Search Results (1,204)

The proliferation of Edge-AI in personalized healthcare has driven significant demand for energy-efficient Systems-on-Chip (SoCs) capable of executing high-accuracy, real-time disease detection. Conventional accelerator architectures struggle to simultaneously accommodate disparate AI models. Specifically, memory-intensive Convolutional Neural Networks (CNNs) for high-fidelity feature extraction and event-driven Spiking Neural Networks (SNNs) for ultra-low-power, brain-inspired computation. To address this bottleneck, this paper presents a comprehensive survey of Reconfigurable Hybrid Neural Networks (RHNNs), an emerging paradigm that dynamically merges the strengths of CNNs and SNNs to meet the stringent resource constraints of biomedical edge devices. We establish a comprehensive taxonomy of existing RHNN architectures, categorizing them by hardware interconnection topologies, dataflow orchestration strategies, and internal structural adaptation mechanisms. Furthermore, we examine the integration of these hybrid accelerators within the open-source RISC-V processor ecosystem, evaluating how custom instruction set extensions optimize control efficiency and minimize energy overhead. The survey also analyzes commonly used datasets based on three major biomedical signal modalities, including electroencephalography (EEG), electrocardiography (ECG), and electromyography (EMG), in the context of processing systems for hardware accelerators. Finally, we highlight the open research challenges and outline future research directions to guide the development of next-generation biomedical intelligent systems. Full article

Photocatalytic antibacterial coatings have emerged as a promising antibiotic-free strategy for combating healthcare-associated infections, biofilm formation, marine biofouling, and environmental microbial contamination. Unlike conventional antimicrobial approaches, photocatalytic systems continuously generate reactive oxygen species (ROS) under light irradiation, enabling broad-spectrum antimicrobial activity while minimizing the risk of antimicrobial resistance. This review systematically summarizes the fundamental mechanisms underlying photocatalytic antibacterial activity, including photogenerated charge-carrier dynamics, ROS generation pathways, and microbial inactivation processes. We further highlight recent advances in photocatalyst design, spanning conventional semiconductor photocatalysts, heterojunction engineering, cocatalyst modification, and two-dimensional material-assisted strategies for enhanced photocatalytic performance. Crucially, particular emphasis is placed on coating architectures and interfacial regulation, including encompassing fabrication methodologies, coating–substrate adhesion, internal heterointerface design, and coating–microorganism interactions, which dictate long-term durability and antibacterial efficacy. Finally, we explore the diverse applications of these coatings in medical devices, environmental remediation, and marine antifouling, while identifying current bottlenecks and future research trajectories toward developing durable, highly efficient, and clinically translatable antimicrobial surface technologies. Full article

The rapid adoption of Internet of Things (IoT)-enabled applications has significantly expanded the cyberattack surface across a wide range of critical systems such as industrial IoT (IIoT), smart grids, transportation, healthcare, industrial control systems, and smart cities. False data injection attack (FDIA) has emerged as a serious security threat to these applications due to its stealthiness and adversarial nature, silently corrupting the data integrity of critical operational processes without triggering conventional detection mechanisms. Existing FDIA solutions rely on single-model architectures that are built based on classical or limited predefined attack scenarios. Such solutions often fail to achieve robust detection under adversarial and evolving attack conditions; accordingly, they lack generalisability and are insufficient to capture the broader scope of FDIAs. In this study, a hybrid detection framework is proposed that integrates a Random Forest classifier with an unsupervised anomaly detection model based on a deep autoencoder combined through a Logistic Regression metaclassifier. The proposed framework addresses the gap in single-model detectors that either rely on fixed decision boundaries that struggle with gradually evolving stealthy FDIA patterns or on anomaly detection that lacks strong discriminative power in separating subtle adversarial deviations from normal operational variability. Different types of stealthy and adversarial FDIA have been modelled and injected into the dataset samples for use in training the proposed model. The results show that the overall detection performance of the proposed architecture improved by 2.39 percentage points in terms of F1-score while maintaining a low false-positive rate of 0.49%. These findings reflect the effectiveness of feature representation learning via autoencoders and hybrid classification strategies against stealthy and adversarial FDIA patterns. Future work should include temporal modelling for further advancing robust detection against evolving adversarial threats. Full article

