Search Results (1,034)

By allowing continuous connectivity, automation, and data-driven decision-making across these areas, Internet of Things (IoT) has transformed certain facets of daily life, including home automation and healthcare, as well as business operations like supply chain management and smart manufacturing. IoT systems are susceptible to different cyberattacks, though, because of different designs, lack of funds, and inadequate security policies, which creates major security issues given their fast growth. Covering important topics including protocols, architectures, attack classification, detection methods, countermeasures, and research issues, this paper offers a thorough study of IoT security. Emphasizing their relevance in enhancing the security of IoTs, the article offers a thorough analysis of machine and deep learning-based detection techniques. It also offers recommendations for future paths to handle changing risks by means of particular proposals and provides tools and datasets required for IoT security studies. When considering recent progress, however, there are still some major limitations in scaling, real-time detection, dataset availability, and versatility of current solutions. We identified these issues and provided guidance on future research; we also offered a selected set of tools and datasets for further research. Additionally, this paper provides an overview of the most important issues related to IoT security as documented in the current literature, providing a framework for developing resilient and adaptable IoT security solutions in the future. Full article

The explosive growth of the Internet of Things (IoT) has expanded the attack surface across industrial systems, smart cities, healthcare, and homes, motivating a synthesis of recent advances in machine learning for IoT security and a clear statement of remaining gaps. This review conducted a systematic search of MDPI, IEEE Xplore, Nature, ScienceDirect, and SpringerLink for publications from 2023 to 2025, screening them for domain relevance and organizing findings into a taxonomy of ML methods, threat types, and deployment contexts, with particular attention to datasets, edge constraints, and privacy considerations. We find that the field is shifting from signature-based detection to supervised and deep learning approaches that report high accuracy on benchmark traffic, while federated learning enables privacy-preserving, distributed intrusion detection with near-real-time edge performance. Across domains, prevalent threats include DDoS, unauthorized access, and malware; persistent challenges include device heterogeneity, rapid exploit weaponization, nonstandardized evaluation, concept drift, adversarial/poisoning risks, and governance and privacy constraints that hinder real world rollouts. We conclude that ML materially strengthens IoT resilience but requires rigorous, industry-scale validation, lightweight and explainable models, protocol-aware designs, robust federated aggregation, and SDN/NFV orchestration; we outline benchmark and deployment priorities to translate laboratory gains into operational security. Full article

Objectives: Improving the interoperability of electronic health records is critical for efficient, cost-effective delivery of quality services, enhanced care coordination, and improved treatment outcomes within the United States healthcare system. Healthcare leaders and administrators often experience EHR interoperability issues, restricting communication between health systems and impacting electronic health data utilization. Methods: This systematic literature review explored innovative strategies to improve electronic health record interoperability between health information systems to enhance data exchange efficiency, accuracy, and security in U.S. healthcare settings. A search transpired using the Public Medline, Institute of Electrical and Electronics Engineers Xplore Digital Library, and Cumulative Index to Nursing and Allied Health Literature following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results: Data from 24 relevant articles were analyzed using screening criteria revolving around the research question. Five themes emerged during data analysis. The themes included the utilization of blockchain-based EHR systems (67%), the drive of the Cures Act to achieve interoperability (17%), the advent of artificial intelligence and how it can be used (33%), how the Internet of Things drives the industry to strategically enhance the system (33%), and how the value of interoperability drives outcomes (79%). Conclusions: Findings indicate strategies from a technical perspective and from policy initiatives can improve communication between health information systems. Findings suggest that by strategically leveraging available resources and implementing innovative strategies, healthcare leaders can achieve comprehensive EHR interoperability long term. Full article

