Intelligent Systems with Applications最新文献_第3页

WOA-FCM-CNN-WNN-informer: An advanced hybrid deep learning model for ultra-accurate PV power forecasting in electric mobility woa - fcm - cnn - cnn - inforformer：一种先进的混合深度学习模型，用于超精确的电动汽车光伏功率预测

IF 4.3

Intelligent Systems with Applications

Pub Date : 2026-03-01 Epub Date: 2026-01-29 DOI: 10.1016/j.iswa.2026.200630

Lazhar Manai , Walid Mchara , Mohamed Abdellatif Khalfa , Monia Raissi , Wissem Dimassi

Effective prediction of photovoltaic (PV) power generation is essential for enhancing energy management in solar-powered electric vehicles. This study introduces an innovative hybrid forecasting framework that combines Fuzzy C-Means (FCM) clustering, Convolutional Neural Networks (CNN), Wavelet Neural Networks (WNN), the Informer architecture, and the Whale Optimization Algorithm (WOA) to improve prediction accuracy. This approach introduces a condition-aware, end-to-end FCM-CNN-WNN-Informer pipeline tailored for PV dynamics, where: (i) similar-day fuzzy clustering normalizes weather heterogeneity before learning; (ii) wavelet-based multi-scale features are injected into a long-horizon Informer; (iii) a global, cross-module hyperparameter search via Whale Optimization Algorithm (WOA) jointly tunes all stages; (iv) a Generalization Index (GI) is proposed for robust model selection; and (v) Monte-Carlo dropout quantifies predictive uncertainty for practical deployment.

The proposed WOA-FCM-CNN-WNN-Informer model is evaluated on a comprehensive dataset of 70,080 hourly PV power recordings gathered over eight years in Tunisia. Results show superior performance compared to standard deep learning models like LSTM and BiLSTM. The framework reduces Mean Absolute Percentage Error (MAPE) by as much as 98.52% and Root Mean Squared Error (RMSE) by 93.84%, while maintaining a high coefficient of determination (

R^{2} = 0.98

) across varying meteorological conditions. These outcomes underscore the model’s robustness and its promise for advancing energy utilization, refining charging strategies, and supporting intelligent route planning in solar-electric transportation systems.

有效的光伏发电预测是提高太阳能电动汽车能源管理水平的关键。本研究提出了一种创新的混合预测框架，该框架结合了模糊c均值（FCM）聚类、卷积神经网络（CNN）、小波神经网络（WNN）、Informer架构和鲸鱼优化算法（WOA）来提高预测精度。该方法引入了一种针对PV动力学的状态感知、端到端FCM-CNN-WNN-Informer管道，其中：(i)相似日模糊聚类在学习前对天气异质性进行归一化；（ii）将基于小波的多尺度特征注入到长视界信息中；（iii）通过鲸鱼优化算法（WOA）进行全局跨模块超参数搜索，共同调整所有阶段；（iv）提出了稳健模型选择的概化指数（GI）；蒙特卡罗误差量化了实际部署的预测不确定性。提出的WOA-FCM-CNN-WNN-Informer模型是在突尼斯8年来收集的70,080小时光伏发电记录的综合数据集上进行评估的。结果显示，与LSTM和BiLSTM等标准深度学习模型相比，性能优越。该框架将平均绝对百分比误差（MAPE）降低了98.52%，均方根误差（RMSE）降低了93.84%，同时在不同的气象条件下保持了较高的决定系数（R2=0.98）。这些结果强调了该模型的稳健性及其在提高能源利用率、改进充电策略和支持太阳能电力运输系统智能路线规划方面的前景。

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引用次数: 0

Musipainter: A music-conditioned generative architecture for artistic image synthesis Musipainter：一种以音乐为条件的艺术图像合成生成建筑

IF 4.3

Intelligent Systems with Applications

Pub Date : 2026-03-01 Epub Date: 2025-11-19 DOI: 10.1016/j.iswa.2025.200611

Alfredo Baione , Giuseppe Rizzo , Luca Barco , Angelica Urbanelli , Luigi Di Biasi , Genoveffa Tortora

