Machine learning with applications最新文献

Document Layout Error Rate (DLER) metric to evaluate image segmentation methods 评估图像分割方法的文档布局错误率 (DLER) 指标

Machine learning with applications Pub Date : 2024-11-19 DOI: 10.1016/j.mlwa.2024.100606

Ari Vesalainen , Mikko Tolonen , Laura Ruotsalainen

{"title":"Document Layout Error Rate (DLER) metric to evaluate image segmentation methods","authors":"Ari Vesalainen , Mikko Tolonen , Laura Ruotsalainen","doi":"10.1016/j.mlwa.2024.100606","DOIUrl":"10.1016/j.mlwa.2024.100606","url":null,"abstract":"<div><div>Scholarly editions play a crucial role in humanities research, particularly in the study of literature and historical documents. The primary objective of these editions is to reconstruct the original text or provide insights into the author’s intentions. Traditionally, crafting a critical edition required a lifetime of dedication. However, thanks to recent advancements in deep learning and computer vision, modern text recognition tools can now be used to expedite this process. A key part of these tools is document layout analysis (DLA), where image segmentation methods are used to detect different text elements. Most existing DLA solutions have focused on evaluating the accuracy of these methods, often neglecting to study the practical consequences of method selection. In this study, we have developed a new metric, the Document Layout Error Rate (DLER), which evaluates the performance of fine-grained DLA methods within the overall pipeline. This metric helps identify the method with the lowest error rate, thereby minimizing the manual effort required for corrections. We applied this evaluation method to assess four different methods and their efficacy for the DLA task in the context of David Hume’s <em>History of England</em>.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"18 ","pages":"Article 100606"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Supervised machine learning for microbiomics: Bridging the gap between current and best practices 用于微生物组学的有监督机器学习：缩小当前实践与最佳实践之间的差距

Machine learning with applications Pub Date : 2024-11-14 DOI: 10.1016/j.mlwa.2024.100607

Natasha Katherine Dudek , Mariami Chakhvadze , Saba Kobakhidze , Omar Kantidze , Yuriy Gankin

{"title":"Supervised machine learning for microbiomics: Bridging the gap between current and best practices","authors":"Natasha Katherine Dudek , Mariami Chakhvadze , Saba Kobakhidze , Omar Kantidze , Yuriy Gankin","doi":"10.1016/j.mlwa.2024.100607","DOIUrl":"10.1016/j.mlwa.2024.100607","url":null,"abstract":"<div><div>Machine learning (ML) is poised to drive innovations in clinical microbiomics, such as in disease diagnostics and prognostics. However, the successful implementation of ML in these domains necessitates the development of reproducible, interpretable models that meet the rigorous performance standards set by regulatory agencies. This study aims to identify key areas in need of improvement in current ML practices within microbiomics, with a focus on bridging the gap between existing methodologies and the requirements for clinical application. To do so, we analyze 100 peer-reviewed articles from 2021 to 2022. Within this corpus, datasets have a median size of 161.5 samples, with over one-third containing fewer than 100 samples, signaling a high potential for overfitting. Limited demographic data further raises concerns about generalizability and fairness, with 24% of studies omitting participants' country of residence, and attributes like race/ethnicity, education, and income rarely reported (11%, 2%, and 0%, respectively). Methodological issues are also common; for instance, for 86% of studies we could not confidently rule out test set omission and data leakage, suggesting a strong potential for inflated performance estimates across the literature. Reproducibility is a concern, with 78% of studies abstaining from sharing their ML code publicly. Based on this analysis, we provide guidance to avoid common pitfalls that can hinder model performance, generalizability, and trustworthiness. An interactive tutorial on applying ML to microbiomics data accompanies the discussion, to help establish and reinforce best practices within the community.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"18 ","pages":"Article 100607"},"PeriodicalIF":0.0,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Playing with words: Comparing the vocabulary and lexical diversity of ChatGPT and humans 玩文字游戏比较 ChatGPT 和人类的词汇量和词汇多样性

Machine learning with applications Pub Date : 2024-11-12 DOI: 10.1016/j.mlwa.2024.100602

Pedro Reviriego , Javier Conde , Elena Merino-Gómez , Gonzalo Martínez , José Alberto Hernández

