Machine learning and knowledge extraction最新文献_第5页

Autoencoder-Based Visual Anomaly Localization for Manufacturing Quality Control 基于自动编码器的视觉异常定位，用于制造质量控制

Machine learning and knowledge extraction Pub Date : 2023-09-13 DOI: 10.3390/make6010001

Devang Mehta, Noah Klarmann

{"title":"Autoencoder-Based Visual Anomaly Localization for Manufacturing Quality Control","authors":"Devang Mehta, Noah Klarmann","doi":"10.3390/make6010001","DOIUrl":"https://doi.org/10.3390/make6010001","url":null,"abstract":"Manufacturing industries require the efficient and voluminous production of high-quality finished goods. In the context of Industry 4.0, visual anomaly detection poses an optimistic solution for automatically controlled product quality with high precision. In general, automation based on computer vision is a promising solution to prevent bottlenecks at the product quality checkpoint. We considered recent advancements in machine learning to improve visual defect localization, but challenges persist in obtaining a balanced feature set and database of the wide variety of defects occurring in the production line. Hence, this paper proposes a defect localizing autoencoder with unsupervised class selection by clustering with k-means the features extracted from a pretrained VGG16 network. Moreover, the selected classes of defects are augmented with natural wild textures to simulate artificial defects. The study demonstrates the effectiveness of the defect localizing autoencoder with unsupervised class selection for improving defect detection in manufacturing industries. The proposed methodology shows promising results with precise and accurate localization of quality defects on melamine-faced boards for the furniture industry. Incorporating artificial defects into the training data shows significant potential for practical implementation in real-world quality control scenarios.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139340434","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

Automatic Genre Identification for Robust Enrichment of Massive Text Collections: Investigation of Classification Methods in the Era of Large Language Models 面向海量文本集鲁棒扩充的自动体裁识别:大语言模型时代分类方法研究

Machine learning and knowledge extraction Pub Date : 2023-09-12 DOI: 10.3390/make5030059

Taja Kuzman, Igor Mozetič, Nikola Ljubešić

{"title":"Automatic Genre Identification for Robust Enrichment of Massive Text Collections: Investigation of Classification Methods in the Era of Large Language Models","authors":"Taja Kuzman, Igor Mozetič, Nikola Ljubešić","doi":"10.3390/make5030059","DOIUrl":"https://doi.org/10.3390/make5030059","url":null,"abstract":"Massive text collections are the backbone of large language models, the main ingredient of the current significant progress in artificial intelligence. However, as these collections are mostly collected using automatic methods, researchers have few insights into what types of texts they consist of. Automatic genre identification is a text classification task that enriches texts with genre labels, such as promotional and legal, providing meaningful insights into the composition of these large text collections. In this paper, we evaluate machine learning approaches for the genre identification task based on their generalizability across different datasets to assess which model is the most suitable for the downstream task of enriching large web corpora with genre information. We train and test multiple fine-tuned BERT-like Transformer-based models and show that merging different genre-annotated datasets yields superior results. Moreover, we explore the zero-shot capabilities of large GPT Transformer models in this task and discuss the advantages and disadvantages of the zero-shot approach. We also publish the best-performing fine-tuned model that enables automatic genre annotation in multiple languages. In addition, to promote further research in this area, we plan to share, upon request, a new benchmark for automatic genre annotation, ensuring the non-exposure of the latest large language models.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135885309","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

Cyberattack Detection in Social Network Messages Based on Convolutional Neural Networks and NLP Techniques 基于卷积神经网络和NLP技术的社交网络信息网络攻击检测

Machine learning and knowledge extraction Pub Date : 2023-09-01 DOI: 10.3390/make5030058

Jorge E. Coyac-Torres, Grigori Sidorov, E. Aguirre-Anaya, Gerardo Hernández-Oregón

{"title":"Cyberattack Detection in Social Network Messages Based on Convolutional Neural Networks and NLP Techniques","authors":"Jorge E. Coyac-Torres, Grigori Sidorov, E. Aguirre-Anaya, Gerardo Hernández-Oregón","doi":"10.3390/make5030058","DOIUrl":"https://doi.org/10.3390/make5030058","url":null,"abstract":"Social networks have captured the attention of many people worldwide. However, these services have also attracted a considerable number of malicious users whose aim is to compromise the digital assets of other users by using messages as an attack vector to execute different types of cyberattacks against them. This work presents an approach based on natural language processing tools and a convolutional neural network architecture to detect and classify four types of cyberattacks in social network messages, including malware, phishing, spam, and even one whose aim is to deceive a user into spreading malicious messages to other users, which, in this work, is identified as a bot attack. One notable feature of this work is that it analyzes textual content without depending on any characteristics from a specific social network, making its analysis independent of particular data sources. Finally, this work was tested on real data, demonstrating its results in two stages. The first stage detected the existence of any of the four types of cyberattacks within the message, achieving an accuracy value of 0.91. After detecting a message as a cyberattack, the next stage was to classify it as one of the four types of cyberattack, achieving an accuracy value of 0.82.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42027449","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

