Machine learning with applications最新文献_第7页

Hybrid-hierarchical fashion graph attention network for compatibility-oriented and personalized outfit recommendation 面向兼容性和个性化服装推荐的混合分层时尚图关注网络

IF 4.9

Machine learning with applications

Pub Date : 2026-03-01 Epub Date: 2025-11-26 DOI: 10.1016/j.mlwa.2025.100802

Sajjad Saed, Babak Teimourpour

The rapid expansion of the fashion industry and the growing variety of products have made it increasingly challenging for users to identify compatible items on e-commerce platforms. Effective fashion recommendation systems are therefore crucial for filtering irrelevant options and suggesting suitable ones. However, simultaneously addressing outfit compatibility and personalized recommendations remains a significant challenge, as these aspects are typically treated independently in existing studies, thereby overlooking the complex interactions between items and user preferences. This research introduces a new framework named FGAT, which leverages a hierarchical graph representation together with attention mechanisms to address this problem. The framework constructs a three-tier graph of users, outfits, and items, integrating visual and textual features to jointly model outfit compatibility and user preferences. By dynamically weighting node importance during representation propagation, the graph attention mechanism captures key interactions and produces precise embeddings for both user preferences and outfit compatibility. Evaluated on the POG dataset, FGAT outperforms strong baselines such as HFGN, achieving notable improvements in accuracy, precision, hit ratio (HR), recall, and NDCG. These results demonstrate that combining multimodal visual–textual features with a hierarchical graph structure and attention mechanisms significantly enhances the effectiveness and efficiency of personalized fashion recommendation systems.

时尚产业的迅速扩张和产品的日益多样化使得用户在电子商务平台上识别兼容的商品越来越具有挑战性。因此，有效的时尚推荐系统对于过滤不相关的选项并推荐合适的选项至关重要。然而，同时解决服装兼容性和个性化推荐仍然是一个重大挑战，因为在现有的研究中，这些方面通常是独立处理的，从而忽略了物品和用户偏好之间复杂的相互作用。本研究引入了一个名为FGAT的新框架，它利用分层图表示和注意机制来解决这个问题。该框架构建了一个用户、服装和物品的三层图，集成了视觉和文本特征，共同为服装兼容性和用户偏好建模。通过在表示传播过程中动态加权节点重要性，图注意机制捕获关键交互，并为用户偏好和装备兼容性生成精确的嵌入。在POG数据集上进行评估，FGAT优于强基线（如HFGN），在准确性、精度、命中率（HR）、召回率和NDCG方面取得了显着改善。这些结果表明，将多模态视觉文本特征与分层图结构和注意机制相结合，可以显著提高个性化时尚推荐系统的有效性和效率。

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引用次数: 0

ISO-DeTr: A novel detection transformer for industrial small object detection ISO-DeTr：一种用于工业小物体检测的新型检测变压器

IF 4.9

Machine learning with applications

Pub Date : 2026-03-01 Epub Date: 2025-12-02 DOI: 10.1016/j.mlwa.2025.100809

Faisal Saeed , Anand Paul

Effectively detecting and assessing real-time structural and ecological parameters in contemporary manufacturing environments poses significant challenges, particularly in identifying minute objects within product images. The swift evolution of the industrial sector underscores the necessity for intelligent manufacturing environments to uphold stringent product quality standards. However, accelerating production processes at high speeds heightens the risk of defective product outcomes. This research addresses the challenges inherent in small object detection within industrial contexts, proposing an innovative detection transformer model tailored to modern manufacturing environments. The proposed model integrates a feature-enhanced multi-head self-attention block (FEMSA), merging cross-channel communication network and multiple multi-head self-attention (MSA) components to refine image features. A query proposal network is also introduced within the detection transformer framework to discern high-ranking proposals using Intersection over Union (IoU) and Non-Maximum Suppression (NMS) algorithms. Through extensive experimentation on custom industrial small objects, our proposed model demonstrates superior performance compared to existing models based on Non-Maximum Suppression and transformers. By tackling the challenges associated with small object detection, our model contributes to the dynamic synchronization between virtual and physical manufacturing realms, enhancing quality control in industrial production.

