The generalization capabilities of deep neural networks have been significantly improved by applying a wide spectrum of label modification approaches, e.g., label smoothing, confidence penalty, label correction, etc. However, less attention has been paid to label correction. In this paper, we propose self-ensemble label correction (SEELC), a unified training framework that dynamically calibrates and distills own knowledge to leverage the training process for better generalization. We analyze the generalization of SEELC from three different perspectives: 1) Our analysis shows that SEELC not only alleviates model miscalibration but also improves model robustness to random noise and adversarial noise, highlighting that SEELC enhances the generalization under supervised learning settings; 2) Our analysis shows that on the power of pseudo-label and noisy student, SEELC can be easily extended to semi-supervised learning and effectively handle domain discrepancies in unsupervised domain adaptation (UDA); 3) Our analysis also sheds light on understanding self-supervised learning, e.g., avoiding degenerate solutions, and can be well explained from alignment and uniformity. Finally, experiments on three applications show the superiority of our approach, i.e., classification on clean and noisy data, UDA and linear evaluation protocol in self-supervised learning.

Online fault diagnosis is crucial for improving the reliability of safety–critical industrial systems. Fault diagnosis becomes increasingly complex if the time-variations, fuzziness, and uncertain causal relationships related to the various internal factors in the system are present. In view of the time-varying working conditions of industrial systems, as well as the fuzzy uncertainty of fault data and knowledge, a C-DUCG (Cubic dynamic uncertain causality graph) approach is proposed for fault diagnosis. Such a solution is intended to help develop a generative cubic causality graph modeling scheme and a FUTURE (fuzzy temporal causality reasoning) algorithm, both of which facilitate the representation and reasoning about complex fault situations (involving temporal causalities and uncertain evidence). In C-DUCG, the strategies of causality simplification and EELA (event-oriented early logical absorption) are proposed to mitigate the complexities of modeling and reasoning. Comparative experiments and a sequence of fault diagnosis tests on a nuclear power plant (NPP) simulator validate the efficiency, recall, accuracy, and interpretability of C-DUCG in large-scale dynamic systems. Experiments reveal that the proposed algorithm achieves 0.62 and 0.999 in terms of recall rate AC@1 and AC@k, respectively, with the average reasoning time being 10 ms, and the average time spent in NPP fault diagnosis tests is 9.42 ms.

Diabetes mellitus is one of the most pressing global health challenges, and early prediction is critical to reducing complications, mortality, and healthcare costs. Conventional diagnostic tools remain limited, as they often rely on a small set of biomarkers and fail to capture lifestyle, genetic, or environmental risk factors. This review systematically evaluates how artificial intelligence (AI), including machine learning (ML) and deep learning (DL), enhances diabetes prediction by integrating multimodal data and improving clinical interpretability. Following PRISMA 2020 guidelines, a systematic search was conducted across PubMed, Scopus, IEEE Xplore, Web of Science, and ACM Digital Library for studies published between 2010 and 2024. Inclusion criteria required AI-based diabetes prediction models with reported performance metrics. From 2134 records, 155 studies met the criteria and were synthesized. AI models consistently outperformed conventional approaches (60–75% accuracy), Ensemble methods such as Random Forests and XGBoost achieved accuracy of 85–90% and AUC-ROC values > 0.90 (Abnoosian in BMC Bioinf 24:373, 2023. https://doi.org/10.1186/s12859-023-05465-z; Chen and Guestrin in Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, Association for Computing Machinery, 2016. https://doi.org/10.1145/2939672.2939785). DL architectures showed notable strengths in unstructured data: CNNs achieved AUC > 0.95 in retinal image analysis (Gulshan et al. in JAMA 316(22):2402–2410, 2016. https://doi.org/10.1001/jama.2016.17216; Kermany et al. in Cell 172(5):1122–1131, 2018. https://doi.org/10.1016/j.cell.2018.02.010), while LSTMs reached 85–90% accuracy in continuous glucose monitoring predictions (De Bois et al. in Prediction-coherent LSTM-based recurrent neural network for safer glucose predictions in diabetic people, arXiv:2009.03722, 2020; Bian et al. in PLoS ONE 19(9):e0310084, 2024. https://doi.org/10.1371/journal.pone.0310084). Emerging methods such as federated learning enable privacy-preserving cross-institutional collaboration with comparable accuracy (~ 88%), while explainable AI techniques (e.g., SHAP, LIME, attention mechanisms) enhance transparency and clinical trust. Real-world case studies demonstrate improvements in early detection, reduced hospitalizations, and increased patient engagement when AI models are integrated into EHRs or mobile health apps. This review contributes a novel synthesis by combining methodological insights with clinical applications. The central takeaway is that AI-driven diabetes prediction offers significant advantages over traditional methods, but challenges in data quality, generalizability, fairness, and regulatory compliance remain. Addressing these will be essential to ensure safe, equitable, and clinically meaningful adoption in healthcare practice.