Artificial intelligence applications in mental health diagnosis face persistent challenges related to interpretability, trust, and the integrity of results. This study presents a trust-aware explainable deep learning framework that combines systematic benchmarking, SHAP-based interpretability, and permissioned blockchain verification to achieve secure mental health classification. The Depression & Mental Health Classification Dataset was used, which contains 1999 records, 21 features, and 12 classes. Data preprocessing included categorical encoding and Z-score normalization for continuous variables. To ensure robust evaluation, a stratified train–test split was applied, and class imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE). Eight machine learning and deep learning models were assessed under identical preprocessing and validation settings. In addition, two models were proposed: Feature Attention XGBoost (FA-XGBoost) and Feature Attention Feedforward Neural Network (FA-FNN). The FA-FNN model achieved the best performance, attaining an accuracy of 96.00%, precision of 98.31%, recall of 97.31%, and F1-score of 98.04%. To address deep learning’s black-box limitation, SHapley Additive ExPlanations (SHAPs) were used to provide both global feature importance and instance-level explanations, enabling transparent identification of the most influential mental health markers. Beyond interpretability, a permissioned blockchain layer was added to provide tamper-proof logging and traceable verification of AI results. The framework securely stores cryptographic hashes of model versions, prediction results, and generated SHAP artifacts, including visualization images, without exposing sensitive medical data. By integrating explainable decision-making, high-performance classification, and blockchain-based trust enforcement, the proposed framework creates a transparent and secure pipeline suitable for real-world mental healthcare systems. Controlled experiments on a permissioned Ethereum-InterPlanetary File System (IPFS) network demonstrated predictable latency, stable throughput (≈28–30 transactions/s), and lower operational costs, proving the framework’s suitability for enterprise and healthcare deployments. Full article

Leveraging Artificial Intelligence (AI) ethically in connected healthcare systems requires a quantifiable framework that measures not only outcome correctness, but also the clarity, auditability, and ethical acceptability of model explanations in high-stakes clinical and cybersecurity workflows. This manuscript first presents a narrative review of ethical risks and countermeasures in Healthcare Internet of Things (HIoT) and explains why existing performance metrics are insufficient for trustworthy deployment. We then formalize a quantitative metric called Ethical Explainability (E_e) as a composite index integrating (1) a Human Agreement Ratio (HAR), capturing concordance between AI recommendations (and their rationale) and a calibrated expert consensus, and (2) an Entropy Reduction Index (ERI), capturing the proportional reduction in expert uncertainty after receiving an explanation, operationalized via probability-elicitation questionnaires mapped to Shannon entropy. Designed for HIoT security monitoring, E_e links transparency with governance-ready evidence of trustworthiness for human–AI collaboration. Full article

Agricultural performance is often interpreted through agronomic inputs and technological progress; however, the translation of knowledge into production depends on the structural environments in which food systems operate. This study examined the association between food-security-related publication activity and crop production across global income settings from 2000 to 2025, while testing whether governance, health-system, and financial indicators modify that association. A longitudinal ecological panel was constructed, integrating 61,158 Scopus-indexed peer-reviewed articles on food security and related dimensions of healthy food access and availability with 23 crop production indicators grouped into staple, horticultural, and commodity domains. Income-stratified regression models were followed by hierarchical mixed-effects models and moderator screening. In exploratory stratified models, 67 of 92 income-specific associations reached nominal significance; however, only 5 of those 67 associations (7.5%) remained statistically significant after multilevel modelling and false discovery rate correction. Robust associations were concentrated in selected staple and horticultural outcomes, whereas most commodity indicators lost significance after hierarchical adjustment. Structural moderators related to territorial control, corruption, healthy life expectancy, health researcher density, healthcare access and quality, and official development assistance shifted the conditional slopes linking publication activity to crop output. These findings do not support a uniform linear relationship between publication growth and production volume. Instead, they suggest that the alignment between research ecosystems and agricultural output is structurally conditioned and likely mediated by institutional capacity, health-system resilience, and implementation environments. The ecological design, the use of publication counts as an indirect proxy, and the reliance on production volume rather than yield or efficiency should be considered when interpreting these results. Full article