Falls represent a threat to older adults, overload healthcare systems, and reduce quality of life. Vision-based fall detection has advanced recently through deep learning, yet most proposed models lack validation on physical hardware and do not report inference-time metrics. This systematic review, following PRISMA and Kitchenham guidelines, targets this gap. We focus exclusively on vision-based systems that report inference speed on a specified device. We define real-time performance using a threshold of 10 fps, based on the reported duration of the critical fall phase in real-life falls. From 588 records across IEEE Xplore, ACM Digital Library, Web of Science Core Collection, and PubMed (2019–2024), only 11 met all inclusion criteria, highlighting how few studies validate real-time performance on physical hardware. The findings show that CNN-based architectures dominate algorithm choice, edge devices dominate deployment platforms, and optimization remains central to real-time inference on constrained hardware. Across these studies, we identify two persistent limitations: no real-world testing with older adults and reliance on small, controlled datasets with simulated falls. Full article

Continuous monitoring of Activities of daily living (ADLs) requires sensing systems that are privacy-preserving, low-power, and robust to environmental variation. Ambient sensing technologies provide an alternative to RGB video and wearable devices, but individual sensing modalities exhibit characteristic limitations. Sparse mmWave radar provides strong motion sensitivity but limited posture detail, low-resolution thermal sensing preserves posture-related spatial information, and smart plug telemetry captures only appliance-mediated behavioural interaction. To address these limitations, this paper proposes a layered multimodal ambient-sensing framework comprising a sparse-track 24-GHz FMCW radar, a $32\times 24$ low-resolution thermal sensor, and a Moko smart plug. It experimentally evaluates a radar–thermal HAR branch together with a separate smart plug appliance-recognition branch. The framework proposes three streams to enable continuous non-wearable monitoring while maintaining redundancy and reduced privacy exposure for intelligent-building and ambient assisted living environments. Radar and thermal streams are jointly evaluated on binary motion and four-class posture and activity recognition tasks collected across multiple environmental configurations using recording-grouped cross-validation, while the appliance stream is evaluated using per-plug telemetry from residential-grade appliances. The radar–thermal streams use a single-subject, fixed-placement dataset of binary-motion windows and four-class posture and motion windows collected across six furniture configurations. The separate intrusive load monitoring stream utilises smart plugs to classify appliances. Regarding binary motion recognition, radar $(F_{1,\mathrm{Transformer}}=0.882\pm 0.034)$ and thermal $(F_{1,\mathrm{XGBoost}}=0.870\pm 0.069)$ pipelines achieved similar macro F1 performance. On the four-class posture and activity recognition task, thermal features $(F_{1,\mathrm{thermal}}=0.775\pm 0.053)$ substantially outperformed radar $(F_{1,\mathrm{radar}}=0.609\pm 0.110)$. Weighted late fusion produced only modest descriptive gains. Separately, smart plug telemetry demonstrated strong appliance recognition performance using lightweight tree-based models suitable for constrained edge deployment. The results support a scoped redundancy argument. Sparse track-level radar carries gross motion, while low-resolution thermal sensing carries posture. The smart plug appliance monitoring extends the framework toward appliance-mediated instrumental activity of daily living (IADL) monitoring, with lightweight tree-based models achieving strong recognition performance under constrained edge deployment conditions. The findings support a layered multimodal sensing architecture for privacy-preserving ADL monitoring, where radar contributes motion-sensitive coverage, thermal sensing contributes posture-aware spatial context, and smart plug telemetry contributes appliance-level behavioural evidence within intelligent healthcare and ambient assisted living environments. Full article

Large language models (LLMs) are expanding the capabilities of socially assistive robots (SARs) through natural dialogue, personalisation, multimodal reasoning, retained interaction context, and adaptive behaviour in healthcare. Integrating generative language models into robots, however, complicates evaluation because fluent output may exaggerate perceived competence and increase the risks of hallucination, overtrust, privacy exposure, relationship dependency, and unsafe reliance on advice or actions. This PRISMA-informed review synthesises healthcare robotics, human–robot interaction, LLM-enabled systems, ethics, implementation, and care delivery. Database searches returned 128 records, of which 110 were unique after deduplication. Supplementary retrieval and assessment yielded 85 substantive sources spanning background mapping, primary analysis, and governance. Studies focused mainly on feasibility, usability, acceptability, dialogue quality, and short-term engagement, whereas longitudinal safety, governance of retained interaction context, comparative effectiveness, workflow integration, and sustained healthcare value received limited attention. These gaps indicate that evaluation of LLM-enabled SARs must account for physical presence, social role, interaction memory, and potential actions rather than focus on conversational performance alone. The review therefore proposes HEART, a healthcare-specific evaluative architecture comprising Human-Centred Communication, Ethical and Trustworthy Deployment, Adaptive and Embodied Intelligence, Relationship Continuity, and Translational Healthcare Value. HEART uses boundary rules, operational indicators, qualitative labels, and non-additive deployment gates to separate evaluative domains, define assessable outcomes, summarise reported support, and prevent strengths in one area from masking critical safety or governance failures. Future research should validate HEART through longitudinal and comparative assessment of hallucination severity, language-to-action safety, long-term effects, equity, and post-deployment monitoring. Full article

