Machine learning with applications最新文献

A Configurable Intrinsic Curiosity Module for a Testbed for Developing Intelligent Swarm UAVs 智能蜂群无人机试验台可配置固有好奇心模块

IF 4.9

Machine learning with applications Pub Date : 2025-08-04 DOI: 10.1016/j.mlwa.2025.100714

Jawad Mahmood , Muhammad Adil Raja , John Loane , Fergal McCaffery

{"title":"A Configurable Intrinsic Curiosity Module for a Testbed for Developing Intelligent Swarm UAVs","authors":"Jawad Mahmood , Muhammad Adil Raja , John Loane , Fergal McCaffery","doi":"10.1016/j.mlwa.2025.100714","DOIUrl":"10.1016/j.mlwa.2025.100714","url":null,"abstract":"<div><div>This paper introduces an Intrinsic Curiosity Module (ICM) based Reinforcement Learning (RL) framework for swarm Unmanned Aerial Vehicles (UAVs) target tracking, leveraging the actor–critic architecture to control the roll, pitch, yaw, and throttle motions of UAVs. A key challenge in RL-based UAV coordination is the delayed reward problem, which hinders effective learning in dynamic environments. Existing UAV testbeds rely primarily on extrinsic rewards and lack mechanisms for adaptive exploration and efficient UAV coordination. To address these limitations, we propose a testbed that integrates an ICM with the Asynchronous Advantage Actor-Critic (A3C) algorithm for tracking UAVs. It incorporates the Self-Reflective Curiosity-Weighted (SRCW) hyperparameter tuning mechanism for the ICM, which adaptively modifies hyperparameters based on the ongoing RL agent’s performance. In this testbed, the target UAV is guided by the Advantage Actor-Critic (A2C) model, while a swarm of two tracking UAVs is controlled by using the A3C-ICM approach. The proposed framework facilitates real-time autonomous coordination among UAVs within a simulated environment. This system is developed using the FlightGear flight simulator and the JSBSim Flight Dynamics Model (FDM), which enables dynamic simulations and continuous interaction between UAVs. Experimental results demonstrate that the tracking UAVs can effectively coordinate and maintain precise paths even under complex conditions.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"21 ","pages":"Article 100714"},"PeriodicalIF":4.9,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144766641","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 fine-tuned deep learning model for detecting Japanese beetles in soybeans using unmanned aircraft systems (UAS) and mobile imaging 一个微调的深度学习模型，用于使用无人机系统（UAS）和移动成像检测大豆中的日本甲虫

Machine learning with applications Pub Date : 2025-07-21 DOI: 10.1016/j.mlwa.2025.100711

Ivan Grijalva , H. Braden Adams , Brian McCornack

{"title":"A fine-tuned deep learning model for detecting Japanese beetles in soybeans using unmanned aircraft systems (UAS) and mobile imaging","authors":"Ivan Grijalva , H. Braden Adams , Brian McCornack","doi":"10.1016/j.mlwa.2025.100711","DOIUrl":"10.1016/j.mlwa.2025.100711","url":null,"abstract":"<div><div>Since the early 1900s, the Japanese beetle (<em>Popillia japonica</em>, Newman) has invaded soybean crops in the U.S. It has emerged as a significant economic threat due to its habit of defoliating plants, often leaving them skeletal and reducing yields. The conventional approach for monitoring Japanese beetles uses visual assessments and sweep counts, which are impractical for larger soybean acreages. Furthermore, frequent manual sampling demands labor and time resources that could otherwise be allocated to enhancing soybean production. To address this challenge, we fine-tuned a deep learning model capable of automatically detecting Japanese beetles using images, thereby improving the monitoring process. The YOLOv8s model can detect Japanese beetles 87.90 % of the time on images collected from mobile devices and unmanned aircraft systems (UAS) at certain distances. The model was deployed in a web application as a prototype platform to understand the capabilities of deep learning in pest monitoring. This web application is a server that autonomously analyzes images captured by mobile devices and UAS to detect and count beetles in the soybean canopy. This study aimed to transform the traditional method of pest monitoring in soybean production by transitioning to a digital monitoring system.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"21 ","pages":"Article 100711"},"PeriodicalIF":0.0,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694594","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