Wireless sensor networks (WSNs) have increasing demand on lightweight, efficient, and secure encryption techniques for devices with limited resources, since traditional algorithms require high computation which make them impractical. This preliminary study presents an encryption algorithm based on chaos designed for transmitting short data, using the Lorenz system and Euler’s method for computation. It is combined with a synchronization model based on data array. It inserts iteration parameters within the ciphertext to ensure consistent key reproduction while decrypting. Within the broader context of e-health data streams, encryption efficiency is critical: continuous ECG signals generate large volumes of data that challenge real-time secure transmission, whereas individual blood pressure readings are far smaller and lightweight. While this work delimits its scope to short, low-power transmissions, simulations and hardware implementation on an nRF chip using the Enhanced ShockBurst (ESB) protocol demonstrated efficiency, with the lowest encryption speed of 0.154 ms for a 1-byte payload. Security analysis using the NIST Statistical Test Suite confirmed high statistical randomness of the generated keystream, and theoretical key-space analysis supports robustness. By focusing on short-stream encryption in preliminary form, the scheme contributes toward inclusive secure communication technologies for resource-constrained IoT healthcare systems and diverse user populations. Full article

Globalization refers to the increasing density of economic, social, and technological interconnections on a global scale. In the healthcare industry, it simultaneously accelerates innovation and increases systemic vulnerabilities. This study aims to review and conceptually synthesise the main channels of impact: (1) pharmaceuticals, clinical development, and regulation; (2) supply chains and resilience; (3) service mobility (health tourism); (4) human resources and competencies; (5) digitalization, artificial intelligence (AI), and data governance; (6) ethics, law, and public policy; and (7) sustainability and climate change. The COVID-19 pandemic highlighted the risks associated with global interdependencies, particularly in supply chains, while also demonstrating the innovation-accelerating effects of knowledge sharing and international cooperation. Particular attention is given to artificial intelligence and digital health, which open up new potential for efficiency and quality improvement from research and development through diagnostics to healthcare organization, while simultaneously intensifying concerns related to data protection, cyber security, and liability. Telemedicine, platform-based systems, and real-world data may contribute to addressing the care needs of ageing societies, but only when supported by appropriate competencies and sound data governance. As global data flows intensify, the importance of data protection, bias mitigation, transparency, and accountability correspondingly increases. Through the cultural channels of globalization, health-conscious lifestyles and complementary approaches are also spreading, which we address in a brief, separate subsection. The guidelines of international organizations foster standardization; however, due to differences in local capacities and institutional environments, the effects are not homogeneous. In conclusion, the study emphasises the dual nature of globalization; it expands access and accelerates innovation, while at the same time creating new vulnerabilities—in supply chains, labour mobility, and data security—and, together with climate-related risks, generating complex adaptive pressures for the healthcare industry. Full article

Ensuring data security and privacy has emerged as a serious concern in the realm of blood supply chain. This is mainly because of sensitivity of donor information, the involvement of multiple stakeholders, and the need for transparent traceability. This paper proposes a novel privacy-preserving, permissioned blockchain framework for blood supply chain management that integrates Hyperledger Fabric, the InterPlanetary File System (IPFS), and a Zero-Knowledge Proof (ZKP)-based authentication protocol. The framework introduces a Pseudonymous Role-Bound Zero-Knowledge Authentication (PRZKA) mechanism that enables donors to authenticate and authorize access to their medical data without revealing their real identities. Context-specific pseudonyms derived through cryptographic hash-to-curve operations ensure unlinkability across different healthcare interactions, while Schnorr-style challenge–response proofs prevent replay attacks and credential misuse. Sensitive donor information is protected using Fabric Private Data Collections, whereas encrypted medical records are stored off-chain in IPFS, with only secure content identifiers recorded on the blockchain. Smart contracts enforce fine-grained, consent-aware access control policies and maintain immutable audit logs of all access events. The proposed system architecture combines an off-chain ZKP gateway with on-chain authorization logic to minimize blockchain overhead while preserving strong security guarantees. Furthermore, a performance evaluation framework is defined, including metrics, workload scenarios, and system configurations, to support future empirical validation. Security analysis indicates that the proposed framework enhances privacy, prevents identity linkage, and enables auditable, consent-driven data sharing compared with existing blockchain-based healthcare solutions. Full article