Generative art is a challenging area of research in deep generative modeling. Exploring AI’s role in human–machine co-creative processes requires understanding machine learning’s potential in the arts. Building on this premise, this paper presents Musipainter, a cross-modal generative framework adapted to create artistic images that are historically and stylistically aligned with 30-second musical inputs, with a focus on creative and semantic coherence. To support this goal, we introduce Museart, a dataset designed explicitly for this research, and GIILS, a creativity-oriented metric that enables us to assess both artistic-semantic consistency and diversity in the generated outputs. The results indicate that Musipainter, supported by the Museart dataset and the exploratory GIILS metric, can offer a foundation for further research on AI’s role in artistic generation, while also highlighting the need for systematic validation and future refinements.

生成艺术是深度生成建模中一个具有挑战性的研究领域。探索人工智能在人机共同创造过程中的作用需要理解机器学习在艺术中的潜力。在此前提下，本文介绍了Musipainter，这是一个跨模态生成框架，用于创建与30秒音乐输入在历史和风格上一致的艺术图像，重点是创造性和语义一致性。为了实现这一目标，我们引入了专门为本研究设计的数据集Museart和GIILS，这是一个以创造力为导向的度量标准，使我们能够评估生成输出中的艺术语义一致性和多样性。结果表明，在Museart数据集和探索性GIILS指标的支持下，Musipainter可以为进一步研究人工智能在艺术生成中的作用提供基础，同时也强调了系统验证和未来改进的必要性。

引用次数: 0

Video anomaly detection for edge-based IoT systems: A survey of input modalities and real-time applications 基于边缘的物联网系统的视频异常检测：输入方式和实时应用的调查

IF 4.3

Intelligent Systems with Applications

Pub Date : 2026-03-01 Epub Date: 2026-02-05 DOI: 10.1016/j.iswa.2026.200635

Hoangcong Le, Cheng-Kai Lu, Chen-Chien Hsu

With the vast amount of video data generated daily, researchers have become increasingly interested in extracting meaningful information, particularly for analyzing abnormal events. This growing interest has accelerated progress in video anomaly detection (VAD) as a specialized subfield of computer vision, attracting considerable attention due to its potential applications in real-time scenarios such as elderly care, smart homes, and intelligent surveillance. To provide a comprehensive understanding of this rapidly evolving field, several systematic reviews have been conducted to help new researchers enter the field and assist experienced groups in keeping pace with recent advancements. However, existing surveys lack a focused analysis of how different input data modalities impact the performance of VAD systems, particularly from a privacy-preserving perspective. Understanding the effectiveness of various data modalities and data collection strategies is essential for protecting personal information in computer vision applications. Furthermore, the feasibility of deploying VAD models in real-time Internet of Things (IoT) environments remains underexplored, where low latency, limited resources, and strict privacy requirements are critical considerations. Although edge computing has been increasingly adopted to address these challenges, most studies overlook the deployment of VAD frameworks on resource-constrained devices. Integrating edge-based VAD systems with federated learning algorithms represents a promising direction for enabling privacy-aware and scalable real-world systems. Rather than providing a method-centric summary, this survey reorganizes the VAD literature from a deployment-oriented viewpoint, highlighting how input modality choices fundamentally affect privacy preservation and real-time feasibility on edge-based IoT systems. This work specifically reviews studies published between 2020 and 2025.

随着每天产生的大量视频数据，研究人员对提取有意义的信息越来越感兴趣，特别是对异常事件的分析。这种日益增长的兴趣加速了视频异常检测（VAD）作为计算机视觉的一个专门子领域的进展，由于其在老年人护理，智能家居和智能监控等实时场景中的潜在应用而引起了相当大的关注。为了全面了解这一快速发展的领域，已经进行了几次系统综述，以帮助新的研究人员进入该领域，并帮助有经验的小组跟上最新的进展。然而，现有的调查缺乏对不同输入数据模式如何影响VAD系统性能的重点分析，特别是从隐私保护的角度。了解各种数据模式和数据收集策略的有效性对于保护计算机视觉应用中的个人信息至关重要。此外，在实时物联网（IoT）环境中部署VAD模型的可行性仍未得到充分探索，在这些环境中，低延迟、有限资源和严格的隐私要求是关键考虑因素。尽管边缘计算已被越来越多地用于解决这些挑战，但大多数研究忽略了在资源受限设备上部署VAD框架。将基于边缘的VAD系统与联邦学习算法集成为实现隐私感知和可扩展的现实世界系统提供了一个有前途的方向。本调查不是提供以方法为中心的总结，而是从面向部署的角度重新组织了VAD文献，强调了输入模式选择如何从根本上影响基于边缘的物联网系统的隐私保护和实时可行性。这项工作特别回顾了2020年至2025年之间发表的研究。

