Pub Date : 2025-11-07DOI: 10.1016/j.mlwa.2025.100792
Mohammad Amin Amiri , Saeid Afshari , Ali Soltani
Road traffic injuries continue to pose a significant public health challenge in Australia, with pedestrians representing one of the most vulnerable road user groups. Accurate prediction of injury severity, particularly fatal outcomes, is essential for improving road safety interventions and resource allocation. This study applies advanced machine learning techniques to predict pedestrian crash severity using national hospitalization and mortality data collected from 2011 to 2021. The analysis focuses on addressing class imbalance, a common issue in injury data by evaluating the impact of several data balancing methods, including SMOTE, ADASYN, Random Oversampling (ROS), and Threshold Moving. We implement and compare four supervised learning algorithms: Logistic Regression, Support Vector Machine (SVM), Decision Tree, and XGBoost. Model performance is assessed using F1-score and macro-accuracy, with a focus on the minority (fatality) class. Results show that XGBoost combined with Threshold Moving achieves the highest performance, yielding an F1-score of 72% for fatality classification and a macro-accuracy of 84%. Additionally, feature importance analysis using SHAP values reveals age, gender, road user type, and crash location as key predictors of injury severity. The study highlights the critical role of data balancing strategies in enhancing predictive accuracy for rare but high-impact outcomes. These findings provide actionable insights for transport authorities and policymakers seeking to develop data-driven, targeted safety measures to protect pedestrians and reduce the severity of crash outcomes.
{"title":"Machine learning approaches to traffic accident severity prediction: Addressing class imbalance","authors":"Mohammad Amin Amiri , Saeid Afshari , Ali Soltani","doi":"10.1016/j.mlwa.2025.100792","DOIUrl":"10.1016/j.mlwa.2025.100792","url":null,"abstract":"<div><div>Road traffic injuries continue to pose a significant public health challenge in Australia, with pedestrians representing one of the most vulnerable road user groups. Accurate prediction of injury severity, particularly fatal outcomes, is essential for improving road safety interventions and resource allocation. This study applies advanced machine learning techniques to predict pedestrian crash severity using national hospitalization and mortality data collected from 2011 to 2021. The analysis focuses on addressing class imbalance, a common issue in injury data by evaluating the impact of several data balancing methods, including SMOTE, ADASYN, Random Oversampling (ROS), and Threshold Moving. We implement and compare four supervised learning algorithms: Logistic Regression, Support Vector Machine (SVM), Decision Tree, and XGBoost. Model performance is assessed using F1-score and macro-accuracy, with a focus on the minority (fatality) class. Results show that XGBoost combined with Threshold Moving achieves the highest performance, yielding an F1-score of 72% for fatality classification and a macro-accuracy of 84%. Additionally, feature importance analysis using SHAP values reveals age, gender, road user type, and crash location as key predictors of injury severity. The study highlights the critical role of data balancing strategies in enhancing predictive accuracy for rare but high-impact outcomes. These findings provide actionable insights for transport authorities and policymakers seeking to develop data-driven, targeted safety measures to protect pedestrians and reduce the severity of crash outcomes.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100792"},"PeriodicalIF":4.9,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528029","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}
Pub Date : 2025-11-07DOI: 10.1016/j.mlwa.2025.100790
Trushal Sardhara , Ravi Dadsena , Roland C. Aydin , Ralf-Dieter Hilgers , Leon Horn , Jörg B. Schulz , Kathrin Reetz , Sandro Romanzetti , Imis Dogan
Dentate nucleus (DN) degeneration is a key neuropathological feature in Friedreich’s ataxia (FRDA), and its accurate quantification is critical for understanding disease progression. However, its visualization and volumetry require iron-sensitive imaging techniques and time-consuming segmentation procedures, posing challenges for conventional ML approaches due to small datasets typical of rare diseases. We present a transfer learning–based machine learning pipeline for automated DN segmentation that directly uses standard T2*-weighted Magnetic Resonance Imaging (MRI), which highlights the DN without additional processing, and is designed to perform robustly with limited annotated data. Using 38 manually labeled subjects (18 FRDA, 20 controls), the model was validated via five-fold cross-validation and an independent hold-out test set, achieving Dice scores of 0.81–0.87 and outperforming classical atlas-based methods. Pretraining improved performance by ∼10% in patients and >5% in controls. Applied to 181 longitudinal scans from 33 FRDA patients and 33 controls, the model revealed significantly reduced DN volumes in FRDA, with reductions correlating with disease duration and clinical severity over time. Our approach provides a scalable and reproducible segmentation framework, requiring minimal annotated data and no preprocessing, while demonstrating robust performance across cross-validation and independent testing. Additionally, it enables the first longitudinal volumetric analysis of DN in FRDA using standard T2*-weighted MRI, demonstrating its practical utility for monitoring neurodegenerative changes. Overall, this work illustrates how transfer learning can overcome data scarcity in rare diseases and provides a robust methodology for automated MRI segmentation in both research and clinical applications.