Diagnosing skin diseases remains a clinical challenge due to the visual similarity among diverse dermatological conditions. This study presents a prototype deep learning–powered system for multi-class dermatological screening, implemented through a cloud-based architecture and accessed via a smartphone interface. A pre-trained Convolutional Neural Network (CNN), EfficientNetV2B3, was fine-tuned on a composite dataset encompassing nine disease categories. The model achieved promising performance, with an accuracy of 0.87, precision of 0.87, recall of 0.87, and an F1 score of 0.86, indicating its potential reliability for automated classification. Prototype validation was conducted using a cloud API (Google Cloud Storage + PostMan) to verify the inference pipeline and user interaction. While the current implementation demonstrates the feasibility of cloud-based dermatological screening, real-device mobile performance metrics such as latency, model size, and memory consumption remain future work. Users can capture or upload skin images, which are processed to generate preliminary diagnostic feedback, including symptom descriptions and general treatment information. While not intended to replace professional medical evaluation, the prototype serves as a proof-of-concept tool for initial screening and early intervention. This work illustrates how artificial intelligence (AI) can be harnessed in mobile health applications to expand access to dermatological care and supports broader initiatives to integrate AI into healthcare delivery. Full article

Terahertz (THz) technology has emerged as a versatile platform enabling advancements across materials characterization, sensing, wireless communication, and biomedical diagnostics. This review provides a unified perspective on these application domains by highlighting the central role of terahertz time-domain spectroscopy (THz-TDS) as a fundamental tool for probing material electrodynamics. THz-TDS enables simultaneous measurement of amplitude and phase of the electric field, allowing contact-free direct extraction of complex permittivity, conductivity and other dielectric properties. Building on this capability, the review connects material-level properties to device and system-level functionalities, including metamaterial-based sensors, graphene-enabled reconfigurable intelligent surfaces (RISs), and beam-steering architectures relevant to 6G and beyond communication systems. Furthermore, the potential of THz techniques in biomedical applications is discussed in detail, particularly for non-invasive tumor detection through dielectric contrast mapping and imaging-based reconstruction methods. By integrating developments across these domains, this review presents THz-TDS as a unifying framework that links materials physics to emerging technologies in sensing, communication, and healthcare, offering insights into future directions for THz research and applications. The principal contribution of this review is to present a cross-domain framework that relates THz field measurements and extracted material electrodynamics to sensing, reconfigurable wavefront control, communication technologies, and biomaterials characterization. Full article

Continual learning (CL) aims to develop intelligent systems capable of learning continuously from sequential data while retaining previously acquired knowledge. As AI systems are increasingly deployed in dynamic real-world environments, CL has become essential for enabling long-term adaptation without catastrophic forgetting. This review provides a structured overview of major CL paradigms, including task-incremental, domain-incremental, class-incremental, online, multimodal, and federated CL. We examine the theoretical foundations of CL, particularly the stability–plasticity dilemma, catastrophic forgetting, transfer dynamics, and representation learning. In addition, we analyze major methodological categories, including regularization-based, replay-based, architecture-based, optimization-based, representation-learning, and parameter-efficient approaches. Recent developments involving transformers, prompt learning, foundation models, and multimodal adaptation are also discussed as emerging directions in modern CL research. Furthermore, this review highlights important issues related to benchmark fragmentation, evaluation inconsistency, memory constraints, computational efficiency, scalability, and privacy-aware learning. We also summarize key application domains, including computer vision, natural language processing, robotics, healthcare, and medical imaging. Finally, we identify open research challenges and future directions toward scalable, reliable, and deployment-oriented lifelong learning systems capable of operating effectively in continuously evolving environments. Full article

