Machine learning with applications最新文献

Towards an intelligent review helpfulness estimation: A novel dataset and machine learning framework 迈向智能评论有用估计：一个新的数据集和机器学习框架

IF 4.9

Machine learning with applications

Pub Date : 2026-01-21 DOI: 10.1016/j.mlwa.2026.100849

Rakibul Hassan, Shubhashish Kar, Jorge Fonseca Cacho, Shaikh Arifuzzaman

The rise of online rental platforms has led to an overwhelming amount of user-generated content, making it difficult for prospective consumers to discern which reviews are helpful. Existing approaches often rely on raw helpfulness votes, which are sparse, subjective, and temporally inconsistent. Also, there is lack of labeled dataset in the field of rental review usefulness prediction. This paper introduces a novel dataset of apartment reviews collected from online website and proposes an intelligent machine learning framework to predict the helpfulness of rental reviews. To address the challenge of obtaining reliable labels from sparse and subjective user votes, a scoring-based labeling strategy is developed that uses helpful vote count and timeliness. A diverse set of features including TF–IDF vectors, sentiment polarity, rating deviation, and review length are used to capture both textual and behavioral aspects of the reviews. Multiple classifiers, including Logistic Regression, Naive Bayes, and XGBoost, are systematically evaluated under 5-fold cross-validation, along with a rule-based and deep learning models.

Experimental results show that XGBoost consistently achieves the best overall performance with an accuracy of 0.71 and ROC-AUC of 0.75 when leveraging all features. This research makes three key contributions: (i) the first large-scale dataset for rental review, (ii) auto annotation technique that uses clustering approach with score from user votes and time since posted, and (iii) comprehensive evaluation pipeline spanning rule-based, traditional, and deep learning classifiers. Together, these advances establish a foundation for intelligent rental review helpfulness estimation, with broader implications for e-commerce, hospitality, and user-generated content analysis.

在线租赁平台的兴起导致了大量的用户生成内容，这使得潜在的消费者很难辨别哪些评论是有用的。现有的方法通常依赖于原始的有用性投票，这些投票是稀疏的、主观的，并且在时间上不一致。此外，在租房评论有用性预测领域也缺乏标记数据集。本文介绍了一个从在线网站收集的公寓评论数据集，并提出了一个智能机器学习框架来预测租房评论的有用性。为了解决从稀疏和主观的用户投票中获得可靠标签的挑战，开发了一种基于分数的标签策略，该策略使用有用的投票计数和及时性。包括TF-IDF向量、情感极性、评级偏差和评论长度在内的一系列不同的特征被用来捕获评论的文本和行为方面。多个分类器，包括逻辑回归，朴素贝叶斯和XGBoost，在5倍交叉验证下进行系统评估，以及基于规则和深度学习模型。实验结果表明，在利用所有特征时，XGBoost始终保持最佳的整体性能，精度为0.71，ROC-AUC为0.75。本研究做出了三个关键贡献：(i)第一个用于租赁评论的大规模数据集，（ii）使用用户投票和发布时间评分的聚类方法的自动注释技术，以及（iii）跨越基于规则的、传统的和深度学习分类器的综合评估管道。总之，这些进步为智能租赁评论有用性评估奠定了基础，并对电子商务、酒店业和用户生成内容分析产生了更广泛的影响。

{"title":"Towards an intelligent review helpfulness estimation: A novel dataset and machine learning framework","authors":"Rakibul Hassan, Shubhashish Kar, Jorge Fonseca Cacho, Shaikh Arifuzzaman","doi":"10.1016/j.mlwa.2026.100849","DOIUrl":"10.1016/j.mlwa.2026.100849","url":null,"abstract":"<div><div>The rise of online rental platforms has led to an overwhelming amount of user-generated content, making it difficult for prospective consumers to discern which reviews are helpful. Existing approaches often rely on raw helpfulness votes, which are sparse, subjective, and temporally inconsistent. Also, there is lack of labeled dataset in the field of rental review usefulness prediction. This paper introduces a novel dataset of apartment reviews collected from online website and proposes an intelligent machine learning framework to predict the helpfulness of rental reviews. To address the challenge of obtaining reliable labels from sparse and subjective user votes, a scoring-based labeling strategy is developed that uses helpful vote count and timeliness. A diverse set of features including TF–IDF vectors, sentiment polarity, rating deviation, and review length are used to capture both textual and behavioral aspects of the reviews. Multiple classifiers, including Logistic Regression, Naive Bayes, and XGBoost, are systematically evaluated under 5-fold cross-validation, along with a rule-based and deep learning models.</div><div>Experimental results show that XGBoost consistently achieves the best overall performance with an accuracy of 0.71 and ROC-AUC of 0.75 when leveraging all features. This research makes three key contributions: (i) the first large-scale dataset for rental review, (ii) auto annotation technique that uses clustering approach with score from user votes and time since posted, and (iii) comprehensive evaluation pipeline spanning rule-based, traditional, and deep learning classifiers. Together, these advances establish a foundation for intelligent rental review helpfulness estimation, with broader implications for e-commerce, hospitality, and user-generated content analysis.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"23 ","pages":"Article 100849"},"PeriodicalIF":4.9,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synonym extraction from Japanese patent documents using term definition sentences 使用术语定义句从日语专利文件中提取同义词

IF 4.9

Machine learning with applications

Pub Date : 2026-01-21 DOI: 10.1016/j.mlwa.2026.100848

Koji Marusaki , Seiya Kawano , Asahi Hentona , Hirofumi Nonaka

Conducting prior patent searches before developing technologies and filing patent applications in companies or universities is essential for understanding technological trends among competitors and academic institutions, as well as for increasing the likelihood of obtaining patent rights. In these searches, it is important not only to include relevant keywords in the search queries but also to incorporate related terms retrieved from a thesaurus. To support this, methods using word embeddings for automatically extracting such synonyms have recently been proposed. However, patent documents often contain unique expressions and compound terms, such as specialized technical terminology and abstract conceptual terms, which are difficult to accurately capture using existing large language models trained at the token level.

