New Approaches in Engineering Research Vol. 14最新文献

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Lung Cancer Detection with Prediction Employing Machine Learning Algorithms: A Recent Study 利用机器学习算法预测肺癌检测:一项最新研究

New Approaches in Engineering Research Vol. 14 Pub Date : 2021-08-25 DOI: 10.9734/bpi/naer/v14/4218f

S. Prasad, Aneesha Johnson, S. M. Kumar

{"title":"Lung Cancer Detection with Prediction Employing Machine Learning Algorithms: A Recent Study","authors":"S. Prasad, Aneesha Johnson, S. M. Kumar","doi":"10.9734/bpi/naer/v14/4218f","DOIUrl":"https://doi.org/10.9734/bpi/naer/v14/4218f","url":null,"abstract":"Every year, the number of people dying from lung cancer rises around the world. It is second most cancer affecting among population worldwide. The ability to forecast the onset of cancer in patients can aid clinicians in making decisions about their drugs and therapies.This study suggests a new technique for detecting and predicting the existence of malignant nodules in the lungs of patients. To conduct the classification, the suggested system uses a machine learning technique called support vector machine (SVM) and a deep learning algorithm called convolutional neural network (CNN) and a large lung cancer repository database called the UCI repository. Images are pre-processed and then post-processed in the initial step of cleaning. The RGB to greyscale conversion is included in the pre-processing step, and the noise is removed using the Non-Local Means (NLM) filter in the post-processing step. Image segmentation was achieved using Otsu's method in the second stage of development, and feature extraction was achieved using Grey Level Co-occurrence Matrix (GLCM). Finally, the two classifiers are used to classify lung malignant images, and the accuracy of their classifications is compared and recorded.","PeriodicalId":262600,"journal":{"name":"New Approaches in Engineering Research Vol. 14","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127835892","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

A Study on TCal (Touchless Calibration): Contribution to Metrology for Industry 4.0 TCal(非接触式校准)研究:对工业4.0计量的贡献

New Approaches in Engineering Research Vol. 14 Pub Date : 2021-08-25 DOI: 10.9734/bpi/naer/v14/12855d

S. Andonov

{"title":"A Study on TCal (Touchless Calibration): Contribution to Metrology for Industry 4.0","authors":"S. Andonov","doi":"10.9734/bpi/naer/v14/12855d","DOIUrl":"https://doi.org/10.9734/bpi/naer/v14/12855d","url":null,"abstract":"In 2018, as contribution to the area of calibrations in Industry 4.0, the novel concept of Touchless Calibration (TCal) was introduced. In this paper, a study, accompanied by explanations, results, uncertainty and cost-benefit analysis, followed by practical experiment for the validation of TCal in the area of DC voltage calibrations are presented. Touchless Calibration (TCal) is introduced as a future development in calibration metrology on the XXII IMEKO Congress in Belfast in 2018. TCal is dealing mostly with decreasing the steps necessary to provide calibration traceability. Missing steps actually contribute to considerable decreasing of time and costs of calibration keeping the calibration traceability on high level. In the scope of Metrology for Industry 4.0, it could be one of the important improvements bringing increased effectiveness and efficiency for manufacturing industry. \u0000It is shown by this study, implementing the worst-case scenarios, that TCal can be used in manufacturing companies for the calibration of any measurement system used to measure DC voltages in the range from 0 V to 10 V with tolerances (USL – LSL) bigger than 0.44825 V (± 0.22413 V).","PeriodicalId":262600,"journal":{"name":"New Approaches in Engineering Research Vol. 14","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123805776","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

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