Pub Date : 2015-10-01DOI: 10.1109/ICACSIS.2015.7415194
A. Baskoro, Irwan Haryanto
This paper explains about travel speed detection method that can be applied as the welding simulator using augmented reality. In welding process, the travel speed is an important parameter that influences the welding quality. In the future, this simulator can be used in welder training with a relatively low cost. This method uses ARToolkit, OpenGL library, and Autodesk 3Ds Max software for building the simulator. This method is the development of welding simulator that will show the deviation and accuracy of moving marker detection. This method uses differences in distance of the coordinate per unit time algorithm, taken from the amount of frames per second (FPS) of a camera. After this method was successfully built, the measurement data is taken to analyze the accuracy and the number of error in speed detection by the simulator from the actual speed with different light intensity parameter. The result is that the error in speed detection is not too large, so this simulator was successfully built and it can be developed further to get more sophisticated features on welding process in the future.
{"title":"Development of travel speed detection method in welding simulator using augmented reality","authors":"A. Baskoro, Irwan Haryanto","doi":"10.1109/ICACSIS.2015.7415194","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415194","url":null,"abstract":"This paper explains about travel speed detection method that can be applied as the welding simulator using augmented reality. In welding process, the travel speed is an important parameter that influences the welding quality. In the future, this simulator can be used in welder training with a relatively low cost. This method uses ARToolkit, OpenGL library, and Autodesk 3Ds Max software for building the simulator. This method is the development of welding simulator that will show the deviation and accuracy of moving marker detection. This method uses differences in distance of the coordinate per unit time algorithm, taken from the amount of frames per second (FPS) of a camera. After this method was successfully built, the measurement data is taken to analyze the accuracy and the number of error in speed detection by the simulator from the actual speed with different light intensity parameter. The result is that the error in speed detection is not too large, so this simulator was successfully built and it can be developed further to get more sophisticated features on welding process in the future.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116831618","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}
Pub Date : 2015-10-01DOI: 10.1109/ICACSIS.2015.7415156
W. W. Kalengkongan, B. P. Silalahi, Y. Herdiyeni, S. Douady
This research proposes a method to extract landmark of leaf shape using dynamic threshold polygonal approximation. Landmark-based shape analysis is the core of geometric morphometric and has been used as a quantitative tool in evolutionary and developmental biology. Also, this analysis has been used by botanist and taxonomist to discriminate species. In this research, the polygonal approximation is used to select the best points that can represent the leaf shape variability. We used a dynamic threshold as the control parameter of fitting a series of line segment over a digital curve of leaf shape. This research focuses on seven leaf shape, i.e., cordate, eliptic, lanceolate, obovate, obriculate, ovate and reniform. Experimental results show dynamic polygonal approximation shows can be used to find the important points of leaf shape. This research is promising for discriminating species of plants.
{"title":"Landmark analysis of leaf shape using dynamic threshold polygonal approximation","authors":"W. W. Kalengkongan, B. P. Silalahi, Y. Herdiyeni, S. Douady","doi":"10.1109/ICACSIS.2015.7415156","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415156","url":null,"abstract":"This research proposes a method to extract landmark of leaf shape using dynamic threshold polygonal approximation. Landmark-based shape analysis is the core of geometric morphometric and has been used as a quantitative tool in evolutionary and developmental biology. Also, this analysis has been used by botanist and taxonomist to discriminate species. In this research, the polygonal approximation is used to select the best points that can represent the leaf shape variability. We used a dynamic threshold as the control parameter of fitting a series of line segment over a digital curve of leaf shape. This research focuses on seven leaf shape, i.e., cordate, eliptic, lanceolate, obovate, obriculate, ovate and reniform. Experimental results show dynamic polygonal approximation shows can be used to find the important points of leaf shape. This research is promising for discriminating species of plants.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131870266","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}
Pub Date : 2015-10-01DOI: 10.1109/ICACSIS.2015.7415177
Thia Sabel Permata, A. Bustamam
The formation and proliferation of tumor cells occurs if a special protein that regulates cell division experience any changing on their function, gene expression or both of them. One of the tumor suppressor proteins that plays a significant role in controlling the cell cycle is the TP53 protein. In most of the genetic changes in the tumor, it found that mutant of TP53 is a high risk factor for cancer. Therefore, it is important to conduct studies on clustering protein-protein interactions (PPI) network of TP53. PPI networks are generally presented in the graph network with proteins as vertices and interactions as edges. Markov clustering (MCL) algorithm is a graph clustering method which based on a simulation of stochastic flow on a graph. In implementation, we applied MCL process using the Python programming language. The clustering datasets are the PPI of TP53 obtained from the STRING database. MCL algorithm consists of three main operations such as expansion, inflation, and prune. We conduct the clustering simulation using different parameter of expansion, inflation and the multiplier factor of identity matrix. As the results we found the MCL algorithm is proven to produce robust cluster with TP53 protein as a centroid for each clustering results.
