Machine learning with applications最新文献

{"title":"Combining style and semantics for robust authorship verification","authors":"Britt van Leeuwen ,&nbsp;Sandjai Bhulai ,&nbsp;Rob van der Mei","doi":"10.1016/j.mlwa.2025.100732","DOIUrl":"10.1016/j.mlwa.2025.100732","url":null,"abstract":"<div><div>Authorship Verification is a key task in Natural Language Processing, essential for applications like plagiarism detection and content authentication. This paper analyzes the use of deep learning models for Authorship Verification, focusing on combining semantic and style features to enhance model performance. We propose three models: the Feature Interaction Network, Pairwise Concatenation Network, and Siamese Network, which aim to determine if two texts are written by the same author. Each model uses RoBERTa embeddings to capture semantic content and incorporates style features such as sentence length, word frequency, and punctuation to differentiate authors based on writing style.</div><div>Our results confirm that incorporating style features consistently improves model performance, with the extent of improvement varying by architecture. This demonstrates the value of combining semantic and stylistic information for Authorship Verification. While limitations such as RoBERTa’s fixed input length and the use of predefined style features exist, they do not hinder model effectiveness and point to clear opportunities for future enhancement through extended input handling and dynamic style feature extraction.</div><div>In contrast to prior studies such as Bevendorff et al., (2020) and Kestemont, et al., (2022), which relied on balanced and homogeneous datasets with consistent topics and well-formed language, our work evaluates models on a more challenging, imbalanced, and stylistically diverse dataset, better reflecting real-world Authorship Verification conditions. Despite the increased difficulty, our models achieve competitive results, underscoring their robustness and practical applicability.</div><div>These findings support the value of combining semantic and style features for real-world Authorship Verification.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100732"},"PeriodicalIF":4.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222738","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}

{"title":"Enhanced prediction of karst spring discharge using a hybrid LSTM-XGBoost model optimized with grid search","authors":"Xiaomei Liu","doi":"10.1016/j.mlwa.2025.100740","DOIUrl":"10.1016/j.mlwa.2025.100740","url":null,"abstract":"<div><div>Globally, intensifying droughts taxed water supplies, particularly in karst areas where it is difficult to predict spring discharge due to complex hydrology. Data-driven models represent a viable alternative, with the significance of karst aquifers to freshwater production. To enhance the accuracy of spring discharge prediction, this study introduces a new LSTM-XGBoost hybrid model for more accurate karst spring discharge prediction in Chaharmahal Bakhtiari Province, Iran. The hybrid model exploits the benefits of LSTM in capturing temporal dependency and the strength of XGBoost in modeling nonlinear relationships, and Grid Search is utilized for tuning hyperparameters. The performance of the LSTM-XGBoost model is compared with the optimized ML models. The study utilizes a dataset of 3,266 day, month, and spring discharge records of the Dehghara Springs. The results depict the excellence of the suggested LSTM-XGBoost hybrid model with the highest test R² = 0.8798, Explained Variance (EV) = 0.8857, and the lowest error metrics (MAE = 0.3355, RMSE = 0.5795, MAPE = 21.84%). The hybrid model outperforms both the baseline traditional and Deep Learning (DL). Feature importance analysis reveals that seasonal factors, particularly the month with an importance score of 0.919, have a significantly greater impact on spring discharge than daily variations. The proposed LSTM-XGBoost hybrid model provides a reliable and accurate tool for karst spring discharge prediction, offering valuable insights for water resource management in regions affected by climate change and increasing water demand.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100740"},"PeriodicalIF":4.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268143","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}

