Pub Date : 2016-10-01DOI: 10.1109/IWBIS.2016.7872887
Herrnansyah, Y. Ruldeviyani, R. F. Aji
Library Automation and Digital Archive (Lontar) is a liblary information system developed by Universitas Indonesia and used by its main library. Rapid increase of library collections will soon make query performance of current SQL DBMS, which is MySQL, not fast enough to satisfy users and need to be complemented by NoSQL database, an emerging technology that specially developed for managing big data. The goal of this research is to implement and analyze the usage of NoSQL database to improve the query performance of Lontar. MongoDB is selected as NoSQL DBMS and the result shows that MongoDB is signficantly faster than MySQL.
{"title":"Enhancing query performance of library information systems using NoSQL DBMS: Case study on library information systems of Universitas Indonesia","authors":"Herrnansyah, Y. Ruldeviyani, R. F. Aji","doi":"10.1109/IWBIS.2016.7872887","DOIUrl":"https://doi.org/10.1109/IWBIS.2016.7872887","url":null,"abstract":"Library Automation and Digital Archive (Lontar) is a liblary information system developed by Universitas Indonesia and used by its main library. Rapid increase of library collections will soon make query performance of current SQL DBMS, which is MySQL, not fast enough to satisfy users and need to be complemented by NoSQL database, an emerging technology that specially developed for managing big data. The goal of this research is to implement and analyze the usage of NoSQL database to improve the query performance of Lontar. MongoDB is selected as NoSQL DBMS and the result shows that MongoDB is signficantly faster than MySQL.","PeriodicalId":193821,"journal":{"name":"2016 International Workshop on Big Data and Information Security (IWBIS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124611510","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 : 2016-10-01DOI: 10.1109/IWBIS.2016.7872884
I. Azimi, A. Anzanpour, A. Rahmani, P. Liljeberg, T. Salakoski
Remote patient monitoring is essential for many patients that are suffering from acute diseases such as different heart conditions. Continuous health monitoring can provide medical services that consider the current medical state of the patient and to predict or early-detect future potentially critical situations. In this regard, Internet of Things as a multidisciplinary paradigm can provide profound impacts. However, the current IoT-based systems may encounter difficulties to provide continuous and real time patient monitoring due to issues in data analytics. In this paper, we introduce a new IoT-based approach to offer smart medical warning in personalized patient monitoring. The proposed approach consider local computing paradigm enabled by machine learning algorithms and automate management of system components in computing section. The proposed system is evaluated via a case study concerning continuous patient monitoring to early-detect patient deterioration via arrhythmia in ECG signal.
{"title":"Medical warning system based on Internet of Things using fog computing","authors":"I. Azimi, A. Anzanpour, A. Rahmani, P. Liljeberg, T. Salakoski","doi":"10.1109/IWBIS.2016.7872884","DOIUrl":"https://doi.org/10.1109/IWBIS.2016.7872884","url":null,"abstract":"Remote patient monitoring is essential for many patients that are suffering from acute diseases such as different heart conditions. Continuous health monitoring can provide medical services that consider the current medical state of the patient and to predict or early-detect future potentially critical situations. In this regard, Internet of Things as a multidisciplinary paradigm can provide profound impacts. However, the current IoT-based systems may encounter difficulties to provide continuous and real time patient monitoring due to issues in data analytics. In this paper, we introduce a new IoT-based approach to offer smart medical warning in personalized patient monitoring. The proposed approach consider local computing paradigm enabled by machine learning algorithms and automate management of system components in computing section. The proposed system is evaluated via a case study concerning continuous patient monitoring to early-detect patient deterioration via arrhythmia in ECG signal.","PeriodicalId":193821,"journal":{"name":"2016 International Workshop on Big Data and Information Security (IWBIS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132337075","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 : 2016-10-01DOI: 10.1109/IWBIS.2016.7872904
Novian Habibie, Rindra Wiska, A. Nugraha, A. Wibisono, P. Mursanto, W. S. Nugroho, S. Yazid
Wireless Sensor Network (WSN) is a system used to conduct a remote monitoring in a wide monitoring area. It has a sensor node — a sampling point — which communicate each other to passing their data to central node for recapitulation or transmit it to data center. Because of that, communication system is a crucial thing for WSN. However, WSN may be deployed in a environment that far from ideal condition. Placed in an unattended area with far distance between nodes, WSN is very vulnerable with security threats. To overcome that, the good combination between communication protocol and encryption algorithm for WSN is needed to gather an accurate and representative data with high transmission speed. This research focused on finding those combination for our own-made low-cost sensor node for CO2 monitoring. In this research, two routing protocols (AODV and TARP) and several encryption algorithms (AES, ChaCha, and Speck) tested to determine which combination is give the best result. As the result, combination between routing protocol AODV and encryption algorithm Speck give the best result in the term of performance.