{"title":"Clustering protein-protein interaction network of TP53 tumor suppressor protein using Markov clustering algorithm","authors":"Thia Sabel Permata, A. Bustamam","doi":"10.1109/ICACSIS.2015.7415177","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415177","url":null,"abstract":"The formation and proliferation of tumor cells occurs if a special protein that regulates cell division experience any changing on their function, gene expression or both of them. One of the tumor suppressor proteins that plays a significant role in controlling the cell cycle is the TP53 protein. In most of the genetic changes in the tumor, it found that mutant of TP53 is a high risk factor for cancer. Therefore, it is important to conduct studies on clustering protein-protein interactions (PPI) network of TP53. PPI networks are generally presented in the graph network with proteins as vertices and interactions as edges. Markov clustering (MCL) algorithm is a graph clustering method which based on a simulation of stochastic flow on a graph. In implementation, we applied MCL process using the Python programming language. The clustering datasets are the PPI of TP53 obtained from the STRING database. MCL algorithm consists of three main operations such as expansion, inflation, and prune. We conduct the clustering simulation using different parameter of expansion, inflation and the multiplier factor of identity matrix. As the results we found the MCL algorithm is proven to produce robust cluster with TP53 protein as a centroid for each clustering results.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132041412","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}
Pub Date : 2015-10-01DOI: 10.1109/ICACSIS.2015.7415159
P. Ochieng, Taufik Djatna, W. Kusuma
The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including Mapping and Assembly with Quality (MAQ), which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Therefore, we carried out an in-depth performance analysis of BWA a popular BWT-based aligner and discovered that its performance is significantly better than MAQ although, it has drawbacks regarding execution speed, time complexity and accuracy. Based on those factors we implemented an improved Burrows-Wheeler Alignment algorithm (BWA), anew read alignment package which is original BWT optimized by source code of Ziv-Lempel (LZ-77) sliding window technique and prefix trie string matching, to efficiently search for inexact and exact matches on tandem repeats against a large reference sequence genome. Our analysis show that search speed of improved BWA significantly increased by approximately 1.40 ×faster than MAQ-32 while achieving sufficiently higher accuracy with percent confidence of 96.7 % and 93.0 %. Moreover, it is more efficient to search exact and inexact matches supported by percent error of 0.05 % single ends and 0.04 % for paired end reads also more effective to search for left and right overlap tandem repeat at percent confidence of 88.9%.
