K-nearest neighbor

K - 最近邻
  • 文章类型: Journal Article
    近年来,使用人工智能算法对色素性皮肤病变进行分类的准确性有了显著提高。智能分析和分类系统明显优于皮肤科医生和肿瘤学家使用的视觉诊断方法。然而,由于缺乏通用性和潜在错误分类的风险,此类系统在临床实践中的应用受到严重限制。在临床病理实践中成功实施基于人工智能的工具需要对现有模型的有效性和性能进行全面研究,以及潜在研究发展的进一步有希望的领域。本系统综述的目的是调查和评估人工智能技术用于检测色素性皮肤病变的恶性形式的准确性。对于这项研究,从电子科学出版商中选择了10,589篇科学研究和评论文章,其中171篇文章被纳入本系统综述。所有选定的科学文章都根据所提出的神经网络算法从机器学习到多模态智能架构进行分发,并在手稿的相应部分进行了描述。这项研究旨在探索自动皮肤癌识别系统,从简单的机器学习算法到基于高级编码器-解码器模型的多模态集成系统,视觉变压器(ViT),以及生成和尖峰神经网络。此外,作为分析的结果,未来的研究方向,前景,并讨论了进一步开发用于对色素性皮肤病变进行分类的自动神经网络系统的潜力。
    In recent years, there has been a significant improvement in the accuracy of the classification of pigmented skin lesions using artificial intelligence algorithms. Intelligent analysis and classification systems are significantly superior to visual diagnostic methods used by dermatologists and oncologists. However, the application of such systems in clinical practice is severely limited due to a lack of generalizability and risks of potential misclassification. Successful implementation of artificial intelligence-based tools into clinicopathological practice requires a comprehensive study of the effectiveness and performance of existing models, as well as further promising areas for potential research development. The purpose of this systematic review is to investigate and evaluate the accuracy of artificial intelligence technologies for detecting malignant forms of pigmented skin lesions. For the study, 10,589 scientific research and review articles were selected from electronic scientific publishers, of which 171 articles were included in the presented systematic review. All selected scientific articles are distributed according to the proposed neural network algorithms from machine learning to multimodal intelligent architectures and are described in the corresponding sections of the manuscript. This research aims to explore automated skin cancer recognition systems, from simple machine learning algorithms to multimodal ensemble systems based on advanced encoder-decoder models, visual transformers (ViT), and generative and spiking neural networks. In addition, as a result of the analysis, future directions of research, prospects, and potential for further development of automated neural network systems for classifying pigmented skin lesions are discussed.
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  • 文章类型: Systematic Review
    机器学习(ML)技术提供了有效评估和分析人类皮肤和头发评估的能力。这项研究的目的是系统地回顾应用机器学习(ML)方法和人工智能(AI)技术来评估头发和皮肤评估的有效性。PubMed,WebofScience,IEEEXplore,为了检索2010年1月1日至2020年3月31日之间的研究出版物,使用适当的关键词,如“头发和皮肤分析”。“经过准确的筛查,选择了20篇同行评审的出版物纳入本系统综述。分析表明,流行的机器学习(ML)方法由支持向量机(SVM)组成,k-近邻,和人工神经网络(ANN)。观察到人工神经网络产生95%的最高准确度,其次是SVM产生90%。这些技术最常用于起草诸如黑色素瘤之类的框架评估。参数的值,如灵敏度,特异性,和曲线下面积(AUC)从研究中提取,并在比较的帮助下,也做出了相关推论。观察到ANN产生82.30%的最高灵敏度和96.90%的特异性。因此,有了这个系统的审查,起草了研究的摘要,其中概述了如何将机器学习(ML)技术用于头发和皮肤评估的分析和评估。
    Machine Learning (ML) techniques provide the ability to effectively evaluate and analyze human skin and hair assessments. The aim of this study is to systematically review the effectiveness of applying Machine Learning (ML) methods and Artificial Intelligence (AI) techniques in order to evaluate hair and skin assessments. PubMed, Web of Science, IEEE Xplore, and Science Direct were searched in order to retrieve research publications between 1 January 2010 and 31 March 2020 using appropriate keywords such as \"hair and skin analysis.\" Following accurate screening, 20 peer-reviewed publications were selected for inclusion in this systematic review. The analysis demonstrated that prevalent Machine Learning (ML) methods comprised of Support Vector Machine (SVM), k-nearest Neighbor, and Artificial Neural Networks (ANN). ANN\'s were observed to yield the highest accuracy of 95% followed by SVM generating 90%. These techniques were most commonly applied for drafting framework assessments such as that of Melanoma. Values of parameters such as Sensitivity, Specificity, and Area under the Curve (AUC) were extracted from the studies and with the help of comparisons, relevant inferences were also made. ANN\'s were observed to yield the highest sensitivity of 82.30% as well as a 96.90% specificity. Hence, with this systematic review, a summarization of the studies was drafted that encapsulated how Machine Learning (ML) techniques have been employed for the analysis and evaluation of hair and skin assessments.
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  • 文章类型: Journal Article
    由于正常人和受影响者之间缺乏交流,手语识别具有挑战性。许多社会和生理影响是由于说话或听力障碍造成的。以前已经提出了许多不同的尺寸技术来克服这个差距。用于手语识别(SLR)的基于传感器的智能手套被证明有助于根据与特定标志相关的各种手部动作生成数据。本文对用于手语识别的所有类型的可用技术和传感器进行了详细的比较审查。本文的重点是探索手语识别的新兴趋势和策略,并指出现有系统中的不足。本文将作为其他研究人员的指南,以了解所有材料和技术,如基于柔性电阻传感器,基于视觉传感器,或基于混合系统的技术用于手语到现在为止。
    Sign language recognition is challenging due to the lack of communication between normal and affected people. Many social and physiological impacts are created due to speaking or hearing disability. A lot of different dimensional techniques have been proposed previously to overcome this gap. A sensor-based smart glove for sign language recognition (SLR) proved helpful to generate data based on various hand movements related to specific signs. A detailed comparative review of all types of available techniques and sensors used for sign language recognition was presented in this article. The focus of this paper was to explore emerging trends and strategies for sign language recognition and to point out deficiencies in existing systems. This paper will act as a guide for other researchers to understand all materials and techniques like flex resistive sensor-based, vision sensor-based, or hybrid system-based technologies used for sign language until now.
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  • 文章类型: Journal Article
    The K-nearest neighbor (KNN) classifier is one of the simplest and most common classifiers, yet its performance competes with the most complex classifiers in the literature. The core of this classifier depends mainly on measuring the distance or similarity between the tested examples and the training examples. This raises a major question about which distance measures to be used for the KNN classifier among a large number of distance and similarity measures available? This review attempts to answer this question through evaluating the performance (measured by accuracy, precision, and recall) of the KNN using a large number of distance measures, tested on a number of real-world data sets, with and without adding different levels of noise. The experimental results show that the performance of KNN classifier depends significantly on the distance used, and the results showed large gaps between the performances of different distances. We found that a recently proposed nonconvex distance performed the best when applied on most data sets comparing with the other tested distances. In addition, the performance of the KNN with this top performing distance degraded only ∼20% while the noise level reaches 90%, this is true for most of the distances used as well. This means that the KNN classifier using any of the top 10 distances tolerates noise to a certain degree. Moreover, the results show that some distances are less affected by the added noise comparing with other distances.
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