Frontiers in Artificial Intelligence最新文献

Obstructive sleep apnea (OSA) results from repeated collapses of the upper airway during sleep, which can lead to serious health complications. Although polysomnography (PSG) is the diagnostic gold standard, it is costly, labor-intensive, and associated with long waiting times. With the rapid evolution of automated scoring solutions and the emergence of machine learning (ML) and deep learning (DL) in many disciplines, there is a need for tools that use fewer signals and can provide accurate diagnoses. DL models can an process large amounts of data and often generalize effectively to new instances. This makes them a suitable choice for classifying continuous time series data. This study introduces a transformer-based deep learning approach using a single-lead electrocardiogram (ECG) for OSA detection. The proposed architecture, designed to handle raw signals with high sampling rates, preserves temporal continuity over unlimited durations. Without any preprocessing, the model tolerates high-noise raw data. The model is tested with different positional embedding techniques. Additionally, a novel positional encoding technique using an autoencoder is introduced. The proposed approach achieves a high F1 score, outperforming other published work by an average margin of more than 13%. In addition, the model classifies apnea episodes at one-second intervals, providing clinicians with nuanced insights.

This work presents a conceptual framework for causal concept-based post-hoc explainable artificial intelligence (XAI), based on the requirements that explanations for non-interpretable models must be both understandable and faithful to the model being explained. Local and global explanations are generated by calculating the probability of sufficiency of concept interventions. Example explanations are presented, generated with a proof-of-concept model made to explain classifiers trained on the CelebA dataset. Understandability is demonstrated through a clear concept-based vocabulary, subject to an implicit causal interpretation. Fidelity is addressed by highlighting important framework assumptions, stressing that the context of explanation interpretation must align with the context of explanation generation.

Artificial intelligence is approaching the point at which it can complete the scientific cycle, from hypothesis generation to experimental design and validation, within a closed loop that requires little human intervention. Yet, the loop is not fully autonomous: humans still curate data, set hyperparameters, adjudicate interpretability, and decide what counts as a satisfactory explanation. As models scale, they begin to explore regions of hypothesis and solution space that are inaccessible to human reasoning because they are too intricate or alien to our intuitions. Scientists may soon rely on AI strategies they do not fully understand, trusting goals and empirical payoffs rather than derivations. This prospect forces a choice about how much control to relinquish to accelerate discovery while keeping outputs human relevant. The answer cannot be a blanket policy to deploy LLMs or any single paradigm everywhere. It demands principled matching of methods to domains, hybrid causal and neurosymbolic scaffolds around generative models, and governance that preserves plurality and counters recursive bias. Otherwise, recursive training and uncritical reuse risk model collapse in AI and an epistemic collapse in science, as statistical inertia amplifies flaws and narrows the investigation. We argue for graded autonomy in AI-conducted science: systems that can close the loop at machine speed, while remaining anchored to human priorities, verifiable mechanisms, and domain-appropriate forms of understanding.

Background: Digital twin and agentic artificial intelligence technology provide innovative systems for testing behavioral science theory, which can improve emergency communication in crisis situations. More advanced and effective evidence-based messaging is needed for better safety preparation for extreme weather and more trusted evacuation communication.

Methods: This study developed a digital twin of Miami-Dade County populated with a synthetic population embedded with behavioral theory (Extended Parallel Process Model, Theory of Planned Behavior) and the development of a Message Assessment Framework (MAF) to systematically test theory-based crisis messages. Agents were exposed to fear-only, efficacy-only, norm-only, combined fear+efficacy, combined fear+efficacy+norm, and a neutral control message.

Results: Messages grounded in behavioral theory were more effective than the control message at encouraging evacuation. Messages that combined fear and efficacy provided the best results in the synthetic population's decision to evacuate (OR = 15.45, p < 0.001), while adding social cues did not produce a statistically distinguishable added benefit.

Discussion: This research demonstrates a proof-of-concept approach for using agentic AI and digital twins to pre-test communication strategies, offering a scalable method for optimizing emergency messaging prior to real-world implementation.

While the adoption of Artificial Intelligence (AI) is advancing globally, its pace varies significantly across nations. This study statistically examines the associations between Hofstede's cultural dimensions and national-level AI readiness. A correlation analysis was conducted using data from the Oxford Insights' "Government AI Readiness Index 2024" and Hofstede's cultural dimension scores. The findings reveal that Individualism and Long-Term Orientation have a significant positive correlation with AI readiness, whereas Power Distance and Uncertainty Avoidance show a significant negative correlation. Conversely, Masculinity and Indulgence did not have a statistically significant relationship. These results suggest that national cultural characteristics are associated with differences in the adoption of advanced technologies such as AI. To contextualize the statistics, we include an illustrative, non-causal comparison of Japan, the United States, and Singapore.

Customer churn prediction is critical for telecommunications companies to maintain profitability and inform retention strategies. This study builds upon existing work by implementing a comprehensive machine learning framework using the Telco Customer Churn dataset (n = 7,043). Our methodology integrated comprehensive feature engineering, SMOTE oversampling, and training of seven machine learning models including XGBoost, Random Forest, and a Multi-layer Perceptron. Model interpretation was conducted via SHAP analysis and customer segmentation. Key results demonstrated that gradient boosting algorithms (XGBoost, LightGBM, Gradient Boosting) achieved the highest balanced performance with accuracy, precision, recall, and F1-scores of 0.84, with XGBoost attaining the best discriminative ability (AUC-ROC: 0.932). A soft-voting ensemble of the top models matched this performance (F1-score: 0.84, AUC-ROC: 0.918). SHAP analysis revealed that contract type, tenure, and technical support were the features contributing most to the model's churn predictions. Threshold optimization at 0.528 balanced precision (0.90) and recall (0.91) while reducing false negatives by 15%. The findings provide actionable insights for prioritizing high-risk customers and designing targeted retention strategies in the telecom sector.