新的DNA测序技术产生了大量的短读，这要求开发快速准确的读比对程序。第一代基于哈希表的方法已经被开发出来，包括Mapping and Assembly with Quality (MAQ)，它准确、功能丰富、速度足够快，可以对来自单个个体的短读取进行对齐。然而，MAQ不支持单端读取的间隙对齐，这使得它不适合经常出现索引的较长读取的对齐。当校准规模扩大到数百个个体的重测序时，MAQ的速度也是一个问题。因此，我们对流行的基于bwt的对齐器BWA进行了深入的性能分析，发现它的性能明显优于MAQ，尽管它在执行速度，时间复杂度和准确性方面存在缺点。基于这些因素，我们实现了改进的Burrows-Wheeler比对算法(BWA)，该算法是基于Ziv-Lempel (LZ-77)滑动窗口技术和前缀三串匹配的原始BWT优化的新的读取比对包，用于在大参考序列基因组上高效地搜索串联重复序列的不精确和精确匹配。我们的分析表明，改进的BWA的搜索速度比MAQ-32显著提高了约1.40 ×faster，同时获得了足够高的准确率，百分比置信度分别为96.7%和93.0%。此外，精确匹配和不精确匹配的搜索效率更高，单端误差为0.05%，成对端读长为0.04%，左右重叠串联重复的搜索效率更高，置信度为88.9%。
{"title":"Tandem repeats analysis in DNA sequences based on improved Burrows-Wheeler transform","authors":"P. Ochieng, Taufik Djatna, W. Kusuma","doi":"10.1109/ICACSIS.2015.7415159","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415159","url":null,"abstract":"The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including Mapping and Assembly with Quality (MAQ), which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Therefore, we carried out an in-depth performance analysis of BWA a popular BWT-based aligner and discovered that its performance is significantly better than MAQ although, it has drawbacks regarding execution speed, time complexity and accuracy. Based on those factors we implemented an improved Burrows-Wheeler Alignment algorithm (BWA), anew read alignment package which is original BWT optimized by source code of Ziv-Lempel (LZ-77) sliding window technique and prefix trie string matching, to efficiently search for inexact and exact matches on tandem repeats against a large reference sequence genome. Our analysis show that search speed of improved BWA significantly increased by approximately 1.40 ×faster than MAQ-32 while achieving sufficiently higher accuracy with percent confidence of 96.7 % and 93.0 %. Moreover, it is more efficient to search exact and inexact matches supported by percent error of 0.05 % single ends and 0.04 % for paired end reads also more effective to search for left and right overlap tandem repeat at percent confidence of 88.9%.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134344073","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}
Pub Date : 2015-10-01DOI: 10.1109/ICACSIS.2015.7415197
J. Pidanic, T. Shejbal, Z. Nemec, H. Suhartanto
The precise and quick association of targets is one of the main challenging tasks in the signal processing field of the Multistatic Radar System (MRS). The paper deals with target association techniques based on the computation of the Probability Hypothetic Density (PHD) Function. The Computation time makes solving the PHD a very demanding task. The speedup of a newly developed algorithm depends on vectorization and parallel processing techniques. This paper describes the comparison between the original and parallel version of the target association algorithm with the full set of input data (without any knowledge about the approximation of targets direction) and the comparison with the advanced target association algorithm using additional input information about the direction of the target. All algorithms are processed in the MATLAB environment and Microsoft Visual Studio - C. The comparison also includes Central Processor Unit (CPU) and Graphics Processor Unit (GPU) version of all algorithms.