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引用次数: 0

A systematic review of vision transformer and explainable AI advances in multimodal facial expression recognition 系统回顾了视觉转换器和可解释人工智能在多模态面部表情识别中的进展

IF 4.3

Intelligent Systems with Applications

Pub Date : 2026-03-01 Epub Date: 2025-12-06 DOI: 10.1016/j.iswa.2025.200615

Ilya Kus , Cemal Kocak , Ayse Keles

Facial expression is one of the most important indicators used to convey human emotions. Facial expression recognition is the process of automatically detecting and classifying these expressions by computer systems. Multimodal facial expression recognition aims to perform a more accurate and comprehensive emotion analysis by combining facial expressions with different modalities such as image, speech, Electroencephalogram (EEG), or text. This study systematically reviews research conducted between 2021 and 2025 on the Vision Transformer (ViT) based approaches and Explainable Artificial Intelligence (XAI) techniques in multimodal facial expression recognition, as well as the datasets employed in these studies. The findings indicate that ViT-based models outperform conventional Convolutional Neural Networks (CNNs) by effectively capturing long-range dependencies between spatially distant facial regions, thereby enhancing emotion classification accuracy. However, significant challenges remain, including data privacy risks arising from the collection of multimodal biometric information, data imbalance and inter-modality incompatibility, high computational costs hindering real-time applications, and limited progress in model explainability. Overall, this study highlights that integrating advanced ViT architectures with robust XAI and privacy-preserving techniques can enhance the reliability, transparency, and ethical deployment of multimodal facial expression recognition systems.

面部表情是用来传达人类情感的最重要的指标之一。面部表情识别是计算机系统对面部表情进行自动检测和分类的过程。多模态面部表情识别旨在通过将面部表情与图像、语音、脑电图（EEG）或文本等不同模态相结合，进行更准确、更全面的情绪分析。本研究系统回顾了2021年至2025年间在多模态面部表情识别中基于视觉变形（ViT）的方法和可解释人工智能（XAI）技术的研究，以及这些研究中使用的数据集。研究结果表明，基于vit的模型可以有效地捕获空间距离较远的面部区域之间的远程依赖关系，从而提高情绪分类的准确性，从而优于传统的卷积神经网络（cnn）。然而，重大挑战仍然存在，包括多模态生物特征信息收集带来的数据隐私风险、数据不平衡和模态间不兼容、阻碍实时应用的高计算成本，以及模型可解释性方面的有限进展。总之，本研究强调，将先进的ViT架构与强大的XAI和隐私保护技术相结合，可以提高多模态面部表情识别系统的可靠性、透明度和道德部署。

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引用次数: 0

Advancing decision-making: A comprehensive review of intelligent systems, applications, and challenges 推进决策：对智能系统、应用和挑战的全面回顾

IF 4.3

Intelligent Systems with Applications

Pub Date : 2026-03-01 Epub Date: 2026-01-31 DOI: 10.1016/j.iswa.2026.200631

Hussein A.A. Al-Khamees , Ahmad AL Smadi , Mutasem K. Alsmadi , Abdulrahman A. Alkannad , Ahed Abugabah , Latifa Abdullah Almusfar , Bashair Althani