In this study, we investigate a method for extracting synonyms from patent documents by embedding the definition sentences that explain technical terms. The experimental results demonstrate that the proposed method achieves more precise synonym extraction than conventional word embedding approaches, and it can contribute to the expansion of existing thesauri.

Thus, this research is expected to improve the recall of prior art searches and support the automatic extraction of technical elements for identifying technological trends.

在开发技术和向公司或大学提交专利申请之前进行事先专利检索，对于了解竞争对手和学术机构之间的技术趋势以及增加获得专利权的可能性至关重要。在这些搜索中，重要的是不仅要在搜索查询中包含相关的关键字，而且要合并从同义词库中检索到的相关术语。为了支持这一点，最近提出了使用词嵌入来自动提取此类同义词的方法。然而，专利文献通常包含独特的表达和复合术语，例如专门的技术术语和抽象的概念术语，使用在令牌级别训练的现有大型语言模型很难准确捕获这些术语。在这项研究中，我们研究了一种通过嵌入解释技术术语的定义句来从专利文件中提取同义词的方法。实验结果表明，该方法比传统的词嵌入方法获得了更精确的同义词提取，并有助于现有同义词库的扩展。因此，本研究有望提高现有技术检索的召回率，并支持自动提取技术元素以识别技术趋势。

{"title":"Synonym extraction from Japanese patent documents using term definition sentences","authors":"Koji Marusaki , Seiya Kawano , Asahi Hentona , Hirofumi Nonaka","doi":"10.1016/j.mlwa.2026.100848","DOIUrl":"10.1016/j.mlwa.2026.100848","url":null,"abstract":"<div><div>Conducting prior patent searches before developing technologies and filing patent applications in companies or universities is essential for understanding technological trends among competitors and academic institutions, as well as for increasing the likelihood of obtaining patent rights. In these searches, it is important not only to include relevant keywords in the search queries but also to incorporate related terms retrieved from a thesaurus. To support this, methods using word embeddings for automatically extracting such synonyms have recently been proposed. However, patent documents often contain unique expressions and compound terms, such as specialized technical terminology and abstract conceptual terms, which are difficult to accurately capture using existing large language models trained at the token level.</div><div>In this study, we investigate a method for extracting synonyms from patent documents by embedding the definition sentences that explain technical terms. The experimental results demonstrate that the proposed method achieves more precise synonym extraction than conventional word embedding approaches, and it can contribute to the expansion of existing thesauri.</div><div>Thus, this research is expected to improve the recall of prior art searches and support the automatic extraction of technical elements for identifying technological trends.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"23 ","pages":"Article 100848"},"PeriodicalIF":4.9,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intrinsic Dimension Estimating Autoencoder (IDEA) using CancelOut layer and a projected loss 使用CancelOut层和投影损失的自编码器（IDEA）固有维估计

IF 4.9

Machine learning with applications

Pub Date : 2026-01-20 DOI: 10.1016/j.mlwa.2026.100850

Antoine Oriou , Philipp Krah , Julian Koellermeier

This paper introduces the Intrinsic Dimension Estimating Autoencoder (IDEA), which identifies the underlying intrinsic dimension of a wide range of datasets whose samples lie on either linear or nonlinear manifolds. Beyond estimating the intrinsic dimension, IDEA is also able to reconstruct the original dataset after projecting it onto the corresponding latent space, which is structured using re-weighted double CancelOut layers. Our key contribution is the introduction of the projected reconstruction loss term, guiding the training of the model by continuously assessing the reconstruction quality under the removal of an additional latent dimension.

We first assess the performance of IDEA on a series of theoretical benchmarks to validate its robustness. These experiments allow us to test its reconstruction ability and compare its performance with state-of-the-art intrinsic dimension estimators. The benchmarks show good accuracy and high versatility of our approach. Subsequently, we apply our model to data generated from the numerical solution of a vertically resolved one-dimensional free-surface flow, following a pointwise discretization of the vertical velocity profile in the horizontal direction, vertical direction, and time. IDEA succeeds in estimating the dataset’s intrinsic dimension and then reconstructs the original solution by working directly within the projection space identified by the network.

