2016 International Conference on Informatics and Computing (ICIC)最新文献

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Stomach disorder detection through the Iris Image using Backpropagation Neural Network 基于反向传播神经网络的虹膜图像胃疾病检测

2016 International Conference on Informatics and Computing (ICIC) Pub Date : 1900-01-01 DOI: 10.1109/IAC.2016.7905714

Aisyah Kumala Dewi, Astri Novianty, T. Purboyo

{"title":"Stomach disorder detection through the Iris Image using Backpropagation Neural Network","authors":"Aisyah Kumala Dewi, Astri Novianty, T. Purboyo","doi":"10.1109/IAC.2016.7905714","DOIUrl":"https://doi.org/10.1109/IAC.2016.7905714","url":null,"abstract":"Stomach is a digestive organ which is the most vulnerable to diseases which are caused by the increased stomach acid production due to wrong diet. Many people sometimes ignore, even worse underestimate this, but if it's been ignored too long, it will lead to death. Thus it's necessary for routine check to determine whether there is disturbance in the stomach organ or not. One simple way to check is through the iris or called iridology. Iridology in science is based on an analysis of the composition of the iris. In particular slice has specific advantages, which can record all state organs, body construction, also psychological condition. In this final project will be made a system which can detect the presence or absence of disturbances in someone's stomach. This software works by taking an image by camera. After that, system will do the feature extraction by using Principal Component Analysis (PCA) and classify it with method Backpropagation Neural Network. From result of testing that has been done, the conclusion is the system is very good at doing classification process with one hidden layer and produce a level of accuracy up to 87,5% from 40 iris image data.","PeriodicalId":404904,"journal":{"name":"2016 International Conference on Informatics and Computing (ICIC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125501800","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: 17

Discovering traceability between business process and software component using Latent Dirichlet Allocation 使用潜狄利克雷分配发现业务流程和软件组件之间的可跟踪性

2016 International Conference on Informatics and Computing (ICIC) Pub Date : 1900-01-01 DOI: 10.1109/IAC.2016.7905724

A. Baskara, R. Sarno, Adhatus Solichah

{"title":"Discovering traceability between business process and software component using Latent Dirichlet Allocation","authors":"A. Baskara, R. Sarno, Adhatus Solichah","doi":"10.1109/IAC.2016.7905724","DOIUrl":"https://doi.org/10.1109/IAC.2016.7905724","url":null,"abstract":"Software system is built to support business process. Software system needs to evolve over time because there are some changes on business processes. A relationship exists between business processes and supporting software system which can help maintainers to understand the system and carried maintenance tasks. Such kind of relation is called traceability links. One approach to discover traceability links is analyzing the similarity of textual content. This paper proposed an approach to discover a traceability links between two software artefacts, which are business processes and software components, using Latent Dirichlet Allocation (LDA). In the proposed method, each label of business process model and software components model are formed into documents. Then, the topic probability distributions are calculated using LDA. The similarities between those two artefacts are calculated using Jensen-Shannon (JS) Divergence The result of LDA is compared to the real software components and business process documents and it shows that LDA and JS Divergence are applicable for discovering traceability links with average Cohens Kappa value of 0.67.","PeriodicalId":404904,"journal":{"name":"2016 International Conference on Informatics and Computing (ICIC)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127861698","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: 7

Algorithm for updating n-grams word dictionary for web classification 用于web分类的n-grams词字典更新算法

2016 International Conference on Informatics and Computing (ICIC) Pub Date : 1900-01-01 DOI: 10.1109/IAC.2016.7905758

T. Abidin, R. Ferdhiana

{"title":"Algorithm for updating n-grams word dictionary for web classification","authors":"T. Abidin, R. Ferdhiana","doi":"10.1109/IAC.2016.7905758","DOIUrl":"https://doi.org/10.1109/IAC.2016.7905758","url":null,"abstract":"In this paper, we examine an algorithm to update n-grams word dictionary (thesaurus) and evaluate its effectiveness in binary classification problem. The thesaurus is used as a reference to generate the numerical feature attributes of web pages. Generally, the n-grams word dictionary is built once using a set of training data and its content is never updated. Hence, the content is static and its coverage is limited to the n-grams word found in the initial training set. Actually, the content of a thesaurus must be dynamic, especially because the n-grams word dictionary is used repeatedly as a reference in generating the numerical feature attributes of web pages. We argue that a dynamic thesaurus is better than a static one in a long-term. Thus, n-grams word dictionary should be updated frequently using new data without degrading the classification accuracy. We validate our proposed algorithm using several test sets, each of which contains one hundred web pages, except for the last one. The experimental results show that our proposed algorithm works well. On average, the accuracy of feature dataset generated using the existing (old) dictionary is 57.75%, while the accuracy of feature dataset generated using updated (new) dictionary is 76.75%. The proposed algorithm increases classification accuracy about 32.90%.","PeriodicalId":404904,"journal":{"name":"2016 International Conference on Informatics and Computing (ICIC)","volume":"1 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125815560","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: 5

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