精确、快速的目标关联是多基地雷达信号处理领域的主要挑战之一。本文研究了基于概率假设密度函数计算的目标关联技术。由于计算时间的限制，求解PHD是一项非常艰巨的任务。一种新开发的算法的加速依赖于向量化和并行处理技术。本文描述了原始版本的目标关联算法与并行版本的目标关联算法在完整的输入数据(不知道目标方向的近似)下的比较，以及与使用额外的目标方向输入信息的高级目标关联算法的比较。所有算法都在MATLAB环境和Microsoft Visual Studio - c中进行处理，并比较了所有算法的中央处理器(CPU)和图形处理器(GPU)版本。
{"title":"Advanced targets association based on GPU computation of PHD function","authors":"J. Pidanic, T. Shejbal, Z. Nemec, H. Suhartanto","doi":"10.1109/ICACSIS.2015.7415197","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415197","url":null,"abstract":"The precise and quick association of targets is one of the main challenging tasks in the signal processing field of the Multistatic Radar System (MRS). The paper deals with target association techniques based on the computation of the Probability Hypothetic Density (PHD) Function. The Computation time makes solving the PHD a very demanding task. The speedup of a newly developed algorithm depends on vectorization and parallel processing techniques. This paper describes the comparison between the original and parallel version of the target association algorithm with the full set of input data (without any knowledge about the approximation of targets direction) and the comparison with the advanced target association algorithm using additional input information about the direction of the target. All algorithms are processed in the MATLAB environment and Microsoft Visual Studio - C. The comparison also includes Central Processor Unit (CPU) and Graphics Processor Unit (GPU) version of all algorithms.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"260 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132780428","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}
Pub Date : 2015-10-01DOI: 10.1109/ICACSIS.2015.7415168
Khumaisa Nur'Aini, Ibtisami Najahaty, Lina Hidayati, H. Murfi, S. Nurrohmah
Online social media are growing very rapidly in recent years, such as Twitter. Even the interaction and communication in the social media can reflect on the events of the real world. This causes the value of the information increasing significantly. However, the huge amount of the information requires a method of automatically detecting topics, one of which is the K-means Clustering. Moreover, the large dimensions of data become obstacles. So, we used singular value decomposition (SVD) to reduce the dimension of the data prior to the learning process using the K-means Clustering. The accuracy of the combination of SVD and K-means Clustering methods showed comparative results, while the computation time required is likely to be faster than the method of K-means Clustering without any reduction in advance.
{"title":"Combination of singular value decomposition and K-means clustering methods for topic detection on Twitter","authors":"Khumaisa Nur'Aini, Ibtisami Najahaty, Lina Hidayati, H. Murfi, S. Nurrohmah","doi":"10.1109/ICACSIS.2015.7415168","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415168","url":null,"abstract":"Online social media are growing very rapidly in recent years, such as Twitter. Even the interaction and communication in the social media can reflect on the events of the real world. This causes the value of the information increasing significantly. However, the huge amount of the information requires a method of automatically detecting topics, one of which is the K-means Clustering. Moreover, the large dimensions of data become obstacles. So, we used singular value decomposition (SVD) to reduce the dimension of the data prior to the learning process using the K-means Clustering. The accuracy of the combination of SVD and K-means Clustering methods showed comparative results, while the computation time required is likely to be faster than the method of K-means Clustering without any reduction in advance.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"38 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133022336","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}
Pub Date : 2015-10-01DOI: 10.1109/ICACSIS.2015.7415157
T. Abidin, M. Subianto, T. A. Gani, R. Ferdhiana
A lot of tropical disease cases that occurred in Indonesia are reported online in Indonesian news portals. Online news portals are now becoming great sources of information because online news articles are updated frequently. A rule-based, combined with machine learning algorithm, to identify the location of the cases has been developed. In this paper, a complete flow to routinely search, crawl, clean, classify, extract, and integrate the extracted entities into Google Earth is presented. The algorithm is started by searching for Indonesian news articles using a set of selected queries and Google Site Search API, and then crawling them. After the articles are crawled, they are cleaned and classified. The articles that discuss about tropical disease cases (classified as positive) are further examined to extract the locution of the incidence and to determine the sentences containing the date of occurrence and the number of casualties. The extracted entities are then stored in a relational database and annotated in an XML keyhole markup language notation to create a geographic visualization in Google Earth. The evaluation shows that it takes approximately 6 minutes to search, crawl, clean, classify, extract, and annotate the extracted entities into an XML keyhole markup language notation from 5 Web articles. In other words, it takes about 72.40 seconds to process a new page.