在当代制造环境中，有效地检测和评估实时结构和生态参数提出了重大挑战，特别是在识别产品图像中的微小物体方面。工业部门的快速发展强调了智能制造环境维护严格的产品质量标准的必要性。然而，高速加速生产过程会增加产品缺陷的风险。本研究解决了工业环境中小物体检测固有的挑战，提出了一种适合现代制造环境的创新检测变压器模型。该模型集成了一个特征增强的多头自注意块（FEMSA），融合了跨信道通信网络和多个多头自注意（MSA）组件来细化图像特征。在检测变压器框架中还引入了一个查询提议网络，该网络使用交联（IoU）和非最大抑制（NMS）算法来识别高级提议。通过在定制工业小型对象上的大量实验，与基于非最大抑制和变压器的现有模型相比，我们提出的模型表现出优越的性能。通过解决与小物体检测相关的挑战，我们的模型有助于虚拟和物理制造领域之间的动态同步，增强工业生产中的质量控制。

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引用次数: 0

DAIRE: A lightweight AI model for real-time detection of Controller Area Network attacks in the Internet of Vehicles DAIRE：用于实时检测车联网控制器区域网络攻击的轻量级AI模型

IF 4.9

Machine learning with applications

Pub Date : 2026-03-01 Epub Date: 2026-02-02 DOI: 10.1016/j.mlwa.2026.100859

Shahid Alam , Amina Jameel , Zahida Parveen , Ehab Alnfrawy , Adeela Ashraf , Raza Uddin , Jamal Aqib

The Internet of Vehicles (IoV) is advancing modern transportation by improving safety, efficiency, and intelligence. However, the reliance on the Controller Area Network (CAN) introduces critical security risks, as CAN-based communication is highly vulnerable to cyberattacks. Addressing this challenge, we propose DAIRE (Detecting Attacks in IoV in REal-time), a lightweight machine learning framework designed for real-time detection and classification of CAN attacks. DAIRE is built on a lightweight artificial neural network (ANN) where each layer contains

N_{i} = i \times c

neurons, with

N_{i}

representing the number of neurons in the

i

th layer and

c

corresponding to the total number of attack classes. Other hyperparameters are determined empirically to ensure real-time operation. To support the detection and classification of various IoV attacks, such as Denial-of-Service, Fuzzy, and Spoofing, DAIRE employs the sparse categorical cross-entropy loss function and root mean square propagation for loss minimization. In contrast to more resource-intensive architectures, DAIRE leverages a lightweight ANN to reduce computational demands while still delivering strong performance. Experimental results on the CICIoV2024 and Car-Hacking datasets demonstrate DAIRE’s effectiveness, achieving an average detection rate of 99.88%, a false positive rate of 0.02%, and an overall accuracy of 99.96%. Furthermore, DAIRE significantly outperforms state-of-the-art approaches in inference speed, with a classification time of just 0.03 ms per sample. These results highlight DAIRE’s effectiveness in detecting IoV cyberattacks and its practical suitability for real-time deployment in vehicular systems, underscoring its vital role in strengthening automotive cybersecurity.

车联网（IoV）通过提高安全性、效率和智能，推动着现代交通的发展。然而，对控制器区域网络（CAN）的依赖带来了严重的安全风险，因为基于CAN的通信非常容易受到网络攻击。为了应对这一挑战，我们提出了DAIRE (detection Attacks in IoV in REal-time)，这是一个轻量级的机器学习框架，旨在实时检测和分类CAN攻击。DAIRE建立在一个轻量级的人工神经网络（ANN）上，每层包含Ni=i×c神经元，Ni表示第i层神经元的数量，c对应攻击类的总数。其他超参数是经验确定的，以确保实时运行。为了支持各种IoV攻击的检测和分类，如拒绝服务、模糊和欺骗，DAIRE采用稀疏分类交叉熵损失函数和均方根传播来最小化损失。与更多的资源密集型架构相比，DAIRE利用轻量级ANN来减少计算需求，同时仍然提供强大的性能。在CICIoV2024和Car-Hacking数据集上的实验结果证明了DAIRE的有效性，平均检测率为99.88%，假阳性率为0.02%，总体准确率为99.96%。此外，DAIRE在推理速度上明显优于最先进的方法，每个样本的分类时间仅为0.03 ms。这些结果突出了DAIRE在检测车联网网络攻击方面的有效性及其在车载系统中实时部署的实际适用性，强调了其在加强汽车网络安全方面的重要作用。

