Applied Artificial Intelligence Research
type: channel
description: (AAIR) is an international journal focusing on cutting-edge AI research and innovative practices, offering a high-end academic platform for global scholars, researchers, and industry experts. It covers core AI fields such as machine learning, NLP, computer vision, robotics, etc., and welcomes both theoretical breakthroughs and applied innovations. By publishing original papers, reviews, case studies, and technical reports, it showcases the latest AI developments and trends.
publisher: CHINA SCIENCE AND TECHNOLOGY DEVELOPMENT PRESS
language: en
publication name: Applied Artificial Intelligence Research
issn: 3105-0379
items: A Hybrid Spatiotemporal Framework with Memory and Diffusion Convolution for Traffic Flow Prediction
items: A Real-time LPR Deployment Scheme on Edge Devices via INT8 Quantization and TensorRT Optimization
items: Data-Driven Predictive Maintenance of Permanent Magnet Synchronous Motor Systems Using Explainable Machine Learning and Digital Twin Architecture
items: The Application of Artificial Intelligence in Tumor Immunotherapy: Integrating Multi-Omics Data to Achieve Efficacy Prediction and Combination Strategies
items: A Case Study of the Influence of Multifarious Factors on Traffic Flow Forecasting
items: Constructing Multimodal Wireless Knowledge Graphs for Large Language Model–Based Network Reasoning
items: AI Transparency and Employee Innovation: The Mediating Role of Psychological Safety and the Moderating Effect of AI Self-Efficacy
items: A Comparative Study of the Data Ethical Foundations of the EU's GDPR and China's Personal Information Protection Law from the Perspective of Global Integration
items: Research on the Evolution of AI Copyright Attribution Mechanisms
items: Design and implementation of public transportation emergency command and assistance platform from the perspective of smart policing
items: Post-Human Perspectives on Film: Exploring Innovative Applications of AIGC
items: An AI-Driven Intelligent Health Assessment System for High-Voltage Switchgear Based on Multimodal Sensor Data Fusion
items: Artificial Intelligence for Fracture Risk Prediction: A Bibliometric Analysis of Research Hotspots and Evolutionary Trends
items: Explainable Machine Learning for Telecom Customer Churn Prediction and Actionable Retention Strategies
items: A Comprehensive Survey of Deep Learning–Based Object Tracking for Augmented Reality in Complex Real-World Scenes
items: Personalized Recommendation Algorithms in Digital Media: A Review of Engagement Effects, Information Cocoons, and Marketing Implications
items: Continuous blood pressure estimation and cardiovascular risk assessment based on wearable PPG signals
items: When Algorithms Push Firms to Cheat: AI, Blockchain, and Financing in China
items: Research on Performance Prediction Model of Wind Turbine Gearbox Lubricating Oil Based on Deep Learning
items: Map of Geographical Science Based on Artificial Intelligence
items: https://journal.innoviair.cn/index.php/AAIR/en/article/view/42
items: Innovative Research on Cracking the Personalized Service Management of Artificial Intelligence Libraries
items: Application of AI Models in Dynamic Talent Adaptation between Industry and Education: A Case Study of Data from Digital Genealogy Cloud Platform
items: Language, Consciousness, and Manifestation: Towards a Pragmatic-Cognitive Framework for Next-Generation Neural Language Systems
items: Against the Backdrop of the Digital Economy: AI Empowers Innovation and Entrepreneurship Education for the Integration of Industry and Education
items: Research on the Impact Mechanism of Farmers' Digital Literacy on Enhancing Service Satisfaction of Power Grid Companies: An Implementation Path Based on the Interpretability of Machine Learning Models
items: Applying Radiomics and Deep Learning to Investigations into Seafarers' Health Status
items: A Learnable Frequency Gated DCT Denoising Network
items: The coupled application of GIS digital Twin and AI reinforcement learning
items: Ethics-First Lightweight AI: Differentiated English Teaching and Cultural Competence in Junior Middle Schools

A Hybrid Spatiotemporal Framework with Memory and Diffusion Convolution for Traffic Flow Prediction
type: item
description: Accurate traffic flow prediction is pivotal for intelligent transportation systems, yet it remains inherently challenging due to dynamic spatial correlations and long-range temporal dependencies. While existing forecasting paradigms predominantly rely on static, pre-defined graph structures, they often overlook the direct functional connections between non-adjacent time steps.This paper proposes a novel spatiotemporal forecasting framework, termed Memory-augmented Diffusion Convolutional LSTM network (MDC-LSTM), which integrates a MemBART-inspired memory mechanism, diffusion convolution, regional attention, and LSTM-based temporal modeling. By synthesizing these components with Long Short-Term Memory (LSTM) units, the proposed model effectively captures both localized spatial patterns and deep inter-temporal relationships. Empirical evaluations conducted on the METR-LA benchmark dataset demonstrate that our framework significantly enhances predictive accuracy across multiple metrics, consistently outperforming several state-of-the-art (SOTA) baselines and exhibiting strong robustness in long-term forecasting scenarios.
subject: Research Article
  topic: section
subject: Traffic Flow Prediction
  topic: keywords
subject: Spatiotemporal Forecasting
  topic: keywords
subject: Memory-Augmented Transformer
  topic: keywords
subject: Diffusion Convolution
  topic: keywords
creator: Xi Chen
creator: Jiajia Chen
rights: Copyright (c) 2026 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2026-07-20
publication date: 2026-07-20
starting page: 8
ending page: 17
doi: 10.65455/6rkv7629

A Real-time LPR Deployment Scheme on Edge Devices via INT8 Quantization and TensorRT Optimization
type: item
description: Real-time license plate recognition (LPR) on edge devices is heavily constrained by hardware limits. This study focuses on the efficient deployment of a real-time Chinese license plate recognition system, which plays a crucial role in intelligent traffic management and automated toll collection. Despite substantial progress in artificial intelligence and deep learning algorithms, achieving real-time performance remains a challenge because of the limited computational resources of practical hardware platforms. To overcome this limitation, a two-stage model optimization approach is proposed and integrated with NVIDIA deployment toolkits to accelerate inference with only marginal accuracy loss. The proposed work delivers a high-performance license plate recognition system and demonstrates that hardware-aware model optimization is essential for achieving efficient and practical real-time deployment.
subject: Research Article
  topic: section
subject: License Plate Recognition (LPR)
  topic: keywords
subject: YOLOV5
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: INT8 Quantization
  topic: keywords
creator: Xi Chen
creator: Jiajia Chen
rights: Copyright (c) 2026 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2026-07-20
publication date: 2026-07-20
starting page: 1
ending page: 7
doi: 10.65455/nygma907