Recent developments in cognitive neuroscience, neuroimaging, and embodied cognition have stimulated growing interest in neuroscience-informed architecture. However, despite the rapid expansion of the field, neuroarchitectural scholarship remains conceptually fragmented and methodologically diverse, and its institutional evolution, thematic structure, and translational implications have not been comprehensively synthesised. Using a narrative critical synthesis supported by translational qualitative structuring and conceptual synthesis, this study examines foundational publications, institutional documents, conference outputs, and applied studies that have shaped neuroscience-informed architecture since the early 2000s. The review identifies recurring thematic domains, including sensory perception, spatial cognition, emotion, empathy, embodied cognition, environmental behaviour, healthcare design, and evidence-based architectural practice. It indicates that ANFA-related scholarship has played a significant role in legitimising neuroscience as a relevant framework for understanding architectural experience and informing design practices in therapeutic, educational, and behavioural contexts. The review also highlights persistent methodological challenges in translating neuroscientific findings into architectural research, particularly given that architectural experience is multisensory, embodied, socially situated, and temporally dynamic. The study concludes that neuroscience-informed architecture should be understood not as a deterministic explanatory model but as a translational framework that connects neuroscientific knowledge with architectural theory and practice. This expanding evidence base calls for more ecologically valid, interdisciplinary, and methodologically rigorous research, while encouraging architects and practitioners to adopt evidence-based, human-centred design strategies that enhance health, well-being, and user experience across diverse built environments. Full article

Smart cities (SCs) use the Internet of Things (IoT) to collect and process data to communicate with their infrastructure and assets in real time. A great deal of techniques, such as encryption protocols, Random Forest-based AI-driven threat detection, and blockchain architectures, have been developed to address cybersecurity challenges in smart cities (SCs). These techniques, however, have limitations such as their scalability, high computational expenses, and energy inefficiency. Therefore, in this study, to overcome these challenges, we propose a blockchain-based infrastructure called BlockSafeNet. This uses artificial intelligence, big data, and blockchain to enhance cybersecurity in SCs. The effectiveness of the proposed BlockSafeNet framework was evaluated using responsiveness, computational time, encryption quality score, detection rate, false positive rate, latency, throughput, and energy consumption as the primary cybersecurity performance metrics. These metrics were selected to assess communication efficiency, threat detection capability, privacy preservation, scalability, and overall security performance within smart-city IoT environments. To ensure secure data transactions, robust threat detection, and efficient communication. The system’s high calculation speed and detection rate show potential for managing sensitive maternal health data collected by IoT devices. The platform also shows how IoT may be used by healthcare services to monitor public health in real time, allowing hospitals, emergency services, and public health agencies to securely share data. This aids in resource optimization, improving service delivery, and preserving data privacy and trust in SCs. Data was obtained from the UCI Machine Learning Repository on Kaggle to validate the developed framework. By evaluating the effectiveness of BlockSafeNet in tackling cybersecurity challenges, we establish its practical relevance and usability in SCs. The proposed BlockSafeNet framework achieved a responsiveness of 24 s, an encryption quality score of 0.89, computational time of 85 s, and a detection rate of 91%, demonstrating significant improvements in secure IoT communication, privacy preservation, and AI-driven cyber threat detection within smart city infrastructures. shows that SC IoT security has significantly improved through the adoption of new data protection methods and better measures of security, providing a positive impact on the SC ecosystem. Full article

Deepfakes, synthetic audiovisual content produced by deep generative models, have escalated into a critical threat across civilian and military domains, enabling identity fraud, disinformation campaigns, and evidence fabrication. In high-stakes environments, ranging from journalism and finance to healthcare and legal contexts, the consequences extend to severe misinformation, market manipulation, identity fraud, and the erosion of institutional trust. This entry explores how modern visual intelligence and computer-vision techniques are used to detect deepfakes. It outlines key deepfake generation models, such as GANs, autoencoders, neural rendering, and diffusion systems, while also explaining how adversarial methods enhance realism and challenge existing detectors. The overview highlights visual artifacts, digital patterns, and physiological cues commonly leveraged in detection and reviews major CNN, transformer, and frequency-based approaches. It also summarizes evaluation practices and the difficulty of achieving strong generalization. Finally, it identifies emerging directions, including modern intelligence techniques for civilian and military content verification. This survey covers generation architectures (GANs, latent diffusion, neural rendering, video synthesis), the spatial, temporal, frequency-domain, and physiological artifacts they produce, and the detector families that exploit them. We examine evaluation benchmarks and protocols, highlighting cross-generator generalization as the field’s central open challenge. Beyond detection, we discuss cryptographic provenance standards, watermarking, and regulatory frameworks (EU AI Act, DSA, GDPR). We conclude that effective deepfake governance requires defense in depth integrating forensic detection, verifiable provenance, and institutional accountability. Full article