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引用次数: 0

Combining style and semantics for robust authorship verification 结合风格和语义，实现健壮的作者身份验证

IF 4.9

Machine learning with applications

Pub Date : 2025-12-01 Epub Date: 2025-09-23 DOI: 10.1016/j.mlwa.2025.100732

Britt van Leeuwen , Sandjai Bhulai , Rob van der Mei

Authorship Verification is a key task in Natural Language Processing, essential for applications like plagiarism detection and content authentication. This paper analyzes the use of deep learning models for Authorship Verification, focusing on combining semantic and style features to enhance model performance. We propose three models: the Feature Interaction Network, Pairwise Concatenation Network, and Siamese Network, which aim to determine if two texts are written by the same author. Each model uses RoBERTa embeddings to capture semantic content and incorporates style features such as sentence length, word frequency, and punctuation to differentiate authors based on writing style.

Our results confirm that incorporating style features consistently improves model performance, with the extent of improvement varying by architecture. This demonstrates the value of combining semantic and stylistic information for Authorship Verification. While limitations such as RoBERTa’s fixed input length and the use of predefined style features exist, they do not hinder model effectiveness and point to clear opportunities for future enhancement through extended input handling and dynamic style feature extraction.

In contrast to prior studies such as Bevendorff et al., (2020) and Kestemont, et al., (2022), which relied on balanced and homogeneous datasets with consistent topics and well-formed language, our work evaluates models on a more challenging, imbalanced, and stylistically diverse dataset, better reflecting real-world Authorship Verification conditions. Despite the increased difficulty, our models achieve competitive results, underscoring their robustness and practical applicability.

These findings support the value of combining semantic and style features for real-world Authorship Verification.

作者身份验证是自然语言处理中的一项关键任务，对于抄袭检测和内容认证等应用至关重要。本文分析了作者身份验证中深度学习模型的使用，重点是结合语义和风格特征来提高模型的性能。我们提出了三个模型：特征交互网络、配对连接网络和连体网络，旨在确定两个文本是否由同一作者撰写。每个模型都使用RoBERTa嵌入来捕获语义内容，并结合句子长度、词频和标点符号等风格特征，以根据写作风格区分作者。我们的结果证实，结合风格特征可以持续改善模型性能，改善的程度因架构而异。这说明了将语义和风格信息结合起来进行作者身份验证的价值。虽然RoBERTa的固定输入长度和预定义样式特征的使用等限制存在，但它们并不妨碍模型的有效性，并为将来通过扩展输入处理和动态样式特征提取进行增强指明了明确的机会。与Bevendorff等人（2020）和Kestemont等人（2022）等先前的研究不同，Bevendorff等人（2020）和Kestemont等人（2022）依赖于具有一致主题和格式良好的语言的平衡和同构数据集，我们的工作在更具挑战性、不平衡和风格多样化的数据集上评估模型，更好地反映了现实世界的作者身份验证条件。尽管难度增加，但我们的模型取得了有竞争力的结果，强调了它们的鲁棒性和实用性。这些发现支持了将语义和风格特征结合起来进行真实作者身份验证的价值。

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引用次数: 0

Text-enhanced multimodal deep learning models for predicting chloride transport in concrete 用于预测混凝土中氯化物迁移的文本增强多模态深度学习模型

IF 4.9

Machine learning with applications

Pub Date : 2025-12-01 Epub Date: 2025-10-10 DOI: 10.1016/j.mlwa.2025.100753

Bingbing Guo , Yujie Jiao , Yan Wang , Fengling Zhang , Yuanfei Guo , Qinghao Guan

