Frontiers in Artificial Intelligence最新文献

Background: Accurate antenatal prediction of SGA at birth is essential to improve development and delivery of preventative and therapeutic interventions. This study aimed to assess the performance of machine learning (ML) models to predict SGA at birth among Chinese pregnancies classified according to the Chinese birthweight standard and three international birthweight standards.

Methods: We collected multimodal, longitudinal, antenatal surveillance data on 350,135 singleton pregnancies in Wenzhou City, China, between Jan 1, 2014 and Dec 31, 2016. For three pregnancy intervals we developed ML prediction models for newborns classified as SGA using the China, Intergrowth 21st, Fetal Medicine Foundation (FMF), and Gestation-related Optimal Weight (GROW) standards. We applied lasso regression to conduct feature selection, and CatBoost, XGBoost, LightBoost, Artificial Neural Networks, Random Forest, Stacked ensemble model, and logistic regression for predictive modeling in training data sets, with validation in testing data sets.

Results: Among 22,603 singleton pregnancies with complete data, the rate of SGA using the China standard was 6.1%, compared to 4.3, 6.0, and 9.7% for the Intergrowth 21st, GROW, and FMF standards, respectively. This pattern was maintained in the imputed data set (n = 225,523), with corresponding SGA rates of 6.8, 4.8, 7.4, and 10.7%. Late pregnancy models (<37 weeks) had the best power to predict SGA, compared to middle (<26 weeks) and early pregnancy (<18 weeks) models. With the China standard, the logistic regression model in late pregnancy performed best with an area under the receiver operating characteristic curve (ROC-AUC) of 0.74. Logistic regression also performed better than ML algorithms with the Intergrowth-21st and GROW standards at each pregnancy interval, although differences were small. The Random Forest model with the FMF standard achieved superior performance at each pregnancy interval, reaching a ROC-AUC of 0.79 in late pregnancy. Notably, the middle pregnancy Random Forest model with the FMF standard already attained a ROC-AUC of 0.72 at 26 weeks' gestation. Symphysis-fundal height, maternal abdominal circumference, maternal age, maternal height and weight, and parity were consistently identified as key predictors of SGA across the different standards.

Conclusion: There are important differences in the classification of SGA at birth between national and international birthweight standards. Both machine learning models and traditional logistic regression demonstrated comparable predictive performance for SGA identification. These findings hold promise for guiding risk-stratified prenatal care and optimizing resource allocation in clinical settings.

Introduction: Potato foliar diseases, particularly early and late blight, pose a serious threat to yield and food security, yet reliable visual recognition remains challenging due to cultivar heterogeneity, variable symptom expression, and acquisition noise in field-like imagery. To address these issues, we propose PotatoLeafNet, a two-stage deep learning framework that combines a fixed-sequence image-augmentation pipeline with a compact, task-optimized 11-layer convolutional neural network (CNN) using 3 × 3 kernels for robust, data-efficient classification of potato leaf conditions (Healthy, Early Blight, Late Blight).

Methods: We construct a dataset of 4,072 labeled potato leaf images from the PlantVillage-Potato subset and standardize all inputs to 224 × 224 RGB tensors with pixel intensities normalized to [0,1]. A balanced, fixed-order augmentation policy-comprising rotation, translation, shear, zoom, horizontal flipping, brightness adjustment, and channel jitter-is applied exclusively to the training split, increasing it to 6,000 images (2,000 per class) while keeping the validation and test sets free of synthetic samples. The second stage consists of an 11-layer CNN implemented in TensorFlow/Keras and trained with categorical cross-entropy loss and the Adam optimizer under a unified training and evaluation protocol. Performance is benchmarked against strong CNN and hybrid baselines, including ResNet-50 + VGG-16, VGG-16 + MobileNetV2, MobileNetV2, and Inception-V3.

Results: On the PlantVillage-Potato test set, PotatoLeafNet achieves 98.52% accuracy, 98.67% macro-precision, 99.67% macro-recall, 99.16% macro-F1, and 1.00 macro-AUC, outperforming all baseline models under identical preprocessing and training conditions. In particular, PotatoLeafNet surpasses ResNet-50 + VGG-16 (97.10% accuracy, AUC 0.98), VGG-16 + MobileNetV2 (94.80% accuracy, AUC 0.93), MobileNetV2 (93.20% accuracy, AUC 0.92), and Inception-V3 (92.50% accuracy, AUC 0.91). Short 10-epoch runs yield stable convergence (training accuracy 88.22%, validation accuracy 86.91%, test accuracy 88.15%), indicating efficient learning from the augmented distribution.

Discussion: The results demonstrate that explicitly coupling a fixed sequential augmentation stage with a lightweight 3×3-kernel CNN enables high tri-class accuracy, strong recall for disease classes, and improved generalization relative to deeper or fused architectures, without incurring substantial computational cost. By emphasizing disease-relevant structure while limiting overfitting, PotatoLeafNet provides a practical and resource-efficient solution for automated screening of potato leaf health in real-world agronomic settings, supporting timely and data-driven disease management.