The growing interconnection of critical engineering infrastructure through IoT introduces unprecedented exposure to cyber threats. Emerging quantum computing capabilities pose a transformative risk to classical cryptographic primitives such as Rivest–Shamir–Adleman and Elliptic-Curve Cryptography, which underpin secure communication and device authentication in industrial control systems, power grids, transportation networks, and healthcare infrastructure. This paper investigates quantum-resistant encryption, often termed post-quantum cryptography (PQC), as a sustainable security paradigm for IoT communication within critical systems. By analyzing lattice-based, code-based, multivariate, and hash-based schemes, the study evaluates trade-offs between computational cost, memory footprint, and latency constraints intrinsic to resource-limited IoT nodes. A hybrid architectural framework integrating the National Institute of Standards and Technology-standardized algorithms (e.g., Cryptographic Suite for Algebraic Lattices—Kyber, Dilithium) with lightweight symmetric primitives (e.g., Ascon, GIFT block cipher in Combined Feedback mode) is proposed for secure data transmission across heterogeneous IoT layers. Experimental simulations benchmark key-exchange throughput, ciphertext expansion, and resilience against quantum-adversarial models, demonstrating up to 65% reduction in handshake latency compared to baseline lattice implementations under constrained conditions. The paper concludes with policy and engineering recommendations for the adoption of quantum-resistant IoT protocols in energy, transportation, and industrial automation sectors, highlighting alignment with global PQC migration roadmaps and IEC 62443 cybersecurity standards. Full article

With the increasing demand for enhanced security and surveillance, the integration of multimodal AI has shown significant promise. We developed and fine-tuned a transformer-based model, the Large Language and Vision Assistant–OneVision, tailored for human event and action recognition. By utilizing a multimodal approach, we identified specific human actions, including eating, running, fighting, sitting, and sleeping, within diverse real-world settings. Through knowledge distillation and Low-Rank Adaptation, the model’s performance was optimized in demonstrating substantial improvements in context-aware recognition and response generation. Evaluation results showed recall-oriented understudy for obtaining evaluation (ROUGE)-1 score of 0.6844, ROUGE-2 score of 0.5751, ROUGE-L score of 0.6520, and the bilingual evaluation understudy score of 68.20, demonstrating significant gains in accuracy and interpretability. The model’s success highlights its potential for real-time applications in surveillance, healthcare, and interactive AI systems, providing reliable, efficient, and context-sensitive human action detection. Full article

Artificial Intelligence (AI) systems based on traditional Deep Learning (DL) are expected to play a leading role in the early detection of various diseases in healthcare applications. However, there are two major drawbacks of these systems: protecting patient privacy and obtaining sufficiently large, high-quality datasets to train reliable models. In traditional DL, collecting data from different sources on a single central server increases system complexity and raises serious privacy and security concerns. Federated Learning (FL) makes it possible to train models locally at multiple data locations while collaboratively improving a global model without exposing raw data, making it a promising architectural solution for privacy preservation. Although previous studies have reported that FL can achieve performance comparable to centralized DL approaches, traditional FL approaches often struggle to maintain consistent performance across different settings. This limitation becomes more noticeable when heterogeneous data distributions, modalities, and domains are involved. In these situations, client drift, overfitting, and generalization capability of the global model arise as major challenges. Thus, this study presents FedCycle as an incremental improvement of the FedAvg algorithm. It modifies the aggregation frequency. It aims to overcome these drawbacks and make the global model more stable and efficient. The FedCycle eliminates centralized data collection, enhances data security, and effectively reduces client drift and overfitting by supporting model training across heterogeneous data distributions, modalities, and domains. The performance evaluation involves extensive experiments using various real-world breast cancer image datasets, namely BREAKHIS, ROBOFLOW, RSNA, BUSI, and BCFPP. The presented method is evaluated against both traditional DL and FL approaches using accuracy, precision, recall, F1-score, and AUC. The findings confirm that applying fine-tuning within FedCycle reduces overfitting during training. As a result, FedCycle achieves performance improvements of 7.75% and 4.65% in accuracy and F1-score on the RSNA and BCFPP datasets compared to traditional DL approaches, while also providing an average improvement of approximately 1.5% in accuracy and F1-score across BREAKHIS, ROBOFLOW, and BUSI datasets compared to FedAvg. Full article