Hyperdimensional Computing (HDC) is a neurally inspired computing paradigm that leverages lightweight, high-dimensional operations to emulate key brain functions. Recent advances in HDC have primarily targeted two domains: learning, where the goal is to extract and generalize patterns for tasks such as classification, and cognitive computation, which requires accurate information retrieval for human-like reasoning. Although state-of-the-art HDC methods achieve strong performance in both areas, they lack a principled understanding of the fundamentally different requirements imposed by learning vs. cognition. In particular, existing works provide limited guidance on designing encoding methods that generate optimal hyperdimensional representations for these distinct tasks. In this study, we proposed the first universal hyperdimensional encoding method that dynamically adapts to the needs of both learning and cognitive computation. Our approach is based on neural-symbolic techniques that assign random complex hypervectors to atomic bases (e.g., alphabet definitions) and then apply algebraic operations in the high-dimensional hyperspace to control the correlation structure among encoded data points. Through theoretical analysis, we show that learning tasks benefit from correlated representations to maximize memorization and generalization capacity, whereas cognitive tasks require orthogonal, highly separable representations to enable accurate decoding and reasoning. We further derived a separation metric that quantifies this trade-off and validated it empirically across image classification and decoding tasks. Our results demonstrate that tuning the encoder to increase correlation improves classification accuracy from 65% to 95%, while maximizing separation enhances decoding accuracy from 85% to 100%. These findings provide the first systematic framework for designing hyperdimensional encoders that unify learning and cognition under a single, theoretically grounded representation model.

Introduction: The increasing adoption of Software-Defined Networking (SDN) in 5G networks has revolutionized network management. However, this paradigm shift has introduced critical security vulnerabilities, including data-plane anomalies, control-layer intrusions, and Distributed Denial-of-Service (DDoS) attacks. Existing intrusion detection approaches based on Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks suffer from high computational overhead, long detection latency, and limited scalability, making them unsuitable for real-time 5G-SDN environments.

Methods: This article proposes a novel multi-layered security framework for 5G-SDN that integrates EfficientNet with Knowledge Distillation (KD), Transformer Networks, Spiking Neural Networks (SNNs), Federated Reinforcement Learning (FRL), and blockchain technology. EfficientNet-KD enables lightweight and accurate anomaly detection at the data-plane layer. Transformer networks capture long-range temporal dependencies to enhance control-layer attack detection. SNNs are employed for ultra-low-latency attack classification by mimicking human brain neural processing. FRL supports decentralized and privacy-preserving mitigation across SDN controllers, improving scalability, while blockchain technology ensures the integrity and immutability of attack logs for forensic reliability.

Results: The proposed framework was evaluated using multiple benchmark datasets, including CICIDS2017, UNSW-NB15, IoT-23, and InSDN. Experimental results demonstrate an average detection accuracy of 97.75%, detection latency of 15 ms, and less than 5% throughput degradation. Each detection consumes only 0.25 J of energy, achieving a 40% reduction in energy usage compared to traditional CNN- and LSTM-based approaches.

Discussion: The results verify that the proposed framework provides a scalable, energy-efficient, and low-latency intrusion detection and mitigation solution for 5G-SDN environments. By integrating lightweight deep learning, neuromorphic computing, decentralized learning, and blockchain-based security, the framework effectively addresses the limitations of existing methods and offers a robust approach for securing next-generation 5G-SDN networks.

[This corrects the article DOI: 10.3389/frai.2025.1685376.].

Introduction: Loss functions play a critical role in machine learning, particularly in training neural networks for classification tasks. In this work, we establish a theoretical framework for distance-based loss functions by adapting the Hassanat distance for binary classification.

Methods: Through gradient analysis, we prove that Hassanat losses exhibit bounded gradients with finite Lipschitz constants, providing convergence guarantees and robustness to outliers. We formulate six variants with different error sensitivities and validate these theoretical properties empirically. Their effectiveness is evaluated on synthetic datasets and nine real-world datasets, ranging from a few hundred to nearly 48,000 samples, under controlled experimental conditions. A comprehensive comparison is conducted against widely used loss functions, including Binary Cross-Entropy (BCE), Focal Loss, Mean Squared Error (MSE), and L1 Loss.

Results: Results show that the proposed Hassanat-based losses achieve competitive performance across evaluation metrics, with comparable or slightly improved results in calibration, convergence speed (in terms of epochs), precision, recall, F1-score, and AUC on several datasets, while exhibiting notable robustness to outliers and noise. The estimated Floating Point Operations (FLOPs) shows that the wall-clock time difference is due to implementation gap, not algorithmic. Importantly, Cohen's d effect size and confidence interval analyses shows that some of the proposed variants introduce a larger practical effect size than popular loss functions such as BCE.

Discussion: This work establishes both theoretical foundations and empirical validation for distance-based loss functions. The bounded gradient framework with finite Lipschitz constants provides principled optimization guarantees while explaining observed robustness and convergence behavior. This foundation enables systematic development of robust loss functions tailored to specific application requirements.