{"title":"Playing with words: Comparing the vocabulary and lexical diversity of ChatGPT and humans","authors":"Pedro Reviriego , Javier Conde , Elena Merino-Gómez , Gonzalo Martínez , José Alberto Hernández","doi":"10.1016/j.mlwa.2024.100602","DOIUrl":"10.1016/j.mlwa.2024.100602","url":null,"abstract":"<div><div>The introduction of Artificial Intelligence (AI) generative language models such as GPT (Generative Pre-trained Transformer) and conversational tools such as ChatGPT has triggered a revolution that can transform how text is generated. This has many implications, for example, as AI-generated text becomes a significant fraction of the text, would this affect the language capabilities of readers and also the training of newer AI tools? Would it affect the evolution of languages? Focusing on one specific aspect of the language: words; will the use of tools such as ChatGPT increase or reduce the vocabulary used or the lexical diversity? This has implications for words, as those not included in AI-generated content will tend to be less and less popular and may eventually be lost. In this work, we perform an initial comparison of the vocabulary and lexical diversity of ChatGPT and humans when performing the same tasks. In more detail, two datasets containing the answers to different types of questions answered by ChatGPT and humans, and a third dataset in which ChatGPT paraphrases sentences and questions are used. The analysis shows that ChatGPT-3.5 tends to use fewer distinct words and lower diversity than humans while ChatGPT-4 has a similar lexical diversity as humans and in some cases even larger. These results are very preliminary and additional datasets and ChatGPT configurations have to be evaluated to extract more general conclusions. Therefore, further research is needed to understand how the use of ChatGPT and more broadly generative AI tools will affect the vocabulary and lexical diversity in different types of text and languages.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"18 ","pages":"Article 100602"},"PeriodicalIF":0.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A survey on knowledge distillation: Recent advancements 知识提炼调查：最新进展

Machine learning with applications Pub Date : 2024-11-10 DOI: 10.1016/j.mlwa.2024.100605

Amir Moslemi , Anna Briskina , Zubeka Dang , Jason Li

{"title":"A survey on knowledge distillation: Recent advancements","authors":"Amir Moslemi , Anna Briskina , Zubeka Dang , Jason Li","doi":"10.1016/j.mlwa.2024.100605","DOIUrl":"10.1016/j.mlwa.2024.100605","url":null,"abstract":"<div><div>Deep learning has achieved notable success across academia, medicine, and industry. Its ability to identify complex patterns in large-scale data and to manage millions of parameters has made it highly advantageous. However, deploying deep learning models presents a significant challenge due to their high computational demands. Knowledge distillation (KD) has emerged as a key technique for model compression and efficient knowledge transfer, enabling the deployment of deep learning models on resource-limited devices without compromising performance. This survey examines recent advancements in KD, highlighting key innovations in architectures, training paradigms, and application domains. We categorize contemporary KD methods into traditional approaches, such as response-based, feature-based, and relation-based knowledge distillation, and novel advanced paradigms, including self-distillation, cross-modal distillation, and adversarial distillation strategies. Additionally, we discuss emerging challenges, particularly in the context of distillation under limited data scenarios, privacy-preserving KD, and the interplay with other model compression techniques like quantization. Our survey also explores applications across computer vision, natural language processing, and multimodal tasks, where KD has driven performance improvements and enhanced model compression. This review aims to provide researchers and practitioners with a comprehensive understanding of the state-of-the-art in knowledge distillation, bridging foundational concepts with the latest methodologies and practical implications.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"18 ","pages":"Article 100605"},"PeriodicalIF":0.0,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142698990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Texas rural land market integration: A causal analysis using machine learning applications 得克萨斯州农村土地市场一体化：利用机器学习应用进行因果分析

Machine learning with applications Pub Date : 2024-11-08 DOI: 10.1016/j.mlwa.2024.100604

Tian Su , Senarath Dharmasena , David Leatham , Charles Gilliland

{"title":"Texas rural land market integration: A causal analysis using machine learning applications","authors":"Tian Su , Senarath Dharmasena , David Leatham , Charles Gilliland","doi":"10.1016/j.mlwa.2024.100604","DOIUrl":"10.1016/j.mlwa.2024.100604","url":null,"abstract":"<div><div>Texas rural land markets have several special features that makes it unique from other rural land markets in the United States. In 2021, Texas agricultural land, including buildings, is valued at $299.88 billion, which is almost 10% of the nation's total agricultural real estate value and 83% of the state's land is categorized as rural. In addition, due to its size and geologic features, Texas’ diverse landscape creates complex and widely divergent conditions affecting ownership and marketing of the land. Despite this complexity, lack of granular level and reliable transactional data on land sales has prevented thorough investigation into Texas land markets to uncover various interdependencies. Using quarterly transactional land value data from 1966 to 2017, this study uses cutting-edge machine learning algorithms and probabilistic graphical models to uncover causal interaction patterns of different land markets in Texas. The results reveal that Texas rural land markets are interdependent. Current and potential landholders and lenders can use the results from this work to aid strategic decision making. Financial institutions and investment groups could be made aware of the trend of one land market relative to other markets and adjust their holdings accordingly. Landowners may better understand changes in net wealth, which affect their ability to borrow capital and operate efficiently. Moreover, lenders may also benefit from the information to manage collateral and thus maintain the stability of their operation.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"18 ","pages":"Article 100604"},"PeriodicalIF":0.0,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Applications of cluster-based transfer learning in image and localization tasks 基于聚类的迁移学习在图像和定位任务中的应用