{"title":"Forecasting political voting: A high dimensional machine learning approach","authors":"Pedro Caiua Campelo Albuquerque ,&nbsp;Daniel Oliveira Cajueiro","doi":"10.1016/j.mlwa.2025.100739","DOIUrl":"10.1016/j.mlwa.2025.100739","url":null,"abstract":"<div><div>We present a novel machine learning approach to predict voting patterns in Brazil’s Chamber of Deputies. Using a high-dimensional dataset and a time-series methodology, our models aim to accurately forecast legislative decisions. Unlike prior studies that often focus on single ideological dimensions, our approach integrates a broad feature set, including party guidelines, proposition characteristics, and deputy voting history, to improve predictive power. We train time-series models for each legislature, comparing ensembles like Random Forests and Gradient Boosting, which are validated using three-fold chronological splits to ensure temporal integrity. Our analysis highlights the significant influence of party guidelines and pork-barrel politics on voting behavior. Additionally, we identify key predictors, including the theme and source of the legislative proposition, as well as the deputies’ voting history. This work demonstrates the feasibility of accurately forecasting legislative votes, offering a valuable tool for stakeholders to anticipate legislative outcomes and enhancing the transparency of the political process.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100739"},"PeriodicalIF":4.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222735","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}

{"title":"Multi-source plume tracing via multi-agent reinforcement learning under common UAV-faults","authors":"Pedro Antonio Alarcon Granadeno,&nbsp;Theodore Chambers,&nbsp;Jane Cleland-Huang","doi":"10.1016/j.mlwa.2025.100737","DOIUrl":"10.1016/j.mlwa.2025.100737","url":null,"abstract":"<div><div>Hazardous airborne gas releases from accidents, leaks, or wildfires require rapid localization of emission sources under uncertain and turbulent conditions. Traditional gradient-based or biologically inspired strategies struggle in multi-source environments where odor cues are intermittent, aliased, and partially observed. We address this challenge by formulating multi-source plume tracing in three-dimensional fields as a cooperative partially observable Markov game. To solve it, we introduce an Action-Specific Double Deep Recurrent Q-Network (ADDRQN) that conditions on action–observation pairs to improve latent-state inference, and integrates teammate information through a permutation-invariant set encoder. Training follows a randomized centralized-training and decentralized-execution regime with host randomization, team-size variation, and noise injection. This yields a policy that is robust to agent failures (hardware malfunction, battery depletion, etc.), resilient to intermittent communication blackouts, and tolerant of sensor noise. Empirical evaluation in simulated Gaussian plume environments shows that ADDRQN achieves higher success rates and shorter localization times than non-action baselines, maintains strong performance under mid-mission disruptions, and scales predictably with team size.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100737"},"PeriodicalIF":4.9,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121157","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}

{"title":"Conformal validation: A deferral policy using uncertainty quantification with a human-in-the-loop for model validation","authors":"Paul Horton,&nbsp;Alexandru Florea,&nbsp;Brandon Stringfield","doi":"10.1016/j.mlwa.2025.100733","DOIUrl":"10.1016/j.mlwa.2025.100733","url":null,"abstract":"<div><div>Validating performance is a key challenge facing the adoption of machine learning models in high risk applications. Current validation methods assess performance marginally over the entire testing dataset, which can fail to identify regions in the distribution with insufficient performance. In this paper, we propose Conformal Validation, a systems-based approach with a calibrated form of uncertainty quantification using a conformal prediction framework as a part of the validation process to reduce performance gaps. Specifically, the policy defers a subset of observations for which the predictive model is most uncertain and provides a human with informative prediction sets to make the ancillary decision. We evaluate this policy on an image classification task where images are distorted with varying levels of gaussian blur for a quantifiable measure of added difficulty. The model is compared to human performance on the most difficult observations, i.e., those where the model is most uncertain, to simulate the scenario when a human is the alternative decision-maker. We evaluate performance on three arms: the model independently, humans with access to a set of classes the model is most confident in, and humans independently. The deferral policy is simple to understand, applicable to any predictive model, and easy to implement while, in this case, keeping humans in the loop for improved trustworthiness. Conformal Validation incorporates a risk assessment that is conditioned on the prediction set length and can be tuned to the needs of the application.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100733"},"PeriodicalIF":4.9,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145108028","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}