Comparing the Performance of Machine Learning Algorithms in the Automatic Classification of Psychotherapeutic Interactions in Avatar Therapy 比较机器学习算法在阿凡达治疗中心理治疗相互作用自动分类中的性能

Machine learning and knowledge extraction Pub Date : 2023-08-24 DOI: 10.3390/make5030057

A. Hudon, Kingsada Phraxayavong, S. Potvin, A. Dumais

{"title":"Comparing the Performance of Machine Learning Algorithms in the Automatic Classification of Psychotherapeutic Interactions in Avatar Therapy","authors":"A. Hudon, Kingsada Phraxayavong, S. Potvin, A. Dumais","doi":"10.3390/make5030057","DOIUrl":"https://doi.org/10.3390/make5030057","url":null,"abstract":"(1) Background: Avatar Therapy (AT) is currently being studied to help patients suffering from treatment-resistant schizophrenia. Facilitating annotations of immersive verbatims in AT by using classification algorithms could be an interesting avenue to reduce the time and cost of conducting such analysis and adding objective quantitative data in the classification of the different interactions taking place during the therapy. The aim of this study is to compare the performance of machine learning algorithms in the automatic annotation of immersive session verbatims of AT. (2) Methods: Five machine learning algorithms were implemented over a dataset as per the Scikit-Learn library: Support vector classifier, Linear support vector classifier, Multinomial Naïve Bayes, Decision Tree, and Multi-layer perceptron classifier. The dataset consisted of the 27 different types of interactions taking place in AT for the Avatar and the patient for 35 patients who underwent eight immersive sessions as part of their treatment in AT. (3) Results: The Linear SVC performed best over the dataset as compared with the other algorithms with the highest accuracy score, recall score, and F1-Score. The regular SVC performed best for precision. (4) Conclusions: This study presented an objective method for classifying textual interactions based on immersive session verbatims and gave a first comparison of multiple machine learning algorithms on AT.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"56 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41273478","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: 1

Analyzing Quality Measurements for Dimensionality Reduction 分析降维的质量测量

Machine learning and knowledge extraction Pub Date : 2023-08-21 DOI: 10.3390/make5030056

Michael C. Thrun, Julian Märte, Quirin Stier

{"title":"Analyzing Quality Measurements for Dimensionality Reduction","authors":"Michael C. Thrun, Julian Märte, Quirin Stier","doi":"10.3390/make5030056","DOIUrl":"https://doi.org/10.3390/make5030056","url":null,"abstract":"Dimensionality reduction methods can be used to project high-dimensional data into low-dimensional space. If the output space is restricted to two dimensions, the result is a scatter plot whose goal is to present insightful visualizations of distance- and density-based structures. The topological invariance of dimension indicates that the two-dimensional similarities in the scatter plot cannot coercively represent high-dimensional distances. In praxis, projections of several datasets with distance- and density-based structures show a misleading interpretation of the underlying structures. The examples outline that the evaluation of projections remains essential. Here, 19 unsupervised quality measurements (QM) are grouped into semantic classes with the aid of graph theory. We use three representative benchmark datasets to show that QMs fail to evaluate the projections of straightforward structures when common methods such as Principal Component Analysis (PCA), Uniform Manifold Approximation projection, or t-distributed stochastic neighbor embedding (t-SNE) are applied. This work shows that unsupervised QMs are biased towards assumed underlying structures. Based on insights gained from graph theory, we propose a new quality measurement called the Gabriel Classification Error (GCE). This work demonstrates that GCE can make an unbiased evaluation of projections. The GCE is accessible within the R package DR quality available on CRAN.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46759622","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

Tabular Machine Learning Methods for Predicting Gas Turbine Emissions 预测燃气轮机排放的表格式机器学习方法

Machine learning and knowledge extraction Pub Date : 2023-08-14 DOI: 10.3390/make5030055

Rebecca Potts, Rick Hackney, Georgios Leontidis

引用次数: 1

Defining a Digital Twin: A Data Science-Based Unification 定义数字孪生:基于数据科学的统一

Machine learning and knowledge extraction Pub Date : 2023-08-12 DOI: 10.3390/make5030054