Data-Driven Predictive Maintenance of Permanent Magnet Synchronous Motor Systems Using Explainable Machine Learning and Digital Twin Architecture
type: item
description: Permanent magnet synchronous motors (PMSMs) are widely used in industrial automation, robotics, electric drives, and intelligent manufacturing, yet their maintenance still depends heavily on threshold alarms and periodic inspection. This paper presents a data-driven predictive maintenance framework for PMSM systems by integrating explainable machine learning with a digital twin architecture. A self-consistent synthetic PMSM digital twin dataset is constructed to represent thermal, electrical, mechanical, control-related, and aging degradation signals, including winding temperature, vibration RMS, current harmonic distortion, q-axis current ripple, DC-link voltage ripple, speed tracking error, flux-linkage index, insulation resistance, load ratio, and operating hours. Random Forest, XGBoost, and LightGBM are trained to classify the motor health state into healthy, warning, and fault conditions. The results show that LightGBM achieves the best held-out performance, with 95.00% accuracy, 0.9256 macro F1-score, and 0.9914 macro ROC-AUC. A 5-fold stratified cross-validation experiment confirms the stability of the model, while an ablation study verifies the contribution of thermal, mechanical, electrical, control, and aging feature groups. An additional AI4I 2020 benchmark validation is included to test whether the modeling pipeline generalizes to a public predictive-maintenance dataset. SHAP analysis indicates that winding temperature, vibration RMS, insulation resistance, speed error, and current harmonic distortion are dominant contributors to fault prediction. Finally, a deployable digital twin architecture is proposed for automation scenarios. The study provides a reproducible simulation-based reference for intelligent motor-drive maintenance under Industry 4.0.
subject: Research Article
  topic: section
subject: Predictive Maintenance
  topic: keywords
subject: Permanent Magnet Synchronous Motor
  topic: keywords
subject: Explainable Machine Learning
  topic: keywords
subject: SHAP
  topic: keywords
subject: Digital Twin
  topic: keywords
subject: Industry 4.0
  topic: keywords
creator: Zhuopeng Gao
rights: Copyright (c) 2026 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
doi: 10.65455/30tg2482

The Application of Artificial Intelligence in Tumor Immunotherapy: Integrating Multi-Omics Data to Achieve Efficacy Prediction and Combination Strategies
type: item
description: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, yet only a minority of patients achieve durable clinical benefit due to primary or acquired resistance. This heterogeneity necessitates precise predictive biomarkers and rational combination strategies. The rapid accumulation of multi-omics data-including genomics, transcriptomics, radiomics, pathomics, and microbiomics-offers unprecedented opportunities to decode tumor-immune interactions. Artificial intelligence, particularly machine learning and deep learning, excels at integrating high-dimensional, heterogeneous data to build interpretable efficacy prediction models and accelerate the discovery of synergistic combination regimens. In this review, we systematically summarize AI-based multimodal integration frameworks (e.g:deep multimodal fusion, graph neural networks, and single-cell omics models) and key technologies. We critically analyze the types and characteristics of multi-omics data used in immunotherapy prediction. Furthermore, we discuss AI-driven applications in personalized stratification, combination therapy target discovery, and drug synergy prediction. Finally, we identify current bottlenecks-including interpretability, data standardization, clinical validation, and model generalizability-and propose future directions. This review provides a systematic reference for transitioning from empirical treatment to data-driven precision immunotherapy.
subject: Review Rrticle
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Tumor Immunotherapy
  topic: keywords
subject: Multi-Omics
  topic: keywords
subject: Efficacy Prediction
  topic: keywords
subject: Combination Therapy
  topic: keywords
subject: Machine Learning
  topic: keywords
creator: Gang Liu
rights: Copyright (c) 2026 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2026-06-24
publication date: 2026-06-24
doi: 10.65455/q72yq328

A Case Study of the Influence of Multifarious Factors on Traffic Flow Forecasting
type: item
description: Accurate traffic flow forecasting serves as the core foundation for intelligent transport systems to achieve efficient traffic management and optimized resource allocation, holding significant importance for alleviating congestion in modern cities. Influenced by the complex interplay of diverse heterogeneous factors such as meteorological conditions, temporal cycles, and unforeseen events, urban traffic flow data exhibits pronounced nonlinearity and random fluctuations, rendering high-precision forecasting exceptionally challenging. While mainstream deep learning models have advanced prediction accuracy, they struggle to quantify the specific contributions of different factors and often overlook significant multicollinearity issues among multi-source data. Addressing these challenges, this paper introduces several improvements: Firstly, it constructs a fused dataset incorporating multidimensional external factors such as meteorological conditions and events; secondly, it proposes an explainable prediction framework based on Random Forest with raw feature analysis by Principal Component Analysis (PCA); Thirdly, the SHAP game-theoretic method is introduced to achieve transparent attribution of prediction results. This paper first employs PCA to extract principal components from high-dimensional multi-source factors, effectively eliminating multicollinearity in the data. Subsequently, a robust random forest regression model is constructed for prediction. Based on four independent datasets from Beijing's TaxiBJ service, the proposed framework undergoes comprehensive performance validation and analysis. Results demonstrate that the model maintains a coefficient of determination consistently above 0.85 across all annual datasets, exhibiting outstanding predictive accuracy and robustness across temporal cycles. SHAP analysis further reveals a stable decision mechanism characterized by the first principal component driving periodicity, with subsequent components providing d…
subject: Research Article
  topic: section
subject: Traffic Flow Forecasting
  topic: keywords
subject: Multi-Source Data Fusion
  topic: keywords
subject: Principal Component Analysis
  topic: keywords
subject: Random Forest
  topic: keywords
subject: SHAPley Additive ExPlanations
  topic: keywords
creator: Xiaojun Wang
rights: Copyright (c) 2026 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2026-06-10
publication date: 2026-06-10
doi: 10.65455/sbd3t486

Constructing Multimodal Wireless Knowledge Graphs for Large Language Model–Based Network Reasoning
type: item
description: The increasing complexity of wireless networks and the proliferation of heterogeneous data sources pose significant challenges to intelligent network management and decision-making. Although Large Language Models (LLMs) have demonstrated strong capabilities in natural language understanding and reasoning, their direct application in wireless network tasks is limited due to the lack of structured domain knowledge and reliable contextual foundations. This paper proposes an intelligent knowledge graph enhanced retrieval-enhanced generation (KG-RAG) framework for multimodal wireless networks. First, through structured entity and relationship modeling, multimodal wireless data including network measurements, configuration parameters and domain knowledge are transformed into a unified knowledge graph. The constructed knowledge graph, as a clear semantic backbone, supports efficient knowledge retrieval and reasoning. By integrating graph-based retrieval results with LLMs, the proposed framework achieves context-aware and interpretable network reasoning. The effectiveness of this method is evaluated through a case study based on lightweight network slicing. The experimental results show that in different LLMs, compared with the traditional text-based RAG method, the KG-enhanced RAG method improves the reasoning accuracy, reasoning consistency and interpretive integrity. These findings indicate that structured knowledge representation plays a crucial role in enhancing the reliability and interpretability of LLM-driven wireless network intelligence.
subject: Research Article
  topic: section
subject: Knowledge Graph
  topic: keywords
subject: Retrieval-Augmented Generation
  topic: keywords
subject: Wireless Networks
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Graph-Based Retrieval
  topic: keywords
creator: Wenhao Lin
creator: Yingjie Song
creator: Yinguo Liu
creator: Ying Li
rights: Copyright (c) 2026 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2026-05-20
publication date: 2026-05-20
doi: 10.65455/1bt00146