本文介绍了一种固有维数估计自编码器（IDEA），它可以识别大量样本位于线性或非线性流形上的数据集的内在维数。除了估计固有维数之外，IDEA还能够将原始数据集投影到相应的潜在空间后重建原始数据集，该潜在空间使用重新加权的双CancelOut层进行结构化。我们的主要贡献是引入了预测重建损失项，通过在去除额外潜在维度的情况下持续评估重建质量来指导模型的训练。我们首先评估了IDEA在一系列理论基准上的表现，以验证其稳健性。这些实验使我们能够测试其重建能力，并将其性能与最先进的内维估计器进行比较。基准测试表明，我们的方法具有很高的准确性和通用性。随后，我们将我们的模型应用于垂直分解的一维自由表面流的数值解生成的数据，随后在水平方向、垂直方向和时间上对垂直速度剖面进行点向离散化。IDEA成功地估计了数据集的内在维度，然后通过直接在网络识别的投影空间内工作来重建原始解。

{"title":"Intrinsic Dimension Estimating Autoencoder (IDEA) using CancelOut layer and a projected loss","authors":"Antoine Oriou , Philipp Krah , Julian Koellermeier","doi":"10.1016/j.mlwa.2026.100850","DOIUrl":"10.1016/j.mlwa.2026.100850","url":null,"abstract":"<div><div>This paper introduces the Intrinsic Dimension Estimating Autoencoder (IDEA), which identifies the underlying intrinsic dimension of a wide range of datasets whose samples lie on either linear or nonlinear manifolds. Beyond estimating the intrinsic dimension, IDEA is also able to reconstruct the original dataset after projecting it onto the corresponding latent space, which is structured using re-weighted double CancelOut layers. Our key contribution is the introduction of the <em>projected reconstruction loss</em> term, guiding the training of the model by continuously assessing the reconstruction quality under the removal of an additional latent dimension.</div><div>We first assess the performance of IDEA on a series of theoretical benchmarks to validate its robustness. These experiments allow us to test its reconstruction ability and compare its performance with state-of-the-art intrinsic dimension estimators. The benchmarks show good accuracy and high versatility of our approach. Subsequently, we apply our model to data generated from the numerical solution of a vertically resolved one-dimensional free-surface flow, following a pointwise discretization of the vertical velocity profile in the horizontal direction, vertical direction, and time. IDEA succeeds in estimating the dataset’s intrinsic dimension and then reconstructs the original solution by working directly within the projection space identified by the network.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"23 ","pages":"Article 100850"},"PeriodicalIF":4.9,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Trust but verify: Image-aware evaluation of radiology report generators 信任但要验证：放射学报告生成器的图像感知评估

IF 4.9

Machine learning with applications

Pub Date : 2026-01-20 DOI: 10.1016/j.mlwa.2026.100851

Sayeh Gholipour Picha, Dawood Al Chanti, Alice Caplier

Large language and vision-language models have greatly advanced automated chest X-ray report generation (RRG),. yet current evaluation practices remain largely text-based and detached from image evidence. Traditional machine translation metrics fail to determine whether generated findings are clinically correct or visually grounded, limiting their suitability for medical applications.

This study introduces a comprehensive, image-aware evaluation framework that integrates the VICCA (Visual Interpretation and Comprehension of Chest X-ray Anomalies) protocol with the domain-specific semantic metric MCSE (Medical Corpus Similarity Evaluation). VICCA combines visual grounding and text-guided image generation to assess visual-textual consistency, while MCSE measures semantic and factual fidelity through clinically meaningful entities, negations, and modifiers. Together, they provide a unified, semi-reference-free assessment of pathology-level accuracy, semantic coherence, and visual consistency.

Five representative RRG models, R2Gen, M2Trans, CXR-RePaiR, RGRG, and MedGemma, are benchmarked on 2461 MIMIC-CXR studies using a standardized pipeline. Results reveal systematic trade-offs: models with high pathology agreement often generate semantically weak or visually inconsistent reports, whereas textually fluent models may lack proper image grounding. By integrating clinical semantics and visual reliability within a single multimodal framework, VICCA establishes a robust paradigm for evaluating the trustworthiness and interpretability of AI-generated radiology reports.

大型语言和视觉语言模型大大提高了自动胸部x射线报告生成（RRG）。然而，目前的评估实践仍然主要基于文本，与图像证据脱节。传统的机器翻译指标无法确定生成的结果是否临床正确或视觉基础，限制了它们对医学应用的适用性。本研究引入了一个全面的图像感知评估框架，该框架将VICCA（胸部x射线异常的视觉解释和理解）协议与领域特定语义度量MCSE（医学语料库相似性评估）集成在一起。VICCA结合了视觉基础和文本引导图像生成来评估视觉-文本一致性，而MCSE通过临床有意义的实体、否定和修饰语来测量语义和事实的保真度。总之，他们提供了一个统一的，半参考自由的评估病理水平的准确性，语义一致性和视觉一致性。R2Gen、M2Trans、CXR-RePaiR、RGRG和MedGemma这5种具有代表性的RRG模型在2461项MIMIC-CXR研究中使用标准化管道进行基准测试。结果揭示了系统的权衡：具有高病理一致性的模型通常生成语义弱或视觉不一致的报告，而文本流畅的模型可能缺乏适当的图像基础。通过将临床语义和视觉可靠性整合到一个单一的多模态框架中，VICCA为评估人工智能生成的放射学报告的可信度和可解释性建立了一个强大的范例。

