Machine learning with applications最新文献

Performance evaluation of Complex-Valued Neural Networks on real and complex-valued classification and reconstruction tasks 复值神经网络在实值和复值分类重构任务上的性能评价

IF 4.9

Machine learning with applications Pub Date : 2025-09-30 DOI: 10.1016/j.mlwa.2025.100742

Mahmood K.M. Almansoori , Miklos Telek

引用次数: 0

Evaluating the impact of model scale and prompting strategies on corruption allegation classification using thai-specialized typhoon2 language models 使用泰国专门的台风2语言模型评估模型规模和提示策略对腐败指控分类的影响

IF 4.9

Machine learning with applications Pub Date : 2025-09-29 DOI: 10.1016/j.mlwa.2025.100743

Patipan Sriphon , Pattrawut Khunwipusit , Bamisaye Mayowa Emmanuel , Issara Sereewatthanawut

{"title":"Evaluating the impact of model scale and prompting strategies on corruption allegation classification using thai-specialized typhoon2 language models","authors":"Patipan Sriphon , Pattrawut Khunwipusit , Bamisaye Mayowa Emmanuel , Issara Sereewatthanawut","doi":"10.1016/j.mlwa.2025.100743","DOIUrl":"10.1016/j.mlwa.2025.100743","url":null,"abstract":"<div><div>Corruption complaint classification is a critical yet resource-intensive task in public sector governance, particularly in low-resource linguistic environments. This study assesses the capacity of Thai-specialized large language models (LLMs) from the Typhoon2 family to automate the classification of corruption complaints submitted to Thailand’s National Anti-Corruption Commission (NACC). Three variants—Typhoon2–3B (base), Typhoon2–3B (fine-tuned), and Typhoon2–7B (base)—were evaluated under zero-shot, one-shot, and two-shot prompting strategies and benchmarked against strong traditional machine learning models (Random Forest, XGBoost) trained on TF-IDF features. Results reaffirm the competitiveness of tree-based classifiers, which delivered consistently high and stable performance. Among the LLMs, the Typhoon2–7B model with two-shot prompting achieved the most balanced performance (Macro F1 = 0.514), highlighting emergent few-shot reasoning capabilities and improved handling of class imbalance. By contrast, fine-tuning the smaller 3B model induced severe overfitting and significant degradation on minority classes. These outcomes emphasize that model scale and prompt design are more reliable drivers of performance than direct fine-tuning in small, imbalanced settings. The study contributes practical guidance for deploying scalable and ethically aligned AI in governance, demonstrating that while traditional models remain robust benchmarks, large-scale prompted LLMs represent a promising complement for future public sector innovation.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100743"},"PeriodicalIF":4.9,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222739","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

Enhancing IDS performance through a comparative analysis of Random Forest, XGBoost, and Deep Neural Networks 通过比较分析随机森林、XGBoost和深度神经网络来增强IDS性能

IF 4.9

Machine learning with applications Pub Date : 2025-09-27 DOI: 10.1016/j.mlwa.2025.100738

Sow Thierno Hamidou, Adda Mehdi

{"title":"Enhancing IDS performance through a comparative analysis of Random Forest, XGBoost, and Deep Neural Networks","authors":"Sow Thierno Hamidou, Adda Mehdi","doi":"10.1016/j.mlwa.2025.100738","DOIUrl":"10.1016/j.mlwa.2025.100738","url":null,"abstract":"<div><div>Intrusion Detection Systems (IDS) face major challenges in network security, notably the need to combine a high detection rate with reliable performance. This reliability is often affected by class imbalances and inadequate hyperparameter optimization. This article addresses the issue of improving the detection rate of IDS by evaluating and comparing three machine learning algorithms: Random Forest (RF), XGBoost, and Deep Neural Networks (DNN), using the NSL-KDD dataset. In our methodology, we integrate SMOTE (Synthetic Minority Oversampling Technique) to tackle the unbalanced nature of the data, ensuring a more balanced representation of the different classes. This approach helps optimize model performance, reduce bias, and enhance robustness. Additionally, hyperparameter optimization is performed using Optuna, ensuring that each algorithm operates at its optimal level. The results show that our model, using the Random Forest algorithm, achieves an accuracy of 99.80%, surpassing the performance of XGBoost and Deep Neural Networks (DNN). This makes our approach a true asset for intrusion detection methods in computer networks.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100738"},"PeriodicalIF":4.9,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222733","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

Auxiliary evaluation of marginal ridge discrepancy in periodontal disease using deep learning on periapical radiographs 根尖周x线片深度学习辅助评估牙周病边缘嵴差异

IF 4.9

Machine learning with applications Pub Date : 2025-09-27 DOI: 10.1016/j.mlwa.2025.100727

Yuan-Jin Lin , Chiung-An Chen , Yi-Cheng Mao , Chin-Hao Liang , Tsung-Yi Chen , Kuo-Chen Li , Shih-Lun Chen , Wei-Chen Tu

