2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA)最新文献_第5页

Cancer Detection Based on Microarray Data Classification using Genetic Bee Colony (GBC) and Conjugate Gradient Backpropagation with Modified Polak Ribiere (MBP-CGP) 基于遗传蜂群(GBC)和改进Polak Ribiere (MBP-CGP)共轭梯度反向传播的微阵列数据分类癌症检测

2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA) Pub Date : 2018-11-01 DOI: 10.1109/IC3INA.2018.8629538

Melati Suci Pratiwi, Adiwijaya, A. Aditsania

{"title":"Cancer Detection Based on Microarray Data Classification using Genetic Bee Colony (GBC) and Conjugate Gradient Backpropagation with Modified Polak Ribiere (MBP-CGP)","authors":"Melati Suci Pratiwi, Adiwijaya, A. Aditsania","doi":"10.1109/IC3INA.2018.8629538","DOIUrl":"https://doi.org/10.1109/IC3INA.2018.8629538","url":null,"abstract":"Cancer is one of the major health problems in the world, and should therefore be detected as early as possible. The development of technology has given rise to microarray technology, which can help researchers gather information from thousands of genes in a human being simultaneously, which is useful for the detection of cancer. Each feature of microarray data has a high dimension, so dimensional selection is done to improve the accuracy of microarray data classification; the Genetic Bee Colony (GBC) algorithm and Conjugate Gradient Backpropagation with Modified Polak Ribiere (MBP-CGP) can be used to detect whether or not an individual has cancer. GBC is a metaheuristic hybrid algorithm based on the Artificial Bee Colony (ABC) algorithm and Genetic Algorithm. MBP-CGP is a modification of the Artificial Neural Network (ANN), designed to accelerate backpropagation training. By implementing GBC and MBP-CGP as the feature selection method and classifier, respectively, the system is able to select features of up to 47-51% for all datasets with the performance generated for all datasets (without GBC) ranging between 63.75-84.44% for the MBP-CGP architecture with two hidden layers and 63.75-82.77% for the MBP-CGP with one hidden layer. Meanwhile, the accuracy of results using MBP-CGP and GBC classifications ranged between 88.75-100% for all datasets with one hidden layer.","PeriodicalId":179466,"journal":{"name":"2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA)","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122050578","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: 4

IC3INA 2018 Advisory Board IC3INA 2018顾问委员会

2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA) Pub Date : 2018-11-01 DOI: 10.1109/ic3ina.2018.8629499

引用次数: 0

Wood Identification on Microscopic Image with Daubechies Wavelet Method and Local Binary Pattern 基于涂抹小波和局部二值模式的显微图像木材识别

2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA) Pub Date : 2018-11-01 DOI: 10.1109/IC3INA.2018.8629529

Salma, P. H. Gunawan, E. Prakasa, B. Sugiarto, R. Wardoyo, Y. Rianto, R. Damayanti, Krisdianto, L. M. Dewi

{"title":"Wood Identification on Microscopic Image with Daubechies Wavelet Method and Local Binary Pattern","authors":"Salma, P. H. Gunawan, E. Prakasa, B. Sugiarto, R. Wardoyo, Y. Rianto, R. Damayanti, Krisdianto, L. M. Dewi","doi":"10.1109/IC3INA.2018.8629529","DOIUrl":"https://doi.org/10.1109/IC3INA.2018.8629529","url":null,"abstract":"Wood is one of Indonesia’s very rich natural resources abundant because the number reaches around 4,000 species. The process of identifying wood species currently it is still done manually in a relatively long time by observing types of fibers, vessels, rays, and other structures directly because there is not a much automatic application of identification of wood species is made. This is an obstacle for experts anatomy of wood because it must check wood species accurately and quickly. Therefore that, the field of Computer Vision is the right solution to develop the process Identification of wood species automatically. In this research program will be made application of Computer Vision to identify wood species with using the Daubechies Wavelet (DW) and Local Binary Pattern (LBP) methods for The extraction of the wood pattern is then classified Support Vector Machine (SVM) method. Results obtained in this study is able to identify the microscopic image of wood as a species of wood with average SVM accuracy is 85%.","PeriodicalId":179466,"journal":{"name":"2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA)","volume":"220 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127527995","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: 8

[Copyright notice] (版权)

2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA) Pub Date : 2018-11-01 DOI: 10.1109/ic3ina.2018.8629536

引用次数: 0