The rapid evolution of intelligent systems, powered by artificial intelligence and machine learning, has created a fragmented research landscape. While numerous studies exist on specific applications, a holistic synthesis of their architectures, taxonomies, applications, and challenges is absent. This paper will bridge this gap by providing a comprehensive systematic review that integrates these disparate elements. This paper conducts a systematic review of over 100 peer-reviewed scientific publications, following a structured process to identify, analyze, and synthesize the current state of intelligent systems research. The review encompasses a wide range of domains, including healthcare, cybersecurity, data mining, and industrial automation. Our analysis yields a unified taxonomy and clarifies the core architectural components of intelligent systems. We identify and categorize key application domains and demonstrate their transformative impact. The review also synthesizes prevailing challenges, such as data quality, scalability, and ethical concerns, and pinpoints emerging trends, including the rise of multimodal AI and hybrid intelligent systems. To the best of our knowledge, this is the first review to offer a consolidated framework that integrates the architecture, taxonomy, applications, and cross-domain challenges of intelligent systems into a single reference. This work serves as a foundational guide for researchers and practitioners, facilitating future advancements in the development of efficient, scalable, and context-aware intelligent systems.

在人工智能和机器学习的推动下，智能系统的快速发展创造了一个碎片化的研究格局。虽然有许多关于特定应用程序的研究，但缺乏对它们的体系结构、分类法、应用程序和挑战的全面综合。本文将通过提供集成这些不同元素的全面系统回顾来弥合这一差距。本文对100多篇同行评议的科学出版物进行了系统的综述，遵循结构化的过程来识别、分析和综合智能系统研究的现状。该审查涵盖了广泛的领域，包括医疗保健、网络安全、数据挖掘和工业自动化。我们的分析产生了一个统一的分类，并澄清了智能系统的核心架构组件。我们对关键应用领域进行识别和分类，并展示它们的变革性影响。该报告还综合了当前面临的挑战，如数据质量、可扩展性和伦理问题，并指出了新兴趋势，包括多模式人工智能和混合智能系统的兴起。据我们所知，这是第一个提供整合架构、分类、应用和智能系统跨领域挑战的综合框架的综述。这项工作为研究人员和实践者提供了基础指导，促进了未来高效、可扩展和上下文感知智能系统的发展。

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引用次数: 0

Blind steganalysis-driven secure transmission validation using feature-based classification in JPEG images 在JPEG图像中使用基于特征分类的盲隐写分析驱动的安全传输验证

IF 4.3

Intelligent Systems with Applications

Pub Date : 2026-03-01 Epub Date: 2025-12-22 DOI: 10.1016/j.iswa.2025.200623

Deepa D. Shankar , Adresya Suresh Azhakath

Information technology and digital media have significantly improved in recent years, facilitating the internet as an effective channel for communication and data transmission. Nevertheless, the rapid advancement of technology has rendered data a source of mismanagement and vulnerable to exploitation. Consequently, technologies such as data concealment were devised to mitigate exploitation. Steganalysis is a technique for data concealment. Various processes, including breaches of information security, can be mitigated by steganalysis. This work aims to encapsulate the notion of blind statistical steganalysis within image processing methodologies and ascertain the accuracy percentage of secure transmission. This work discusses the extraction of features that indicate a change during embedding. A specific percentage of text is integrated into a JPEG image of a predetermined size. The text embedding utilizes various steganographic techniques in both the spatial and transform domains. The steganographic techniques include LSB Matching, LSB Replacement, Pixel Value Differencing, and F5. Due to the blind nature of steganalysis, there are no cover images available for comparative analysis. An estimation of the cover image is obtained by a calibration concept. After embedding, the images are partitioned into 8 × 8 blocks, from which certain features are extraction for classification. This paper utilizes interblock dependent features and intrablock dependent features. Both dependencies are regarded as means to mitigate the shortcomings of each individually. The approach of machine learning is employed using a classifier to distinguish between the stego image and the cover image. This research does a comparative investigation of the classifiers SVM and SVM-PSO. Comparative research is frequently performed both with and without use cross-validation methodology. The study incorporates the concept of cross-validation for comparative analysis. There are six unique kernel functions and four sample methods for grouping. The embedding ratio employed in this investigation is 50%.