F. Emmert-Streib

{"title":"Defining a Digital Twin: A Data Science-Based Unification","authors":"F. Emmert-Streib","doi":"10.3390/make5030054","DOIUrl":"https://doi.org/10.3390/make5030054","url":null,"abstract":"The concept of a digital twin (DT) has gained significant attention in academia and industry because of its perceived potential to address critical global challenges, such as climate change, healthcare, and economic crises. Originally introduced in manufacturing, many attempts have been made to present proper definitions of this concept. Unfortunately, there remains a great deal of confusion surrounding the underlying concept, with many scientists still uncertain about the distinction between a simulation, a mathematical model and a DT. The aim of this paper is to propose a formal definition of a digital twin. To achieve this goal, we utilize a data science framework that facilitates a functional representation of a DT and other components that can be combined together to form a larger entity we refer to as a digital twin system (DTS). In our framework, a DT is an open dynamical system with an updating mechanism, also referred to as complex adaptive system (CAS). Its primary function is to generate data via simulations, ideally, indistinguishable from its physical counterpart. On the other hand, a DTS provides techniques for analyzing data and decision-making based on the generated data. Interestingly, we find that a DTS shares similarities to the principles of general systems theory. This multi-faceted view of a DTS explains its versatility in adapting to a wide range of problems in various application domains such as engineering, manufacturing, urban planning, and personalized medicine.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42597676","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: 2

Artificial Intelligence Ethics and Challenges in Healthcare Applications: A Comprehensive Review in the Context of the European GDPR Mandate 医疗保健应用中的人工智能伦理和挑战:欧洲GDPR授权背景下的全面审查

Machine learning and knowledge extraction Pub Date : 2023-08-07 DOI: 10.3390/make5030053

Mohammad MohammadAmini, Marcia Jesus, Davood Fanaei Sheikholeslami, Paulo Alves, Aliakbar Hassanzadeh Benam, Fatemeh Hariri

{"title":"Artificial Intelligence Ethics and Challenges in Healthcare Applications: A Comprehensive Review in the Context of the European GDPR Mandate","authors":"Mohammad MohammadAmini, Marcia Jesus, Davood Fanaei Sheikholeslami, Paulo Alves, Aliakbar Hassanzadeh Benam, Fatemeh Hariri","doi":"10.3390/make5030053","DOIUrl":"https://doi.org/10.3390/make5030053","url":null,"abstract":"This study examines the ethical issues surrounding the use of Artificial Intelligence (AI) in healthcare, specifically nursing, under the European General Data Protection Regulation (GDPR). The analysis delves into how GDPR applies to healthcare AI projects, encompassing data collection and decision-making stages, to reveal the ethical implications at each step. A comprehensive review of the literature categorizes research investigations into three main categories: Ethical Considerations in AI; Practical Challenges and Solutions in AI Integration; and Legal and Policy Implications in AI. The analysis uncovers a significant research deficit in this field, with a particular focus on data owner rights and AI ethics within GDPR compliance. To address this gap, the study proposes new case studies that emphasize the importance of comprehending data owner rights and establishing ethical norms for AI use in medical applications, especially in nursing. This review makes a valuable contribution to the AI ethics debate and assists nursing and healthcare professionals in developing ethical AI practices. The insights provided help stakeholders navigate the intricate terrain of data protection, ethical considerations, and regulatory compliance in AI-driven healthcare. Lastly, the study introduces a case study of a real AI health-tech project named SENSOMATT, spotlighting GDPR and privacy issues.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"28 1","pages":"1023-1035"},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80681861","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: 3

Improving Spiking Neural Network Performance with Auxiliary Learning 用辅助学习改进脉冲神经网络性能

Machine learning and knowledge extraction Pub Date : 2023-08-05 DOI: 10.3390/make5030052

P. G. Cachi, S. Ventura, Krzysztof J. Cios

引用次数: 0

Identifying the Regions of a Space with the Self-Parameterized Recursively Assessed Decomposition Algorithm (SPRADA) 基于自参数化递归评估分解算法(SPRADA)的空间区域识别

Machine learning and knowledge extraction Pub Date : 2023-08-04 DOI: 10.3390/make5030051

Dylan Molinié, K. Madani, V. Amarger, A. Chebira

{"title":"Identifying the Regions of a Space with the Self-Parameterized Recursively Assessed Decomposition Algorithm (SPRADA)","authors":"Dylan Molinié, K. Madani, V. Amarger, A. Chebira","doi":"10.3390/make5030051","DOIUrl":"https://doi.org/10.3390/make5030051","url":null,"abstract":"This paper introduces a non-parametric methodology based on classical unsupervised clustering techniques to automatically identify the main regions of a space, without requiring the objective number of clusters, so as to identify the major regular states of unknown industrial systems. Indeed, useful knowledge on real industrial processes entails the identification of their regular states, and their historically encountered anomalies. Since both should form compact and salient groups of data, unsupervised clustering generally performs this task fairly accurately; however, this often requires the number of clusters upstream, knowledge which is rarely available. As such, the proposed algorithm operates a first partitioning of the space, then it estimates the integrity of the clusters, and splits them again and again until every cluster obtains an acceptable integrity; finally, a step of merging based on the clusters’ empirical distributions is performed to refine the partitioning. Applied to real industrial data obtained in the scope of a European project, this methodology proved able to automatically identify the main regular states of the system. Results show the robustness of the proposed approach in the fully-automatic and non-parametric identification of the main regions of a space, knowledge which is useful to industrial anomaly detection and behavioral modeling.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"64 1","pages":"979-1009"},"PeriodicalIF":0.0,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84020672","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