The advent of the Intelligence of Social Things (IoST) paradigm has created new prospects for improving false news detection by utilizing interconnected social networks, facilitating the amalgamation of many data sources including user behaviors, social interactions, and contextual information. Multiple techniques exist for identifying false information, with individual methods often concentrating on aspects such as news substance, social context, or external veracity. Establishing dissemination networks, examining the structural traits and methods of fake news spread on Weibo and Twitter. Nonetheless, it possesses limitations in enabling the two modes to concentrate more efficiently on their individual preferences. By using entity linking to expand the entity terminology in news content and semantic mining to augment the style vocabulary in news material, the Pref-FEND model was developed. The graph neural network’s capacity to effectively capture node properties was improved by learning and using five different types of words as node representations in the graph network. A heterogeneous degree-aware graph convolutional network was concurrently incorporated, yielding enhancements of 2.8% and 1.9% in F1-score relative to the fact-based singular model GET. Additionally, when integrated with LDAVAE+GET for concurrent detection, the F1-scores were enhanced by 1.1% and 1.3%, respectively, in comparison to Pref-FEND. The experimental findings confirm the efficacy of the suggested enhancements to the model.

Intelligent reconfigurable surfaces (IRS) have emerged as a promising technology to enhance wireless communications by dynamically controlling the propagation environment. Despite their potential, practical challenges such as effective integration with existing systems and efficient optimization remain critical. This paper investigates the sum capacity enhancement of NOMA-enabled uncrewed aerial vehicle (UAV) communications in vehicular networks assisted IRS. In urban environments where direct links from UAV to vehicles are often obstructed by buildings or other obstacles, the IRS plays a critical role in improving signal quality by reflecting signals toward vehicles. We consider a downlink NOMA transmission scenario, where the UAV serves multiple ground vehicles, and signals are delivered through both direct and IRS-assisted links. A joint optimization problem is formulated to maximize the sum capacity by simultaneously optimizing UAV power allocation and IRS passive beamforming while ensuring a minimum signal-to-interference plus noise ratio requirement for each vehicle. To address the non-convex nature and reduce the complexity of the optimization, we first transform the original problem using the first-order Taylor expansion method. Then, we employ a two-step solution based on the fixed-point iteration method for passive beamforming at the IRS and standard convex optimization for UAV power allocation. The proposed solution is compared with a benchmark scheme with direct UAV-to-vehicle communication without IRS assistance. Numerical results demonstrate that our proposed framework converges quickly and significantly outperforms the benchmarks in terms of system capacity.

Abstract Autonomous intelligent systems are artificial intelligence (AI) tools that act autonomously without direct human supervision. Cloud computing (CC) and Internet of Things (IoT) technologies find it challenging to deploy sufficient security defences because of the different structures, storage, and limited computing capabilities that make them more vulnerable to attacks. Security threats against IoT structures, devices, and applications are increasing with the demand for IoT technology. The training data available to AI models may be limited, which could impact their performance and generalizability. Adopting AI solutions in real‐world situations may be impeded by compatibility concerns and the requirement for flawless integration. Malware classification errors can occur due to a lack of contextual knowledge, particularly in cases where benign files behave identically to malicious. Various studies were carried out on detecting IoT malware to evade the menaces posed by malicious code. However, prevailing techniques of IoT malware classification supported particular platforms or demanded complicated methods for attaining higher accuracy. This study introduces an enhanced slime mould optimization with a convolutional BLSTM autoencoder‐based malware classification (ESMO‐CBLSTMAE) system in the IoT cloud platform. The projected ESMO‐CBLSTMAE system focuses on detecting and classifying malware in the IoT cloud platform. To achieve that, the ESMO‐CBLSTMAE algorithm employs a min–max normalization technique for scaling the input dataset. The ESMO‐CBLSTMAE method uses a convolutional bidirectional long short‐term memory autoencoder (CBLSTM‐AE) model for the malware detection process. Lastly, the ESMO method is executed for the optimum hyperparameter tuning of the CBLSTM‐AE technique, which boosts the malware classification results. The experimental analysis of the ESMO‐CBLSTMAE method is tested against a benchmark database, and the outcomes portray the greater efficacy of the ESMO‐CBLSTMAE approach over other existing techniques. The proposed malware classification model achieved an accuracy of 98.57 and F Score of 80.77 and outperformed the existing models.