近年来，信息技术和数字媒体发展迅速，使互联网成为沟通和数据传输的有效渠道。然而，技术的迅速进步使数据成为管理不善和容易被利用的来源。因此，设计了诸如数据隐藏之类的技术来减轻利用。隐写分析是一种数据隐藏技术。各种过程，包括对信息安全的破坏，都可以通过隐写分析来缓解。这项工作旨在将盲统计隐写分析的概念封装在图像处理方法中，并确定安全传输的准确性百分比。这项工作讨论了在嵌入过程中指示变化的特征的提取。将特定百分比的文本集成到预定大小的JPEG图像中。文本嵌入利用了空间域和变换域的各种隐写技术。隐写技术包括LSB匹配、LSB替换、像素值差分和F5。由于隐写分析的盲目性，没有可用于比较分析的封面图像。利用标定概念对覆盖图像进行估计。嵌入后，将图像分割成8 × 8块，从中提取一定的特征进行分类。本文利用了块间依赖特征和块内依赖特征。这两种依赖关系都被视为减轻各自缺点的手段。采用机器学习的方法，使用分类器区分隐写图像和封面图像。本文对SVM和SVM- pso分类器进行了比较研究。比较研究经常在使用或不使用交叉验证方法的情况下进行。本研究采用交叉验证的概念进行比较分析。有六个独特的核函数和四个用于分组的示例方法。本研究采用的包埋率为50%。

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引用次数: 0

Authenticating devices based on audio feature selection with scene-specific tuning and landmark augmentation 基于音频特征选择与场景特定的调谐和地标增强验证设备

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-01 Epub Date: 2025-10-15 DOI: 10.1016/j.iswa.2025.200593

Andi Bahtiar Semma , Kusrini , Arief Setyanto , Bruno da Silva , An Braeken

Authentication methods have evolved significantly, transitioning from traditional passwords to biometric and multi-factor techniques, with audio-based systems now emerging as a promising frontier. These systems leverage ambient sounds or device-generated noise for continuous authentication but encounter challenges such as environmental noise, spoofing risks, and standardized audio feature selection. This study tackles these issues by focusing on robust handling of environmental variations and interference and optimizing audio feature selection for effective environmental audio authentication. A key innovation introduced is the concept of “audio landmarks,” randomly generated signals embedded into audio samples. These landmarks enhance device authentication by enriching feature representation and reducing sensitivity to noise, leading to significant improvements in precision, recall, and F1 scores across various scenarios. In some cases, features achieved a perfect F1 score 1.00 under ideal conditions. Among audio features analyzed, the Constant-Q Transform (CQT) excels, particularly in music or speech scenes. However, combining multiple features often introduces redundancy due to overlapping information and varying optimal thresholds, which may not constantly improve performance. Additionally, spectral centroids and spectral contrast, which are computationally lightweight at 9 ms and 10 ms, respectively, deliver excellent performance, making them ideal for real-time or resource-constrained applications, as tested on the Raspberry Pi 4. These findings provide practical guidelines for audio-based device authentication by leveraging cryptographic hashing for deterministic landmark generation and the balanced fusion of landmark and acoustic features. This enables robust authentication even in challenging scenarios where environmental sounds are insufficient.

身份验证方法已经发生了重大变化，从传统的密码过渡到生物识别和多因素技术，基于音频的系统现在正在成为一个有前途的前沿。这些系统利用环境声音或设备产生的噪声进行持续身份验证，但会遇到环境噪声、欺骗风险和标准化音频特征选择等挑战。本研究通过关注环境变化和干扰的鲁棒处理以及优化音频特征选择以实现有效的环境音频认证来解决这些问题。引入的一个关键创新是“音频地标”概念，即嵌入音频样本中随机生成的信号。这些标志通过丰富特征表示和降低对噪声的敏感性来增强设备认证，从而显著提高了各种场景下的精度、召回率和F1分数。在某些情况下，功能在理想条件下获得了完美的F1分数1.00。在分析的音频特征中，恒定q变换（CQT）表现出色，特别是在音乐或语音场景中。然而，组合多个特征往往会由于信息重叠和最优阈值变化而引入冗余，这可能不会持续提高性能。此外，光谱质心和光谱对比度，分别在9毫秒和10毫秒的计算轻量级，提供了出色的性能，使它们成为实时或资源受限应用的理想选择，正如在Raspberry Pi 4上测试的那样。这些发现为基于音频的设备认证提供了实用指南，通过利用加密散列来确定地标生成以及地标和声学特征的平衡融合。即使在环境声音不足的挑战性场景中，这也可以实现健壮的身份验证。