{"title":"Deep learning-based 3D reconstruction of dentate nuclei in Friedreich’s ataxia from T2*weighted MR images","authors":"Trushal Sardhara , Ravi Dadsena , Roland C. Aydin , Ralf-Dieter Hilgers , Leon Horn , Jörg B. Schulz , Kathrin Reetz , Sandro Romanzetti , Imis Dogan","doi":"10.1016/j.mlwa.2025.100790","DOIUrl":"10.1016/j.mlwa.2025.100790","url":null,"abstract":"<div><div>Dentate nucleus (DN) degeneration is a key neuropathological feature in Friedreich’s ataxia (FRDA), and its accurate quantification is critical for understanding disease progression. However, its visualization and volumetry require iron-sensitive imaging techniques and time-consuming segmentation procedures, posing challenges for conventional ML approaches due to small datasets typical of rare diseases. We present a transfer learning–based machine learning pipeline for automated DN segmentation that directly uses standard T2*-weighted Magnetic Resonance Imaging (MRI), which highlights the DN without additional processing, and is designed to perform robustly with limited annotated data. Using 38 manually labeled subjects (18 FRDA, 20 controls), the model was validated via five-fold cross-validation and an independent hold-out test set, achieving Dice scores of 0.81–0.87 and outperforming classical atlas-based methods. Pretraining improved performance by ∼10% in patients and >5% in controls. Applied to 181 longitudinal scans from 33 FRDA patients and 33 controls, the model revealed significantly reduced DN volumes in FRDA, with reductions correlating with disease duration and clinical severity over time. Our approach provides a scalable and reproducible segmentation framework, requiring minimal annotated data and no preprocessing, while demonstrating robust performance across cross-validation and independent testing. Additionally, it enables the first longitudinal volumetric analysis of DN in FRDA using standard T2*-weighted MRI, demonstrating its practical utility for monitoring neurodegenerative changes. Overall, this work illustrates how transfer learning can overcome data scarcity in rare diseases and provides a robust methodology for automated MRI segmentation in both research and clinical applications.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100790"},"PeriodicalIF":4.9,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528533","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}
Pub Date : 2025-11-06DOI: 10.1016/j.mlwa.2025.100786
Movaffaq Kateb , Sahar Safarian
This work demonstrates that modern tree‑based models can effectively model complex, temperature-dependent mechanical responses, including highly nonlinear and even non-monotonic trends, in austenitic stainless steel and highlights limitations of composition‑only empirical models. To ensure robust model evaluation, we employed multiple validation strategies including repeated random train and test partitions and leave-one-out cross-validation. While one might assume that steel grade is fully captured by its composition, local assessments within narrower compositional ranges reveal different feature importance rankings than those observed in the full dataset. Grade-specific (AISI 304, 316, 321 and 347) feature importance analysis offered deeper insights into local alloy behavior and demonstrated the advantage of disaggregated modeling in avoiding misleading conclusions. Clustering and SHAP analyses further revealed a temperature-sensitive role of nitrogen, which strengthens the alloy through interstitial and fine precipitate mechanisms at lower temperatures but loses effectiveness at elevated temperatures due to precipitate coarsening. This highlights how data-driven methods can uncover metallurgically consistent, temperature-dependent strengthening behaviors not captured by simpler models. Our results confirm that temperature governs the mechanical performance of austenitic stainless steels, with other features contributing marginally, particularly for UTS. Additionally, the model achieved a notably high score for elongation, highlighting the critical role of testing temperature in addressing the long-standing challenge of poor elongation predictions in composition-only or composition-processing models. This suggests that low accuracy in previous studies is more likely due to dataset limitations rather than shortcomings of tree-based models.