{"title":"Trust but verify: Image-aware evaluation of radiology report generators","authors":"Sayeh Gholipour Picha, Dawood Al Chanti, Alice Caplier","doi":"10.1016/j.mlwa.2026.100851","DOIUrl":"10.1016/j.mlwa.2026.100851","url":null,"abstract":"<div><div>Large language and vision-language models have greatly advanced automated chest X-ray report generation (RRG),. yet current evaluation practices remain largely text-based and detached from image evidence. Traditional machine translation metrics fail to determine whether generated findings are clinically correct or visually grounded, limiting their suitability for medical applications.</div><div>This study introduces a comprehensive, image-aware evaluation framework that integrates the VICCA (<em>Visual Interpretation and Comprehension of Chest X-ray Anomalies</em>) protocol with the domain-specific semantic metric MCSE (<em>Medical Corpus Similarity Evaluation</em>). VICCA combines visual grounding and text-guided image generation to assess visual-textual consistency, while MCSE measures semantic and factual fidelity through clinically meaningful entities, negations, and modifiers. Together, they provide a unified, semi-reference-free assessment of pathology-level accuracy, semantic coherence, and visual consistency.</div><div>Five representative RRG models, R2Gen, M2Trans, CXR-RePaiR, RGRG, and MedGemma, are benchmarked on 2461 MIMIC-CXR studies using a standardized pipeline. Results reveal systematic trade-offs: models with high pathology agreement often generate semantically weak or visually inconsistent reports, whereas textually fluent models may lack proper image grounding. By integrating clinical semantics and visual reliability within a single multimodal framework, VICCA establishes a robust paradigm for evaluating the trustworthiness and interpretability of AI-generated radiology reports.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"23 ","pages":"Article 100851"},"PeriodicalIF":4.9,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of major segmentation models for intracranial artery time-of-flight magnetic resonance angiography images 颅内动脉飞行时间磁共振血管造影图像的主要分割模型分析

IF 4.9

Machine learning with applications

Pub Date : 2026-01-19 DOI: 10.1016/j.mlwa.2026.100843

Mekhla Sarkar , Yen-Chu Huang , Tsong-Hai Lee , Jiann-Der Lee , Prasan Kumar Sahoo

Intracranial arterial stenosis (ICAS) is a leading cause of cerebrovascular accidents, and accurate morphological assessment of intracranial arteries is critical for diagnosis and treatment planning. Complex vascular structures, imaging noise, and variability in time-of-flight magnetic resonance angiography (TOF-MRA) images are challenging issues for the manual delineation that motivates the use of deep learning (DL) for automatic segmentation of the intracranial arteries. DL based automatic segmentation offers a promising solution by providing consistent and noise-reduced vessel delineation. However, selecting an optimal segmentation architecture remains challenging due to the diversity of network designs and encoder backbones. Therefore, this study presents a systematic benchmarking of five widely used DL segmentation architectures, UNet, LinkNet, Feature Pyramid Networks (FPN), Pyramid Scene Parsing Network (PSPNet), and DeepLabV3+, each combined with nine backbone networks, yielding 45 model variants, including previously unexplored configurations for intracranial artery segmentation in TOF-MRA. Models were trained and cross-validated on four datasets: in-house, CereVessMRA, IXI and ADAM, and evaluated on held-out independent test set. Performance metrics included Intersection over Union (IoU), Dice Similarity Coefficient (DSC), and a Stability Score, combining the coefficient of variation of IoU and DSC to quantify segmentation consistency and reproducibility. Experimental results demonstrated highest DSC score was achieved with UNet–SE-ResNeXt50, LinkNet-SE-ResNeXt50, FPN-DenseNet169, FPN-SENet154. The most stable configurations were LinkNet–EfficientNetB6, LinkNet–SENet154, UNet–DenseNet169, and UNet–EfficientNetB6. Conversely, DeepLabV3+ and PSPNet variants consistently underperformed. These findings provide actionable guidance for selecting backbone–segmentation pairs and highlight trade-offs between accuracy, robustness, and reproducibility for complex intracranial artery TOF-MRA segmentation tasks.

颅内动脉狭窄（ICAS）是脑血管意外的主要原因，准确的颅内动脉形态学评估对诊断和治疗计划至关重要。复杂的血管结构、成像噪声和飞行时间磁共振血管造影（TOF-MRA）图像的可变性是人工描绘的挑战问题，这促使使用深度学习（DL）来自动分割颅内动脉。基于深度学习的自动分割提供了一个很有前途的解决方案，它提供了一致的、降噪的血管描绘。然而，由于网络设计和编码器主干网的多样性，选择一个最佳的分割架构仍然是一个挑战。因此，本研究对五种广泛使用的深度学习分割架构UNet、LinkNet、特征金字塔网络（FPN）、金字塔场景解析网络（PSPNet）和DeepLabV3+进行了系统的基准测试，每一种都与9个骨干网络相结合，产生45种模型变体，包括以前未开发的TOF-MRA颅内动脉分割配置。模型在内部、CereVessMRA、IXI和ADAM四个数据集上进行训练和交叉验证，并在独立测试集上进行评估。性能指标包括交联（Intersection over Union, IoU）、骰子相似系数（Dice Similarity Coefficient， DSC）和稳定性评分，结合IoU和DSC的变异系数来量化分割的一致性和可重复性。实验结果表明，UNet-SE-ResNeXt50、LinkNet-SE-ResNeXt50、FPN-DenseNet169、FPN-SENet154的DSC得分最高。最稳定的配置是LinkNet-EfficientNetB6、LinkNet-SENet154、UNet-DenseNet169和UNet-EfficientNetB6。相反，DeepLabV3+和PSPNet变体一直表现不佳。这些发现为选择骨干分割对提供了可操作的指导，并突出了复杂颅内动脉TOF-MRA分割任务的准确性，稳健性和可重复性之间的权衡。