Reinforced concrete (RC) structures are widely used in civil engineering, and accurate prediction of chloride transport is essential for durability design and service life estimation. Existing machine learning models for predicting the chloride transport in concrete have primarily relied on researchers' expertise for feature construction. However, the factors affecting the chloride transport are numerous and highly complex, making manual feature engineering inefficient and labor-intensive. This study developed text-enhanced multimodal models that integrate natural language processing (NLP) with deep neural network (DNN) to automatically extract features from textual information, including properties of raw materials, experimental methods, chloride attack mechanisms and comments. The results demonstrate that the developed multimodal models have learned prior knowledge, which enables them to achieve significantly higher accuracy than numerical-data-only DNN models. Among these models, the multi-head self-attention model performs the best by capturing features from multiple angles and enabling parallel computation. Crucially, the text-enhanced multimodal models can maintain high accuracy even with limited numerical data.

钢筋混凝土（RC）结构在土木工程中应用广泛，其氯离子输运量的准确预测对其耐久性设计和使用寿命估计至关重要。用于预测混凝土中氯离子迁移的现有机器学习模型主要依赖于研究人员在特征构建方面的专业知识。然而，影响氯离子迁移的因素众多且非常复杂，使得人工特征工程效率低下且劳动强度大。本研究开发了文本增强多模态模型，该模型将自然语言处理（NLP）与深度神经网络（DNN）相结合，从文本信息中自动提取特征，包括原材料属性、实验方法、氯化物攻击机制和评论。结果表明，所开发的多模态模型已经学习了先验知识，这使得它们比仅使用数字数据的深度神经网络模型具有更高的精度。在这些模型中，多头自注意模型从多个角度捕获特征并实现并行计算，性能最好。关键是，文本增强的多模态模型即使在有限的数值数据下也能保持较高的精度。

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引用次数: 0

A comprehensive survey on deep reinforcement learning in object tracking 深度强化学习在目标跟踪中的研究综述

IF 4.9

Machine learning with applications

Pub Date : 2025-12-01 Epub Date: 2025-10-10 DOI: 10.1016/j.mlwa.2025.100745

Hy Nguyen , Srikanth Thudumu , Hung Du , Rajesh Vasa , Kon Mouzakis

The exploration of Deep Reinforcement Learning (DRL) in Object Tracking (OT) represents an emerging paradigm and is gaining traction as an alternative to conventional CNN-based methods. DRL’s ability to integrate spatial and temporal context and learn from interactions makes it particularly suited for the sequential decision-making required in OT. The survey reviews a range of DRL-based methods for OT, systematically collating and analyzing existing research to highlight trends and challenges. It also provides an evaluation of different DRL algorithms, categorizing them based on their performance in various dynamic environments. Additionally, we analyze existing evaluation benchmarks and simulators, along with the challenges, potential solutions, and trends in DRL-based OT methods. This paper aims to bridge the fragmented literature on DRL applications in OT, providing a unified view that identifies common approaches, challenges, and potential synergies.

深度强化学习（DRL）在目标跟踪（OT）中的探索代表了一种新兴的范式，并且作为传统的基于cnn的方法的替代方案正在获得关注。DRL整合空间和时间背景并从交互中学习的能力使其特别适合OT所需的顺序决策。该调查回顾了一系列基于drl的OT方法，系统地整理和分析了现有研究，以突出趋势和挑战。它还提供了对不同DRL算法的评估，根据它们在各种动态环境中的性能对它们进行分类。此外，我们还分析了现有的评估基准和模拟器，以及基于drl的OT方法的挑战、潜在解决方案和趋势。本文旨在弥合关于DRL在OT中的应用的零散文献，提供一个统一的观点，确定通用的方法、挑战和潜在的协同作用。

引用次数: 0

On the retraining frequency of global models in retail demand forecasting 零售需求预测中全局模型的再训练频率研究