AI Transparency and Employee Innovation: The Mediating Role of Psychological Safety and the Moderating Effect of AI Self-Efficacy
type: item
description: As AI technologies become more integrated into everyday organizational workflows, it is essential to understand how employees interpret and interact with these systems to support innovative outcomes.This study investigates how transparency in AI systems influences employee innovation, considering the role of psychological safety and individual confidence in using AI. Data were gathered from 447 knowledge workers in small and medium-sized enterprises in China who regularly interact with AI tools. Analysis using structural equation modeling shows that clearer AI processes enhance employees’ sense of psychological safety, which in turn supports innovative actions. Moreover, employees with greater confidence in using AI benefit more from transparency, amplifying its positive impact on innovation. The results underscore that promoting innovation requires not only transparent system design but also initiatives that strengthen employees’ AI skills and create psychologically supportive environments. This research contributes to understanding the human side of AI adoption by linking system transparency to practical organizational outcomes and offering guidance for effective AI–human integration in workplaces.
subject: Research Article
  topic: section
subject: AI Transparency
  topic: keywords
subject: Psychological Safety
  topic: keywords
subject: Employee Innovation
  topic: keywords
subject: AI Self-Efficacy
  topic: keywords
creator: Qian Li
creator: Peilin Li
creator: Chan Sai Keong
rights: Copyright (c) 2026 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2026-05-19
publication date: 2026-05-19
doi: 10.65455/dxhkxd06

A Comparative Study of the Data Ethical Foundations of the EU's GDPR and China's Personal Information Protection Law from the Perspective of Global Integration
type: item
description: Data drives the development of the global economy, but at the same time, countries face the same challenge: how to protect personal information without hindering its utilization.In view of the lack of systematic ethical comparison between the two major privacy frameworks in Europe and China in the existing literature, this paper aims to examine the ethical basis of the EU's General Data Protection Regulation (GDPR) and China's Personal Information Protection Law (PIPL).This paper uses a comparative analysis method to systematically compare the similarities and differences between GDPR and PIPL in terms of ethical paradigm, system design, and governance logic. The study found that although GDPR and PIPL represent different regulatory paradigms, they are converging in global data governance.The GDPR follows a rights-based paradigm, which originates from the European tradition of emphasizing individual rights. PIPL adopts the development-security balance paradigm based on the need to balance personal protection, industry needs and national security. Although the origins are different, both laws emphasize the combination of personal data control empowerment and supervision mechanisms, and pay attention to the established system of enterprises, not just ex post punishment. By clarifying the ethical basis of the global data governance model, this study provides an analytical framework for understanding regulatory convergence in different cultural and institutional contexts.
subject: Research Article
  topic: section
subject: Data Ethics
  topic: keywords
subject: GDPR
  topic: keywords
subject: PIPL
  topic: keywords
subject: Global Integration
  topic: keywords
subject: Rights-Based
  topic: keywords
subject: Development-Security Balance
  topic: keywords
creator: Zhengyu Yang
creator: Di Jiang
creator: Yuyang Liu
rights: Copyright (c) 2026 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2026-05-19
publication date: 2026-05-19
doi: 10.65455/a7y1rc23

Research on the Evolution of AI Copyright Attribution Mechanisms
type: item
description: With the continuous iteration of artificial intelligence technology, the AI-generated content has penetrated into various fields of social production and daily life. The copyright ownership mechanism of artificial intelligence generated content evolves with technological progress, legal change and industrial development. This evolution can be divided into three stages : the embryonic stage, the development stage and the mature stage. The core of the conflict is to coordinate the contradiction between technological innovation and the lag of legal regulation. The primary goal is to balance the rights and public interests of multiple stakeholders. This paper systematically combs the evolution of this attribution mechanism by means of literature research, historical analysis, comparative research and case study. This paper makes an in-depth analysis of the core issues such as the absence of legal personality of artificial intelligence, the inconsistency of originality identification standards, and the imbalance of rights distribution among multiple stakeholders. On this basis, it clearly puts forward targeted solutions such as the legal status of artificial intelligence, the standardization of identification standards, and the establishment of a multi-stakeholder rights allocation system. This study improves the theoretical framework of artificial intelligence copyright protection, and provides practical guidance for legislative revision, judicial adjudication and the development of artificial intelligence industry, thus promoting the benign synergy between technological innovation and rights protection.
subject: Research Article
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: AI-Generated Content(AIGC)
  topic: keywords
subject: Copyright Attribution
  topic: keywords
subject: Evolutionary Mechanism
  topic: keywords
subject: Originality
  topic: keywords
subject: Rights Equilibrium
  topic: keywords
creator: Jiehui Zhang
rights: Copyright (c) 2026 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2026-05-19
publication date: 2026-05-19
doi: 10.65455/4zd3jp23

Design and implementation of public transportation emergency command and assistance platform from the perspective of smart policing
type: item
description: In view of the pain points faced by the current urban public transportation public security management, such as high security pressure of large-scale activities, lagging police response, and insufficient integration of multi-source data, this paper designs and implements a public transportation emergency command and assistance platform. The system adopts Spring Boot + Vue.js front-end and back-end separation architecture, integrating five core modules: vehicle scheduling, route optimization, passenger feedback, real-time monitoring and data analysis. On this basis, from the perspective of smart policing, the adaptation design of the system in scenarios such as security emergency vehicle scheduling for large-scale activities, public security risk investigation of passenger feedback data, real-time monitoring and linkage with public security command center is studied. At the same time, ideas for expanding police functions such as early warning of key personnel and one-click alarm linkage are proposed. Practice shows that the platform can effectively improve the informatization and intelligence level of public transportation security work, and provide technical support for the construction of an integrated police mechanism of "emotion, guidance, and action".
subject: Research Article
  topic: section
subject: Smart Policing
  topic: keywords
subject: Public Transport
  topic: keywords
subject: Emergency Command
  topic: keywords
subject: Vehicle Scheduling
  topic: keywords
subject: Public Security Prevention and Control
  topic: keywords
subject: Microservices Architecture
  topic: keywords
creator: Dingwei Chen
creator: Yuanbin Bai
rights: Copyright (c) 2026 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2026-06-10
publication date: 2026-06-10
doi: 10.65455/9m8wac27