Machine learning with applications Pub Date : 2024-11-07 DOI: 10.1016/j.mlwa.2024.100601

Liuyi Yang, Patrick Finnerty, Chikara Ohta

引用次数: 0

Vulnerability detection using BERT based LLM model with transparency obligation practice towards trustworthy AI 使用基于 BERT 的 LLM 模型进行漏洞检测，履行透明义务，实现可信赖的人工智能

Machine learning with applications Pub Date : 2024-11-02 DOI: 10.1016/j.mlwa.2024.100598

Jean Haurogné , Nihala Basheer , Shareeful Islam

{"title":"Vulnerability detection using BERT based LLM model with transparency obligation practice towards trustworthy AI","authors":"Jean Haurogné , Nihala Basheer , Shareeful Islam","doi":"10.1016/j.mlwa.2024.100598","DOIUrl":"10.1016/j.mlwa.2024.100598","url":null,"abstract":"<div><div>Vulnerabilities in the source code are one of the main causes of potential threats in software-intensive systems. There are a large number of vulnerabilities published each day, and effective vulnerability detection is critical to identifying and mitigating these vulnerabilities. AI has emerged as a promising solution to enhance vulnerability detection, offering the ability to analyse vast amounts of data and identify patterns indicative of potential threats. However, AI-based methods often face several challenges, specifically when dealing with large datasets and understanding the specific context of the problem. Large Language Model (LLM) is now widely considered to tackle more complex tasks and handle large datasets, which also exhibits limitations in terms of explaining the model outcome and existing works focus on providing overview of explainability and transparency. This research introduces a novel transparency obligation practice for vulnerability detection using BERT based LLMs. We address the black-box nature of LLMs by employing XAI techniques, unique combination of SHAP, LIME, heat map. We propose an architecture that combines the BERT model with transparency obligation practices, which ensures the assurance of transparency throughout the entire LLM life cycle. An experiment is performed with a large source code dataset to demonstrate the applicability of the proposed approach. The result shows higher accuracy of 91.8 % for the vulnerability detection and model explainability outcome is highly influenced by “vulnerable”, “function”, \"mysql_tmpdir_list\", “strmov” tokens using both SHAP and LIME framework. Heatmap of attention weights, highlights the local token interactions that aid in understanding the model's decision points.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"18 ","pages":"Article 100598"},"PeriodicalIF":0.0,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Moral decision making: Explainable insights into the role of working memory in autonomous driving 道德决策：关于工作记忆在自动驾驶中作用的可解释见解

Machine learning with applications Pub Date : 2024-10-31 DOI: 10.1016/j.mlwa.2024.100599

Amandeep Singh, Yovela Murzello, Hyowon Lee, Shene Abdalla, Siby Samuel

{"title":"Moral decision making: Explainable insights into the role of working memory in autonomous driving","authors":"Amandeep Singh, Yovela Murzello, Hyowon Lee, Shene Abdalla, Siby Samuel","doi":"10.1016/j.mlwa.2024.100599","DOIUrl":"10.1016/j.mlwa.2024.100599","url":null,"abstract":"<div><div>The intersection of Artificial Intelligence (AI) and moral philosophy presents unique challenges in the development of autonomous vehicles, particularly in scenarios requiring split-second ethical decisions. This study examines the relationship between working memory (WM) and moral judgments in simulated AV scenarios, quantifying the effects of varying cognitive load on utilitarian decision-making under different time constraints. We experimented with 336 participants, each completing 16 simulated driving trials presenting unique ethical dilemmas. Results reveal a complex interplay between cognitive load and ethical choices. Under high temporal pressure (1-second response window), utilitarian decisions decreased significantly from 92.77 % to 70.08 %. Extended time constraints led to increased utilitarian choices. Statistical analyses validated these findings across diverse ethical contexts. Chi-square tests revealed significant associations between WM load and utilitarian decisions in 1-second conditions, particularly for high-stakes scenarios. Logistic regression showed that WM significantly decreased the likelihood of utilitarian decisions in these scenarios. Six supervised machine learning models were employed, with Gaussian Naive Bayes achieving the highest predictive accuracy (82.2 % to 97.0 %) in distinguishing utilitarian decisions. Partial Dependence analysis revealed a strong negative correlation between WM and utilitarian decisions, especially in the 1-second interval. The 2-second interval emerged as potentially optimal for balancing time constraints and cognitive load. These findings contribute to the theoretical understanding of ethical decision-making under cognitive load and provide practical insights for developing ethically aligned autonomous systems, with implications for improving safety, optimizing takeover protocols, and enhancing the ethical reasoning capabilities of autonomous driving systems.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"18 ","pages":"Article 100599"},"PeriodicalIF":0.0,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142650983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep learning applications for real-time and early detection of fall armyworm, African armyworm, and maize stem borer 深度学习在实时和早期检测秋虫、非洲象鼻虫和玉米螟中的应用