The convergence of multi-omics strategies and big data analytics is transforming modern healthcare by shifting medicine from a reactive discipline to a proactive, personalized system. This review explores how integrating genomics, transcriptomics, proteomics, and metabolomics provides a holistic view of complex biological systems. While current literature heavily documents theoretical models, a persistent gap remains in translating these high-dimensional architectures into validated healthcare workflows. We highlight the critical role of big data infrastructure, specifically cloud computing and artificial intelligence (AI), in processing massive datasets and we benchmark our approach against existing reviews to emphasize the path toward routine clinical deployment. Through concrete case studies in oncology and metabolic disorders, we illustrate the potential clinical utility of these technologies in advancing precision medicine. Finally, we address persistent challenges including data heterogeneity, privacy concerns, and computational bottlenecks, and discuss future directions required to translate multi-omics insights into routine clinical practice. Full article

The rapid deployment of Internet of Things (IoT)-enabled video surveillance systems has expanded the capabilities of real-time monitoring in smart cities, healthcare facilities, industrial environments and critical infrastructure. However, integrating resource-constrained cameras, heterogeneous communication protocols, edge/cloud analytics, and sensitive video data creates a complex cybersecurity landscape. This systematic review synthesizes recent evidence on security challenges and mitigation strategies in IoT-enabled video surveillance systems. Following the PRISMA 2020 guidelines, four bibliographic databases (Scopus, IEEE Xplore, Web of Science, and Google Scholar) were searched for peer-reviewed journal articles and conference papers published between January 2021 and July 2025. After duplicate removal, title/abstract screening, full-text assessment, and quality appraisal, 21 studies were included for qualitative synthesis. The findings show that vulnerabilities occur across three interdependent architectural layers: device/perception, network/communication, and application/cloud. The frequently reported weaknesses were default credentials, insecure firmware, unencrypted video streams, weak protocol configuration, metadata leakage, and inadequate cloud access control. Existing mitigation strategies, including multi-factor authentication, role-based access control, TLS/DTLS, lightweight encryption, intrusion detection systems, and secure boot, provide partial protection but remain constrained by latency, computational overhead, energy consumption, scalability, cost and legacy device compatibility. This review further identifies a persistent research–practice gap: only a small subset of studies provides evidence of real-world deployments, while most solutions remain evaluated in simulations, testbeds, or conceptual frameworks. This review contributes a domain-specific taxonomy of IoT video surveillance security, a comparative evaluation of mitigation strategies using technical, operational, and economic criteria, and deployment-oriented recommendations for smart city, industrial, healthcare, residential, and critical infrastructure settings. The study highlights the need for cross-layer security architectures, lightweight and post-quantum-ready cryptography, privacy preservation, edge AI, federated learning, zero-trust access control, and standardized security baselines. Full article

Medical image analysis has undergone transformative progress with the application of deep learning models. However, existing architectures often struggle to effectively balance local feature extraction with global contextual understanding, which is crucial for complex diagnostic tasks such as Retinopathy of Prematurity (ROP) detection. In this study, we present a pretrained lightweight Conformer model tailored for medical image classification. The model integrates convolutional layers for capturing fine-grained spatial features with transformer blocks that capture long-range dependencies, creating a unified architecture capable of robust representation learning. We evaluate the model across multiple benchmark medical imaging datasets, including ROP, BloodMNIST, RetinalMNIST and other MedMNIST benchmark datasets. With 93.61% accuracy on the ROP dataset and 99.12% accuracy on BloodMNIST, experimental results show competitive classification performance while lowering model complexity to 12.4 million parameters and 3.2 GFLOPs. Experimental results demonstrate that the comparative studies versus CNN-based and transformer-based architectures, such as ResNet50, Swin-Tiny, ConvNeXt-Tiny, Vision Transformer, and MedViT. The findings show that in clinical settings with limited resources, the suggested lightweight Conformer offers a practical and computationally efficient alternative for medical image interpretation. Furthermore, the lightweight design ensures computational efficiency, making it suitable for deployment in resource-constrained healthcare environments. These findings validate the lightweight Conformer model’s potential for scalable, accurate, and real-time medical image classification. Full article