Modeling of settlement of shallow-founded rocking structures using explainable physics-guided machine learning 使用可解释的物理引导机器学习对浅基础摇摆结构的沉降进行建模

Machine learning with applications Pub Date : 2025-07-20 DOI: 10.1016/j.mlwa.2025.100702

Sivapalan Gajan, Christopher Kantor

{"title":"Modeling of settlement of shallow-founded rocking structures using explainable physics-guided machine learning","authors":"Sivapalan Gajan, Christopher Kantor","doi":"10.1016/j.mlwa.2025.100702","DOIUrl":"10.1016/j.mlwa.2025.100702","url":null,"abstract":"<div><div>Rocking foundation is an unorthodox seismic design philosophy of structures that enhances the performance of structures by absorbing and dissipating seismic energy into soil. This paper examines the application of physics-guided machine learning (PGML) technique to model the settlement of shallow-founded rocking structures during earthquake loading. An approximate physics-based model (PBM) is derived for rocking-induced total settlement as a function of critical contact area ratio and cumulative rotation of the foundation. The output of the PBM is fed as an additional input feature to machine learning (ML) algorithms to develop PGML models. The performances of PGML models are compared with the performances of purely data-driven ML models, the PBM outputs, and results obtained from an empirical relationship. To shed light on the explainability of ML and PGML models, Shapley Additive Explanations (SHAP values) are used to decipher and interpret the model predictions and their dependency on input features. It is found that PGML models, especially physics-guided gradient boosting and random forest regression, improve the prediction accuracy when compared to their purely data-driven ML counterparts by combining the knowledge extracted from experimental data with the mechanics of the problem considered. SHAP analysis reveals that the PGML model predictions and their dependency on input features are consistent with the existing domain knowledge, and that the inclusion of physics in PGML models help improve the prediction accuracy, especially in cases where other input features fail to capture the combined complex interaction among the variables involved.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"21 ","pages":"Article 100702"},"PeriodicalIF":0.0,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144678937","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

Multi-objective deep learning: Taxonomy and survey of the state of the art 多目标深度学习：分类和现状调查

Machine learning with applications Pub Date : 2025-07-18 DOI: 10.1016/j.mlwa.2025.100700

Sebastian Peitz, Sèdjro Salomon Hotegni

{"title":"Multi-objective deep learning: Taxonomy and survey of the state of the art","authors":"Sebastian Peitz, Sèdjro Salomon Hotegni","doi":"10.1016/j.mlwa.2025.100700","DOIUrl":"10.1016/j.mlwa.2025.100700","url":null,"abstract":"<div><div>Simultaneously considering multiple objectives in machine learning has been a popular approach for several decades, with various benefits for multi-task learning, the consideration of secondary goals such as sparsity, or multicriteria hyperparameter tuning. However – as multi-objective optimization is significantly more costly than single-objective optimization – the recent focus on deep learning architectures poses considerable additional challenges due to the very large number of parameters, strong nonlinearities and stochasticity. On the other hand considering multiple criteria in deep learning presents many benefits, such as the just-mentioned multi-task learning, the consideration of performance versus adversarial robustness, or a more interpretable way for interactively adapting to changing preferences. This survey covers recent advancements in the area of multi-objective deep learning. We introduce a taxonomy of existing methods – based on the type of training algorithm as well as the decision maker’s needs – before listing recent advancements, and also successful applications. All three main learning paradigms supervised learning, unsupervised learning and reinforcement learning are covered, and we also address the recently very popular area of generative modeling. With a focus on the advantages and disadvantages of the existing training algorithms, this survey is formulated from an optimization perspective rather than organizing according to different learning paradigms or application areas.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"21 ","pages":"Article 100700"},"PeriodicalIF":0.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662492","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

Improving neural network training using dynamic learning rate schedule for PINNs and image classification 使用动态学习率计划改进神经网络训练，用于pinn和图像分类

Machine learning with applications Pub Date : 2025-07-18 DOI: 10.1016/j.mlwa.2025.100697