IF 4.9

Machine learning with applications

Pub Date : 2025-12-01 Epub Date: 2025-10-23 DOI: 10.1016/j.mlwa.2025.100769

Marco Zanotti

In an era of increasing computational capabilities and growing environmental consciousness, organizations face a critical challenge in balancing the accuracy of forecasting models with computational efficiency and sustainability. Global forecasting models, lowering the computational time, have gained significant attention over the years. However, the common practice of retraining these models with new observations raises important questions about the costs of forecasting. Using ten different machine learning and deep learning models, we analyzed various retraining scenarios, ranging from continuous updates to no retraining at all, across two large retail demand datasets. We showed that less frequent retraining strategies maintain the forecast accuracy while reducing the computational costs, providing a more sustainable approach to large-scale forecasting. We also found that machine learning models are a marginally better choice to reduce the costs of forecasting when coupled with less frequent model retraining strategies as the frequency of the data increases. Our findings challenge the conventional belief that frequent retraining is essential for maintaining forecasting accuracy. Instead, periodic retraining offers a good balance between predictive performance and efficiency, both in the case of point and probabilistic forecasting. These insights provide actionable guidelines for organizations seeking to optimize forecasting pipelines while reducing costs and energy consumption.

在计算能力不断增强和环境意识不断增强的时代，组织面临着平衡预测模型准确性与计算效率和可持续性的关键挑战。减少计算时间的全球预测模型多年来获得了极大的关注。然而，用新的观测结果对这些模型进行再训练的常见做法引发了有关预测成本的重要问题。使用十种不同的机器学习和深度学习模型，我们分析了两个大型零售需求数据集的各种再培训场景，从持续更新到根本不进行再培训。我们发现，较少频率的再训练策略在保持预测准确性的同时降低了计算成本，为大规模预测提供了一种更可持续的方法。我们还发现，随着数据频率的增加，机器学习模型是降低预测成本的一个稍微更好的选择，当加上不太频繁的模型再训练策略时。我们的发现挑战了传统观念，即频繁的再培训对于保持预测的准确性至关重要。相反，在点预测和概率预测的情况下，定期再训练提供了预测性能和效率之间的良好平衡。这些见解为寻求优化预测管道，同时降低成本和能源消耗的组织提供了可操作的指导方针。

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引用次数: 0

Optimization of genomic breeding value prediction for growth traits in Rongchang pigs through machine learning techniques 利用机器学习技术优化荣昌猪生长性状基因组育种价值预测

IF 4.9

Machine learning with applications

Pub Date : 2025-12-01 Epub Date: 2025-10-02 DOI: 10.1016/j.mlwa.2025.100747

Pingxian Wu , Junge Wang , Xinyou Chen , Tao Wang , Zongyi Guo , Shuqi Diao , Jinyong Wang

Background

The increasing volume of genome sequencing data poses significant challenges for traditional genome-wide prediction methods in handling large datasets. Machine learning (ML) techniques are well-suited for processing high-dimensional data, and offer promising solutions. This study aimed to identify an optimal genome-wide prediction approach for local pig breeds using 10 datasets with varying single nucleotide polymorphism (SNP) densities, derived from imputed sequencing data of 485 Rongchang pigs and the results of genome-wide association studies (GWAS). Three growth traits, namely, backfat (BF) thickness, loin and thoracic height (LTH), and girth circumference (GC), were predicted using six traditional methods and six ML-based methods, including Kernel Ridge Regression (KRR), Support Vector Regression (SVR), Random Forest, Gradient Boosting Decision Tree, Light Gradient Boosting Machine, and Adaboost.

Results

The efficacy of the different methods was evaluated using a five-fold cross-validation strategy and independent tests. The predictive performance of both the traditional and ML-based methods was initially enhanced through the incorporation of significantly associated SNPs and weighted data, with the KRR method exhibiting exceptional resistance to overfitting at a SNP density of 300,000. The ML-based methods outperformed the traditional methods, with improvements of 6.6–8.1 %. The integration of GWAS data enhanced the prediction accuracy of the ML-based methods. KRR and Gradient Boosting Decision Tree demonstrated significant computational efficiency, indicating their potential as promising strategies for genomic prediction in livestock breeding.

Conclusions

This study provides a comprehensive analysis of genome-wide predictions in Rongchang pigs, and highlights the potential of ML-based techniques in enhancing prediction accuracy and efficiency. The study provides valuable insights into GP and holds key implications for advancing genome breeding practices in local pig breeds.