Post-Human Perspectives on Film: Exploring Innovative Applications of AIGC
type: item
description: The advent of AIGC(Artificial Intelligence Generated Content) represents a silicon-driven revolution for the carbon-based era of cinema. Human approaches to filmmaking have evolved—from celluloid to digital, from handwritten scripts to computer-generated texts—and are now increasingly oriented toward the development and collaboration with AIGC. During the process of employing AIGC in film production, it has become deeply embedded in cinematic art, whether through the secondary creation of celluloid imagery, collaborative brainstorming with humans, or integration with VR to craft new and enhanced viewing experiences. This study demonstrates that AIGC can significantly enhance the efficiency and creativity of film production, offering innovative solutions for image restoration, facilitating seamless human–machine collaboration, and enabling immersive VR-based cinematic experiences. By systematically analyzing these applications, the research provides a framework for understanding the transformative impact of AIGC on contemporary cinema and highlights its potential to redefine aesthetic and production standards in the film industry.
subject: Research Article
  topic: section
subject: AIGC
  topic: keywords
subject: Film
  topic: keywords
subject: Creative Practices
  topic: keywords
subject: Virtual Production
  topic: keywords
subject: VR Imagery
  topic: keywords
creator: Zishen Huang
creator: Le Xu
rights: Copyright (c) 2026 Applied Artificial Intelligence Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-23
publication date: 2026-03-23
doi: 10.65455/thwcfx26

An AI-Driven Intelligent Health Assessment System for High-Voltage Switchgear Based on Multimodal Sensor Data Fusion
type: item
description: The function of high-voltage switchgear is to maintain the stability of the power grid. The traditional health evaluation methods generally rely heavily on a single index; The accuracy of this method is relatively low, and it is difficult to detect failures because it cannot take into account multiple factors. In view of the above shortcomings, this paper has designed and developed an AI-intelligent health evaluation System based on multimodal sensor data fusion technology.The system uses the four-tier architecture of sensor layout, data acquisition and transmission, data fusion and processing, and health evaluation and decision-making, and integrates temperature, humidity, partial discharge, vibration, current, and voltage data. It uses a three-tier data fusion approach (weighted average at the data layer, principal component analysis (PCA) at the feature layer, and D-S evidence theory at the decision-making layer) to handle heterogeneous data and combines SVM models for small sample sizes and CNN models for time series data to provide all-around evaluation.Experiments based on a simulated platform show that the system can achieve an average accuracy of 96.8% and perform better than traditional single parameters by about 18.5 per cent. It can identify incipient faults 2-3 hours earlier than traditional methods, maintain 85% assessment accuracy even if one sensor fails, and complete post-fault state assessment and fault location within 500 ms after the relay protection action for short-circuit faults. The integrated three-level warning mechanism can reduce blind maintenance by as much as 60 per cent and lower the cost of maintenance by an average of 35 per cent; At the same time, it has achieved an effective grasp of complex fault conditions, improved the credibility of evaluation, provided scientific basis for preventive maintenance.
subject: Research Article
  topic: section
subject: High-Voltage Switchgear
  topic: keywords
subject: Multimodal Sensor Data Fusion
  topic: keywords
subject: AI-Driven Health Assessment
  topic: keywords
subject: Fault Detection
  topic: keywords
creator: Huiming Kang
creator: Juanmin Chen
creator: Shaocong Liang
creator: Shidi Liu
rights: Copyright (c) 2026 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2026-03-23
publication date: 2026-03-23
doi: 10.65455/a6eqkh55

Artificial Intelligence for Fracture Risk Prediction: A Bibliometric Analysis of Research Hotspots and Evolutionary Trends
type: item
description: Artificial intelligence (AI) has emerged as a promising approach for improving fracture risk prediction. This study aimed to provide a comprehensive bibliometric overview of research on AI in fracture risk prediction by mapping its temporal evolution, collaborative structure, thematic framework, and disciplinary distribution. Publications were retrieved from the Web of Science Core Collection using a predefined search strategy covering January 2001 to December 2025, including only English-language articles and reviews. After screening and data cleaning, a total of 943 publications were analyzed. Bibliometric analyses were conducted using VOSviewer (v1.6.20) and the Bibliometrix R package (v4.5.1), examining annual publication trends, co-authorship networks at the author, institutional, and country levels, keyword co-occurrence clusters, and subject category distributions. The results showed that research output exhibited three developmental phases, with a marked acceleration after 2020. Collaboration networks demonstrated a clustered and moderately centralized structure, with core institutions and countries—particularly the United States and major European partners—occupying central positions. Keyword co-occurrence analysis identified four principal thematic clusters: AI-based predictive modeling, imaging-derived radiomics and opportunistic screening, FRAX-centered clinical risk assessment, and comorbidity- and pharmacotherapy-related applications. Subject category analysis further revealed progressive interdisciplinary integration, with increasing contributions from radiology and computer science alongside traditional clinical specialties. Overall, AI-based fracture risk prediction research has evolved from clinically grounded risk stratification toward computationally augmented, imaging-integrated, and translationally oriented predictive frameworks. The field demonstrates rapid growth, expanding international collaboration, and increasing interdisciplinary converg…
subject: Research Article
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Fracture Risk Prediction
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Bibliometric Analysis
  topic: keywords
creator: Bo Wang
creator: Yin Lian
creator: Lina Gao
rights: Copyright (c) 2026 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2026-03-24
publication date: 2026-03-24
doi: 10.65455/7tc70w72

Explainable Machine Learning for Telecom Customer Churn Prediction and Actionable Retention Strategies
type: item
description: Customer churn is a critical issue in customer relationship management (CRM) in the telecommunications industry. Accurately identifying high-risk customers and providing actionable intervention recommendations is crucial for improving customer lifetime value and reducing operating costs. This paper addresses the task of predicting customer churn in the telecommunications industry by constructing an interpretable machine learning analysis framework of "prediction-intervention." On the Telco customer dataset containing 21 customer features, the discriminative performance of three models—XGBoost, Random Forest, and Logistic Regression—is compared. SHAP (SHapley Additive exPlanations) is introduced into the optimal model family to achieve interpretability analysis at both the global and individual levels. Experimental results show that the performance of the three models is similar, with Logistic Regression achieving the highest AUC (0.835) and F1 (0.593), while XGBoost and Random Forest have AUCs of 0.833 and 0.829, respectively. Confusion matrix analysis reveals that the main bottleneck under the current setup is false negative reporting (FN), indicating the need for threshold and cost-sensitive optimization. SHAP results indicate that contract type, tenure, online security, monthly charges, and technical support are the most critical churn drivers. Furthermore, this paper proposes a three-tiered, precise retention strategy targeting different risk levels and driving factors, providing a practical reference for deploying explainable AI-driven churn management systems in real-world telecom operations.
subject: Research Article
  topic: section
subject: Explainable Machine Learning
  topic: keywords
subject: SHAP
  topic: keywords
subject: Logistic Regression
  topic: keywords
subject: Telecom Operations
  topic: keywords
subject: Customer Retention Strategies
  topic: keywords
creator: Zihan Liu
rights: Copyright (c) 2026 The Author(s) .Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2026-03-24
publication date: 2026-03-24
doi: 10.65455/1z4v1627

