Machine learning with applications最新文献

{"title":"The association between mindfulness, psychological flexibility, and rumination in predicting mental health and well-being among university students using machine learning and structural equation modeling","authors":"Ruohan Feng ,&nbsp;Vaibhav Mishra ,&nbsp;Xin Hao ,&nbsp;Paul Verhaeghen","doi":"10.1016/j.mlwa.2024.100614","DOIUrl":"10.1016/j.mlwa.2024.100614","url":null,"abstract":"<div><h3>Objectives</h3><div>This study explores the intricate relationships between mindfulness, psychological flexibility, rumination, and their combined impact on mental health and well-being.</div></div><div><h3>Methods</h3><div>Random forest regression on survey data from 524 undergraduate students was used to identify significant predictors from a comprehensive set of psychological variables. Neural networks were then trained on various combinations of these predictors to evaluate their performance in predicting mental health and well-being outcomes. Finally, structural equation modeling (SEM) was employed to validate a model based on the identified key predictors, focusing on pathways from mindfulness through psychological flexibility to rumination and well-being.</div></div><div><h3>Results</h3><div>The random forest analysis revealed that the mindfulness variables exerted their influence partially indirectly through psychological flexibility and rumination. The deep neural network analysis supported these findings and additionally showed that the mindfulness manifold model (consisting of self-awareness, self-regulation, and self-transcendence) was superior to the Five Facet Mindfulness Questionnaire variables in predicting mental health outcomes. The SEM analysis confirmed that psychological flexibility, particularly its avoidance and acceptance components, mediated the relationship between mindfulness and mental health. The hypothesized serial mediation pathway—mindfulness affecting psychological flexibility, which then influences rumination and subsequently mental health and well-being—was supported by the data. Self-transcendence was a particularly powerful predictor of mental health outcomes.</div></div><div><h3>Conclusions</h3><div>The findings underscore the critical role of psychological flexibility and rumination in mediating the effects of mindfulness on mental health and well-being, suggesting that enhancing mindfulness and psychological flexibility might significantly reduce rumination, thereby improving overall mental health and well-being.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"19 ","pages":"Article 100614"},"PeriodicalIF":0.0,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143170242","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}

{"title":"Detection of fraud in IoT based credit card collected dataset using machine learning","authors":"Mohammed Naif Alatawi","doi":"10.1016/j.mlwa.2024.100603","DOIUrl":"10.1016/j.mlwa.2024.100603","url":null,"abstract":"<div><div>Due in large part to the proliferation of electronic financial transactions, credit card fraud is a serious problem for customers, merchants, and banks. For this reason, a novel approach is offered to fraud detection that makes use of cutting-edge ML methods in an IoT setting. The method in this paper employs a carefully selected set of cutting-edge ML algorithms specifically designed to handle the complexities of fraud detection, in contrast to older approaches that have difficulty adapting to shifting fraud patterns. In order to address the many facets of the problem, the methodology employs a large collection of ML models. These models include deep neural networks, decision trees, support vector machines, random forests, and clustering methods. This paper provides a solution that is able to detect fraudulent activity in real time by efficiently analyzing massive amounts of transactional data thanks to the power of big data processing and cloud computing. The model is able to distinguish between valid and fraudulent transactions thanks to careful feature engineering and anomaly detection methods. Extensive experiments on a large and diverse collection of real and simulated credit card transactions, both legitimate and fraudulent, prove the success of this technique. The findings demonstrate state-of-the-art performance in fraud detection, with increased precision and recall rates compared to traditional methods. And because the presented ML models are easy to understand, they improve fraud risk management and prevention techniques. The findings of this study provide banking institutions, government agencies, and policymakers with vital information for combating the negative effects of credit card fraud on consumers, companies, and the economy as a whole. This study provides a solution to the problem of fraud in the Internet of Things (IoT) ecosystem and paves the way for future developments in this crucial area by proposing a unique ML-driven approach to the problem.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"19 ","pages":"Article 100603"},"PeriodicalIF":0.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169033","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}

{"title":"Demographic bias mitigation at test-time using uncertainty estimation and human–machine partnership","authors":"Anoop Krishnan Upendran Nair ,&nbsp;Ajita Rattani","doi":"10.1016/j.mlwa.2024.100610","DOIUrl":"10.1016/j.mlwa.2024.100610","url":null,"abstract":"<div><div>Facial attribute classification algorithms frequently manifest demographic biases by obtaining differential performance across gender and racial groups. Existing bias mitigation techniques are mostly in-processing techniques, i.e., implemented during the classifier’s training stage, that often lack generalizability, require demographically annotated training sets, and exhibit a trade-off between fairness and classification accuracy. In this paper, we propose a technique to mitigate bias at the test time i.e., during the deployment stage, by harnessing prediction uncertainty and human–machine partnership. To this front, we propose to utilize those lowest percentages of test data samples identified as outliers with high prediction uncertainty. These identified uncertain samples at test-time are labeled by human analysts for decision rendering and for subsequently re-training the deep neural network in a continual learning framework. With minimal human involvement and through iterative refinement of the network with human guidance at test-time, we seek to enhance the accuracy as well as the fairness of the already deployed facial attribute classification algorithms. Extensive experiments are conducted on gender and smile attribute classification tasks using four publicly available datasets and with gender and race as the protected attributes. The obtained outcomes consistently demonstrate improved accuracy by up to 2% and 5% for the gender and smile attribute classification tasks, respectively, using our proposed approaches. Further, the demographic bias was significantly reduced, outperforming the State-of-the-Art (SOTA) bias mitigation and baseline techniques by up to 55% for both classification tasks. The demo shall be released on <span><span>https://github.com/hashtaglensman/HumanintheLoop</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"19 ","pages":"Article 100610"},"PeriodicalIF":0.0,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143170237","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}