随着基因组测序数据量的不断增加，传统的全基因组预测方法在处理大数据集时面临着巨大的挑战。机器学习（ML）技术非常适合处理高维数据，并提供了有前途的解决方案。本研究旨在利用来自485头荣昌猪的序列数据和全基因组关联研究（GWAS）结果的10个不同单核苷酸多态性（SNP）密度的数据集，确定一种最佳的地方猪品种全基因组预测方法。采用核脊回归（Kernel Ridge Regression， KRR）、支持向量回归（Support Vector Regression， SVR）、随机森林、梯度增强决策树、轻梯度增强机（Light Gradient Boosting Machine）和Adaboost等6种传统方法和6种基于ml的方法预测了毛畜的3个生长特征，即背膘厚度（BF）、腰胸高度（LTH）和周长（GC）。结果采用五重交叉验证策略和独立试验对不同方法的疗效进行评价。通过纳入显著相关的SNP和加权数据，传统方法和基于ml的方法的预测性能最初都得到了增强，其中KRR方法在SNP密度为30万时表现出卓越的过拟合抵抗能力。基于ml的方法优于传统方法，提高了6.6 - 8.1%。GWAS数据的集成提高了基于ml方法的预测精度。KRR和梯度增强决策树显示出显著的计算效率，表明它们有潜力作为家畜育种基因组预测策略。结论本研究对荣昌猪全基因组预测进行了全面分析，并强调了基于ml的技术在提高预测准确性和效率方面的潜力。该研究为GP提供了有价值的见解，并对推进本地猪品种的基因组育种实践具有重要意义。

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引用次数: 0

Production scheduling optimisation using mixed integer programming with machine learning dilution prediction capabilities for underground open stoping operations 利用混合整数规划和机器学习贫化预测能力进行地下露天采场生产调度优化

IF 4.9

Machine learning with applications

Pub Date : 2025-12-01 Epub Date: 2025-11-05 DOI: 10.1016/j.mlwa.2025.100776

Prosper Chimunhu , Erkan Topal , Mohammad Waqar Ali Asad , Roohollah Shirani Faradonbeh , Ajak Duany Ajak

For decades, Mixed Integer Programming (MIP) has been successfully utilised to optimise production schedules in underground mining, with increasingly notable results reported. However, recurrent inconsistencies between schedule forecasts and actual production due to imprecise input assumptions, such as mining dilution factors, subtly impair the robustness of optimal solutions, with detrimental hierarchical effects on the business’s cashflow projections and profitability. To address this, this study leverages emerging applications of Machine Learning (ML) and adjacent technologies that are revolutionising intelligent prediction of dilution in underground mining operations. The study proposes a synergistic nexus between MIP and ML models using ML-predicted dilution on a per-stope granularity instead of the traditional single dilution factor to improve the schedule’s forecasting accuracy. A sample of 61 stopes from an underground open-stoping operation was used to create and optimise schedules based on empirically determined and ML-predicted dilution factors. Study findings revealed a 3.1% higher net present value (NPV) for MIP-optimised schedules over manual schedules for the same dilution factor (empirical). Further, it was also noted that the ML-predicted dilution at 74% accuracy on a per-stope granularity enhances the MIP-optimised schedules’ tonnage forecast precision by at least 4 % and the NPV by at least 2 % compared to MIP-optimised schedules using the single dilution factor over a 16-month period. Additionally, results revealed that MIP schedules augmented with ML-predicted dilution demonstrated greater flexibility in navigating schedule constraints, leading to better schedule responsiveness and granularity on forecasts. Thus, the study improves optimal solutions’ robustness, reliability and production scheduling efficacy.