Superiority of images in low light is necessary in the case of medical image as well as autonomous systems but there is still a challenge of balancing between brightness and natural appearance. The presented paper elaborates a new improvement model that combines Interval-Valued Intuitionistic Fuzzy Set as well as Reptile Search Algorithm optimization. The proposed approach automatically tunes the fuzzy membership and hesitation factors to adapt to uncertainty in dark areas while preserving significant structural data. The Performance is evaluated using common objective metrics which are Peak Signal-to-Noise Ratio, Absolute Mean Brightness Error, Contrast Improvement Index and entropy. All the reported percentage improvements are computed using the average metric values of the baseline Interval-Valued Intuitionistic Fuzzy Set method on the complete dataset. The results of the investigations indicate significant and consistent increases in the experimental results with a 3.69% percentage gain in entropy, a 21.71% percentage gain in brightness restoration, an 18.73% percentage gain in contrast and a 66.12% percentage gain in Peak Signal to Noise Ratio compared to the baseline method. As these results show, the given technique yields naturally amplified images that have better qualities in clarity, conciseness and structural conservation, which is extremely applicable in real-life situations involving low-light photography.

In the context of smart manufacturing, improving the quality and efficiency of process planning, especially in the processing of complex parts, has become a key factor influencing the level of intelligence in manufacturing systems. However, most current process planning methods still heavily rely on manual expertise, leading to problems such as difficulty in knowledge reuse, low planning efficiency, and slow response times, which are inadequate to meet the diverse and changing needs of engineering applications. To address these issues, this paper proposes an algorithm for Assembly Process Reasoning and Decision-making based on Bidirectional Long Short-Term Memory with Attention (AP-BiLSTM-ATT), which aims to deeply explore the hidden relationships between the multi-dimensional features of parts and process plans, thereby achieving probabilistic modeling of process decisions. Specifically, the attributes, geometric features, and historical process plans of parts are first labeled and vectorized, transforming traditional process knowledge into structured data representations suitable for deep learning models. A BiLSTM network model, integrated with a multi-head attention mechanism, is then constructed to capture contextual dependencies and semantic weight distributions between features, enhancing the model's ability to express complex process relationships. During training, the model learns the mapping distribution between features and processes from a large-scale historical process dataset, enabling intelligent reasoning and recommendation of process plans for new parts. The results show that this method outperforms traditional methods in terms of accuracy, response speed, and generalization ability in process planning, providing effective support for enhancing the intelligence of complex part process planning and laying a foundation for the structured expression and intelligent application of manufacturing process knowledge.

Current AI benchmarks often equate mimicry with genuine intelligence, emphasizing task performance over the underlying cognitive processes that enable human-like understanding. The Machine Perturbational Complexity & Agency Battery (mPCAB) introduces a new, substrate-independent framework that applies neurophysiological methods used initially to assess consciousness in artificial systems. Unlike existing evaluations, it features four key components-perturbational complexity, global workspace assessment, norm internalization, and agency-that link mechanisms with functions. This enables systematic comparisons across digital, neuromorphic, and biological substrates, addressing three research gaps: long-term reasoning with coherent behavior, norm internalization amid distribution shifts, and transformational creativity involving meta-cognitive rule modification. By analyzing theories of consciousness (GNW, IIT, PP, HOT), we identify targets for AI implementation. Our cognitive architecture analysis maps human functions-such as working memory and executive control-to their computational counterparts, providing guiding principles for design. The creativity taxonomy progresses from combinational to transformational, with measurable criteria like changes in conceptual space and the depth of meta-level reasoning. Ethical considerations are integrated into frameworks for monitoring organoid intelligence, reducing bias in creativity, and addressing rights issues. Pilot studies demonstrate mPCAB's feasibility across different substrates and show that its metrics are comparable. This framework moves evaluation away from superficial benchmarks toward mechanism-based assessment, supporting the development of mind-like machines and responsible AI advancements.

Objectives: The integration of Artificial Intelligence (AI) in healthcare promises to revolutionize patient care, diagnostics, and treatment protocols. Collaborative efforts among healthcare systems, research institutions, and industry are pivotal to leveraging AI's full potential. Understanding these dynamics is essential for addressing current challenges and shaping future AI development in healthcare. This study aims to characterize collaborative networks and stakeholders in AI healthcare initiatives, identify challenges and opportunities within these collaborations, and elucidate priorities for future AI research and development.

Methods: This study analyzed publicly available survey data previously collected by the Chinese Society of Radiology and the Chinese Medical Imaging AI Innovation Alliance. We performed secondary analysis of the national cross-sectional survey that was conducted in China with a total of 5,262 participants (5,142 clinicians and 120 research institution professionals), involving participants from three key groups: clinicians, institution professionals, and industry representatives. The survey explored diverse aspects including current AI usage in healthcare, collaboration dynamics, challenges encountered, and research and development priorities.

Results: Findings reveal high interest in AI among clinicians, with a significant gap between interest and actual engagement in development activities. Key findings include limited establishment of AI research departments and scarce interdisciplinary collaborations. Despite the willingness to share data, progress is hindered by concerns about data privacy and security, and lack of clear industry standards and legal guidelines. Future development interests focus on lesion screening, disease diagnosis, and enhancing clinical workflows.