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引用次数: 0

Beyond algorithms: Artificial intelligence driven talent identification with human insight 超越算法：人工智能驱动的人才识别与人类的洞察力

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-01 Epub Date: 2025-11-07 DOI: 10.1016/j.iswa.2025.200604

Tiago Jacob Fernandes França , José Henrique Pereira São Mamede , João Manuel Pereira Barroso , Vítor Manuel Pereira Duarte dos Santos

The rapid evolution of Artificial Intelligence (AI) is reshaping Human Resource Management (HRM), with growing interest in its role in talent identification. While AI has demonstrated effectiveness in analysing structured data, its limitations in assessing qualitative attributes such as creativity, adaptability, and emotional intelligence remain underexplored. This study addresses these gaps through an exploratory mixed-methods design, combining a global survey (n = 240) with semi-structured interviews of HR professionals. Quantitative analysis highlights patterns of association between key competencies, while qualitative findings provide contextual insights into perceptions of fairness, bias, and cultural resistance. The results suggest that AI can complement, but not replace, human judgement, supporting a Hybrid Evaluative Model that integrates algorithmic efficiency with human interpretation. The study contributes rare empirical evidence to a nascent field, highlights the ethical imperatives of bias mitigation and transparency, and underscores the importance of cultural context (collectivist versus individualist orientations) in shaping the acceptance and effectiveness of AI-enabled HR practices. These findings offer practical guidance for organisations and advance theory-building at the intersection of AI and HRM.

人工智能（AI）的快速发展正在重塑人力资源管理（HRM），人们对其在人才识别中的作用越来越感兴趣。虽然人工智能在分析结构化数据方面已经证明了有效性，但它在评估创造力、适应性和情商等定性属性方面的局限性仍未得到充分探索。本研究通过探索性混合方法设计，将全球调查（n = 240）与人力资源专业人员的半结构化访谈相结合，解决了这些差距。定量分析强调了关键能力之间的关联模式，而定性研究结果提供了对公平、偏见和文化阻力感知的背景见解。结果表明，人工智能可以补充而不是取代人类的判断，支持将算法效率与人类解释相结合的混合评估模型。该研究为这一新兴领域提供了罕见的经验证据，强调了减少偏见和透明度的伦理必要性，并强调了文化背景（集体主义与个人主义取向）在塑造人工智能人力资源实践的接受度和有效性方面的重要性。这些发现为组织提供了实践指导，并推进了人工智能和人力资源管理交叉领域的理论建设。

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引用次数: 0

Liver cirrhosis prediction: The employment of the machine learning-based approaches 肝硬化预测：基于机器学习方法的应用

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-01 Epub Date: 2025-09-02 DOI: 10.1016/j.iswa.2025.200573

Genjuan Ma, Yan Li

Early detection of liver cirrhosis remains problematic due to its asymptomatic onset and the inherent class imbalance in clinical data. This study conducts a comprehensive evaluation of machine learning models for predicting cirrhosis stages, with a focus on addressing these challenges. An approach employing Quadratic Discriminant Analysis (QDA) is benchmarked against seven other models, including powerful ensembles like Stacking and HistGradientBoosting, on a clinical dataset. Methodologies such as SMOTE oversampling, stratified data splitting, and class-specific covariance estimation were implemented to manage data complexity. The results demonstrate that a Stacking ensemble achieves the highest overall predictive performance with a micro-AUC of 0.80. The proposed QDA method also proves to be a highly effective and competitive model, achieving a robust AUC of 0.76 and outperforming several specialized imbalance-learning algorithms. Crucially, QDA offers this strong performance with exceptional computational efficiency. These findings show that while complex ensembles can yield top-tier accuracy, QDA’s capacity to model non-linear feature associations makes it a powerful and practical choice for the diagnosis of cirrhosis.