{"title":"Analysis of major segmentation models for intracranial artery time-of-flight magnetic resonance angiography images","authors":"Mekhla Sarkar , Yen-Chu Huang , Tsong-Hai Lee , Jiann-Der Lee , Prasan Kumar Sahoo","doi":"10.1016/j.mlwa.2026.100843","DOIUrl":"10.1016/j.mlwa.2026.100843","url":null,"abstract":"<div><div>Intracranial arterial stenosis (ICAS) is a leading cause of cerebrovascular accidents, and accurate morphological assessment of intracranial arteries is critical for diagnosis and treatment planning. Complex vascular structures, imaging noise, and variability in time-of-flight magnetic resonance angiography (TOF-MRA) images are challenging issues for the manual delineation that motivates the use of deep learning (DL) for automatic segmentation of the intracranial arteries. DL based automatic segmentation offers a promising solution by providing consistent and noise-reduced vessel delineation. However, selecting an optimal segmentation architecture remains challenging due to the diversity of network designs and encoder backbones. Therefore, this study presents a systematic benchmarking of five widely used DL segmentation architectures, UNet, LinkNet, Feature Pyramid Networks (FPN), Pyramid Scene Parsing Network (PSPNet), and DeepLabV3+, each combined with nine backbone networks, yielding 45 model variants, including previously unexplored configurations for intracranial artery segmentation in TOF-MRA. Models were trained and cross-validated on four datasets: in-house, CereVessMRA, IXI and ADAM, and evaluated on held-out independent test set. Performance metrics included Intersection over Union (IoU), Dice Similarity Coefficient (DSC), and a Stability Score, combining the coefficient of variation of IoU and DSC to quantify segmentation consistency and reproducibility. Experimental results demonstrated highest DSC score was achieved with UNet–SE-ResNeXt50, LinkNet-SE-ResNeXt50, FPN-DenseNet169, FPN-SENet154. The most stable configurations were LinkNet–EfficientNetB6, LinkNet–SENet154, UNet–DenseNet169, and UNet–EfficientNetB6. Conversely, DeepLabV3+ and PSPNet variants consistently underperformed. These findings provide actionable guidance for selecting backbone–segmentation pairs and highlight trade-offs between accuracy, robustness, and reproducibility for complex intracranial artery TOF-MRA segmentation tasks.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"23 ","pages":"Article 100843"},"PeriodicalIF":4.9,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hybrid machine learning and IoT system for driver fatigue monitoring in connected electric vehicles 用于联网电动汽车驾驶员疲劳监测的混合机器学习和物联网系统

IF 4.9

Machine learning with applications

Pub Date : 2026-01-18 DOI: 10.1016/j.mlwa.2026.100845

Obaida AlHousrya, Aseel Bennagi, Petru A. Cotfas, Daniel T. Cotfas

Driver fatigue remains a critical factor in road accidents, particularly in long duration or cognitively demanding driving scenarios. This study presents a comprehensive, low cost, and real time system for monitoring driver health and electric vehicle status through physiological signal analysis. By integrating heart rate, eye movement, and breathing rate sensors, both simulated and real, this hybrid framework detects signs of fatigue using machine learning classifiers trained on publicly available datasets including OpenDriver, DriveDB, MAUS, YawDD, TinyML, and the Driver Respiration Dataset. The system architecture combines Arduino based hardware, cloud integration via Microsoft Azure, and advanced classification and anomaly detection algorithms such as Random Forest and Isolation Forest. Evaluation across diverse datasets revealed robust fatigue detection capabilities, with OpenDriver achieving 97.6% cross validation accuracy and 95.8% F1-score, while image and respiration-based models complemented the electrocardiogram-based analysis. These results demonstrate the feasibility of affordable, multimodal health monitoring in EVs, offering a scalable and deployable solution for enhancing road safety.

驾驶员疲劳仍然是道路交通事故的一个关键因素，特别是在长时间或认知要求高的驾驶情况下。本研究提出了一种全面、低成本、实时的、通过生理信号分析来监测驾驶员健康和电动汽车状态的系统。通过整合模拟和真实的心率、眼动和呼吸频率传感器，这个混合框架使用公开可用数据集（包括OpenDriver、DriveDB、MAUS、YawDD、TinyML和驾驶员呼吸数据集）训练的机器学习分类器检测疲劳迹象。系统架构结合了基于Arduino的硬件，通过Microsoft Azure进行云集成，以及Random Forest和Isolation Forest等高级分类和异常检测算法。对不同数据集的评估显示，OpenDriver具有强大的疲劳检测能力，交叉验证准确率为97.6%，f1评分为95.8%，而基于图像和呼吸的模型补充了基于心电图的分析。这些结果证明了在电动汽车中进行经济实惠的多模式健康监测的可行性，为提高道路安全提供了可扩展和可部署的解决方案。