{"title":"Structure-aware stable diffusion for traditional architectural decoration design","authors":"Jianhong Yang ,&nbsp;Guoyong Wang","doi":"10.1016/j.mlwa.2025.100735","DOIUrl":"10.1016/j.mlwa.2025.100735","url":null,"abstract":"<div><div>The intelligent generation of traditional architectural styles faces significant challenges in structural integrity and style consistency. While existing methods can generate numerous realistic images, they lack a deep understanding of structural elements in traditional architectural decorative design. This paper proposes a Structure-aware Stable Diffusion (SSD) model, which enhances the model's comprehension of architectural features through three key innovations. First, we design a structure-aware feature injection module that adaptively fuses extracted architectural structural information with original features during the U-net upsampling phase, enhancing the model's understanding of geometric structures. Second, we introduce a dual-path text enhancement strategy that combines structural descriptions with original descriptions to provide richer textual guidance signals for the generation process. Finally, we design a progressive injection strategy that dynamically controls the injection intensity of structural information through cosine scheduling, ultimately achieving effective internalization of structural knowledge. Experimental results show that compared to existing methods, our model effectively improves both the diversity of generated traditional architectural decorations and the rationality of their structures, thus providing an effective new technical approach for traditional architectural decorative design.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100735"},"PeriodicalIF":4.9,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145108027","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}

{"title":"Feature importance analysis of optimized machine learning modeling for predicting customers satisfaction at the United States Airlines","authors":"Hamid Mirzahossein,&nbsp;Soheil Rezashoar","doi":"10.1016/j.mlwa.2025.100734","DOIUrl":"10.1016/j.mlwa.2025.100734","url":null,"abstract":"<div><div>Customer experience is crucial in the airline industry, as understanding passenger satisfaction helps airlines improve service quality. This study evaluates hyperparameter optimization and feature interpretability in machine learning models for predicting airline passenger satisfaction. Support Vector Machine (SVM) and Multilayer Perceptron (MLP) models were tested for binary classification, labeling passengers as ‘Satisfied’ or ‘Neutral or Dissatisfied’ using a Kaggle dataset with ∼104,000 training and ∼26,000 test records. Hyperparameter tuning used grid search with 10-fold cross-validation. For SVM, the optimal setup included the RBF kernel, <em>C</em> = 10, and gamma = ‘auto’, achieving a mean score of 0.9606. For MLP, the best configuration used no regularization, \"he\" initialization, ReLU activation, 30 epochs, batch size of 32, two hidden layers with 32 neurons each, and a learning rate of 0.001, yielding a mean score of 0.9556. Performance metrics included accuracy, precision, recall, and F1-Score, with SVM achieving a test accuracy of 0.96, precision of 0.97, and F1-Score of 0.95, slightly outperforming MLP by &lt;1 %, though MLP was faster at 0.3 s versus SVM’s 18 s. Both models surpassed baseline models and prior studies, benefiting from robust preprocessing and a large dataset. Permutation importance analysis identified Type of Travel, Inflight Wi-Fi Service, Customer Type, and Online Boarding as key predictors, emphasizing passenger needs for digital connectivity and personalized services. These insights guide airlines to prioritize reliable Wi-Fi and efficient online boarding to enhance satisfaction, loyalty, and competitive positioning.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100734"},"PeriodicalIF":4.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145108030","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}

