Pub Date : 2015-10-01DOI: 10.1109/ICACSIS.2015.7415163
M. D. Enjat Munajat, D. H. Widyantoro, R. Munir
The purpose of this paper is to describe a new approach in road detection. This research uses two detection processes approaches: RGB histogram Filterization and Boundary Classifer, which is different from previous works on road detection. RGB Histogram Filterization processes the reading from the camera in greyscale form and afterward processes them by color segmentation. The last step for this process is determining area between the slopes, which is considered to be the road area. Boundary classification process then employs the RGB indexing on slope ranges, and mapping them into real pictures of roads and its environments. The next process is specifically looking for line boundaries by using Hough-Transform and Canny Edge Detection, and transforms them into binary numbers of `0' and `1'. `1' represents road boundaries while `0' represents surrounding area. The coordinate of `1', then mapped by cubic spline to produce connecting line between point `1' coordinates, which in the end produce sharp images on boundaries between road and non-road. This model has proven to be able to detect road conditions and distinguish roads from non-road in a precise way. A test is already conducted for the system by using real-time roads in Bandung, Indonesia. The results are really promising for the road condition on both straight and curved road area.
{"title":"Road detection system based on RGB histogram filterization and boundary classifier","authors":"M. D. Enjat Munajat, D. H. Widyantoro, R. Munir","doi":"10.1109/ICACSIS.2015.7415163","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415163","url":null,"abstract":"The purpose of this paper is to describe a new approach in road detection. This research uses two detection processes approaches: RGB histogram Filterization and Boundary Classifer, which is different from previous works on road detection. RGB Histogram Filterization processes the reading from the camera in greyscale form and afterward processes them by color segmentation. The last step for this process is determining area between the slopes, which is considered to be the road area. Boundary classification process then employs the RGB indexing on slope ranges, and mapping them into real pictures of roads and its environments. The next process is specifically looking for line boundaries by using Hough-Transform and Canny Edge Detection, and transforms them into binary numbers of `0' and `1'. `1' represents road boundaries while `0' represents surrounding area. The coordinate of `1', then mapped by cubic spline to produce connecting line between point `1' coordinates, which in the end produce sharp images on boundaries between road and non-road. This model has proven to be able to detect road conditions and distinguish roads from non-road in a precise way. A test is already conducted for the system by using real-time roads in Bandung, Indonesia. The results are really promising for the road condition on both straight and curved road area.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"2004 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114129040","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.2015.7415195
Radu Muschevici
Modern software systems must support a high degree of variability to adapt to a wide range of requirements and operating conditions. While static adaptation based on software product lines is becoming more common, dynamic adaptation is less well-explored. However, runtime adaptation has a host of advantages ranging from downtime avoidance to performance improvements. Auto-adaptation is a particularly promising form of runtime adaptation that enables a running program to adapt autonomously, in swift response to changing conditions in the running environment. This paper focuses on the design of a programming language facility to support the runtime auto-configuration of dynamic software product lines (DSPL). We implement this facility for the Abstract Behavioural Specification (ABS) language by introducing a dynamic, reflection-based meta-programming facility for ABS, called MetaABS and a runtime environment that readily supports dynamically auto-adapting systems written in MetaABS.
{"title":"Dynamic, auto-adaptive software product lines using the ABS language","authors":"Radu Muschevici","doi":"10.1109/ICACSIS.2015.7415195","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415195","url":null,"abstract":"Modern software systems must support a high degree of variability to adapt to a wide range of requirements and operating conditions. While static adaptation based on software product lines is becoming more common, dynamic adaptation is less well-explored. However, runtime adaptation has a host of advantages ranging from downtime avoidance to performance improvements. Auto-adaptation is a particularly promising form of runtime adaptation that enables a running program to adapt autonomously, in swift response to changing conditions in the running environment. This paper focuses on the design of a programming language facility to support the runtime auto-configuration of dynamic software product lines (DSPL). We implement this facility for the Abstract Behavioural Specification (ABS) language by introducing a dynamic, reflection-based meta-programming facility for ABS, called MetaABS and a runtime environment that readily supports dynamically auto-adapting systems written in MetaABS.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"34 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":"129608252","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.2015.7415176
M. N. Puspita, W. Kusuma, A. Kustiyo, R. Heryanto
Jamu is an Indonesia herbal medicine made from natural materials such as roots, leaves, fruits, and animals. The purpose of this research is to develop a classification system for jamu efficacy based on the composition of plants using Support Vector Machine (SVM) and to implement the k-means clustering algorithm as a feature selection method. The result of this study was compared to the previous research that using SVM method without feature selection. This study used variances to evaluate the results of clustering. The total of 3138 data herbs and 465 plant species were grouped into 100 clusters with the variance of 0.0094. The managed group succesfully reduced the data dimension into 3047 of jamu sample and 236 species of herbs and plants as features. The result of SVM classification using feature selection yielded the accuracy of 71.5%.