Machine learning with applications Pub Date : 2024-10-30 DOI: 10.1016/j.mlwa.2024.100596

Ivan Oyege , Harriet Sibitenda , Maruthi Sridhar Balaji Bhaskar

{"title":"Deep learning applications for real-time and early detection of fall armyworm, African armyworm, and maize stem borer","authors":"Ivan Oyege , Harriet Sibitenda , Maruthi Sridhar Balaji Bhaskar","doi":"10.1016/j.mlwa.2024.100596","DOIUrl":"10.1016/j.mlwa.2024.100596","url":null,"abstract":"<div><div>The application of artificial intelligence for identifying Fall armyworm (<em>Spodoptera frugiperda</em>), African armyworm (<em>Spodoptera exempta</em>), and Maize stem borer (<em>Busseola fusca</em>) is critical due to the threats they pose to global food production. This study aims to evaluate and identify the most accurate and robust DL models in detecting and classifying these three significant agricultural pests. Seven traditional DL models: Convolutional Neural Network, Visual Geometry Group (VGG16), Residual Networks (ResNet50), MobileNetV2, InceptionV3, Deep Neural Network (DNN), and InceptionResNetV2 and the advanced You Look Only Once (YOLOv8) model were trained and tested using pest image datasets. The results showed that all traditional models except DNN had high accuracies ranging from 93.17% (InceptionResNetV2) to 99.43% (MobileNet) in training and testing, with losses ranging from 1.71% (MobileNetV2) to 24.99% (InceptionResNetV2). DNN had a slightly lower accuracy range of 55.27% to 56.39% and a loss range of 85.02% to 89.96% in training and testing. YOLOv8 emerged as the best and most robust model in the pest detection and classification tasks, achieving Precision and Recall scores ranging from 98.4% to 100% on single-class and multi-class classifications, making it highly suitable for real-world pest management applications. This research pioneers the use of DL for the classification and detection of maize stem borer, African armyworm and Fall armyworm, unique and separately addressing a critical gap in agricultural pest management in corn. With early and accurate pest identification, crop protection measures can be implemented efficiently. The findings lead to reduced crop damage and enhanced food security.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"18 ","pages":"Article 100596"},"PeriodicalIF":0.0,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing SMOTE for imbalanced data with abnormal minority instances 针对异常少数实例的不平衡数据增强 SMOTE

Machine learning with applications Pub Date : 2024-10-29 DOI: 10.1016/j.mlwa.2024.100597

Surani Matharaarachchi , Mike Domaratzki , Saman Muthukumarana

{"title":"Enhancing SMOTE for imbalanced data with abnormal minority instances","authors":"Surani Matharaarachchi , Mike Domaratzki , Saman Muthukumarana","doi":"10.1016/j.mlwa.2024.100597","DOIUrl":"10.1016/j.mlwa.2024.100597","url":null,"abstract":"<div><div>Imbalanced datasets are frequent in machine learning, where certain classes are markedly underrepresented compared to others. This imbalance often results in sub-optimal model performance, as classifiers tend to favour the majority class. A significant challenge arises when abnormal instances, such as outliers, exist within the minority class, diminishing the effectiveness of traditional re-sampling methods like the Synthetic Minority Over-sampling Technique (SMOTE). This manuscript addresses this critical issue by introducing four SMOTE extensions: Distance ExtSMOTE, Dirichlet ExtSMOTE, FCRP SMOTE, and BGMM SMOTE. These methods leverage a weighted average of neighbouring instances to enhance the quality of synthetic samples and mitigate the impact of outliers. Comprehensive experiments conducted on diverse simulated and real-world imbalanced datasets demonstrate that the proposed methods improve classification performance compared to the original SMOTE and its most competitive variants. Notably, we demonstrate that Dirichlet ExtSMOTE outperforms most other proposed and existing SMOTE variants in terms of achieving better F1 score, MCC, and PR-AUC. Our results underscore the effectiveness of these advanced SMOTE extensions in tackling class imbalance, particularly in the presence of abnormal instances, offering robust solutions for real-world applications.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"18 ","pages":"Article 100597"},"PeriodicalIF":0.0,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0