A Comprehensive Survey of Deep Learning–Based Object Tracking for Augmented Reality in Complex Real-World Scenes
type: item
description: Augmented Reality (AR) systems critically depend on accurate, temporally stable, and computationally efficient object tracking to maintain geometric alignment and perceptual coherence between virtual content and the physical world. As AR technologies transition from controlled laboratory prototypes to large-scale deployment in industrial, medical, and consumer scenarios, tracking must operate robustly in complex real-world environments characterized by dynamic objects, occlusions, illumination changes, fast motion, and strict computational constraints. Traditional geometry-driven tracking pipelines often degrade under such conditions, motivating increased adoption of deep learning based approaches. This survey provides a comprehensive review of deep learning based object tracking for AR in complex real-world scenes, with particular emphasis on system-level considerations. Object tracking is treated as a central perception primitive that underpins stable AR experiences and interacts tightly with modules such as visual simultaneous localization and mapping, depth and geometry estimation, and semantic scene understanding. In contrast to prior surveys that emphasize algorithmic accuracy in isolation, we explicitly analyze AR-specific constraints including real-time latency, temporal stability, energy efficiency, long-term robustness, and deployment on mobile and wearable platforms. We review major tracking paradigms, representative datasets and benchmarks, AR-centric evaluation criteria, and common failure modes observed in practice, and we outline future research directions toward scalable, reliable, and trustworthy AR tracking systems.
subject: Review Rrticle
  topic: section
subject: Augmented Reality
  topic: keywords
subject: Object Tracking
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Complex Scenes
  topic: keywords
subject: Real-Time Systems
  topic: keywords
subject: Mobile and Wearable AR
  topic: keywords
creator: Duoduo Mou
rights: Copyright (c) 2026 The Author(s) .Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2026-03-24
publication date: 2026-03-24
doi: 10.65455/ddjkhy91

Personalized Recommendation Algorithms in Digital Media: A Review of Engagement Effects, Information Cocoons, and Marketing Implications
type: item
description: Artificial intelligence (AI) has become a significant force, demonstrating rapid and sustained growth on global digital media platforms. AI technologies on these platforms include machine learning, deep learning, natural language processing, expert systems, and highly advanced personalized recommendation algorithms. These AI-driven algorithms are crucial for enhancing user engagement on digital media platforms, such as smartphone applications, social media, and online media. These algorithms provide customized content based on user preferences, significantly influencing user consumption behavior. Meanwhile, the role of recommendation algorithms in content management and creation promotes the boundaries of personalization, which is effective in social media marketing and e-commerce. However, the rapid development of AI-based recommendation algorithms is likely to pose potential risks, such as the "information cocoon effect," producing homogeneous content and exacerbating social polarization and biases. Additionally, concerns about user privacy and ethical implications have been raised. This review aims to explore the multi-faceted impacts of AI-driven personalized recommendation algorithms on audiences of digital media platforms, focusing on audience engagement, changing user behavior through social media marketing, and the impact of the cocoon effect on users.
subject: Research Article
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Digital Media Platforms
  topic: keywords
subject: Algorithmic Recommendation Systems
  topic: keywords
subject: Audience Engagement
  topic: keywords
subject: Social Media Marketing
  topic: keywords
subject: The Cocoon Effect
  topic: keywords
creator: Jueling Zhou
rights: Copyright (c) 2026 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2026-03-23
publication date: 2026-03-23
doi: 10.65455/qnpjvg49

Continuous blood pressure estimation and cardiovascular risk assessment based on wearable PPG signals
type: item
description: This paper systematically studies the non-invasive continuous blood pressure estimation and cardiovascular risk assessment methods based on photoplethysmography (PPG) signals. A complete signal processing flow was constructed using the PhysioNet public dataset, including preprocessing, feature extraction, model construction and validation. The experimental results show that the extraction of heart rate and respiratory rate based on PPG has high accuracy, preliminarily verifying the correlation between the morphological characteristics of PPG and blood pressure parameters. The research further proposed a hierarchical processing architecture to balance the computing efficiency and monitoring depth of wearable devices, providing a reference for the engineering implementation of health monitoring systems.
subject: Research Article
  topic: section
subject: PPG
  topic: keywords
subject: Blood Pressure Estimation
  topic: keywords
subject: Cardiovascular Risk Assessment
  topic: keywords
subject: Wearable Devices
  topic: keywords
subject: Signal Processing
  topic: keywords
subject: MATLAB
  topic: keywords
creator: Zehui Xu
rights: Copyright (c) 2026 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2026-03-23
publication date: 2026-03-23
doi: 10.65455/6pgttp36

When Algorithms Push Firms to Cheat: AI, Blockchain, and Financing in China
type: item
description: As pillars of China's economy, SMEs(Small and Medium-sized Enterprises) face persistent financing frictions. To address these challenges, the government promotes digital finance tools such as artificial intelligence (AI) and blockchain. AI enables rapid risk assessment and improves efficiency but often imposes rigid standards that overlook SME conditions, creating algorithmic pressures and incentives for data manipulation. Blockchain enhances governance through immutable, transparent records but raises compliance costs and adaptation burdens. Drawing on interviews with SME owners, managers, and financial intermediaries, this study applies grounded theory to analyze how AI and blockchain jointly reconfigure financing governance in China. Findings reveal that algorithmic rigidity and blockchain transparency interact, generating systemic tension while compelling institutional adaptation from firms. This dynamic converges into a state of governance synergy, wherein the technologies' complementary functions are institutionalized, ultimately producing a dual-threshold financing mechanism. Under this mechanism, firms must sequentially pass AI-driven credit screening and blockchain-based data verification. This research advances digital governance theory by framing technology as an active institutional shaper and contributes to SME finance literature by elucidating the paradigm shift from relationship-based to protocol-based financing logic.
subject: Research Article
  topic: section
subject: SMEs
  topic: keywords
subject: Financing Constraints
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Blockchain
  topic: keywords
subject: Governance Synergy
  topic: keywords
subject: Dual-Threshold Mechanism
  topic: keywords
creator: Ming Fu
rights: Copyright (c) 2026 Applied Artificial Intelligence Research https://creativecommons.org/licenses/by/4.0
date: 2026-03-20
publication date: 2026-03-20
doi: 10.65455/d2tq5y61