{"title":"Comparative study of lightweight secure multiroute communication system in low cost wireless sensor network for CO2 monitoring","authors":"Novian Habibie, Rindra Wiska, A. Nugraha, A. Wibisono, P. Mursanto, W. S. Nugroho, S. Yazid","doi":"10.1109/IWBIS.2016.7872904","DOIUrl":"https://doi.org/10.1109/IWBIS.2016.7872904","url":null,"abstract":"Wireless Sensor Network (WSN) is a system used to conduct a remote monitoring in a wide monitoring area. It has a sensor node — a sampling point — which communicate each other to passing their data to central node for recapitulation or transmit it to data center. Because of that, communication system is a crucial thing for WSN. However, WSN may be deployed in a environment that far from ideal condition. Placed in an unattended area with far distance between nodes, WSN is very vulnerable with security threats. To overcome that, the good combination between communication protocol and encryption algorithm for WSN is needed to gather an accurate and representative data with high transmission speed. This research focused on finding those combination for our own-made low-cost sensor node for CO2 monitoring. In this research, two routing protocols (AODV and TARP) and several encryption algorithms (AES, ChaCha, and Speck) tested to determine which combination is give the best result. As the result, combination between routing protocol AODV and encryption algorithm Speck give the best result in the term of performance.","PeriodicalId":193821,"journal":{"name":"2016 International Workshop on Big Data and Information Security (IWBIS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132627092","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 : 2016-10-01DOI: 10.1109/IWBIS.2016.7872886
W. Jatmiko, D. M. S. Arsa, H. A. Wisesa, Grafiks Jati, M. A. Ma'sum
In the recent years, the volume of data that exists in the world has risen dramatically. Biomedical data are data that are recorded from a living being that is used to help analyzing and diagnosis of a certain illness. Like many other types of data, the volume biomedical data has also risen in the last couple of years. In order to process this large amount of data, conventional processing techniques are not adequate. In this paper, we discuss several approach in processing large amount of biomedical data. This paper will also discuss several variations of biomedical data and the challenge that are faced when processing those biomedical data in large sizes. We also proposed integrated Telehealth system which combine Tele-ECG, Tele-USG, and existing biomedical application. The system will be implemented on Big Data Framework. Then Tele-health development can be done using the phase that we propose. The system is started by developing end-to-end user system, implementation to Big Data Framework, then it is finished by Clinical Practice. The proposed framework can be used for high standard biomedical system.