Conclusion: This study highlights an enthusiastic yet cautious approach toward AI in healthcare, characterized by significant barriers that impede effective collaboration and implementation. Recommendations emphasize the need for AI-specific education and training, secure data-sharing frameworks, establishment of clear industry standards, and formation of dedicated AI research departments.

Introduction: Predictive maintenance has emerged as a critical strategy in modern manufacturing, in the frame of Industry 4.0, enabling proactive intervention before equipment failure. However, traditional machine learning approaches require extensive labeled data and lack adaptability to evolving operational conditions. On the other hand, Reinforcement Learning (RL) enables agents to learn optimal policies through interaction with the environment, eliminating the need for labeled datasets and naturally capturing the sequential, uncertain dynamics of equipment degradation.

Methods: In this paper, we propose an approach that incorporates four model-free RL algorithms, namely Proximal Policy Optimization (PPO), Advantage Actor-Critic (A2C), Deep Deterministic Policy Gradient (DDPG), and Soft Actor-Critic (SAC). We formulate the problem as a Markov Decision Process (MDP), which is solved with the aforementioned RL algorithms.

Results: The proposed approach is validated in the context of CNC machine tool wear prediction, using sensor data from the 2010 PHM Society Data Challenge. We examine algorithmic performance across four custom made environments, corrective and non-corrective environments both with and without delay correction mechanisms in order to compare learning dynamics, convergence behavior, and generalization aspects. Our results reveal that PPO and SAC achieve the most stable and efficient performance, with SAC excelling in structured environments and PPO demonstrating robust generalization. A2C shows consistent long-term learning, while DDPG underperforms due to insufficient exploration.

Discussion: The findings highlight the potential of RL for predictive maintenance applications and underscore the importance of aligning algorithm design with environment characteristics and reward structures.

Introduction: Digital lending, also known as alternative lending, refers to fintech platforms that offer quick and easy loans through digital channels, bypassing many of the limitations of traditional banking. Since the mid-2000s, digital lending has become a major fintech innovation, with rapid growth in India driven by financial inclusion measures. However, the sector continues to face challenges, including fraud, transparency issues, and consumer dissatisfaction. The primary objective of this study was to understand how consumers perceive and assess India's RBI-approved P2P digital lending apps by analyzing a large dataset of customer feedback to identify strengths, weaknesses, and overall satisfaction levels.

Methods: The study analyzed a final dataset of 15,408 user reviews collected from seven RBI-approved digital lending platforms: 5Paisa, Faircent, i2iFunding, LenDenClub, CashKumar, Lendbox, and IndiaMoneyMart derived from an initial 15,537 reviews. The cleaned data was then examined using natural language processing, topic modeling, and supervised machine learning and deep learning models to identify key themes and evaluate predictive performance.

Results: Topic modeling identified 11 recurring topics. Sentiment analysis revealed that 55% of evaluations were positive, 41% were negative, and 4% were neutral. Strengths included loan disbursement, withdrawals, and EMI payments, while weaknesses involved interface design, transparency around rejections, and login functionality. Comparative data revealed that IndiaMoneyMart and i2iFunding received the highest user satisfaction, while 5Paisa and Lendbox trailed due to recurring complaints about transparency, accessibility, and overall user experience. In terms of modeling, the deep learning model VGG16 and ensemble machine learning techniques (XGBoost, CatBoost, and LightGBM) consistently achieved the highest predictive accuracy (up to 0.88), outperforming simpler models such as decision trees and ResNets.

Discussion: The findings indicate that digital lending platforms support financial inclusion but require improvements in user interface and user experience, better transparency in loan decisions, and stronger customer support. Addressing these areas can help strengthen trust and promote long term adoption of digital lending services.

The rise of agentic artificial intelligence (Agentic AI) marks a transition from systems that optimize externally specified objectives to systems capable of representing, evaluating, and revising their own goals. Whereas earlier AI architectures executed fixed task specifications, agentic systems maintain recursive loops of perception, evaluation, goal-updating, and action, allowing them to sustain and adapt purposive activity across temporal and organizational scales. This paper argues that Agentic AI is not an incremental extension of large language models (LLMs) or autonomous agents in the sense we know it from classical AI and multi-agent systems, but a reconstitution of agency itself within computational substrates. Building on the logic of coordination, delegation, and self-regulation developed in early agent-based process management systems, we propose a general theory of synthetic purposiveness, where agency emerges as a distributed and self-maintaining property of artificial systems operating in open-ended environments. We develop the concept of synthetic teleology-the engineered capacity of artificial systems to generate and regulate goals through ongoing self-evaluation-and we formalize its dynamics through a recursive goal-maintenance equation. We further outline design patterns, computational semantics, and measurable indicators of purposiveness (e.g., teleological coherence, adaptive recovery, and reflective efficiency), providing a foundation for the systematic design and empirical investigation of agentic behaviour. By reclaiming agency as a first-class construct in artificial intelligence, we argue for a paradigm shift from algorithmic optimization toward goal-directed reasoning and purposive orchestration-one with far-reaching epistemic, societal, and institutional consequences.