Abstract In the realm of Drug‐Target Interaction (DTI) prediction, this research investigates and contrasts the efficacy of diverse evolutionary algorithms in fine‐tuning a sophisticated hybrid deep learning model. Recognizing the critical role of DTI in drug discovery and repositioning, we tackle the challenges of binary classification by reframing the problem as a regression task. Our focus lies on the Convolution Self‐Attention Network with Attention‐based bidirectional Long Short‐Term Memory Network (CSAN‐BiLSTM‐Att), a hybrid model combining convolutional neural network (CNN) blocks, self‐attention mechanisms, and bidirectional LSTM layers. To optimize this complex model, we employ Differential Evolution (DE), Particle Swarm Optimization (PSO), Memetic Particle Swarm Optimization Algorithm (MPSOA), Fire Hawk Optimization (FHO), and Artificial Hummingbird Algorithm (AHA). Through thorough comparative analysis, we evaluate the performance of these evolutionary algorithms in enhancing the CSAN‐BiLSTM‐Att model's effectiveness. By examining the strengths and weaknesses of each algorithm, our study aims to provide valuable insights into DTI prediction, identifying the most effective evolutionary algorithm for hyperparameter tuning in advanced deep learning models. Notably, Fire‐hawk optimization (FHO) emerges as particularly promising, achieving the highest Concordance Index (C‐index) as 0.974 for KIBA datasets and 0.894 for DAVIS datasets and demonstrating exceptional accuracy in ranking continuous predictions across both the datasets.

The integration of beyond diagonal reconfigurable intelligent surfaces (BD-RIS) with non-orthogonal multiple access (NOMA) in the Internet of Vehicles (IoV) networks presents the transformative potential for 6G vehicular communications, promising unprecedented gains in energy and spectral efficiency. However, existing research fails to address the joint optimization of BD-RIS configuration, NOMA power allocation, and dynamic IoV association–a critical oversight given their inherent interdependence in practical deployment scenarios. Current literature predominantly examines these components in isolation, with conventional RIS architectures and orthogonal multiple access schemes, leading to suboptimal performance in high-mobility vehicular environments. This work bridges this gap by proposing a novel hybrid optimization framework for NOMA-aided BD-RIS assisted IoV networks. The proposed framework simultaneously optimizes the IoV association with the base station (BS), NOMA power allocation, and BD-RIS phase shift design to maximize the energy efficiency of the system while ensuring the minimum signal-to-interference plus noise ratios (SINRs) of IoVs. The proposed framework is formulated as non-linear optimization due to joint decision variables and rate expressions of IoVs, resulting in an NP-hard problem where achieving a joint optimal solution is computationally complex. To handle this complexity, the original joint formulation is decomposed into three subproblems: IoV association with BS, BS power allocation, and BD-RIS phase shift design. An efficient iterative solution is then developed through the synergistic combination of deep reinforcement learning, first-order Taylor expansion, and manifold optimization methods. For comprehensive evaluation, we introduce a NOMA-aided conventional RIS assisted IoV framework as a benchmark. Extensive numerical results based on Monte Carlo simulations demonstrate that the proposed NOMA-aided BD-RIS assisted IoV network achieves significant improvements of 32% in energy efficiency and 28% in spectral efficiency compared to conventional RIS-assisted architectures, validating its potential for next-generation vehicular networks.