{"title":"A review of big data analytics in the biomedical field","authors":"W. Jatmiko, D. M. S. Arsa, H. A. Wisesa, Grafiks Jati, M. A. Ma'sum","doi":"10.1109/IWBIS.2016.7872886","DOIUrl":"https://doi.org/10.1109/IWBIS.2016.7872886","url":null,"abstract":"In the recent years, the volume of data that exists in the world has risen dramatically. Biomedical data are data that are recorded from a living being that is used to help analyzing and diagnosis of a certain illness. Like many other types of data, the volume biomedical data has also risen in the last couple of years. In order to process this large amount of data, conventional processing techniques are not adequate. In this paper, we discuss several approach in processing large amount of biomedical data. This paper will also discuss several variations of biomedical data and the challenge that are faced when processing those biomedical data in large sizes. We also proposed integrated Telehealth system which combine Tele-ECG, Tele-USG, and existing biomedical application. The system will be implemented on Big Data Framework. Then Tele-health development can be done using the phase that we propose. The system is started by developing end-to-end user system, implementation to Big Data Framework, then it is finished by Clinical Practice. The proposed framework can be used for high standard biomedical system.","PeriodicalId":193821,"journal":{"name":"2016 International Workshop on Big Data and Information Security (IWBIS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127822148","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 : 2016-10-01DOI: 10.1109/IWBIS.2016.7872902
G. Jati, Ilham Kusuma, M. Hilman, W. Jatmiko
Compression still become main concern in big data framework. The performance of big data depend on speed of data transfer. Compressed data can speed up transfer data between network. It also save more space for storage. Several compression method is provide by Hadoop as a most common big data framework. That method mostly for general purpose. But the performance still have to optimize especially for Biomedical record like ECG data. We propose Set Partitioning in Hierarchical Tree (SPIHT) for big data compression with study case ECG signal data. In this paper compression will run in Hadoop Framework. The proposed method has stages such as input signal, map input signal, spiht coding, and reduce bit-stream. The compression produce compressed data for intermediate (Map) output and final (reduce) output. The experiment using ECG data to measure compression performance. The proposed method gets Percentage Root-mean-square difference (PRD) is about 1.0. Compare to existing method, the proposed method get better Compression Ratio (CR) with competitive longer compression time. So proposed method gets better performance compare to other method especially for ECG dataset.
{"title":"Big data compression using spiht in Hadoop: A case study in multi-lead ECG signals","authors":"G. Jati, Ilham Kusuma, M. Hilman, W. Jatmiko","doi":"10.1109/IWBIS.2016.7872902","DOIUrl":"https://doi.org/10.1109/IWBIS.2016.7872902","url":null,"abstract":"Compression still become main concern in big data framework. The performance of big data depend on speed of data transfer. Compressed data can speed up transfer data between network. It also save more space for storage. Several compression method is provide by Hadoop as a most common big data framework. That method mostly for general purpose. But the performance still have to optimize especially for Biomedical record like ECG data. We propose Set Partitioning in Hierarchical Tree (SPIHT) for big data compression with study case ECG signal data. In this paper compression will run in Hadoop Framework. The proposed method has stages such as input signal, map input signal, spiht coding, and reduce bit-stream. The compression produce compressed data for intermediate (Map) output and final (reduce) output. The experiment using ECG data to measure compression performance. The proposed method gets Percentage Root-mean-square difference (PRD) is about 1.0. Compare to existing method, the proposed method get better Compression Ratio (CR) with competitive longer compression time. So proposed method gets better performance compare to other method especially for ECG dataset.","PeriodicalId":193821,"journal":{"name":"2016 International Workshop on Big Data and Information Security (IWBIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129809567","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 : 2016-10-01DOI: 10.1109/IWBIS.2016.7872895
Ilham Kusuma, M. A. Ma'sum, Novian Habibie, W. Jatmiko, H. Suhartanto
The growth of data has bring us to the big data generation where the amount of data cannot be computed using conventional environment. There are a lot of computational environment that had been developed to compute big data, one of them is Hadoop that has Distributed File System and MapReduce framework. Spark is newly framework that can be combined with Hadoop and run on top of it. In this paper, we design intelligent k-means based on Spark for big data clustering. Our design is using batch of data instead using original Resilient Distributed Dataset (RDD). We compare our design with the implementation that using original RDD of data. Result of experiment shows that implementation using batch of data is faster than the implementation using original RDD.
