Fake news detection (FND) has emerged as a pivotal domain within the natural language processing field. The primary goal of FND is to discern and evaluate the veracity of pivotal claims in news articles, thereby determining the credibility of the presented news. Employing FND acts as a safeguard against the adverse ramifications of propagating misrepresented information, potentially wreaking socio-political and national havoc on targeted societal segments. Furthermore, with the accelerated spread of false information across social media platforms, spanning both textual and visual formats, an imperative arises to enhance the efficacy and accuracy of FND methodologies tailored to these diverse data sources. This paper presents (Verifiable Fake News Detection), a framework tailored to detect fake news in articles that incorporate both textual and visual content. employs a multi-modal ensemble approach, an integration technique that combines various models and data sources for a holistic analysis, to aggregate feature vectors from different media sources within a news article and effectively classify its credibility. Specifically, uses the SBERT and DeBERT models, both widely-used and pre-trained language representation models, to convert the textual news information into word vector representations. Similarly, uses the ResNet model, a deep convolutional neural network known for its efficacy in image feature extraction and recognition, to derive a feature vector from the image(s) in the new article. then combines these generated vectors using a weighted fusion strategy to obtain a unified feature representation capturing nuances from both textual and visual data. For classification of the resulting vector(s), a deep neural network classifier with dropout regularization is deployed to determine the authenticity of the news article. In analyzing the textual and the image data, a reduction in the number of parameters is performed to optimize the performance of the detection model, all while maintaining high accuracy. Evaluation results performed on the Twitter MediaEval Dataset and the Weibo Corpus indicate that achieves accuracies of 87.40% and 88.34% on the Twitter and Weibo datasets, respectively, a better performance compared to other recent FND studies.

Due to the growing interconnection and sensitive nature of IoT applications, this article provides a comprehensive overview of cybersecurity in 5G-enabled Internet of Things (IoT) networks. The review highlights the significant potential of 5G technology while also emphasizing the need for ongoing work to mitigate its weaknesses and fully harness its advantages. In this context, prospective research avenues are crucial to enhance the resilience and security of 5G-enabled systems. The future directions outlined in this document are diverse and seek to tackle significant deficiencies in 5G-IoT cybersecurity research. A primary objective is to align theoretical analyses with practical implementations by examining specific assaults in IoT contexts, thus improving the comprehension of real-world vulnerabilities. Furthermore, as the 5G standard progresses through its implementation, continuous research must remain synchronized with improvements, focusing on components and features that are currently in development. Formulating effective protection and mitigation strategies to address sophisticated threats is essential for safeguarding this integrated environment. Furthermore, enhancing standards and regulatory frameworks is essential to ensure uniform security procedures across various businesses and areas, especially considering the global nature of 5G-IoT applications. Ultimately, investigating sophisticated attack scenarios that leverage the interaction between IoT devices and 5G infrastructure would enhance understanding of vulnerabilities and aid in the development of more resilient security measures. These directives underscore the necessity for ongoing, flexible research to guarantee the robustness and safety of 5G-enabled IoT networks.

Continuous blood pressure (BP) monitoring plays a crucial role in preventative health care because it provides invaluable assessment of an individual’s cardiovascular health. However, achieving accurate and non-invasive BP estimation remains a significant challenge. Motivated by the need for improved accuracy in BP estimation, this research proposes a novel deep-learning technique to estimate blood pressure from photoplethysmography (PPG) signals based on the temporal attention mechanism by selecting optimal features to improve the estimation accuracy. The proposed deep learning model consists of an embedded temporal attention employed to learn the temporal sequence and selects the optimal competitive features using bidirectional encoder and decoder. PPG features are extracted from traditional feature extraction techniques such as statistical, frequency, time domain-based analysis and chaotic features using two publicly available datasets MIMIC II and PPG-BP. The comparison of proposed model experimental results with the state-of-the-art models demonstrates the advancement of optimal feature selection technique for blood pressure measurement. The mean absolute error (MAE) and standard deviation SD for optimal feature extraction with the temporal attention mechanism model are 2.31 ± 3.16 mmHg and 1.13 ± 2.32 mmHg utilising MIMIC II data and 3.43 ± 3.08 mmHg and 3.29 ± 2.19 mmHg for PPG-BP dataset for SBP and DBP respectively. The proposed model results satisfied the standard requirements published by the British Hypertension Society (BHS) and US Association for the Advancement of Medical Instrumentation (AAMI) standards. The proposed solution can offer continuous BP estimation and can significantly enhance healthcare by helping individuals manage their cardiovascular health more effectively.