With the rapid evolution toward data-intensive applications and sustainable urban mobility, upcoming sixth-generation (6G) wireless networks must deliver enhanced coverage, high spectral efficiency, and energy optimization across densely populated areas. However, achieving these requirements poses significant challenges due to the dynamic nature of urban environments, high interference in multi-cell systems, and limitations in conventional passive beamforming technologies. To address these challenges, reconfigurable intelligent surface (RIS) is considered a highly promising approach for enabling and improving 6G wireless communications. This is because it has the ability to efficiently manipulate wireless channels at a lower cost. Considerable study has focused on the utilization of conventional diagonal RIS, in which each individual RIS component is linked to its own ground load but not interconnected with other elements. Nevertheless, the uncomplicated structure of classical RIS imposes restrictions on its ability to manipulate passive beamforming. In this study, we consider beyond diagonal RIS (BD-RIS) in the multi-cell transportation system, which goes beyond using diagonal phase shift matrices. In particular, we provide a new optimization framework to maximize the sum rate of BD-RIS assisted multi-cell transportation system by optimizing the power allocation of the base station and phase shift design of BD-RIS in each cell. We employ the block coordinate descent method to transform the original optimization problem and achieve a local optimal based on standard convex approaches. Numerical results demonstrate the benefits of adopting BD-RIS in multi-cell transportation systems compared to the classical RIS architecture.

This paper introduces a novel telehealth communication system, designed to enhance the security and integrity of medical data exchange. In the rapidly evolving digital healthcare landscape, the protection of sensitive patient information is paramount. To address this, our system uniquely combines quantum cryptography, specifically the BB84 protocol, with blockchain technology, offering a dual-layered security framework. The Quantum Layer, underpinned by the BB84 protocol, establishes quantum-secure communication channels, effectively encrypting data exchanges between patients, doctors, and hospitals. This layer guarantees that medical information remains confidential and safe from potential quantum-level eavesdropping threats. The subsequent Blockchain Layer further strengthens the system by storing these encrypted communications in an immutable blockchain ledger. This approach not only secures the data against unauthorized alterations but also provides a transparent and permanent record of all transactions, thereby enhancing the auditability of medical communications.

In recent times data breaches in various sectors of industry have become a common threat. It has become very crucial to secure patient data in the health industry. The upcoming Healthcare 4.0 techniques can play an important role in this. We have implemented these techniques in our proposed model to protect personalised health information of the individual health profiles of the patient using blockchain. The proposed model is also a recommending system with the aim to offer relevant advice to the patients to keep a check on the various health parameters like blood pressure, body temperature, blood sugar etc.

The Internet of Medical Things (IoMT) revolution-izes healthcare by integrating medical devices and systems with the internet. However, the vast amounts of sensitive medical data in IoMT networks pose significant security and privacy concerns. Traditional security measures often fall short in identifying the malicious data attacks within the IoMT ecosystems. This paper introduces an AI-based non-malicious data classification scheme based on blockchain. We applied and evaluated the machine learning (ML) classifiers, such as support vector machine (SVM), random forest (RF), and K-Nearest neighbor (KNN). We evaluated the proposed framework based on various performance metrics that includes accuracy, precision, recall, and F1 score. The accuracy using SVM obtained 75.3%, RF is 75.9%, and K-NN is 84.3%. The results shows that KNN performs better than other models hy the factor of 9%.

Abstract Cardiovascular disease (CD) is one of the leading causes of death and disability across the globe. Chest x‐rays (CXR) are crucial in detecting chest and CD. The CXR images present helpful information to the radiologist to identify a disease at an earlier stage. Several convolutional neural network (CNN) models for classifying the CXR images have been established. However, there is a demand for significant improvement in CNN models to generalize them in diverse datasets. In addition, healthcare centers require an effective model for identifying CD with limited resources. Therefore, the authors developed a CNN‐based CD detector using CXR images. The proposed research employs the You Only Look Once, version 7 technique to extract features and DenseNet‐161 for classifying the CXR images into normal and abnormal classes. The authors utilized datasets, including CheXpert and VinDr‐CXR, for the performance evaluation. The findings reveal that the proposed study achieves an accuracy and F1‐measure of 97.9, 97.47, 96.85, and 97.77 for the CheXpert and VinDr‐CXR datasets, respectively. The recommended model required fewer parameters of 5.2 M and less computation time for predicting CD. The study's outcome can assist clinicians in detecting CD at the earliest stage.