{"title":"Design of intelligent k-means based on spark for big data clustering","authors":"Ilham Kusuma, M. A. Ma'sum, Novian Habibie, W. Jatmiko, H. Suhartanto","doi":"10.1109/IWBIS.2016.7872895","DOIUrl":"https://doi.org/10.1109/IWBIS.2016.7872895","url":null,"abstract":"The growth of data has bring us to the big data generation where the amount of data cannot be computed using conventional environment. There are a lot of computational environment that had been developed to compute big data, one of them is Hadoop that has Distributed File System and MapReduce framework. Spark is newly framework that can be combined with Hadoop and run on top of it. In this paper, we design intelligent k-means based on Spark for big data clustering. Our design is using batch of data instead using original Resilient Distributed Dataset (RDD). We compare our design with the implementation that using original RDD of data. Result of experiment shows that implementation using batch of data is faster than the implementation using original RDD.","PeriodicalId":193821,"journal":{"name":"2016 International Workshop on Big Data and Information Security (IWBIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128238497","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 : 2016-10-01DOI: 10.1109/IWBIS.2016.7872898
H. Wisesa, M. A. Ma'sum, A. Wibisono
The number of vehicles that exists on public roads have increased drastically over the years. This have caused several problems, where one of the most common problem is traffic jam. There have been several studies that have tried to solve this problem, such as by using real time videos with computer vision, wireless sensor networks, and traffic data predictions. In this study, we proposed a modification of Fast Incremental Model Trees with Drift Detections (FIMT-DD) to predict the traffic flow from a large traffic data set provided by the Government of United Kingdom. From our experiment results using large datasets, our proposed method have proven to be more accurate in predicting the traffic flow as compared to the conventional FIMT-DD Algorithm.
{"title":"Adaptive range in FIMT-DD tree for large data streams","authors":"H. Wisesa, M. A. Ma'sum, A. Wibisono","doi":"10.1109/IWBIS.2016.7872898","DOIUrl":"https://doi.org/10.1109/IWBIS.2016.7872898","url":null,"abstract":"The number of vehicles that exists on public roads have increased drastically over the years. This have caused several problems, where one of the most common problem is traffic jam. There have been several studies that have tried to solve this problem, such as by using real time videos with computer vision, wireless sensor networks, and traffic data predictions. In this study, we proposed a modification of Fast Incremental Model Trees with Drift Detections (FIMT-DD) to predict the traffic flow from a large traffic data set provided by the Government of United Kingdom. From our experiment results using large datasets, our proposed method have proven to be more accurate in predicting the traffic flow as compared to the conventional FIMT-DD Algorithm.","PeriodicalId":193821,"journal":{"name":"2016 International Workshop on Big Data and Information Security (IWBIS)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130449116","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 : 2016-10-01DOI: 10.1109/IWBIS.2016.7872899
H. Wisesa, M. A. Ma'sum, P. Mursanto, A. Febrian
This paper provides a comparison of processing large traffic data by using decision trees. The experiment was tested in three different classifier tools that are very popular and are widely used in the community. These classifier tools are WEKA classifier, MoA (Massive Online Analysis) classifier, and SPARK MLib that runs on Hadoop infrastructure. We tested the traffic data using decision trees because it is one of the best methods for regressing the large data. The experiment results showed that the WEKA classifier fails to classify dataset with a large number of instance, wheras the MoA has successfully regress the dataset as a datastream. The SPARK MLib decision trees algorithm could also successfully resgress the traffic data quickly with a fairly good accuracy.
{"title":"Processing big data with decision trees: A case study in large traffic data","authors":"H. Wisesa, M. A. Ma'sum, P. Mursanto, A. Febrian","doi":"10.1109/IWBIS.2016.7872899","DOIUrl":"https://doi.org/10.1109/IWBIS.2016.7872899","url":null,"abstract":"This paper provides a comparison of processing large traffic data by using decision trees. The experiment was tested in three different classifier tools that are very popular and are widely used in the community. These classifier tools are WEKA classifier, MoA (Massive Online Analysis) classifier, and SPARK MLib that runs on Hadoop infrastructure. We tested the traffic data using decision trees because it is one of the best methods for regressing the large data. The experiment results showed that the WEKA classifier fails to classify dataset with a large number of instance, wheras the MoA has successfully regress the dataset as a datastream. The SPARK MLib decision trees algorithm could also successfully resgress the traffic data quickly with a fairly good accuracy.","PeriodicalId":193821,"journal":{"name":"2016 International Workshop on Big Data and Information Security (IWBIS)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123748985","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 : 2016-10-01DOI: 10.1109/IWBIS.2016.7872893
A. A. Gunawan, Tania, Derwin Suhartono
Recommender systems are nowadays widely used in e-commerce industry to boost its sale. One of the popular algorithms in recommender systems is collaborative filtering. The fundamental assumption behind this algorithm is that other users' opinions can be filtered and accumulated in such a way as to provide a plausible prediction of the target user's preference. In this paper, we would like to develop a recommender system with big data of one e-commerce company and deliver the recommendations through a personalized email. To address this problem, we propose user-based collaboration filter based on company dataset and employ several similarity functions: Euclidean distance, Cosine, Pearson correlation and Tanimoto coefficient. The experimental results show that: (i) user responses are positive to the given recommendations based on user perception survey (ii) Tanimoto coefficient with 10 neighbors shows the best performance in the RMSE, precision and recall evaluation based on groundtruth dataset.