{"title":"A hybrid machine learning and IoT system for driver fatigue monitoring in connected electric vehicles","authors":"Obaida AlHousrya, Aseel Bennagi, Petru A. Cotfas, Daniel T. Cotfas","doi":"10.1016/j.mlwa.2026.100845","DOIUrl":"10.1016/j.mlwa.2026.100845","url":null,"abstract":"<div><div>Driver fatigue remains a critical factor in road accidents, particularly in long duration or cognitively demanding driving scenarios. This study presents a comprehensive, low cost, and real time system for monitoring driver health and electric vehicle status through physiological signal analysis. By integrating heart rate, eye movement, and breathing rate sensors, both simulated and real, this hybrid framework detects signs of fatigue using machine learning classifiers trained on publicly available datasets including OpenDriver, DriveDB, MAUS, YawDD, TinyML, and the Driver Respiration Dataset. The system architecture combines Arduino based hardware, cloud integration via Microsoft Azure, and advanced classification and anomaly detection algorithms such as Random Forest and Isolation Forest. Evaluation across diverse datasets revealed robust fatigue detection capabilities, with OpenDriver achieving 97.6% cross validation accuracy and 95.8% F1-score, while image and respiration-based models complemented the electrocardiogram-based analysis. These results demonstrate the feasibility of affordable, multimodal health monitoring in EVs, offering a scalable and deployable solution for enhancing road safety.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"23 ","pages":"Article 100845"},"PeriodicalIF":4.9,"publicationDate":"2026-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel hybrid model of flying geese optimization and attention-LSTM for predicting survival outcomes in clear cell renal cell carcinoma 一种预测透明细胞肾细胞癌生存结果的新的雁优化和注意力- lstm混合模型

IF 4.9

Machine learning with applications

Pub Date : 2026-01-16 DOI: 10.1016/j.mlwa.2026.100846

Cheng-Hong Yang , Tin-Ho Cheung , Yi-Ling Chen , Sin-Hua Moi , Li-Yeh Chuang

Clear Cell Renal Cell Carcinoma (ccRCC) is the most aggressive and metastatic subtype of renal cell carcinoma and also the type with the highest mortality rate. To enhance survival prediction accuracy and facilitate informed clinical decision-making, this study presents a hybrid model that combines the Flying Geese Optimization Algorithm (FGOA) with an attention-based Long Short-Term Memory (A-LSTM) network. The proposed framework is trained and evaluated using data from the Cancer Genome Atlas Kidney Clear Cell Carcinoma (TCGA-KIRC) database. The feature selection process employed seven representative optimization algorithms covering evolutionary, swarm intelligence, and bio-inspired paradigms. The selected features were then analyzed using the attention-based A-LSTM network to predict survival outcomes in patients with ccRCC. Evaluation metrics for model performance included accuracy, precision, recall, and F1 score. The results showed that the FGOA-A-LSTM model performed best, with an accuracy of 80.8%, precision of 81.5%, recall of 86.9%, and F1 score of 84.1%, outperforming the other models. This result also indicates that on imbalanced datasets, the F1 score may be higher than the accuracy. Furthermore, Cox proportional hazards regression analysis showed that survival outcomes were significantly correlated with factors such as gender, tumor stage, previous treatment, and treatment method. This study introduces an innovative FGOA-A-LSTM framework that improves survival prediction in ccRCC. By integrating optimization-driven feature selection with an attention-enhanced deep learning architecture, the work makes a contribution to improving clinical risk assessment.

透明细胞肾细胞癌（ccRCC）是肾细胞癌中最具侵袭性和转移性的亚型，也是死亡率最高的类型。为了提高生存预测的准确性，促进临床决策，本研究提出了一种将雁群优化算法（FGOA）与基于注意的长短期记忆（a - lstm）网络相结合的混合模型。所提出的框架使用来自癌症基因组图谱肾透明细胞癌（TCGA-KIRC）数据库的数据进行训练和评估。特征选择过程采用了七种具有代表性的优化算法，包括进化、群体智能和生物启发范式。然后使用基于注意力的A-LSTM网络分析所选择的特征，以预测ccRCC患者的生存结果。模型性能的评估指标包括准确性、精密度、召回率和F1分数。结果表明，FGOA-A-LSTM模型表现最好，准确率为80.8%，精密度为81.5%，召回率为86.9%，F1得分为84.1%，优于其他模型。这一结果也表明，在不平衡的数据集上，F1得分可能高于准确率。此外，Cox比例风险回归分析显示，生存结局与性别、肿瘤分期、既往治疗、治疗方法等因素显著相关。本研究引入了一种创新的FGOA-A-LSTM框架，可提高ccRCC的生存预测。通过将优化驱动的特征选择与注意力增强的深度学习架构相结合，该工作有助于改善临床风险评估。

{"title":"A novel hybrid model of flying geese optimization and attention-LSTM for predicting survival outcomes in clear cell renal cell carcinoma","authors":"Cheng-Hong Yang , Tin-Ho Cheung , Yi-Ling Chen , Sin-Hua Moi , Li-Yeh Chuang","doi":"10.1016/j.mlwa.2026.100846","DOIUrl":"10.1016/j.mlwa.2026.100846","url":null,"abstract":"<div><div>Clear Cell Renal Cell Carcinoma (ccRCC) is the most aggressive and metastatic subtype of renal cell carcinoma and also the type with the highest mortality rate. To enhance survival prediction accuracy and facilitate informed clinical decision-making, this study presents a hybrid model that combines the Flying Geese Optimization Algorithm (FGOA) with an attention-based Long Short-Term Memory (A-LSTM) network. The proposed framework is trained and evaluated using data from the Cancer Genome Atlas Kidney Clear Cell Carcinoma (TCGA-KIRC) database. The feature selection process employed seven representative optimization algorithms covering evolutionary, swarm intelligence, and bio-inspired paradigms. The selected features were then analyzed using the attention-based A-LSTM network to predict survival outcomes in patients with ccRCC. Evaluation metrics for model performance included accuracy, precision, recall, and F1 score. The results showed that the FGOA-A-LSTM model performed best, with an accuracy of 80.8%, precision of 81.5%, recall of 86.9%, and F1 score of 84.1%, outperforming the other models. This result also indicates that on imbalanced datasets, the F1 score may be higher than the accuracy. Furthermore, Cox proportional hazards regression analysis showed that survival outcomes were significantly correlated with factors such as gender, tumor stage, previous treatment, and treatment method. This study introduces an innovative FGOA-A-LSTM framework that improves survival prediction in ccRCC. By integrating optimization-driven feature selection with an attention-enhanced deep learning architecture, the work makes a contribution to improving clinical risk assessment.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"23 ","pages":"Article 100846"},"PeriodicalIF":4.9,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SK-DGCNN: Human activity recognition from point cloud data with skeleton transformation SK-DGCNN：基于骨架变换的点云数据的人类活动识别