{"title":"Machine learning-driven predictive modeling of temperature-dependent mechanical properties in austenitic stainless steels","authors":"Movaffaq Kateb , Sahar Safarian","doi":"10.1016/j.mlwa.2025.100786","DOIUrl":"10.1016/j.mlwa.2025.100786","url":null,"abstract":"<div><div>This work demonstrates that modern tree‑based models can effectively model complex, temperature-dependent mechanical responses, including highly nonlinear and even non-monotonic trends, in austenitic stainless steel and highlights limitations of composition‑only empirical models. To ensure robust model evaluation, we employed multiple validation strategies including repeated random train and test partitions and leave-one-out cross-validation. While one might assume that steel grade is fully captured by its composition, local assessments within narrower compositional ranges reveal different feature importance rankings than those observed in the full dataset. Grade-specific (AISI 304, 316, 321 and 347) feature importance analysis offered deeper insights into local alloy behavior and demonstrated the advantage of disaggregated modeling in avoiding misleading conclusions. Clustering and SHAP analyses further revealed a temperature-sensitive role of nitrogen, which strengthens the alloy through interstitial and fine precipitate mechanisms at lower temperatures but loses effectiveness at elevated temperatures due to precipitate coarsening. This highlights how data-driven methods can uncover metallurgically consistent, temperature-dependent strengthening behaviors not captured by simpler models. Our results confirm that temperature governs the mechanical performance of austenitic stainless steels, with other features contributing marginally, particularly for UTS. Additionally, the model achieved a notably high score for elongation, highlighting the critical role of testing temperature in addressing the long-standing challenge of poor elongation predictions in composition-only or composition-processing models. This suggests that low accuracy in previous studies is more likely due to dataset limitations rather than shortcomings of tree-based models.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100786"},"PeriodicalIF":4.9,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528530","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}
Pub Date : 2025-11-05DOI: 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.
{"title":"Production scheduling optimisation using mixed integer programming with machine learning dilution prediction capabilities for underground open stoping operations","authors":"Prosper Chimunhu , Erkan Topal , Mohammad Waqar Ali Asad , Roohollah Shirani Faradonbeh , Ajak Duany Ajak","doi":"10.1016/j.mlwa.2025.100776","DOIUrl":"10.1016/j.mlwa.2025.100776","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100776"},"PeriodicalIF":4.9,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528529","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}
Pub Date : 2025-11-04DOI: 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.
{"title":"Beyond single-run metrics with CP-fuse: A rigorous multi-cohort evaluation of clinico-pathological fusion for improved survival prediction in TCGA","authors":"Juan Duran , Yujing Zou , Martin Vallières , Shirin A. Enger","doi":"10.1016/j.mlwa.2025.100789","DOIUrl":"10.1016/j.mlwa.2025.100789","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100789"},"PeriodicalIF":4.9,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528537","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}
Pub Date : 2025-11-04DOI: 10.1016/j.mlwa.2025.100770
Seyed Pendar Toufighi , Amir Mohammad Khani , Arman Rezasoltani , Iman Ghasemian Sahebi , Jan Vang
Forecasting financial anomalies in emerging markets is critical for informed investment and risk management. This study proposes a novel machine learning framework that integrates an OPTUNA-optimized Isolation Forest algorithm with K-Means clustering to detect and classify stock market anomalies in Iran Khodro, one of Iran’s largest automotive firms. Leveraging daily stock data from 2001 to 2022, the model enhances anomaly detection accuracy by tuning hyperparameters through Bayesian optimization, significantly reducing false positives compared to standard implementations. The K-Means clustering algorithm further segments the detected anomalies into meaningful behavioral categories based on price and trading volume dynamics. Results reveal distinct periods of market disruption aligned with major political and economic events, including sanctions, currency volatility, and the COVID-19 pandemic. This hybrid approach demonstrates a robust, efficient, and interpretable method for forecasting abnormal market behavior in high-volatility, low-transparency environments. The framework holds promise for broader application in forecasting stock anomalies across other emerging financial markets.
