ABSTRACT Ultra‐Wideband (UWB) is a wireless communication technology that uses Radio Frequency (RF) to transmit and receive signals between devices. Beamforming in UWB is a technique that uses multiple antennas simultaneously to focus on specific directions. In beamforming, Deep Learning (DL) techniques are applied to enhance signal processing and optimise beam pattern generation by utilising neural networks for efficient and accurate spatial filtering. However, existing DL techniques suffer from catastrophic forgetting, in which the testing data forgets previously learnt data due to the lack of knowledge distillation in other layers. Therefore, this research proposes a Regularised Hyperparameter Bilevel Optimisation with Continual Learning‐based Deep Neural Network (RHBO‐CLDNN) for beamforming in UWB systems. RHBO optimises hyperparameter efficiency at both the upper and lower levels, thereby enabling the DNN to accurately capture UWB channel characteristics, which improves channel estimation and enhances the Signal‐to‐Noise Ratio (SNR). CL is applied to dynamically adapt to changing environmental conditions without requiring complete retraining, making it suitable for real‐time applications. Elastic Weight Consolidation (EWC) regularisation is also applied, which mitigates catastrophic forgetting by preserving weights from learnt tasks and enables the model to adapt to channel conditions without losing previous knowledge. Experiments on the DeepMIMO dataset show that RHBO‐CLDNN enhances the sum‐rate by up to 18% and achieves an inference time of 0.025 s over Convolutional Neural Network (CNN), thereby demonstrating its suitability for real‐time beamforming.

The convergence of sixth-generation (6G) communication networks with the Internet of Things (IoT) introduces a transformative paradigm for remote healthcare services, which enables ultra-reliable, low-latency, and intelligent medical communication in geographically challenged or underserved areas. However, the integration of Terrestrial Networks (TNs) and Non-Terrestrial Networks (NTNs) under a unified 6G framework presents unique challenges in resource management, signal robustness, and system trustworthiness—particularly for life-critical healthcare scenarios. In this paper, we present an AI-enhanced TN-NTN architecture designed to optimize the delivery of real-time healthcare applications such as telemedicine, remote diagnostics, and patient monitoring. Leveraging advanced intelligent models including proximal policy optimization, graph neural networks, multi-agent deep reinforcement learning, and federated learning, the proposed architecture dynamically manages network resources, mitigates interference, and adapts to varying traffic conditions. Simulation results demonstrate that the architecture achieves certain improvements in latency, throughput, and packet delivery rate compared to other models, even under high network loads. These findings highlight the potential of the proposed system to enable robust and cost-effective telemedicine services, bridging critical gaps in healthcare delivery for remote and resource-constrained environments. This research paves the way for AI-driven approaches in expanding the reach of healthcare services, with implications for next-generation 6G networks and beyond.

ABSTRACT Wireless sensor networks (WSNs) is a wireless system including the set of distributed sensor nodes used for physical or environmental observation. A network energy expenditure is considered as a significant concern because of battery restricted sensors of the WSN. Clustering and multi hop routing are considered as effective approaches to enhance the network lifecycle and communication. Achieving the anticipated objective of reducing the energy expenditure, thereby increasing the network lifecycle, is considered as an optimisation issue. In recent times, a nature inspired meta‐heuristic approaches are extensively utilised for solving the different optimisation issues. In this context, this research aims to accomplish the objective by proposing the multiobjective‐perturbed learning and mutation strategy based artificial rabbits optimisation namely M‐PMARO for an optimum cluster head (CH) selection and route discovery. The proposed M‐PMARO incorporates an experience based perturbed learning (EPL) and mutation strategy to identify the capable regions over the search space for enhancing the exploration and avoiding the local optima issue. To formulate the multiobjective, the residual energy, average intracluster distance, average base station (BS) distance, CH balancing factor (CHBF) and node centrality are incorporated for optimum CH discovery while the residual energy and average BS distance are considered for multi hop routing. The M‐PMARO is analysed based on alive nodes, dead nodes, energy expenditure, throughput and data received in BS and network lifecycle. The viability of M‐PMARO is validated by comparing it with existing approaches such as fitness based glowworm swarm with fruitfly algorithm (FGF), energy balanced particle swarm optimisation (EBPSO), improved bat optimisation algorithm (IBOA), graph neural network (GNN) and fuzzy logic and particle swarm optimisation (PSO) based clustering routing protocol namely PFCRE. The alive node count of M‐PMARO is 100 for 1200 rounds, which is higher than the EBPSO.