IF 4.9

Machine learning with applications

Pub Date : 2026-01-16 DOI: 10.1016/j.mlwa.2026.100847

Zihan Zhang, Aman Anand, Farhana Zulkernine

Human Activity Recognition (HAR) has become a prominent research topic in artificial intelligence, with applications in surveillance, healthcare, and human–computer interaction. Among various data modalities used for HAR, skeleton and point cloud data offer strong potential due to their privacy-preserving and environment-agnostic properties. However, point cloud-based HAR faces challenges like data sparsity, high computation cost, and a lack of large annotated datasets. In this paper, we propose a novel two-stage framework that first transforms radar-based point cloud data into skeleton data using a Skeletal Dynamic Graph Convolutional Neural Network (SK-DGCNN), and then classifies the estimated skeletons using an efficient Spatial Temporal Graph Convolutional Network++ (ST-GCN++). The SK-DGCNN leverages dynamic edge convolution, attention mechanisms, and a custom loss function that combines Mean Square Error and Kullback–Leibler divergence to preserve the structural integrity of the human pose. Our pipeline achieves state-of-the-art performance on the MMActivity and DGUHA datasets, with Top-1 accuracy of 99.73% and 99.25%, and F1-scores of 99.62% and 99.25%, respectively. The proposed method provides an effective, lightweight, and privacy-conscious solution for real-world HAR applications using radar point cloud data.

人类活动识别（HAR）已成为人工智能领域的一个重要研究课题，在监控、医疗保健和人机交互等领域都有广泛的应用。在用于HAR的各种数据模式中，骨架和点云数据由于其隐私保护和与环境无关的特性而具有强大的潜力。然而，基于点云的HAR面临着数据稀疏性、计算成本高、缺乏大型标注数据集等挑战。在本文中，我们提出了一个新的两阶段框架，首先使用骨骼动态图卷积神经网络（SK-DGCNN）将基于雷达的点云数据转换为骨架数据，然后使用高效的时空图卷积网络（ST-GCN++）对估计的骨架进行分类。SK-DGCNN利用动态边缘卷积、注意机制和结合均方误差和Kullback-Leibler散度的自定义损失函数来保持人体姿势的结构完整性。我们的管道在MMActivity和DGUHA数据集上实现了最先进的性能，Top-1精度分别为99.73%和99.25%，f1分数分别为99.62%和99.25%。所提出的方法为使用雷达点云数据的实际HAR应用提供了一种有效、轻量级和注重隐私的解决方案。

{"title":"SK-DGCNN: Human activity recognition from point cloud data with skeleton transformation","authors":"Zihan Zhang, Aman Anand, Farhana Zulkernine","doi":"10.1016/j.mlwa.2026.100847","DOIUrl":"10.1016/j.mlwa.2026.100847","url":null,"abstract":"<div><div>Human Activity Recognition (HAR) has become a prominent research topic in artificial intelligence, with applications in surveillance, healthcare, and human–computer interaction. Among various data modalities used for HAR, skeleton and point cloud data offer strong potential due to their privacy-preserving and environment-agnostic properties. However, point cloud-based HAR faces challenges like data sparsity, high computation cost, and a lack of large annotated datasets. In this paper, we propose a novel two-stage framework that first transforms radar-based point cloud data into skeleton data using a Skeletal Dynamic Graph Convolutional Neural Network (SK-DGCNN), and then classifies the estimated skeletons using an efficient Spatial Temporal Graph Convolutional Network++ (ST-GCN++). The SK-DGCNN leverages dynamic edge convolution, attention mechanisms, and a custom loss function that combines Mean Square Error and Kullback–Leibler divergence to preserve the structural integrity of the human pose. Our pipeline achieves state-of-the-art performance on the MMActivity and DGUHA datasets, with Top-1 accuracy of 99.73% and 99.25%, and F1-scores of 99.62% and 99.25%, respectively. The proposed method provides an effective, lightweight, and privacy-conscious solution for real-world HAR applications using radar point cloud data.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"23 ","pages":"Article 100847"},"PeriodicalIF":4.9,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-domain convergence of generative models: From biomedical to astronomical applications 生成模型的跨领域收敛：从生物医学到天文学应用

IF 4.9

Machine learning with applications

Pub Date : 2026-01-15 DOI: 10.1016/j.mlwa.2026.100841

Hajer Ghodhbani , Suvendi Rimer , Khmaies Ouahada , Adel M. Alimi

This paper investigates the convergence of generative modeling techniques across diverse image analysis tasks by examining their application in two data-intensive scientific domains: biomedical imaging and astronomy. In these two domains, which tend to be scientifically distinct due to their size and aims, they share common challenges, including noise corruption, limited availability of annotated data, and the demand for high-fidelity image reconstruction. This study provides a critical review of the various variants of generative models, with a particular focus on cross-domain applications. Unlike existing surveys that predominantly focus on a single discipline, this study emphasises the transferability and adaptability of generative models across biomedical and astronomical imaging. The proposed review highlights the potential offered by generative models, particularly Generative Adversarial Networks (GANS), in enhancing data generation, image restoration, and analysis in both biomedical and astronomical studies.