{"title":"Auxiliary evaluation of marginal ridge discrepancy in periodontal disease using deep learning on periapical radiographs","authors":"Yuan-Jin Lin , Chiung-An Chen , Yi-Cheng Mao , Chin-Hao Liang , Tsung-Yi Chen , Kuo-Chen Li , Shih-Lun Chen , Wei-Chen Tu","doi":"10.1016/j.mlwa.2025.100727","DOIUrl":"10.1016/j.mlwa.2025.100727","url":null,"abstract":"<div><h3>Background/Objectives</h3><div><strong>:</strong> Marginal Ridge Discrepancy (MRD) is an important early indicator of periodontal disease, often resulting from tooth inclination or alveolar bone loss, leading to uneven interproximal ridge height. Although periapical radiographs commonly observe bone and root structures, image overlap and angle variation often hinder accurate clinical interpretation. This study proposes a deep learning-based system integrating image segmentation and angular evaluation to assist dentists in objectively classifying MRD severity and improving diagnostic efficiency.</div></div><div><h3>Methods</h3><div><strong>:</strong> We adopted a Mask R-CNN model with ResNet-101 as the backbone, incorporating warm-up and learning rate scheduling strategies to ensure stable convergence. Moreover, Mask R-CNN localized the cemento-enamel junction and alveolar crest by overlapping the mask image. We also introduced a novel angular measurement method to quantify the MRD between adjacent ridges and categorize periodontal disease severity.</div></div><div><h3>Results</h3><div><strong>:</strong> ResNet-101 achieved the best segmentation performance among tested backbones with 98.11 % pixel-wise accuracy. Recall scores reached 97.60 % for teeth, 97.24 % for crowns, and 97.53 % for bone structures. The MRD classification model achieved 93.41 % accuracy with a mean angular error of only 0.85°, demonstrating strong clinical reliability.</div></div><div><h3>Conclusions</h3><div><strong>:</strong> The proposed method effectively addresses challenges in evaluating ridge loss on periapical radiographs. Providing accurate and objective assessment enhances early periodontal diagnosis, reduces clinical workload, and supports improved medical quality and treatment planning.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"22 ","pages":"Article 100727"},"PeriodicalIF":4.9,"publicationDate":"2025-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222734","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

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

IF 4.9

Machine learning with applications Pub Date : 2025-09-23 DOI: 10.1016/j.mlwa.2025.100732

Britt van Leeuwen , Sandjai Bhulai , Rob van der Mei

{"title":"Combining style and semantics for robust authorship verification","authors":"Britt van Leeuwen , Sandjai Bhulai , 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}

引用次数: 0

Forecasting political voting: A high dimensional machine learning approach 预测政治投票：一种高维机器学习方法

IF 4.9

Machine learning with applications Pub Date : 2025-09-23 DOI: 10.1016/j.mlwa.2025.100739

Pedro Caiua Campelo Albuquerque , Daniel Oliveira Cajueiro

{"title":"Forecasting political voting: A high dimensional machine learning approach","authors":"Pedro Caiua Campelo Albuquerque , 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}

引用次数: 0

Multi-source plume tracing via multi-agent reinforcement learning under common UAV-faults 基于多智能体强化学习的无人机常见故障多源羽流追踪

IF 4.9

Machine learning with applications Pub Date : 2025-09-18 DOI: 10.1016/j.mlwa.2025.100737

Pedro Antonio Alarcon Granadeno, Theodore Chambers, Jane Cleland-Huang

{"title":"Multi-source plume tracing via multi-agent reinforcement learning under common UAV-faults","authors":"Pedro Antonio Alarcon Granadeno, Theodore Chambers, 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}

引用次数: 0

Conformal validation: A deferral policy using uncertainty quantification with a human-in-the-loop for model validation 保形验证：一种使用不确定性量化和人在环模型验证的延迟策略

IF 4.9

Machine learning with applications Pub Date : 2025-09-15 DOI: 10.1016/j.mlwa.2025.100733

Paul Horton, Alexandru Florea, Brandon Stringfield

{"title":"Conformal validation: A deferral policy using uncertainty quantification with a human-in-the-loop for model validation","authors":"Paul Horton, Alexandru Florea, 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}

引用次数: 0

Structure-aware stable diffusion for traditional architectural decoration design 传统建筑装饰设计的结构意识稳定扩散

IF 4.9

Machine learning with applications Pub Date : 2025-09-12 DOI: 10.1016/j.mlwa.2025.100735

Jianhong Yang , Guoyong Wang

{"title":"Structure-aware stable diffusion for traditional architectural decoration design","authors":"Jianhong Yang , 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}

引用次数: 0

Feature importance analysis of optimized machine learning modeling for predicting customers satisfaction at the United States Airlines 用于预测美国航空公司客户满意度的优化机器学习模型的特征重要性分析

IF 4.9

Machine learning with applications Pub Date : 2025-09-10 DOI: 10.1016/j.mlwa.2025.100734

Hamid Mirzahossein, Soheil Rezashoar

{"title":"Feature importance analysis of optimized machine learning modeling for predicting customers satisfaction at the United States Airlines","authors":"Hamid Mirzahossein, 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 <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}

引用次数: 0