{"title":"Periodic update and automatic extraction of web data for creating a Google Earth based tool","authors":"T. Abidin, M. Subianto, T. A. Gani, R. Ferdhiana","doi":"10.1109/ICACSIS.2015.7415157","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415157","url":null,"abstract":"A lot of tropical disease cases that occurred in Indonesia are reported online in Indonesian news portals. Online news portals are now becoming great sources of information because online news articles are updated frequently. A rule-based, combined with machine learning algorithm, to identify the location of the cases has been developed. In this paper, a complete flow to routinely search, crawl, clean, classify, extract, and integrate the extracted entities into Google Earth is presented. The algorithm is started by searching for Indonesian news articles using a set of selected queries and Google Site Search API, and then crawling them. After the articles are crawled, they are cleaned and classified. The articles that discuss about tropical disease cases (classified as positive) are further examined to extract the locution of the incidence and to determine the sentences containing the date of occurrence and the number of casualties. The extracted entities are then stored in a relational database and annotated in an XML keyhole markup language notation to create a geographic visualization in Google Earth. The evaluation shows that it takes approximately 6 minutes to search, crawl, clean, classify, extract, and annotate the extracted entities into an XML keyhole markup language notation from 5 Web articles. In other words, it takes about 72.40 seconds to process a new page.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133408213","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}
Pub Date : 2015-10-01DOI: 10.1109/ICACSIS.2015.7415173
D. E. Cahyani, R. Manurung, Rahmad Mahendra
Now the knowledge source in natural language text are available in large quantities. There is an increasing need of knowledge representation, then it would require the knowledge processing on text automatically. The previous research has built on knowledge representation system of natural language text that is OWLizr. However, this study has not been able to handle the knowledge representation in concepts that describe a particular object. This paper developed a knowledge representation system in copula sentences containing concepts in Bahasa Indonesia. If the concept can be handled then the relationship between concepts with components of existing ontology can defined. This supports ontology engineering process that build domain ontology by enrich knowledge of the existing knowledge base. This system combine NLP (Natural Language Processing) techniques and OWL (Web Ontology Language) to model the knowledge contained in the copula sentence. The testing in this system is by unit testing and testing on collection of sentences which adapted from Wikipedia. The results of this research are system can represent knowledge in copula sentences containing concepts in Bahasa Indonesia. The system can generate output in OWL knowledge base that can share and reuse for other systems.
{"title":"Knowledge representation system for copula sentence in Bahasa Indonesia based on Web Ontology Language (OWL)","authors":"D. E. Cahyani, R. Manurung, Rahmad Mahendra","doi":"10.1109/ICACSIS.2015.7415173","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415173","url":null,"abstract":"Now the knowledge source in natural language text are available in large quantities. There is an increasing need of knowledge representation, then it would require the knowledge processing on text automatically. The previous research has built on knowledge representation system of natural language text that is OWLizr. However, this study has not been able to handle the knowledge representation in concepts that describe a particular object. This paper developed a knowledge representation system in copula sentences containing concepts in Bahasa Indonesia. If the concept can be handled then the relationship between concepts with components of existing ontology can defined. This supports ontology engineering process that build domain ontology by enrich knowledge of the existing knowledge base. This system combine NLP (Natural Language Processing) techniques and OWL (Web Ontology Language) to model the knowledge contained in the copula sentence. The testing in this system is by unit testing and testing on collection of sentences which adapted from Wikipedia. The results of this research are system can represent knowledge in copula sentences containing concepts in Bahasa Indonesia. The system can generate output in OWL knowledge base that can share and reuse for other systems.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125654849","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}
Pub Date : 2015-10-01DOI: 10.1109/ICACSIS.2015.7415162
Endang Purnama Giri, A. M. Arymurthy, M. I. Fanany, S. Wijaya
A person with sleep disorder such as apnea will stop breathing for a while during sleep. If frequently occurs, sleep disorder is dangerous for health. An early step for diagnosing apnea is by classifying the sleep stages during sleep. This study explores some shallow classifiers and their feasibility applied to sleep data. Recently, a sleep stages classification system that use deep unsupervised features learning representations have been proposed [9]. In our view, an adequate study on this problem using shallow classifiers still need to be investigated. This study, using some of the data on [9], focuses on evaluating some shallow classifier to the sleep stages classification problem. This study evaluates five classifiers: SVM, Neural Network, Classification Tree, k-Nearest Neighborhood (k-NN), and Naive Bayes. Experiment result shows that neural network gives best performance for sleep stage classification problem. Compared to the SVM (the 2-nd rank of accuracy on S000 data), the neural network is also more efficient than SVM in term of computational time and memory requirement.