Sentiment analysis of social media platforms is crucial for extracting actionable insights from unstructured textual data. However, modern sentiment analysis models using deep learning lack explainability, acting as black box and limiting trust. This study focuses on improving the explainability of sentiment analysis models of social media platforms by leveraging explainable artificial intelligence (XAI). We propose a novel explainable sentiment analysis (XSA) framework incorporating intrinsic and posthoc XAI methods, i.e., local interpretable model-agnostic explanations (LIME) and counterfactual explanations. Specifically, to solve the problem of lack of local fidelity and stability in interpretations caused by the LIME random perturbation sampling method, a new model-independent interpretation method is proposed, which uses the isometric mapping virtual sample generation method based on manifold learning instead of LIMEs random perturbation sampling method to generate samples. Additionally, a generative link tree is presented to create counterfactual explanations that maintain strong data fidelity, which constructs counterfactual narratives by leveraging examples from the training data, employing a divide-and-conquer strategy combined with local greedy. Experiments conducted on social media datasets from Twitter, YouTube comments, Yelp, and Amazon demonstrate XSAs ability to provide local aspect-level explanations while maintaining sentiment analysis performance. Analyses reveal improved model explainability and enhanced user trust, demonstrating XAIs potential in sentiment analysis of social media platforms. The proposed XSA framework provides a valuable direction for developing transparent and trust-worthy sentiment analysis models for social media platforms.

Sentiment analysis, a challenging task in understanding human emotions expressed through diverse modalities, prompts the development of innovative solutions. Multimodal data often contains important complementary information. Effective fusion and extraction of multimodal data features are key issues in sentiment analysis. In this article, we introduce a novel sentiment analysis model that integrates multimodal multiscale features based on a fuzzy-deep neural network. First, we combine multimodal data, namely text, audio, and images, to extract intrinsic feature representations. Second, our model incorporates the fuzzy-deep neural network learning module, infused with fuzzy logic principles to enhance adaptability to the inherent vagueness in sentiment expressions. Furthermore, we integrate the dual attention mechanism that dynamically focuses on pivotal aspects within multimodal data, refining feature extraction for heightened context-awareness. Rigorous validation across three datasets, including the Multimodal Corpus of Sentiment Intensity dataset, the Multimodal Opinion Sentiment and Emotion Intensity dataset, and the Chinese Single and Multimodal Sentiment dataset, demonstrates the model's superior performance in capturing the intricacies of human emotions.

Electrocardiogram (ECG) analysis and diagnosis are important auxiliary means for preventing and detecting cardiovascular diseases. Traditional approaches often face challenges due to the sheer volume of data, difficulty in extracting meaningful features, limitations in model complexity, and the requirement for real-time analysis in clinical settings. This paper presents a pioneering approach for automatic ECG diagnosis through the application of a novel Multiscale Deep Neuro-fuzzy Network (MDNFN) structure. The MDNFN is designed to address the complexity of arrhythmia classification by incorporating deep learning and fuzzy logic processing across multiscale feature extraction. To optimize the performance of the MDNFN, an innovative model optimization technique based on the Particle Swarm Optimization (PSO) algorithm is introduced, offering an efficient exploration of the parameter space. Extensive experiments across diverse datasets validate the superior performance of the proposed model compared to existing methods. The MDNFN demonstrates heightened accuracy and robustness, supported by its adaptability to different frequency and time scales inherent in ECG signals. The study establishes the model's efficacy through comprehensive experimentation, providing compelling evidence for its potential application in real-world clinical scenarios.