{"title":"A classification system for jamu efficacy based on formula using support vector machine and k-means algorithm as a feature selection","authors":"M. N. Puspita, W. Kusuma, A. Kustiyo, R. Heryanto","doi":"10.1109/ICACSIS.2015.7415176","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415176","url":null,"abstract":"Jamu is an Indonesia herbal medicine made from natural materials such as roots, leaves, fruits, and animals. The purpose of this research is to develop a classification system for jamu efficacy based on the composition of plants using Support Vector Machine (SVM) and to implement the k-means clustering algorithm as a feature selection method. The result of this study was compared to the previous research that using SVM method without feature selection. This study used variances to evaluate the results of clustering. The total of 3138 data herbs and 465 plant species were grouped into 100 clusters with the variance of 0.0094. The managed group succesfully reduced the data dimension into 3047 of jamu sample and 236 species of herbs and plants as features. The result of SVM classification using feature selection yielded the accuracy of 71.5%.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"39 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":"116483129","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.2015.7415191
G. Jati, Aprinaldi, S. M. Isa, W. Jatmiko
In this paper we present a method for multi-lead ECG signal compression using Predictive Coding combined with Set Partitioning In Hierarchical Trees (SPIHT). We utilize linear prediction between the beats to exploit the high correlation among those beats. This method can optimize the redundancy between adjacent samples and adjacent beats. Predictive coding is the next step after beat reordering step. The purpose of using predictive coding is to minimize amplitude variance of 2D ECG array so the compression error can be minimize. The experiments from selected records from MIT-BIH arrhythmia database shows that the proposed method is more efficient for ECG signal compression compared with original SPIHT and relatively have lower distortion with the same compression ratios compared to the other wavelet transformation techniques.
{"title":"ECG signal compression by predictive coding and Set Partitioning in Hierarchical Trees (SPIHT)","authors":"G. Jati, Aprinaldi, S. M. Isa, W. Jatmiko","doi":"10.1109/ICACSIS.2015.7415191","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415191","url":null,"abstract":"In this paper we present a method for multi-lead ECG signal compression using Predictive Coding combined with Set Partitioning In Hierarchical Trees (SPIHT). We utilize linear prediction between the beats to exploit the high correlation among those beats. This method can optimize the redundancy between adjacent samples and adjacent beats. Predictive coding is the next step after beat reordering step. The purpose of using predictive coding is to minimize amplitude variance of 2D ECG array so the compression error can be minimize. The experiments from selected records from MIT-BIH arrhythmia database shows that the proposed method is more efficient for ECG signal compression compared with original SPIHT and relatively have lower distortion with the same compression ratios compared to the other wavelet transformation techniques.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"11 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":"133060005","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.2015.7415183
Hengki Muradi, A. Bustamam, D. Lestari
Gene clustering can be achieved through hierarchical or partition method. Both clustering methods can be combined by processing the partition and hierarchical phases alternately. This method is known as a hierarchical clustering ordered partitioning and collapsing hybrid (HOPACH) method. The Partitioning phase can be done by using PAM, SOM, or K-Means methods. The partition process is continued with the ordered process, and then it is corrected with agglomerative process, in order to have more accurate clustering results. Furthermore, the main clusters are determined by using MSS (Median Split Silhouette) value. We selected the clustering results which minimize the MSS value. In this work, we conduct the clustering on 136 Ebola Virus DNA sequences data from GenBank. The global alignment process is initially performed, followed by genetic distance calculation using Jukes-Cantor correction. In our implementation, we applied global alignment process and used the combination of HOPACH-PAM clustering using the R open source programming tool. In our results, we obtained maximum genetic distance is 0.6153407; meanwhile the minimum genetic distance is 0. Furthermore, genetic distance matrix can be used as a basis for sequences clustering and phylogenetic analysis. In our HOPACH-PAM clustering results, we obtained 10 main clusters with MSS value is 0.8873843. Ebola virus clusters can be identified by species and virus epidemic year.
{"title":"Application of hierarchical clustering ordered partitioning and collapsing hybrid in Ebola Virus phylogenetic analysis","authors":"Hengki Muradi, A. Bustamam, D. Lestari","doi":"10.1109/ICACSIS.2015.7415183","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415183","url":null,"abstract":"Gene clustering can be achieved through hierarchical or partition method. Both clustering methods can be combined by processing the partition and hierarchical phases alternately. This method is known as a hierarchical clustering ordered partitioning and collapsing hybrid (HOPACH) method. The Partitioning phase can be done by using PAM, SOM, or K-Means methods. The partition process is continued with the ordered process, and then it is corrected with agglomerative process, in order to have more accurate clustering results. Furthermore, the main clusters are determined by using MSS (Median Split Silhouette) value. We selected the clustering results which minimize the MSS value. In this work, we conduct the clustering on 136 Ebola Virus DNA sequences data from GenBank. The global alignment process is initially performed, followed by genetic distance calculation using Jukes-Cantor correction. In our implementation, we applied global alignment process and used the combination of HOPACH-PAM clustering using the R open source programming tool. In our results, we obtained maximum genetic distance is 0.6153407; meanwhile the minimum genetic distance is 0. Furthermore, genetic distance matrix can be used as a basis for sequences clustering and phylogenetic analysis. In our HOPACH-PAM clustering results, we obtained 10 main clusters with MSS value is 0.8873843. Ebola virus clusters can be identified by species and virus epidemic year.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"3 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":"124083428","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.2015.7415187
I. Pranata, G. Skinner
This paper investigates the use of machine learning clustering technique to segment and target customers of a wholesale distributor. It describes the selection, analysis, and interpretation of clusters for evaluating customers annual spending on the products. We show how circular statistics can categorize customers by looking at the annual spending on six essential product categories. Several clusters were created using k-means clustering algorithm and an in-depth analysis on these clusters were performed using several techniques to carefully select the best cluster. Automated clustering was able to suggest groups that these customers fall into. The evaluation and interpretation of clusters were able to provide insights into various purchase behaviors and to nominate the best customer group to target.