几十年来，混合整数规划（MIP）已经成功地用于优化地下开采的生产计划，并取得了越来越显著的成果。然而，由于不精确的输入假设（如采矿稀释因素），进度预测与实际产量之间的反复不一致会微妙地损害最优解决方案的鲁棒性，对企业的现金流预测和盈利能力产生有害的分层影响。为了解决这个问题，本研究利用了机器学习（ML）和相关技术的新兴应用，这些技术正在彻底改变地下采矿作业中稀释的智能预测。该研究提出了MIP和ML模型之间的协同关系，使用ML预测的每个采场粒度的稀释系数，而不是传统的单一稀释系数，以提高进度预测的准确性。以地下空场开采的61个采场为样本，根据经验确定的稀释系数和ml预测的稀释系数来创建和优化计划。研究结果显示，对于相同的稀释系数（经验），mip优化方案的净现值（NPV）比手动方案高3.1%。此外，在16个月的时间内，与使用单一稀释系数的mip优化计划相比，ml预测的每个采场粒度稀释精度为74%，mip优化计划的吨位预测精度提高了至少4%，NPV提高了至少2%。此外，结果显示，MIP计划与ml预测的稀释度增强，在导航计划约束方面表现出更大的灵活性，从而导致更好的计划响应性和预测粒度。从而提高了最优解的鲁棒性、可靠性和生产调度效率。

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引用次数: 0

Beyond single-run metrics with CP-fuse: A rigorous multi-cohort evaluation of clinico-pathological fusion for improved survival prediction in TCGA 超越单组指标的CP-fuse：一项严格的多队列评估临床病理融合改善TCGA的生存预测

IF 4.9

Machine learning with applications

Pub Date : 2025-12-01 Epub Date: 2025-11-04 DOI: 10.1016/j.mlwa.2025.100789

Juan Duran , Yujing Zou , Martin Vallières , Shirin A. Enger

Accurate prediction of progression-free survival (PFS) is critical for precision oncology. However, most existing multimodal survival studies rely on single fusion strategies, one-off cross-validation runs, and focus solely on discrimination metrics, leaving gaps in systematic evaluation and calibration. We evaluated multimodal fusion approaches combining histopathology whole-slide images (via Hierarchical Image Pyramid Transformer) and clinical variables (via Feature Tokenizer-Transformer) across five TCGA cohorts: bladder cancer (BLCA), uterine corpus endometrial carcinoma (UCEC), lung adenocarcinoma (LUAD), breast cancer (BRCA), and head and neck squamous cell carcinoma (HNSC) (N=2,984). Three intermediate (marginal, cross-attention, Variational Autoencoder or VAE) and two late fusion strategies (trainable-weight, meta-learning) were trained end-to-end with DeepSurv. Our 100-repetition 10-fold cross-validation (CV) framework mitigates the variance overlooked in single-run CV evaluations. VAE fusion achieved superior PFS prediction (Concordance-index) in BLCA (0.739±0.019), UCEC (0.770±0.021), LUAD (0.683±0.018), and BRCA (0.760±0.021), while meta-learning was best for HNSC (0.686±0.022). However, Integrated Brier Score values (0.066–0.142) revealed calibration variability. Our findings highlight the importance of multimodal fusion, combined discrimination and calibration metrics, and rigorous validation for clinically meaningful survival modeling.

准确预测无进展生存期（PFS）对精准肿瘤学至关重要。然而，大多数现有的多模态生存研究依赖于单一的融合策略，一次性交叉验证运行，并且只关注歧视指标，在系统评估和校准方面留下了空白。我们评估了结合组织病理学全切片图像（通过分层图像金字塔转换器）和临床变量（通过特征标记器转换器）的多模式融合方法，涵盖了五个TCGA队列：膀胱癌（BLCA）、子宫内膜癌（UCEC）、肺腺癌（LUAD）、乳腺癌（BRCA）和头颈部鳞状细胞癌（HNSC）（N=2,984）。使用DeepSurv端到端训练了三个中间（边缘、交叉注意、变分自编码器或VAE）和两个后期融合策略（可训练权重、元学习）。我们的100次重复10倍交叉验证（CV）框架减轻了单次CV评估中被忽视的方差。VAE融合对BLCA（0.739±0.019）、UCEC（0.770±0.021）、LUAD（0.683±0.018）和BRCA（0.760±0.021）的PFS（一致性指数）预测效果较好，而元学习对HNSC（0.686±0.022）的PFS预测效果最好。然而，综合Brier评分值（0.066-0.142）显示了校准的可变性。我们的研究结果强调了多模态融合、联合判别和校准指标以及对临床有意义的生存模型进行严格验证的重要性。

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引用次数: 0