Medical image analysis plays a crucial role in healthcare systems of Internet of Medical Things (IoMT), aiding in the diagnosis, treatment planning, and monitoring of various diseases. With the increasing adoption of artificial intelligence (AI) techniques in medical image analysis, there is a growing need for transparency and trustworthiness in decision-making. This study explores the application of explainable AI (XAI) in the context of medical image analysis within medical cyber-physical systems (MCPS) to enhance transparency and trustworthiness. To this end, this study proposes an explainable framework that integrates machine learning and knowledge reasoning. The explainability of the model is realized when the framework evolution target feature results and reasoning results are the same and are relatively reliable. However, using these technologies also presents new challenges, including the need to ensure the security and privacy of patient data from IoMT. Therefore, attack detection is an essential aspect of MCPS security. For the MCPS model with only sensor attacks, the necessary and sufficient conditions for detecting attacks are given based on the definition of sparse observability. The corresponding attack detector and state estimator are designed by assuming that some IoMT sensors are under protection. It is expounded that the IoMT sensors under protection play an important role in improving the efficiency of attack detection and state estimation. The experimental results show that the XAI in the context of medical image analysis within MCPS improves the accuracy of lesion classification, effectively removes low-quality medical images, and realizes the explainability of recognition results. This helps doctors understand the logic of the system's decision-making and can choose whether to trust the results based on the explanation given by the framework.

Integrating 6G networks with emergency medical services (EMS) transportation aims to transform patient care during transit, yet maintaining reliable communication for mobile medical units poses technical hurdles. This paper introduces an intelligent reconfigurable surface-assisted healthcare transport system for the 6G (IRIS-HT6G) framework. We leverage reconfigurable intelligent surfaces to enhance communication links between healthcare vehicles, roadside units, and remote medical facilities. Our framework jointly optimizes RIS phase shifts, power allocation, and spectrum sharing to maximize system capacity while ensuring the reliability of critical healthcare data transmission. Simulation results demonstrate the superiority of IRIS-HT6G over state-of-the-art baseline methods. The proposed framework achieves up to 30% higher link capacity, 50% lower latency, and significantly improved reliability in non-line-of-sight scenarios. Further, based on a 30-day trial involving 15 ambulances serving an urban population of 500 000, IRIS-HT6G achieved tangible improvements in EMS operations. Emergency response times decreased from 12.5 to 10.0 minutes through better fleet coordination. Patient care continuity during transport improved from 76% to 98% uptime, enabling uninterrupted vital sign monitoring and ECG transmission.

Abstract Sentiment analysis is an automated approach which is utilized in process of analysing textual data to describe public opinion. The sentiment analysis has major role in creating impact in the day‐to‐day life of individuals. However, a precise interpretation of text still relies as a major concern in classifying sentiment. So, this research introduced Bidirectional Long Short Term Memory with Ranger AdaBelief Optimizer (Bi‐LSTM RAO) to classify sentiment of tweets. Initially, data is obtained from Twitter API, Sentiment 140 and Stanford Sentiment Treebank‐2 (SST‐2). The raw data is pre‐processed and it is subjected to feature extraction which is performed using Bag of Words (BoW) and Term Frequency‐Inverse Document Frequency (TF‐IDF). The feature selection is performed using Gazelle Optimization Algorithm (GOA) which removes the irrelevant or redundant features that maximized model performance and classification is performed using Bi LSTM–RAO. The RAO optimizes the loss function of Bi‐LSTM model that maximized accuracy. The classification accuracy of proposed method for Twitter API, Sentiment 140 and SST 2 dataset is obtained as 909.44%, 99.71% and 99.86%, respectively. These obtained results are comparably higher than ensemble framework, Robustly Optimized BERT and Gated Recurrent Unit (RoBERTa‐GRU), Logistic Regression‐Long Short Term Memory (LR‐LSTM), Convolutional Bi‐LSTM, Sentiment and Context Aware Attention‐based Hybrid Deep Neural Network (SCA‐HDNN) and Stochastic Gradient Descent optimization based Stochastic Gate Neural Network (SGD‐SGNN).

Vehicular edge computing (VEC) systems face challenges in providing real-time intelligent transportation services due to limited computing resources at VEC servers, which lead to excessive delays or denial of services, especially for latency-critical tasks. This article proposes an augmented intelligence of things (AIoT) framework to enable priority-aware task offloading in VEC for vehicle road cooperation systems, maximizing overall system rewards under latency constraints. The framework incorporates an advanced dynamic resource management mechanism that adapts to real-time data and optimizes resource allocation using augmented intelligence models. The joint priority-aware application offloading and resource optimization problem is formulated as a constrained Markov decision process, and a deep Q-network (DQN)-based learning algorithm is employed to optimize the allocation of communication and computational resources based on application priorities and real-time channel/queue state information. Simulation results demonstrate that the proposed algorithm achieves significant improvements in weighted carrying capacity, high/low-priority task drop rates, and high/low-priority task queuing delays under varying overall task arrival rates, proportions of high/low-priority tasks, vehicle density, and task size compared to benchmark schemes. The proposed AIoT-enhanced DQN-based learning algorithm advances the field of VEC systems for vehicle road cooperation, offering practical advantages, such as increased efficiency, reduced latency, and improved resource utilization, ultimately enhancing user experience and enabling real-world applications in intelligent transportation systems.