{"title":"Sleep stages classification using shallow classifiers","authors":"Endang Purnama Giri, A. M. Arymurthy, M. I. Fanany, S. Wijaya","doi":"10.1109/ICACSIS.2015.7415162","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415162","url":null,"abstract":"A person with sleep disorder such as apnea will stop breathing for a while during sleep. If frequently occurs, sleep disorder is dangerous for health. An early step for diagnosing apnea is by classifying the sleep stages during sleep. This study explores some shallow classifiers and their feasibility applied to sleep data. Recently, a sleep stages classification system that use deep unsupervised features learning representations have been proposed [9]. In our view, an adequate study on this problem using shallow classifiers still need to be investigated. This study, using some of the data on [9], focuses on evaluating some shallow classifier to the sleep stages classification problem. This study evaluates five classifiers: SVM, Neural Network, Classification Tree, k-Nearest Neighborhood (k-NN), and Naive Bayes. Experiment result shows that neural network gives best performance for sleep stage classification problem. Compared to the SVM (the 2-nd rank of accuracy on S000 data), the neural network is also more efficient than SVM in term of computational time and memory requirement.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125551314","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}
Pub Date : 2015-10-01DOI: 10.1109/ICACSIS.2015.7415155
L. Punchoojit, Nuttanont Hongwarittorrn
Diversity of users has become recent design concerns, and children are one of those user groups. Organization of contents is one of research areas in designing for children. Research suggests differences in abilities between adults and children; for instance, attention, logic and memory skills, and linguistic abilities. This is related to the way children navigate and access information. Efficient system organization must correspond with user's categorization scheme. Prior study suggests differences in the way children categorized objects than the predetermined categories; however, it did not provide a comparison between children and adults in the way they generated the categories. Moreover, influence of expertise on categorization schemes has been highlighted in psychological literature. Primary objective of this study was to investigate how children and adults utilize categorization schemes based on their domain expertise. This study was carried out under two different circumstances: 1) when the objects were concrete and both age groups were domain experts, and 2) when objects were more abstract and both age groups could be either novices or experts. Similarity and differences between adults and children were found. The results of both tasks showed indicated that categorization schemes employed by participants depends on the information they were exposed to.
{"title":"Children's and adults' schemes in categorization of basic objects and mobile applications","authors":"L. Punchoojit, Nuttanont Hongwarittorrn","doi":"10.1109/ICACSIS.2015.7415155","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415155","url":null,"abstract":"Diversity of users has become recent design concerns, and children are one of those user groups. Organization of contents is one of research areas in designing for children. Research suggests differences in abilities between adults and children; for instance, attention, logic and memory skills, and linguistic abilities. This is related to the way children navigate and access information. Efficient system organization must correspond with user's categorization scheme. Prior study suggests differences in the way children categorized objects than the predetermined categories; however, it did not provide a comparison between children and adults in the way they generated the categories. Moreover, influence of expertise on categorization schemes has been highlighted in psychological literature. Primary objective of this study was to investigate how children and adults utilize categorization schemes based on their domain expertise. This study was carried out under two different circumstances: 1) when the objects were concrete and both age groups were domain experts, and 2) when objects were more abstract and both age groups could be either novices or experts. Similarity and differences between adults and children were found. The results of both tasks showed indicated that categorization schemes employed by participants depends on the information they were exposed to.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130436781","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}
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