Human cognition falters under overload because working memory is sharply limited, as described by Cognitive Load Theory. Advanced AI systems show parallel failures when tasks exceed context windows or cause model collapse. This review synthesizes these constraints through a unifying lens, revealing shared mechanisms like bounded workspaces and chunking, alongside divergences such as human metacognition. We introduce a “bounded agent complementarity” model that proposes dynamic load-balancing for symbiotic intelligence, with implications for reasoning in domains such as education, medicine, and aviation. The framework highlights ways to mitigate these mutual limits and yields testable predictions for augmented cognition and resilient human-AI systems.

Cancer diagnosis has entered an era where precision depends not only on image quality but on the intelligence that interprets it. While deep learning has revolutionized medical imaging, its reliance on centralized data limits collaboration due to privacy constraints and fragmented data ownership. Federated Learning (FL) offers a breakthrough enabling multiple institutions to co-train robust diagnostic models without sharing sensitive patient data. This survey provides a comprehensive cancer-specific synthesis of state-of-the-art FL applications in medical imaging, spanning five critical domains: lung, breast, brain, skin, and colorectal cancers. Beyond summarizing prior work, we uncover patterns in architecture choice (U-Net variants, Convolutional Neural Network (CNN)–Recurrent Neural Network(RNN) hybrids, dataset reuse, and state-of-the-art privacy frameworks such as homomorphic encryption and blockchain-backed consensus. We expose performance bottlenecks, heterogeneity risks, and critical absences of clinical deployment benchmarks. What emerges is not just a landscape of what has been done but a roadmap for what is needed to build secure, distributed, and clinically validated Artificial Intelligence (AI) systems. This work offers a foundation for researchers and healthcare technologists aiming to close the gap between research silos and real-world deployment.

Artificial Intelligence-Generated Content (AIGC) represents a paradigm shift in biomedical research and healthcare delivery, offering unprecedented capabilities for content creation, medical data analysis, and patient care optimization. This review examines the evolution of AIGC technologies from rule-based systems to advanced multimodal large models, with specific focus on their applications in healthcare settings. We analyze the three core capabilities of AIGC: intelligent digital content twinning, editing, and creation, and their transformative potential in medical imaging, clinical documentation, drug discovery, and personalized medicine. This paper discusses key challenges including algorithmic transparency, data privacy, and regulatory compliance, particularly in light of World Health Organization (WHO) guidelines for AI in health. Our findings indicate that while AIGC technologies show remarkable promise in enhancing diagnostic accuracy, streamlining clinical workflows, and democratizing healthcare access, careful consideration of ethical implications and regulatory frameworks is essential for safe and effective implementation.