{"title":"Developing recommender systems for personalized email with big data","authors":"A. A. Gunawan, Tania, Derwin Suhartono","doi":"10.1109/IWBIS.2016.7872893","DOIUrl":"https://doi.org/10.1109/IWBIS.2016.7872893","url":null,"abstract":"Recommender systems are nowadays widely used in e-commerce industry to boost its sale. One of the popular algorithms in recommender systems is collaborative filtering. The fundamental assumption behind this algorithm is that other users' opinions can be filtered and accumulated in such a way as to provide a plausible prediction of the target user's preference. In this paper, we would like to develop a recommender system with big data of one e-commerce company and deliver the recommendations through a personalized email. To address this problem, we propose user-based collaboration filter based on company dataset and employ several similarity functions: Euclidean distance, Cosine, Pearson correlation and Tanimoto coefficient. The experimental results show that: (i) user responses are positive to the given recommendations based on user perception survey (ii) Tanimoto coefficient with 10 neighbors shows the best performance in the RMSE, precision and recall evaluation based on groundtruth dataset.","PeriodicalId":193821,"journal":{"name":"2016 International Workshop on Big Data and Information Security (IWBIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130097117","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 : 2016-10-01DOI: 10.1109/IWBIS.2016.7872901
A. Wibowo, S. Adhy, R. Kusumaningrum, H. A. Wibawa, K. Sekiyama
The detection of molecular markers such as micro ribonucleic acid (miRNA) expression levels in the cells are essential for diagnosis of a disease, especially cancer. Recently, a huge amount of molecular markers on cell is being extracted by single molecular detection method, as results, excessively detection process and time is required. Meanwhile DNA computing has capabilities to interact with biological nucleic acids and massively parallel processing, we propose parallel rule-based classifier using DNA strand displacement reaction as method of detecting multiple molecular markers. Two DNA reaction of parallel sensing and encoding system of the IF-THEN rules have been developed in our proposed model. In this paper, we compared between the proposed method and the AND gate method for the classification result. Moreover, based on the simulation results show that our method is possible as an alternative solution for the programmable fast big molecular markers detection and diagnosis.
{"title":"Parallel rules based classifier using DNA strand displacement for multiple molecular markers detection","authors":"A. Wibowo, S. Adhy, R. Kusumaningrum, H. A. Wibawa, K. Sekiyama","doi":"10.1109/IWBIS.2016.7872901","DOIUrl":"https://doi.org/10.1109/IWBIS.2016.7872901","url":null,"abstract":"The detection of molecular markers such as micro ribonucleic acid (miRNA) expression levels in the cells are essential for diagnosis of a disease, especially cancer. Recently, a huge amount of molecular markers on cell is being extracted by single molecular detection method, as results, excessively detection process and time is required. Meanwhile DNA computing has capabilities to interact with biological nucleic acids and massively parallel processing, we propose parallel rule-based classifier using DNA strand displacement reaction as method of detecting multiple molecular markers. Two DNA reaction of parallel sensing and encoding system of the IF-THEN rules have been developed in our proposed model. In this paper, we compared between the proposed method and the AND gate method for the classification result. Moreover, based on the simulation results show that our method is possible as an alternative solution for the programmable fast big molecular markers detection and diagnosis.","PeriodicalId":193821,"journal":{"name":"2016 International Workshop on Big Data and Information Security (IWBIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134127167","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：