{"title":"Forecasting stock market anomalies in emerging markets: An OPTUNA-optimized isolation forest and K-means approach","authors":"Seyed Pendar Toufighi , Amir Mohammad Khani , Arman Rezasoltani , Iman Ghasemian Sahebi , Jan Vang","doi":"10.1016/j.mlwa.2025.100770","DOIUrl":"10.1016/j.mlwa.2025.100770","url":null,"abstract":"<div><div>Forecasting financial anomalies in emerging markets is critical for informed investment and risk management. This study proposes a novel machine learning framework that integrates an OPTUNA-optimized Isolation Forest algorithm with K-Means clustering to detect and classify stock market anomalies in Iran Khodro, one of Iran’s largest automotive firms. Leveraging daily stock data from 2001 to 2022, the model enhances anomaly detection accuracy by tuning hyperparameters through Bayesian optimization, significantly reducing false positives compared to standard implementations. The K-Means clustering algorithm further segments the detected anomalies into meaningful behavioral categories based on price and trading volume dynamics. Results reveal distinct periods of market disruption aligned with major political and economic events, including sanctions, currency volatility, and the COVID-19 pandemic. This hybrid approach demonstrates a robust, efficient, and interpretable method for forecasting abnormal market behavior in high-volatility, low-transparency environments. The framework holds promise for broader application in forecasting stock anomalies across other emerging financial markets.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100770"},"PeriodicalIF":4.9,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528536","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}
Pub Date : 2025-11-04DOI: 10.1016/j.mlwa.2025.100787
Marco Piangerelli , Vincenzo Nucci , Flavio Corradini , Luca Giulioni , Barbara Re
Condition monitoring techniques stand as essential instruments for evaluating the health and performance of machinery and systems, serving as a foundational element of modern engineering. However, many existing techniques, including advanced approaches, are often tailored to specific domains, limiting their flexibility and adaptability. This paper introduces the COndition moNitoring Detection via cORrelation-based norms (CONDOR), a fully unsupervised, system-agnostic, and multiscale method that leverages matrix norms and correlation matrices derived from time series data recorded by sensors during machine operation. Designed for real-time application, the approach is particularly effective in manufacturing environments characterized by cyclic processes, where consistent inputs yield predictable behaviors. The methodology was validated on both synthetic and real-world datasets, successfully identifying operational patterns that align with common manufacturing system behaviors. Importantly, patterns identified in synthetic data were consistently detected in real-world scenarios, underscoring CONDOR’s robustness and reliability. Comparisons with state-of-the-art algorithms further highlight its superior ability to detect patterns and establish stable clusters, making it a promising tool for condition monitoring in diverse industrial contexts.
{"title":"Condition monitoring for pattern recognition in manufacturing","authors":"Marco Piangerelli , Vincenzo Nucci , Flavio Corradini , Luca Giulioni , Barbara Re","doi":"10.1016/j.mlwa.2025.100787","DOIUrl":"10.1016/j.mlwa.2025.100787","url":null,"abstract":"<div><div>Condition monitoring techniques stand as essential instruments for evaluating the health and performance of machinery and systems, serving as a foundational element of modern engineering. However, many existing techniques, including advanced approaches, are often tailored to specific domains, limiting their flexibility and adaptability. This paper introduces the <em>CO</em>ndition mo<em>N</em>itoring <em>D</em>etection via c<em>OR</em>relation-based norms (CONDOR), a fully unsupervised, system-agnostic, and multiscale method that leverages matrix norms and correlation matrices derived from time series data recorded by sensors during machine operation. Designed for real-time application, the approach is particularly effective in manufacturing environments characterized by cyclic processes, where consistent inputs yield predictable behaviors. The methodology was validated on both synthetic and real-world datasets, successfully identifying operational patterns that align with common manufacturing system behaviors. Importantly, patterns identified in synthetic data were consistently detected in real-world scenarios, underscoring CONDOR’s robustness and reliability. Comparisons with state-of-the-art algorithms further highlight its superior ability to detect patterns and establish stable clusters, making it a promising tool for condition monitoring in diverse industrial contexts.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100787"},"PeriodicalIF":4.9,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528033","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}
Semi-supervised methods based on non-negative matrix factorisation have emerged as a popular approach for clustering. However, the pressing challenge of capturing complex non-linear relationships within multi-view data is seldom considered in the semi-supervised context.
This study introduces a fundamentally novel framework: Label Propagation Assisted Soft-constrained Deep Non-negative Matrix Factorisation for Semi-supervised Multi-view Clustering (LapSDNMF).