Abstract The relentless growth of connected devices is transforming industrial, urban and domestic environments, yet it also expands the attack surface for distributed denial of service (DDoS), unauthorized access and data manipulation. Centralized security architectures struggle to cope with the scale and heterogeneity of the Internet of Things, creating single points of failure and privacy risks. This review takes a close look at how blockchain and artificial intelligence (AI) can work together to solve these problems. Blockchain plays an important role in decentralizing trust, maintaining data integrity, and enabling transparent audit trails. AI subfields such as machine learning (ML), deep learning (DL), reinforcement learning (RL), and multi-agent systems (MAS) enhance these benefits. They enable real-time anomaly detection, predictive analytics, and adaptive policy control. A seven axis Blockchain–AI Security Integration Schema (BASIS) is proposed to classify solutions by security objectives, intelligence modalities, trust primitives, deployment choices, scalability techniques, privacy controls and interoperability mechanisms. In this study also review Layer-2 consensus protocols, federated learning and lightweight deep learning models that address energy and computational constraints. Case studies from supply chains, healthcare and smart grids illustrate the benefits and limitations of current deployments. The evidence suggests that while AI improves the accuracy and responsiveness of threat detection, blockchain offers tamper-proof data provenance. However, there are still issues in achieving scalability, reducing computational overhead, and striking a balance between auditability and privacy. Hybrid on-chain/off-chain architectures, quantum-safe cryptography, and standardized frameworks to guarantee adoption and interoperability are some future research avenues.

Abstract The proliferation of metaheuristic optimization algorithms has led to concerns about their novelty. This study introduces three key contributions to address this challenge: (1) a novel systematic taxonomic framework that employs nineteen rigorously selected, metaphor-free criteria to evaluate algorithmic distinctiveness; (2) a comprehensive clustering methodology that combines Rogers-Tanimoto distance analysis with principal component analysis (PCA) and hierarchical clustering to quantify algorithmic similarities; and (3) an objective assessment method for evaluating genuine algorithmic innovations. Through the analysis of 145 metaheuristic algorithms, we demonstrate that 74 algorithms (51.0%) exhibit distances below the confidence interval threshold, indicating profound structural overlap. Network analysis reveals 26 algorithms with perfect structural identity (distance = 0.0) and 512 algorithm pairs showing high similarity (distance < 0.039), representing 18.9% of all pairwise comparisons. The results show that numerous algorithms claiming innovation deliver only incremental modifications to existing implementation patterns, lacking fundamental methodological advancement. The framework provides both a theoretical foundation for understanding algorithmic similarities and a practical tool for evaluating new algorithmic proposals, potentially transforming how the field assesses and develops novel optimization methods.

The Metaverse is an emerging virtual reality space that merges digital and physical worlds and provides users with immersive, interactive, and persistent virtual environments. The Metaverse leverages multiple technologies, including digital twins, blockchain, artificial intelligence, extended reality, and edge computing to realize the seamless connectivity and interaction between both worlds: physical and virtual. Artificial Intelligence (AI) empowers intelligent decisions in such complex dynamic environments. More specifically, Reinforcement Learning (RL) is uniquely effective in the context of Metaverse applications due to the natural process of learning through interaction and its modeling of sequential decision making, allowing it to be flexible, dynamic, and able to discover complex strategies and emergent behavior in complicated environments where programming explicit rules is impractical. Although multiple works have explored the research on the Metaverse and AI-based applications, there remains a significant gap in the literature that addresses the contribution of RL algorithms within the Metaverse. Therefore, this review presents a comprehensive overview of RL algorithms for Metaverse applications. We examine the architecture of Metaverse networks, the role of RL in enhancing virtual interactions, and the potential for transferring learned behaviors to real-world applications. Furthermore, we categorize the key challenges, opportunities, and research directions associated with deploying RL in the Metaverse.

Abstract We develop a deep reinforcement learning framework for tactical decision making in an autonomous truck, specifically for Adaptive Cruise Control (ACC) and lane change maneuvers in a highway scenario. Our results demonstrate that it is beneficial to separate high-level decision-making processes and low-level control actions between the reinforcement learning agent and the low-level controllers based on physical models. In the following, we study optimizing the performance with a realistic and multi-objective reward function based on Total Cost of Operation (TCOP) of the truck using different approaches; by adding weights to reward components, by normalizing the reward components and by using curriculum learning techniques.