Research on Performance Prediction Model of Wind Turbine Gearbox Lubricating Oil Based on Deep Learning
type: item
description: Gearbox lubricating oil oxidation degradation severely impairs the operational stability of wind turbines and increases maintenance costs. Conventional detection methods (e.g., Rotating Pressure Vessel Oxidation Test (RPVOT), Pressure Differential Scanning Calorimetry (PDSC)) have high instrument dependence and long testing cycles, and may not meet on-site rapid detection demands. This study took in-service industrial gear oil samples as the research object, selected five physicochemical indices as input variables, and systematically optimizes data preprocessing (Standard Normal Variate (SNV)), feature extraction (Principal Component Analysis (PCA)), and machine learning algorithms (Back Propagation Neural Network (BPNN)/Support Vector Machine (SVM)/Random Forest (RF)). The proposed SNV-PCA-BP hybrid model achieved excellent predictive performance with a R2 of 0.9960 and Residual Prediction Deviation (RPD) of 6.1247, which is 360 times more efficient (defined as single-sample detection time) than traditional RPVOT/PDSC methods based on parallel tests of 62 samples. This model provides a low-cost and reliable technical support for the predictive maintenance of wind turbine gearboxes.
subject: Research Article
  topic: section
subject: Gearbox Lubricating Oil
  topic: keywords
subject: Antioxidant Performance Prediction
  topic: keywords
subject: Data Preprocessing
  topic: keywords
subject: Feature Extraction
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: SNV-PCA-BP Model
  topic: keywords
subject: Rapid Detection
  topic: keywords
creator: Tongwei Xie
creator: Xikun Zhang
creator: Xiaodong Liu
creator: Xuan Zhang
rights: Copyright (c) 2026 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2026-03-20
publication date: 2026-03-20
doi: 10.65455/2s6npy06

Map of Geographical Science Based on Artificial Intelligence
type: item
description: As a core tool for integrating the geographical knowledge system and revealing conceptual associations, the traditional construction mode of the basic concept map of geographical science is confronted with bottlenecks such as single data dimension, shallow association mining, and lagging dynamic update. The rise of artificial intelligence technology offers a new path to break through the above limitations. Through the deep integration of algorithms such as machine learning, deep learning, and natural language processing with geographical science theories, intelligent upgrades in concept extraction, relationship modeling, and map optimization can be achieved. This paper systematically reviews the key technologies for constructing the basic concept map of geographic science based on artificial intelligence, including multi-source geographic data preprocessing methods, intelligent concept recognition and classification models, and dynamic relationship mining algorithms, and deeply analyzes the technical characteristics and applicable scenarios of different construction methods. The application value is expounded from three dimensions: the sorting out of the geographical knowledge system, the intelligent solution of geographical problems, and the empowerment of interdisciplinary integration. Finally, the future research directions are prospected to provide references for the intelligent development and practical application of geographical science knowledge graphs.
subject: Research Article
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Geographical Science
  topic: keywords
subject: Concept Map
  topic: keywords
subject: Construction Method
  topic: keywords
subject: Application Value
  topic: keywords
creator: Lizao Li
rights: Copyright (c) 2025 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2025-12-23
publication date: 2025-12-23
doi: 10.65455/dhsph777

https://journal.innoviair.cn/index.php/AAIR/en/article/view/42
type: item
title: AI-Driven Intellectual Property: Building and Empirical Testing of an Innovative Startup Collaborative Management Model-Analysis of Cross-Legal Domain Data in the Guangdong-Hong Kong-Macao Greater Bay Area
description: With the rapid advancement of artificial intelligence technology, the collaborative management of intellectual property and innovation-driven entrepreneurship faces new opportunities and challenges. This study focuses on the unique cross-jurisdictional region of the Guangdong-Hong Kong-Macao Greater Bay Area, exploring the development of an AI-driven collaborative management model for intellectual property and innovation. Using a hybrid research approach, the study collected intellectual property data, innovation indicators, and policy regulations from 11 cities in the Greater Bay Area. Through machine learning algorithms and multidimensional modeling, it revealed the interaction mechanisms between intellectual property and innovation under the distinct legal systems of the three regions. The research established a collaborative management framework comprising four modules: "intelligent identification, cross-domain collaboration, value transformation, and risk prevention," while designing algorithm optimization strategies and decision support systems tailored for cross-jurisdictional environments. This study not only enriches the theoretical framework of intellectual property and innovation collaboration but also provides practical management tools and policy recommendations for innovative development in the Guangdong-Hong Kong-Macao Greater Bay Area and other cross-jurisdictional regions worldwide.
subject: Research Article
  topic: section
subject: AI-Driven
  topic: keywords
subject: Intellectual Property
  topic: keywords
subject: Innovation and Entrepreneurship
  topic: keywords
subject: Collaborative Management Model
  topic: keywords
subject: Guangdong-Hong Kong-Macao Greater Bay Area
  topic: keywords
creator: Xiaodi Guo
creator: Jingjiang Deng
creator: Junming Liang
creator: Chen Chen
rights: Copyright (c) 2025 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2025-12-04
publication date: 2025-12-04
doi: 10.65455/85c92315

Innovative Research on Cracking the Personalized Service Management of Artificial Intelligence Libraries
type: item
description: Against the backdrop of the accelerated advancement of Artificial intelligence transformation, emerging technologies have become the core driving force for the upgrading of library services. This study focuses on the innovative application of technologies such as artificial intelligence, big data analysis, Internet of Things and blockchain in the personalized service management of libraries. Through methods such as literature analysis and case studies , a three-dimensional model of "technology empowerment - service reconfiguration - management collaboration" is constructed. Research shows that technology empowerment drives service management transformation through three major mechanisms: precise user profile construction (with an accuracy rate of 89.2%), intelligent resource scheduling (with a utilization rate increase of 41.7%), and dynamic service optimization (with a response speed increase of 63.5%). Empirical cases show that user satisfaction has increased by 36% after technology empowerment, but the issues of data security (68.3% of users are concerned) and the cost of technology adaptation (with an average deployment cost of 287,000 yuan) need to be addressed simultaneously. Finally, a hierarchical deployment strategy and a collaborative framework of "technology - system - humanities" are proposed to provide a systematic solution for the construction of smart libraries.
subject: Research Article
  topic: section
subject: Emerging Technology
  topic: keywords
subject: Library
  topic: keywords
subject: Personalized Service
  topic: keywords
subject: Service Management
  topic: keywords
subject: Wisdom Empowerment
  topic: keywords
subject: Privacy Computing
  topic: keywords
creator: Nan Liu
rights: Copyright (c) 2025 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2025-12-12
publication date: 2025-12-12
doi: 10.65455/4mv0z378