LapSDNMF innovatively integrates deep hierarchical modelling with label propagation and soft constraint to jointly exploit the non-linear representation learning and extract accurate latent features from limited labelled data. By embedding a predictive membership matrix as a soft constraint, it enables similarly labelled samples to be projected into shared regions, better reflecting real-world data structures. The incorporation of graph-based regularisation within the deep architecture facilitates effective label propagation while preserving the manifold structure at each layer. LapSDNMF unifies deep learning and graph-theoretic techniques within a coherent optimisation framework. We also develop a novel, efficient algorithm based on multiplicative update rules to solve the resulting optimisation problem.
LapSDNMF significantly outperforms state-of-the-art multi-view clustering methods across five diverse real-world datasets. Specifically, it achieves improvements in F-score of 10.2%, 7.2%, 8.8%, 1.4%, and 6.1% on the Yale, Reuters-MinMax, Caltech7, 3-Sources, and Caltech20 datasets, respectively, compared with the best-performing baseline method.
{"title":"LapSDNMF: Label propagation assisted soft-constrained deep non-negative matrix factorisation for semi-supervised multi-view clustering","authors":"Sohan Dinusha Liyana Gunawardena, Khanh Luong, Thirunavukarasu Balasubramaniam, Richi Nayak","doi":"10.1016/j.mlwa.2025.100783","DOIUrl":"10.1016/j.mlwa.2025.100783","url":null,"abstract":"<div><div>Semi-supervised methods based on non-negative matrix factorisation have emerged as a popular approach for clustering. However, the pressing challenge of capturing complex non-linear relationships within multi-view data is seldom considered in the semi-supervised context.</div><div>This study introduces a fundamentally novel framework: Label Propagation Assisted Soft-constrained Deep Non-negative Matrix Factorisation for Semi-supervised Multi-view Clustering (LapSDNMF).</div><div>LapSDNMF innovatively integrates deep hierarchical modelling with label propagation and soft constraint to jointly exploit the non-linear representation learning and extract accurate latent features from limited labelled data. By embedding a predictive membership matrix as a soft constraint, it enables similarly labelled samples to be projected into shared regions, better reflecting real-world data structures. The incorporation of graph-based regularisation within the deep architecture facilitates effective label propagation while preserving the manifold structure at each layer. LapSDNMF unifies deep learning and graph-theoretic techniques within a coherent optimisation framework. We also develop a novel, efficient algorithm based on multiplicative update rules to solve the resulting optimisation problem.</div><div>LapSDNMF significantly outperforms state-of-the-art multi-view clustering methods across five diverse real-world datasets. Specifically, it achieves improvements in F-score of 10.2%, 7.2%, 8.8%, 1.4%, and 6.1% on the Yale, Reuters-MinMax, Caltech7, 3-Sources, and Caltech20 datasets, respectively, compared with the best-performing baseline method.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100783"},"PeriodicalIF":4.9,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528535","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}
Pub Date : 2025-11-03DOI: 10.1016/j.mlwa.2025.100782
Luke Connolly , James Garland , Diarmuid O’Gorman , Edmond F. Tobin
Visual inspections of aircraft are a vital part of routine procedures for maintenance personnel in the aviation industry. However, these inspections take up a considerable amount of time to perform and are susceptible to human error. To mitigate this, utilising image classification for detecting defects is proposed, leveraging transfer learning and knowledge distillation within MATLAB to develop an efficient and deployable model. Transfer learning is applied to a ResNet-50 model, adapting it to classify aircraft defects using a curated dataset. This fine-tuned model is then utilised as a teacher in the knowledge distillation process, where a compact SqueezeNet model (the student) learns from both hard and soft labels to replicate its performance while significantly reducing computational demands. This allows for optimising deep-learning models for deployment on smaller hardware, making the student model suitable for use on an Unmanned Aircraft System (UAS) to filter out images that do not contain a defect, reducing workload for ground personnel. The proposed method offers a solution for improving the efficiency and accuracy of defect detection during a general visual inspection in the aviation industry. Targeted defects here are damaged_skin, missing_or_damaged_rivets, and panel_missing alongside a class denoting no_defect. The knowledge-distilled SqueezeNet model achieves 95.37% validation accuracy and 90.72% inference accuracy, with a 96.9% reduction in model size compared to ResNet-50. The teacher model has a size of 85.77 MB, while the student model is significantly smaller at 2.66 MB, making it ideal for deployment on embedded systems with limited resources.
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