本文通过研究生成建模技术在两个数据密集型科学领域：生物医学成像和天文学中的应用，研究了生成建模技术在不同图像分析任务中的融合。在这两个领域中，由于它们的大小和目标，它们往往在科学上是不同的，它们面临着共同的挑战，包括噪声损坏，注释数据的有限可用性以及对高保真图像重建的需求。本研究对生成模型的各种变体进行了批判性回顾，特别关注跨领域应用。与现有的主要集中在单一学科的调查不同，这项研究强调了生物医学和天文成像生成模型的可转移性和适应性。该综述强调了生成模型，特别是生成对抗网络（GANS）在增强生物医学和天文学研究中的数据生成、图像恢复和分析方面所提供的潜力。

引用次数: 0

Decoding vision transformer variations for image classification: A guide to performance and usability 解码图像分类用视觉变换器的变化：性能和可用性指南

IF 4.9

Machine learning with applications

Pub Date : 2026-01-14 DOI: 10.1016/j.mlwa.2026.100844

João Montrezol , Hugo S. Oliveira , Hélder P. Oliveira

With the rise of Transformers, Vision Transformers (ViTs) have become a new standard in visual recognition. This has led to the development of numerous architectures with diverse designs and applications. This survey identifies 22 key ViT and hybrid CNN–ViT models, along with 5 top Convolutional Neural Network (CNN) models. These were selected based on their new architecture, relevance to benchmarks, and overall impact. The models are organised using a defined taxonomy formed by CNN-based, pure Transformer-based, and hybrid architectures. We analyse their main components, training methods, and computational features, while assessing performance using reported results on standard benchmarks such as ImageNet and CIFAR, along with our training and fine-tuning evaluations on specific imaging datasets. In addition to accuracy, we look at real-world deployment issues by analysing the trade-offs between accuracy and efficiency in embedded, mobile, and clinical settings. The results indicate that modern CNNs are still very competitive in limited-resource environments, while advanced ViT variants perform well after large-scale pretraining, especially in areas with high variability. Hybrid CNN–ViT architectures, on the other hand, tend to offer the best balance between accuracy, data efficiency, and computational cost. This survey establishes a consolidated benchmark and reference framework for understanding the evolution, capabilities, and practical applicability of contemporary vision architectures.

随着变形金刚的兴起，视觉变形金刚（Vision Transformers, ViTs）已经成为视觉识别的新标准。这导致了许多具有不同设计和应用程序的架构的发展。该调查确定了22个关键的ViT和混合CNN - ViT模型，以及5个顶级卷积神经网络（CNN）模型。它们是根据它们的新架构、与基准的相关性以及总体影响来选择的。这些模型使用由基于cnn、纯基于transformer和混合架构形成的定义分类法进行组织。我们分析了它们的主要组成部分、训练方法和计算特征，同时使用ImageNet和CIFAR等标准基准测试报告的结果评估性能，以及我们对特定成像数据集的训练和微调评估。除了准确性之外，我们还通过分析在嵌入式、移动和临床环境中准确性和效率之间的权衡来研究实际部署问题。结果表明，现代cnn在有限资源环境中仍然具有很强的竞争力，而先进的ViT变体在大规模预训练后表现良好，特别是在高变异性的区域。另一方面，混合CNN-ViT架构倾向于在准确性、数据效率和计算成本之间提供最佳平衡。这个调查建立了一个统一的基准和参考框架，用于理解当代视觉体系结构的演变、能力和实际适用性。

{"title":"Decoding vision transformer variations for image classification: A guide to performance and usability","authors":"João Montrezol , Hugo S. Oliveira , Hélder P. Oliveira","doi":"10.1016/j.mlwa.2026.100844","DOIUrl":"10.1016/j.mlwa.2026.100844","url":null,"abstract":"<div><div>With the rise of Transformers, Vision Transformers (ViTs) have become a new standard in visual recognition. This has led to the development of numerous architectures with diverse designs and applications. This survey identifies 22 key ViT and hybrid CNN–ViT models, along with 5 top Convolutional Neural Network (CNN) models. These were selected based on their new architecture, relevance to benchmarks, and overall impact. The models are organised using a defined taxonomy formed by CNN-based, pure Transformer-based, and hybrid architectures. We analyse their main components, training methods, and computational features, while assessing performance using reported results on standard benchmarks such as ImageNet and CIFAR, along with our training and fine-tuning evaluations on specific imaging datasets. In addition to accuracy, we look at real-world deployment issues by analysing the trade-offs between accuracy and efficiency in embedded, mobile, and clinical settings. The results indicate that modern CNNs are still very competitive in limited-resource environments, while advanced ViT variants perform well after large-scale pretraining, especially in areas with high variability. Hybrid CNN–ViT architectures, on the other hand, tend to offer the best balance between accuracy, data efficiency, and computational cost. This survey establishes a consolidated benchmark and reference framework for understanding the evolution, capabilities, and practical applicability of contemporary vision architectures.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"23 ","pages":"Article 100844"},"PeriodicalIF":4.9,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0