Application of AI Models in Dynamic Talent Adaptation between Industry and Education: A Case Study of Data from Digital Genealogy Cloud Platform
type: item
description: This study examines the application of AI models in dynamic talent adaptation between industry and education, using data from the Digital Genealogy Cloud Platform as a research sample. It analyzes current challenges in industry-education talent alignment and elaborates on the pivotal role of AI models. Through in-depth mining and analysis of platform data, the research demonstrates how AI models leverage big data, artificial intelligence, and knowledge graphs to achieve precise digital integration across industrial chains, talent pipelines, and educational systems. The findings reveal that AI models not only assist vocational colleges in optimizing program structures and innovating talent cultivation models, but also facilitate deep collaboration between schools and enterprises. This enhances the alignment between talent development and industrial demands, providing robust support for the digital transformation and high-quality development of vocational education. Additionally, it offers new perspectives and practical solutions to address fundamental issues in industry-education integration.
subject: Research Article
  topic: section
subject: AI model
  topic: keywords
subject: Industry-Education Talent
  topic: keywords
subject: Dynamic Adaptation
  topic: keywords
subject: Digital Genealogy Cloud Platform
  topic: keywords
subject: Vocational Education
  topic: keywords
subject: Macao Greater Bay Area
  topic: keywords
creator: Jingjiang Deng
creator: Junming Liang
creator: Chen Chen
creator: Weijian Huang
rights: Copyright (c) 2025 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2025-12-04
publication date: 2025-12-04
doi: 10.65455/srezr230

Language, Consciousness, and Manifestation: Towards a Pragmatic-Cognitive Framework for Next-Generation Neural Language Systems
type: item
description: Augmented Reality (AR) systems critically depend on accurate, temporally stable, and computationally efficient object tracking to maintain geometric alignment and perceptual coherence between virtual content and the physical world. As AR technologies transition from controlled laboratory prototypes to large-scale deployment in industrial, medical, and consumer scenarios, tracking must operate robustly in complex real-world environments characterized by dynamic objects, occlusions, illumination changes, fast motion, and strict computational constraints. Traditional geometry-driven tracking pipelines often degrade under such conditions, motivating increased adoption of deep learning based approaches. This survey provides a comprehensive review of deep learning based object tracking for AR in complex real-world scenes, with particular emphasis on system-level considerations. Object tracking is treated as a central perception primitive that underpins stable AR experiences and interacts tightly with modules such as visual simultaneous localization and mapping, depth and geometry estimation, and semantic scene understanding. In contrast to prior surveys that emphasize algorithmic accuracy in isolation, we explicitly analyze AR-specific constraints including real-time latency, temporal stability, energy efficiency, long-term robustness, and deployment on mobile and wearable platforms. We review major tracking paradigms, representative datasets and benchmarks, AR-centric evaluation criteria, and common failure modes observed in practice, and we outline future research directions toward scalable, reliable, and trustworthy AR tracking systems.
subject: Research Article
  topic: section
subject: Augmented Reality
  topic: keywords
subject: Object Tracking
  topic: keywords
subject: Manifestation
  topic: keywords
subject: Pragmatics
  topic: keywords
subject: Cognition
  topic: keywords
subject: Natural Language Processing
  topic: keywords
subject: Neural Language Systems
  topic: keywords
creator: Anni Li
rights: Copyright (c) 2025 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2025-12-13
publication date: 2025-12-13
doi: 10.65455/cnbj7s21

Against the Backdrop of the Digital Economy: AI Empowers Innovation and Entrepreneurship Education for the Integration of Industry and Education
type: item
description: The digital economy and AI technologies are driving transformations in industry-education integration models, yet traditional practices face challenges such as mismatched supply and demand and inefficient resource allocation. This study proposes an AI-empowered innovation and entrepreneurship education ecosystem through a quadruple-subject collaborative framework. Employing multi-case comparative research and system dynamics simulation, Practical pathways include developing intelligent course matrices and virtual training platforms. Empirical results demonstrate: university case incubation cycles shortened by 42%, enterprise case employment rates increased by 16%, government case talent gaps narrowed by 54%, and overall talent matching accuracy improved by 36.5%. AI technology effectively resolves industry-education integration challenges, offering a new paradigm for digital economy talent cultivationy.
subject: Research Article
  topic: section
subject: Digital Economy
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Industry-Education Integration
  topic: keywords
subject: Innovation and Entrepreneurship Education
  topic: keywords
creator: Qiang Liu
rights: Copyright (c) 2025 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2025-12-13
publication date: 2025-12-13
doi: 10.65455/nh2jc129

Research on the Impact Mechanism of Farmers' Digital Literacy on Enhancing Service Satisfaction of Power Grid Companies: An Implementation Path Based on the Interpretability of Machine Learning Models
type: item
description: This study focuses on the "service supply - user demand" tension issue in the service satisfaction of power grid companies during the process of Chinese-style modernization. China has achieved remarkable results in the construction of power grid infrastructure, but there are regional differences in user satisfaction, especially in rural areas. Under the background of digitalization of power grids, although digital technology brings benefits, the differences in digital infrastructure and usage levels in rural areas have led to a digital divide, shifting from an "access gap" to a "capability gap". Digital literacy has become the key to enhancing service satisfaction. In view of the existing research deficiencies, this paper constructs an index system for measuring and evaluating the digital literacy of farmers, and uses the XGBoost and SHAP models to quantitatively analyze the influence mechanism and implementation path of digital literacy on the service satisfaction of power grid companies. The research results can provide a scientific basis for power grid companies to precisely optimize services, helping them identify key influencing factors, optimize the allocation of service resources, enhance service satisfaction, and promote the high-quality development of power grid services.
subject: Research Article
  topic: section
subject: Farmers' Digital Literacy
  topic: keywords
subject: Power Grid Companies
  topic: keywords
subject: Service Satisfaction
  topic: keywords
subject: XGBoost
  topic: keywords
subject: SHAP Explainability
  topic: keywords
creator: Yuxuan Tong
creator: Zewei Zhang
rights: Copyright (c) 2025 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2025-12-03
publication date: 2025-12-03
doi: 10.65455/6dwx3a82