{"title":"Integrating deep learning and econometrics for stock price prediction: A comprehensive comparison of LSTM, transformers, and traditional time series models","authors":"Eyas Gaffar A. Osman,&nbsp;Faisal A. Otaibi","doi":"10.1016/j.mlwa.2025.100730","DOIUrl":"10.1016/j.mlwa.2025.100730","url":null,"abstract":"<div><div>This study presents a comprehensive empirical comparison between state-of-the-art deep learning models including Long Short-Term Memory (LSTM) networks, Transformer architectures, and traditional econometric models (ARIMA and VAR) for stock price prediction, with particular focus on performance during the COVID-19 pandemic crisis. Using daily S&amp;P 500 data from 2015 to 2020, we rigorously evaluate model performance across multiple metrics including Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Our findings demonstrate that while Transformer models achieve the best overall performance with an RMSE of 41.87 and directional accuracy of 69.1 %, LSTM networks provide an optimal balance between performance (RMSE: 43.25) and computational efficiency. Both deep learning approaches significantly outperform traditional econometric methods, with LSTM achieving a 53.3 % reduction in RMSE compared to ARIMA models. During the COVID-19 crisis period, deep learning models demonstrated exceptional robustness, with Transformers showing only 45 % performance degradation compared to over 100 % degradation in traditional models. Through comprehensive attention analysis, we provide insights into model interpretability, revealing adaptive behavior across market regimes. The study contributes to the growing literature on artificial intelligence applications in finance by providing rigorous empirical evidence for the superiority of modern deep learning approaches, while addressing the critical need for comparison with cutting-edge Transformer architectures that have revolutionized machine learning in recent years.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100730"},"PeriodicalIF":4.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222736","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}

{"title":"Implementation of machine learning technologies in construction maintenance: A strategic analysis","authors":"Assane Lo ,&nbsp;Aysha Alshehhi","doi":"10.1016/j.mlwa.2025.100731","DOIUrl":"10.1016/j.mlwa.2025.100731","url":null,"abstract":"<div><div>Current predictive maintenance systems in construction rely on static machine learning approaches that fail to adapt to evolving operational environments, achieving only 3%–7% performance improvements over individual models and suffering 15%–25% performance degradation when transferred across domains. This research develops and validates an Adaptive Ensemble Framework that dynamically optimizes algorithm selection through real-time data assessment and performance feedback.</div><div>The framework’s meta-learning architecture continuously adapts ensemble weights using data complexity measures, temporal pattern analysis, and uncertainty quantification metrics. Unlike static approaches, the system integrates scikit-learn and TensorFlow models through dynamic optimization algorithms that respond to changing conditions without manual reconfiguration. The framework provides uncertainty-aware predictions with confidence intervals essential for safety-critical construction decisions.</div><div>Comprehensive evaluation across four industries using 50,000+ maintenance records from major construction firms demonstrates substantial improvements. The adaptive ensemble achieves F1-score of 0.934 in construction delay prediction, representing 15.3% improvement over individual models and 8.7% enhancement over static ensembles. Cross-industry validation reveals successful knowledge transfer with minimal performance degradation (<span><math><mo>&lt;</mo></math></span>5%).</div><div>This research contributes three scholarly advances: (i) the first real-time adaptive ensemble framework eliminating manual hyperparameter tuning, (ii) uncertainty quantification mechanisms for safety-critical applications, and (iii) robust cross-industry transferability through systematic domain adaptation. The framework extends beyond construction to manufacturing, energy, and transportation sectors, demonstrating computational efficiency with sub-100ms latency and linear scaling characteristics. These contributions establish new benchmarks for adaptive machine learning in industrial predictive maintenance.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100731"},"PeriodicalIF":4.9,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145061577","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}

{"title":"Machine-learning based Li-Ion Cell state prediction using Impedance spectroscopy","authors":"Carl Philipp Klemm ,&nbsp;Till Frömling","doi":"10.1016/j.mlwa.2025.100729","DOIUrl":"10.1016/j.mlwa.2025.100729","url":null,"abstract":"<div><div>Accurate and reliable monitoring of battery state parameters is crucial for ensuring optimal battery performance, safety, and lifetime. Existing methods have limitations, such as requiring modeling of each degradation mechanism involved or relying on direct measurement techniques that impose restrictions on field studies or end-user use. In this paper, we propose a machine learning-based approach that combines the strengths of electrochemical impedance spectroscopy (EIS) and machine learning algorithms to predict battery state parameters. We have developed an efficient prediction system that can learn from EIS data and accurately predict battery state parameters. Our approach is trained on an open dataset comprising of over 30,000 spectra, generated using an automated measurement technique that outperforms current machine learning-based models, particularly in terms of generalization across different cells and measurement setups.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100729"},"PeriodicalIF":4.9,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145108029","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}