Veerababu Dharanalakota , Ashwin Arvind Raikar , Prasanta Kumar Ghosh

引用次数: 0

A comprehensive overview of remaining useful life prediction: From traditional literature review to scientometric analysis 剩余使用寿命预测的全面概述：从传统文献综述到科学计量分析

Machine learning with applications Pub Date : 2025-07-18 DOI: 10.1016/j.mlwa.2025.100704

Yitong Liu, Jiarui Wen, Guoqiang Wang

{"title":"A comprehensive overview of remaining useful life prediction: From traditional literature review to scientometric analysis","authors":"Yitong Liu, Jiarui Wen, Guoqiang Wang","doi":"10.1016/j.mlwa.2025.100704","DOIUrl":"10.1016/j.mlwa.2025.100704","url":null,"abstract":"<div><div>The increasing complexity of industrial systems has heightened the need for precise Remaining Useful Life (RUL) predictions. This paper provides a comprehensive overview of RUL prediction methods, processes, datasets, and tools, alongside insights from scientometric analysis. We categorize RUL prediction methods into model-based, data-driven, and hybrid methods, detailing key models and their applications. Model-based methods utilize physical failure mechanisms, enhancing interpretability, but with limited adaptability for complex systems. Data-driven methods use machine learning to extensive datasets, boosting adaptability but often at the cost of explainability. Hybrid methods aim to strike a balance between these strengths, offering both accuracy and flexibility across various applications. In addition, we outline the RUL prediction process, from data acquisition to implementation, and introduce the primary datasets used in studies of engines, bearings, and batteries as well as commonly used programming frameworks and open-source libraries. We also performed a scientometric analysis of 3442 articles from the Web of Science database, examining research areas, prominent research groups, and emerging trends. Our findings offer a comprehensive overview of the RUL prediction field, outlining its current trends and highlighting potential directions for future research.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"21 ","pages":"Article 100704"},"PeriodicalIF":0.0,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687081","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

Predictive modelling of graphene-enhanced greases using classical feedback control and quantum kernel regression 基于经典反馈控制和量子核回归的石墨烯增强润滑脂预测模型

Machine learning with applications Pub Date : 2025-07-17 DOI: 10.1016/j.mlwa.2025.100705

Ethan Stefan-Henningsen , Amirkianoosh Kiani

{"title":"Predictive modelling of graphene-enhanced greases using classical feedback control and quantum kernel regression","authors":"Ethan Stefan-Henningsen , Amirkianoosh Kiani","doi":"10.1016/j.mlwa.2025.100705","DOIUrl":"10.1016/j.mlwa.2025.100705","url":null,"abstract":"<div><div>This paper investigates two predictive modeling approaches for estimating the thermal and tribological performance of graphene-enhanced greases, aiming to reduce reliance on protracted endurance tests. Seven grease formulations with varying graphene concentrations (0–4 wt%) were prepared and tested under a uniform load to capture temperature evolution, wear scar area and coefficient of friction. A classical piecewise regression model, augmented by a Linear Quadratic Regulator (LQR), leverages feedback control to correct temperature predictions and subsequently estimate wear using a polynomial fit. This framework demonstrated high accuracy in tracking transient thermal behaviour, maintaining temperature deviations within ±1 °C of measured data. In parallel, a quantum-classical hybrid model employs a fidelity-based quantum kernel with support vector regression. By encoding partial early-cycle temperature measurements (e.g., from 30 to 120s) into a higher-dimensional Hilbert space, the quantum approach captures subtle nonlinearities and yields strong correlations for both final temperature and wear scar area. Moreover, consistent performance on IBM Quantum models with realistically simulated noise underscores the model’s potential for practical industrial implementation. Collectively, these results confirm the viability of advanced computational tools, both classical and quantum, for rapid, data-driven lubricant assessments. They highlight opportunities to optimize graphene content while minimizing costly trial and error testing.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"21 ","pages":"Article 100705"},"PeriodicalIF":0.0,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711964","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

Optimizing travel time reliability with XAI: A Virginia interstate network case using machine learning and meta-heuristics 使用XAI优化旅行时间可靠性：使用机器学习和元启发式的弗吉尼亚州州际网络案例