The proliferation of multimedia-enabled IoT devices and edge computing enables a new class of data-intensive applications. However, analyzing the massive volumes of multimedia data presents significant privacy challenges. We propose a novel framework called generative adversarial privacy (GAP) that leverages generative adversarial networks (GANs) to synthesize privacy-preserving surrogate data for multimedia analytics across the IoT-Edge continuum. GAP carefully perturbs the GAN's training process to provide rigorous differential privacy guarantees without compromising utility. Moreover, we present optimization strategies, including dynamic privacy budget allocation, adaptive gradient clipping, and weight clustering to improve convergence and data quality under a constrained privacy budget. Theoretical analysis proves that GAP provides rigorous privacy protections while enabling high-fidelity analytics. Extensive experiments on real-world multimedia datasets demonstrate that GAP outperforms existing methods, producing high-quality synthetic data for privacy-preserving multimedia processing in diverse IoT-Edge applications.

The exponential growth of mobile data traffic in next generation networks has led to a significant increase in energy consumption, posing critical challenges for network operators. We propose DLLF-2EN, a novel energy-efficient framework that integrates deep learning-based load forecasting, an advanced power consumption model, and a comprehensive energy-saving strategy to address this issue. The load forecasting technique utilizes deep convolutional neural network and long short-term memory model, which is based on deep learning. This model is capable of capturing the spatiotemporal dependencies present in network traffic data. The power consumption model accurately characterizes the base stations’ static and dynamic power consumption components, facilitating the assessment of energy efficiency under various network scenarios. The energy-saving strategy combines base station sleep mode with discontinuous transmission and reception, as well as lightweight transmission of common signals, dynamically adapting the network operation based on the predicted traffic load. Furthermore, DLLF-2EN incorporates an intelligent power management system that leverages machine learning algorithms to continuously monitor the network, analyze collected data, and make optimal energy-saving decisions in real-time. Simulation demonstrate that the superior performance of DLLF-2EN in terms of load forecasting accuracy and energy efficiency compared to state-of-the-art baseline methods. The proposed framework represents a comprehensive solution for energy-efficient and sustainable next generation mobile networks, addressing the critical challenges of minimizing energy consumption while meeting the growing demands for high-quality mobile services.

In recent decades, automatic vehicle classification plays a vital role in intelligent transportation systems and visual traffic surveillance systems. Especially in countries that imposed a lockdown (mobility restrictions help reduce the spread of COVID‐19), it becomes important to curtail the movement of vehicles as much as possible. For an effective visual traffic surveillance system, it is essential to detect vehicles from the images and classify the vehicles into different types (e.g., bus, car, and pickup truck). Most of the existing research studies focused only on maximizing the percentage of predictions, which have poor real‐time performance and consume more computing resources. To highlight the problems of classifying imbalanced data, a new technique is proposed in this research article for vehicle type classification. Initially, the data are collected from the Beijing Institute of Technology Vehicle Dataset and the MIOvision Traffic Camera Dataset. In addition, adaptive histogram equalization and the Gaussian mixture model are implemented for enhancing the quality of collected vehicle images and to detect vehicles from the denoised images. Then, the Steerable Pyramid Transform and the Weber Local Descriptor are employed to extract the feature vectors from the detected vehicles. Finally, the extracted features are given as the input to an ensemble deep learning technique for vehicle classification. In the simulation phase, the proposed ensemble deep learning technique obtained 99.13% and 99.28% of classification accuracy on the MIOvision Traffic Camera Dataset and the Beijing Institute of Technology Vehicle Dataset. The obtained results are effective compared to the standard existing benchmark techniques on both datasets.