐ABE) is the recommended best practice for outsourced big data access control in the cloud environment. However, most of the existing CP‐ABE schemes do not address the issue of tracing and revoking the malicious user who leaks the secret key for profit, which in turn reduces the security of the CP‐ABE schemes. In this paper, we propose a dynamic traceable CP‐ABE with revocation (DTCP‐ABE) for outsourced big data in cloud storage. DTCP‐ABE scheme dynamically traces who decrypts the ciphertext during the outsourced decryption process, which helps to find the malicious user who leaks the secret key. Our scheme also automatically revokes the malicious users once they are identified. We prove that our scheme is secure against chosen‐plaintext, secret key forging, user collision, and proxy attacks. Furthermore, our scheme also achieves backward revocation security. Performance evaluation proves that our DTCP‐ABE scheme is efficient than other existing schemes.