Machine learning with applications Pub Date : 2025-07-16 DOI: 10.1016/j.mlwa.2025.100709

Navid Khorshidi , Shahriar Afandizadeh Zargari , Soheil Rezashoar , Hamid Mirzahossein

{"title":"Optimizing travel time reliability with XAI: A Virginia interstate network case using machine learning and meta-heuristics","authors":"Navid Khorshidi , Shahriar Afandizadeh Zargari , Soheil Rezashoar , Hamid Mirzahossein","doi":"10.1016/j.mlwa.2025.100709","DOIUrl":"10.1016/j.mlwa.2025.100709","url":null,"abstract":"<div><div>This paper applies machine learning models to predict travel time reliability in transportation networks, using XGBoost, LightGBM, and CatBoost optimized with seven metaheuristic algorithms. The models were fine-tuned with a four-year dataset (2014–2017) covering 59 interstate sections in Virginia. Key features Link Length, AADT/mile/lane, Total Rate, and PRCP/1000 were identified as influential factors for travel time index prediction. Results revealed that XGBoost optimized with Grey Wolf Optimizer (GWO) achieved the highest accuracy at 92 %, surpassing the base model. LightGBM-GWO and CatBoost-GWO also demonstrated improvements, scoring up to 89 %. GWO outperformed other optimization methods, delivering superior accuracy with fewer control parameters. Feature importance analysis highlighted Link Length and AADT/Lane.mile as critical predictors. This research enhances travel time reliability prediction, providing insights for transportation planning and management. Future work includes exploring multi-objective optimization and integrating additional features to refine model accuracy further.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"21 ","pages":"Article 100709"},"PeriodicalIF":0.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711962","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

Predicting flow-blurring droplet size using neural networks and Bayesian optimization: A data-driven approach 利用神经网络和贝叶斯优化预测流模糊液滴大小：一种数据驱动的方法

Machine learning with applications Pub Date : 2025-07-16 DOI: 10.1016/j.mlwa.2025.100708

S. Amirreza S. Madani , Erfan Vaezi , Seyed Sorosh Mirfasihi , Amir Keshmiri

{"title":"Predicting flow-blurring droplet size using neural networks and Bayesian optimization: A data-driven approach","authors":"S. Amirreza S. Madani , Erfan Vaezi , Seyed Sorosh Mirfasihi , Amir Keshmiri","doi":"10.1016/j.mlwa.2025.100708","DOIUrl":"10.1016/j.mlwa.2025.100708","url":null,"abstract":"<div><div>Flow-blurring injectors, known for producing fine sprays in twin-fluid systems, are essential for applications involving high-viscosity fuels, such as biofuels. This study presents a data-driven approach using neural networks and Bayesian optimization to predict the Sauter Mean Diameter (SMD) of flow-blurring sprays. A dataset from the experimental literature was curated and pre-processed, with critical dimensionless parameters – including the Reynolds number, Weber number, injector’s aspect ratio, and air-to-liquid mass flow rate – used to train multi-layer perceptron (MLP) models. Through Bayesian optimization, hyperparameters such as neuron count, learning rate, and regularization were fine-tuned to enhance model accuracy and avoid overfitting. The optimized models achieved high predictive accuracy, with regression scores exceeding 97% and minimal mean-squared error (MSE), demonstrating that Bayesian-optimized neural networks can significantly reduce reliance on costly experimental and numerical methods. This approach provides a fast, accurate solution for spray modeling, offering a scalable method for optimizing injector designs in fuel systems, particularly for alternative fuel applications.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"21 ","pages":"Article 100708"},"PeriodicalIF":0.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687047","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

Dual radar vision: A feature fusion approach for advanced object detection in IoT radar networks 双雷达视觉：物联网雷达网络中高级目标检测的特征融合方法

Machine learning with applications Pub Date : 2025-07-16 DOI: 10.1016/j.mlwa.2025.100703

Philipp Reitz, Tobias Veihelmann, Norman Franchi, Maximilian Lübke

引用次数: 0