{"title":"Application of convolutional neural networks and ensemble methods in the fiber volume content analysis of natural fiber composites","authors":"Florian Rothenhäusler ,&nbsp;Rodrigo Queiroz Albuquerque ,&nbsp;Marcel Sticher ,&nbsp;Christopher Kuenneth ,&nbsp;Holger Ruckdaeschel","doi":"10.1016/j.mlwa.2024.100609","DOIUrl":"10.1016/j.mlwa.2024.100609","url":null,"abstract":"<div><div>The incorporation of natural fibers into fiber-reinforced polymer composites (FRPC) has the potential to bolster their sustainability. A critical attribute of FRPC is the fiber volume content (<em>FVC</em>), a parameter that profoundly influences their thermo-mechanical characteristics. However, the determination of <em>FVC</em> in natural fiber composites (NFC) through manual analysis of light microscopy images is a labor-intensive process. In this work, it is demonstrated that the pixels from light microscopy images of NFC can be utilized to predict <em>FVC</em> using machine learning (ML) models. In this proof-of-concept investigation, it is shown that convolutional neural network-based models predict <em>FVC</em> with an accuracy required in polymer engineering applications, with a mean average error of 2.72<!--> <!-->% and an <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> coefficient of 0.85. Finally, it is shown that much simpler ML models, non-specialized in image recognition, besides being much easier and more efficient to optimize and train, can also deliver good accuracies required for <em>FVC</em> characterization, which not only contributes to the sustainability, but also facilitates the access of such models by researchers in regions with little computational resources. This study marks a substantial advancement in the area of automated characterization of NFC, and democratization of knowledge, offering a promising avenue for the enhancement of sustainable materials.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"19 ","pages":"Article 100609"},"PeriodicalIF":0.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143170238","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}

{"title":"Sharing is CAIRing: Characterizing principles and assessing properties of universal privacy evaluation for synthetic tabular data","authors":"Tobias Hyrup,&nbsp;Anton Danholt Lautrup,&nbsp;Arthur Zimek,&nbsp;Peter Schneider-Kamp","doi":"10.1016/j.mlwa.2024.100608","DOIUrl":"10.1016/j.mlwa.2024.100608","url":null,"abstract":"<div><div>Data sharing is a necessity for innovative progress in many domains, especially in healthcare. However, the ability to share data is hindered by regulations protecting the privacy of natural persons. Synthetic tabular data provide a promising solution to address data sharing difficulties but does not inherently guarantee privacy. Still, there is a lack of agreement on appropriate methods for assessing the privacy-preserving capabilities of synthetic data, making it difficult to compare results across studies. To the best of our knowledge, this is the first work to identify properties that constitute good universal privacy evaluation metrics for synthetic tabular data. The goal of universally applicable metrics is to enable comparability across studies and to allow non-technical stakeholders to understand how privacy is protected. We identify four principles for the assessment of metrics: Comparability, Applicability, Interpretability, and Representativeness (CAIR). To quantify and rank the degree to which evaluation metrics conform to the CAIR principles, we design a rubric using a scale of 1–4. Each of the four properties is scored on four parameters, yielding 16 total dimensions. We study the applicability and usefulness of the CAIR principles and rubric by assessing a selection of metrics popular in other studies. The results provide granular insights into the strengths and weaknesses of existing metrics that not only rank the metrics but highlight areas of potential improvements. We expect that the CAIR principles will foster agreement among researchers and organizations on which universal privacy evaluation metrics are appropriate for synthetic tabular data.</div></div>","PeriodicalId":74093,"journal":{"name":"Machine learning with applications","volume":"18 ","pages":"Article 100608"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143143187","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}

{"title":"Document Layout Error Rate (DLER) metric to evaluate image segmentation methods","authors":"Ari Vesalainen ,&nbsp;Mikko Tolonen ,&nbsp;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}

{"title":"Supervised machine learning for microbiomics: Bridging the gap between current and best practices","authors":"Natasha Katherine Dudek ,&nbsp;Mariami Chakhvadze ,&nbsp;Saba Kobakhidze ,&nbsp;Omar Kantidze ,&nbsp;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}

{"title":"Playing with words: Comparing the vocabulary and lexical diversity of ChatGPT and humans","authors":"Pedro Reviriego ,&nbsp;Javier Conde ,&nbsp;Elena Merino-Gómez ,&nbsp;Gonzalo Martínez ,&nbsp;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}

{"title":"A survey on knowledge distillation: Recent advancements","authors":"Amir Moslemi ,&nbsp;Anna Briskina ,&nbsp;Zubeka Dang ,&nbsp;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}

{"title":"Texas rural land market integration: A causal analysis using machine learning applications","authors":"Tian Su ,&nbsp;Senarath Dharmasena ,&nbsp;David Leatham ,&nbsp;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}