由于肝硬化的发病无症状，以及临床资料固有的分类不平衡，肝硬化的早期检测一直存在问题。本研究对预测肝硬化阶段的机器学习模型进行了全面评估，重点是解决这些挑战。在临床数据集上，采用二次判别分析（QDA）的方法与其他七个模型进行了基准测试，包括强大的集成，如Stacking和HistGradientBoosting。采用SMOTE过采样、分层数据分割和特定类别协方差估计等方法来管理数据复杂性。结果表明，在微auc为0.80的情况下，堆叠集成实现了最高的整体预测性能。所提出的QDA方法也被证明是一个非常有效和有竞争力的模型，实现了0.76的鲁棒AUC，并且优于几种专门的不平衡学习算法。至关重要的是，QDA以卓越的计算效率提供了这种强大的性能。这些发现表明，虽然复杂的集合可以产生顶级的准确性，但QDA对非线性特征关联的建模能力使其成为肝硬化诊断的一个强大而实用的选择。

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引用次数: 0

A corporate credit evaluation method considering strong feature privacy with non-private label: A vertical heterogeneous feature fusion approach 一种考虑非私有标签强特征隐私的企业信用评价方法：垂直异构特征融合方法

IF 4.3

Intelligent Systems with Applications

Pub Date : 2025-12-01 Epub Date: 2025-11-13 DOI: 10.1016/j.iswa.2025.200603

Xifeng Ning , Chao Yang , Hailu Sun , Xinyuan Song , Zifan Hu , Yu Feng , Jiawei Li , Yifan Zhu

In modern monitoring and operational management, whether in industrial systems, financial risk control, or infrastructure maintenance, decision-making increasingly relies on integrating heterogeneous data from multiple sources. However, due to data privacy regulations, distributed storage, communication constraints, and sensor failures, it is often difficult to centralize modeling when dealing with high-dimensional, incomplete datasets held by different institutions. Federated learning offers a privacy-preserving joint modeling solution, yet still faces challenges such as high communication overhead, low robustness to participant dropout, and risks of gradient leakage. In certain incomplete-data scenarios, not all data is private—labels such as equipment inspection results, fault reports, or corporate blacklists and whitelists published by authoritative bodies may be public—while feature data remains private and partially missing. To address this, we propose an innovative collaborative modeling framework tailored for incomplete-data monitoring and operations, in which each participant independently trains a model on its private features and exchanges only prediction results rather than gradients. Inspired by collective expert scoring, each “expert” evaluates based on its own data, then shares scores that are integrated into a comprehensive assessment. This approach offers multiple advantages: independent model training for each party, improved efficiency by migrating only prediction results, enhanced security by avoiding gradient transmission, and higher robustness since the failure of one participant does not halt others’ training. We present three variants of this prediction-result fusion method and evaluate them on representative datasets, including enterprise credit risk assessment as a case study, comparing against vertical federated logistic regression. Experimental results validate the effectiveness of the proposed approach, which can be widely applied to diverse monitoring and operational scenarios under incomplete data conditions.

在现代监控和运营管理中，无论是工业系统、金融风险控制还是基础设施维护，决策越来越依赖于对多源异构数据的集成。然而，由于数据隐私法规、分布式存储、通信约束和传感器故障，在处理不同机构持有的高维、不完整数据集时，通常很难集中建模。联邦学习提供了一种保护隐私的联合建模解决方案，但仍然面临着诸如高通信开销、参与者退出的低鲁棒性以及梯度泄漏风险等挑战。在某些数据不完整的场景中，并非所有数据都是私有数据，例如权威机构发布的设备检查结果、故障报告或企业黑名单和白名单可能是公开的，而特征数据仍然是私有的，部分缺失。为了解决这个问题，我们提出了一个创新的协作建模框架，为不完整的数据监测和操作量身定制，其中每个参与者根据其私有特征独立训练模型，并且只交换预测结果而不是梯度。受集体专家评分的启发，每个“专家”根据自己的数据进行评估，然后分享分数，这些分数被整合到一个综合评估中。这种方法具有多种优势：对每一方进行独立的模型训练，通过只迁移预测结果提高效率，通过避免梯度传输增强安全性，并且由于一个参与者的失败不会停止其他参与者的训练，因此具有更高的鲁棒性。我们提出了这种预测-结果融合方法的三种变体，并在代表性数据集上对它们进行了评估，其中包括以企业信用风险评估为例的研究，并与垂直联邦逻辑回归进行了比较。实验结果验证了该方法的有效性，可广泛应用于不完全数据条件下的各种监测和操作场景。

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引用次数: 0