The Internet of Medical Things (IoMT) is becoming a vital part of modern healthcare, enabling ongoing patient monitoring and remote diagnosis. However, as more devices connect to the internet, healthcare systems become more vulnerable to serious security issues such as unauthorized access, patient data manipulation, and Man-in-the-Middle attacks. Conventional Intrusion Detection Systems (IDSs) often struggle with the unclear and uncertain characteristics of IoMT traffic, which leads to reduced detection accuracy and increased false alarms. To address these challenges, this paper proposes ML-FSID-FIS, a multi-level feature selection-based Intrusion Detection System that employs a fuzzy inference system (FIS) for classification in IoMT networks. The model combines multiple feature selection techniques into a three-stage multi-level feature selection strategy to improve detection efficiency and strengthen the security of IoMT networks. In the first stage, four feature selection techniques—Random Forest, XGBoost, ReliefF, and Mutual Information—are applied to identify the most relevant features. In the second stage, a frequency-based consensus strategy is utilized to extract consistently selected features from the four top-ranked sets. In the third stage, an ensemble refinement using bagging-based ranking is employed to rank the remaining features, resulting in the selection of the top five features. From these, three candidate 3-feature groups are formed and evaluated, and the best-performing group is selected as the final input set for the fuzzy logic classifier. The FIS produces a continuous risk score that is mapped to a binary decision using a validation-selected threshold. When the proposed method was tested on the WUSTL-EHMS-2020 dataset and compared with other recent work using the same dataset, it showed strong detection performance while maintaining a very low false positive rate of 0.3%. This study is distinguished by its integrated design, which combines a three-stage multi-level feature selection strategy with fuzzy logic-based intrusion classification to improve feature efficiency and support interpretable intrusion detection in IoMT. Full article

The integration of algorithmic systems into critical digital infrastructure is no longer peripheral to governance, it is governance. As AI-mediated decisions influence credit access, clinical diagnoses, criminal risk scores, and infrastructure routing, the question of who controls these algorithms and whether that control is meaningful has become a central concern for states and institutions at every level of development. Existing frameworks, including the NIST AI Risk Management Framework, ISO/IEC 42001, and the EU AI Act, have made real progress toward structured AI governance. However, none treats digital sovereignty as a first-order goal, nor do they provide integrated cross-layer guidance applicable across the diverse institutional landscape found worldwide. From this synthesis, we develop the Integrated AI Risk Governance Framework (IARGF): a four-layer structure covering policy and regulations, institutional oversight, technical controls, and operational execution, organized around five risk categories—technical, ethical, security, systemic, and sovereignty-related. A comparative analysis with major existing frameworks highlights the IARGF’s unique contributions, especially its explicit focus on sovereignty, adaptability across different institutional capacities, and recursive feedback mechanisms that connect all four governance layers. The framework is analyzed across three domains—healthcare AI, financial services, and critical infrastructure—to demonstrate its practical utility. Results confirm that governance effectiveness is a system property, not just a feature of individual layers; that digital sovereignty is both a governance goal and a distinct risk dimension with specific technical and institutional needs; and that context-aware, capacity-scaled governance is a design requirement, not a political compromise. The IARGF is presented as a conceptual governance model based on a systematic literature review rather than an empirically validated tool, and it remains to be tested in actual organizational settings. Its main contribution is the comprehensive theoretical integration of sovereignty, institutional capacity, and inter-layer governance dynamics, rather than proven performance advantages over existing models. Future research should aim to validate this framework through longitudinal case studies, expert panels, and retrospective failure analyses. Full article

The rapid growth in the spectrum of cyber threats, coupled with the evolution of digital uses, services and infrastructures in the healthcare sector, means that security measures need to be reassessed to ensure that they are in step with the reality on the ground and adapted accordingly, as smart healthcare systems show a dearth of privacy and security in the digitization and sharing of health records. Blockchain, being a new decentralized infrastructure, is one of the leading revolutionary emerging technologies that can be used to improve data integrity and traceability in healthcare systems. This study investigates how blockchain technology is affecting the healthcare domain, comprehensively analyzing its implications, challenges, and capabilities. The results indicate that blockchain is a revolutionary technology for creating transparent personal health records that can address the limitations of smart healthcare system management and provide a decentralized environment for exchanging healthcare data. However, there are still plenty of difficulties and obstacles that prevent it from being more widely accepted by healthcare stakeholders. Full article