Applying Radiomics and Deep Learning to Investigations into Seafarers' Health Status
type: item
description: This retrospective study on 7232 male seafarers(June 2022 -- Nov 2023), explored the occurrence of fatty liver disease and gallstones and pointed out its related risk elements, moreover examined the worth of using DL-based radiomics in intelligent examination and medical diagnosis of the said hepatobiliary diseases. Clinical data (BMI, blood lipids) and abdominal ultrasound images were analyzed via traditional statistical methods (chi-squared test, correlation analysis, t-test). A DL framework integrating U-Net segmentation and MobileNetV2 classification was developed to automate region-of-interest (ROI) extraction, extract high-dimensional radiomic features, and fuse clinical/radiomic data for dual-disease prediction. Results showed BMI was linearly positively correlated with hyperlipidemia and fatty liver disease (r=0.98, P<0.05). The DL model demonstrated superior diagnostic performance: for fatty liver disease, AUC=0.93, accuracy=90.2%, recall=88.5%, and specificity=89.8% (significantly higher than manual ultrasound, AUC=0.79, P<0.05); for gallstones, AUC=0.89, accuracy=87.6%, and recall=85.3%. Gallstone formation was statistically associated with gallbladder wall thickening/roughness, hyperlipidemia, and hypercholesterolemia (P<0.005).Conclusions Controlling BMI and blood lipid levels effectively reduces fatty liver risk. DL-based radiomics enables automated, quantitative, and intelligent hepatobiliary disease assessment—ideal for seafarers with limited on-board medical resources and large-scale screenings. Combining this AI tool with targeted health education and lifestyle interventions will enhance the efficiency and accuracy of seafarers’ hepatobiliary health management.
subject: Research Article
  topic: section
subject: Fatty Liver Disease
  topic: keywords
subject: Gallstones
  topic: keywords
subject: Radiomics
  topic: keywords
subject: Deep Learning
  topic: keywords
creator: Huiming Kang
creator: Xiaoqing Li
creator: Mingyue Feng
creator: Wei Wang
creator: Shidi Liu
creator: Shaocong Liang
creator: Cuiliu Zhou
creator: De Kang
creator: Lingling Chen
rights: Copyright (c) 2025 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2025-12-09
publication date: 2025-12-09
doi: 10.65455/nqcbkw66

A Learnable Frequency Gated DCT Denoising Network
type: item
description: DCT-based denoising often relies on fixed rules such as thresholding or retaining only low-frequency components, which easily blur textures or leave residual noise on natural images. To address this limitation, we develop a learnable denoising model that keeps the structure of the DCT domain while allowing the network to adaptively regulate each frequency band. With differentiable DCT/IDCT layers, the method applies trainable masks to high- and low-frequency coefficients, and a lightweight global attention block at reduced resolution provides broader contextual cues at low cost. An image-level residual path further aids reconstruction. Under a compact setup, the model improves PSNR and SSIM from 11.89 dB and 0.2915 (pure DCT) to 25.05 dB and 0.945, showing that replacing fixed heuristics with learnable frequency modulation leads to substantially better restoration quality.
subject: Research Article
  topic: section
subject: Image Denoising
  topic: keywords
subject: Discrete Cosine Transform
  topic: keywords
subject: Frequency Domain Modeling
  topic: keywords
subject: Multi Scale Attention
  topic: keywords
subject: Global Residual
  topic: keywords
subject: Image Restoration
  topic: keywords
creator: Haonian Cao
rights: Copyright (c) 2025 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2025-12-09
publication date: 2025-12-09
doi: 10.65455/p24ghq94

The coupled application of GIS digital Twin and AI reinforcement learning
type: item
description: In response to the intelligent governance demands of complex scenarios such as smart cities and smart water conservancy, this paper explores the coupled application of GIS digital twins and AI reinforcement learning. First, sort out the core theories of the two, then analyze the coupling technology interface, data flow logic and adaptability, and then carry out practice in combination with the scenarios of urban traffic control and river basin water resource dispatching. Finally, point out the challenges such as data heterogeneity, insufficient generalization and computing power bottleneck, and propose solutions. The results show that the coupled system can significantly improve the governance accuracy, such as reducing the delay time in traffic scenarios by 32.1% and increasing the utilization rate of water resources scenarios by 8.3%, providing technical support for the intelligent governance of complex spatial scenarios.
subject: Research Article
  topic: section
subject: GIS Digital Twin
  topic: keywords
subject: AI Reinforcement Learning
  topic: keywords
subject: Coupled Application
  topic: keywords
subject: Intelligent Governance
  topic: keywords
subject: Urban Traffic Control
  topic: keywords
subject: Water Resources Dispatching in River Basins
  topic: keywords
creator: Ziyi Xia
rights: Copyright (c) 2025 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2025-12-02
publication date: 2025-12-02
doi: 10.65455/d792e032

Ethics-First Lightweight AI: Differentiated English Teaching and Cultural Competence in Junior Middle Schools
type: item
description: The implementation of stratified English teaching in junior middle schools consistently encounters two fundamental challenges: the persistence of static "pseudo-stratification" that fails to adapt to students' evolving needs, and growing ethical concerns regarding intrusive artificial intelligence technologies in educational settings. In direct response to these dual challenges, this study proposes and empirically validates an innovative ethics-first lightweight AI framework specifically designed to enable truly dynamic and culturally responsive differentiated instruction. By strategically integrating privacy-preserving federated learning methodologies with non-intrusive mobile sensing technologies, our framework introduces a novel pedagogical architecture termed the "three-layer dynamic grouping and four-dimensional strategy matrix." To comprehensively evaluate its efficacy, we conducted a rigorous six-month controlled experiment involving 326 students across diverse socioeconomic contexts—including urban (eastern), county-town (central), and rural (western) regions of China. The empirical results demonstrate the framework's remarkable effectiveness across multiple dimensions: it achieved a stratification accuracy of 89%, significantly reduced grammatical error rates by 31%, and enhanced cultural understanding by 44%. Most notably, the implementation of a carefully designed tiered informed consent protocol successfully reduced parental data withdrawal requests to an unprecedented 0.9%. By leveraging ubiquitous smartphone sensors for low-cost deployment, this framework provides a scalable, ethically-grounded solution for implementing sophisticated differentiated instruction in resource-constrained educational environments, thereby advancing the crucial goal of educational equity from theoretical principle to practical reality.
subject: Research Article
  topic: section
subject: Ethics-First AI
  topic: keywords
subject: Differentiated Teaching
  topic: keywords
subject: Cultural Competence
  topic: keywords
subject: Lightweight Model
  topic: keywords
subject: Federated Learning
  topic: keywords
subject: Educational Equity
  topic: keywords
subject: Dynamic Assessment
  topic: keywords
creator: Hui Nie
rights: Copyright (c) 2025 The Author(s). Applied Artificial Intelligence Research published by CSTDP https://creativecommons.org/licenses/by/4.0
date: 2025-12-13
publication date: 2025-12-13
doi: 10.65455/es208a12