{"title":"Segmenting and targeting customers through clusters selection & analysis","authors":"I. Pranata, G. Skinner","doi":"10.1109/ICACSIS.2015.7415187","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415187","url":null,"abstract":"This paper investigates the use of machine learning clustering technique to segment and target customers of a wholesale distributor. It describes the selection, analysis, and interpretation of clusters for evaluating customers annual spending on the products. We show how circular statistics can categorize customers by looking at the annual spending on six essential product categories. Several clusters were created using k-means clustering algorithm and an in-depth analysis on these clusters were performed using several techniques to carefully select the best cluster. Automated clustering was able to suggest groups that these customers fall into. The evaluation and interpretation of clusters were able to provide insights into various purchase behaviors and to nominate the best customer group to target.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"23 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":"125764941","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.2015.7415185
Nuttanont Hongwarittorrn, Suttikiat Meelap
This research investigated a new Image-Based CAPTCHA called Gestalt Geometric CAPTCHA, which does not require the use of a database of images, and is based on the Gestalt Theory of human recognition. The aim was to develop a type of CAPTCHA that is easier for human users and harder for bots. We experimentally tested the use and effectiveness of Gestalt Geometric CAPTCHA in terms of time for completion, authentication pass rate, and user satisfaction, in comparison with reCAPTCHA, Ironclad CAPTCHA, and ShapeCAPTCHA. We also tested our novel CAPTCHA for robustness against two shape detection and classification programs, ShapeChecker and Shape-detect. The results were promising, but some issues remain for future improvement.
{"title":"Gestalt geometric CAPTCHA","authors":"Nuttanont Hongwarittorrn, Suttikiat Meelap","doi":"10.1109/ICACSIS.2015.7415185","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415185","url":null,"abstract":"This research investigated a new Image-Based CAPTCHA called Gestalt Geometric CAPTCHA, which does not require the use of a database of images, and is based on the Gestalt Theory of human recognition. The aim was to develop a type of CAPTCHA that is easier for human users and harder for bots. We experimentally tested the use and effectiveness of Gestalt Geometric CAPTCHA in terms of time for completion, authentication pass rate, and user satisfaction, in comparison with reCAPTCHA, Ironclad CAPTCHA, and ShapeCAPTCHA. We also tested our novel CAPTCHA for robustness against two shape detection and classification programs, ShapeChecker and Shape-detect. The results were promising, but some issues remain for future improvement.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"40 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":"129886462","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.2015.7415169
Prasetya Ajie Utama, Bayu Distiawan
Mining sequence patterns in form of n-grams (sequences of words that appear consecutively) from a large text data is one of the fundamental parts in several information retrieval and natural language processing applications. In this work, we present Spark-gram, a method for large scale frequent sequence mining based on Spark that was adapted from its equivalent method in MapReduce called Suffix-σ. Spark-gram design allows the discovery of all n-grams with maximum length σ and minimum occurrence frequency τ, using iterative algorithm with only a single shuffle phase. We show that Spark-gram can outperform Suffix-σ mainly when τ is high but potentially worse when the value of σ grows higher.
{"title":"Spark-gram: Mining frequent N-grams using parallel processing in Spark","authors":"Prasetya Ajie Utama, Bayu Distiawan","doi":"10.1109/ICACSIS.2015.7415169","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415169","url":null,"abstract":"Mining sequence patterns in form of n-grams (sequences of words that appear consecutively) from a large text data is one of the fundamental parts in several information retrieval and natural language processing applications. In this work, we present Spark-gram, a method for large scale frequent sequence mining based on Spark that was adapted from its equivalent method in MapReduce called Suffix-σ. Spark-gram design allows the discovery of all n-grams with maximum length σ and minimum occurrence frequency τ, using iterative algorithm with only a single shuffle phase. We show that Spark-gram can outperform Suffix-σ mainly when τ is high but potentially worse when the value of σ grows higher.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"16 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":"129927550","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.2015.7415199
W. Jatmiko, M. A. Ma'sum, S. M. Isa, E. Imah, R. Rahmatullah, B. Wiweko
Indonesia is developing country with high population. There are more than 200 million residents living in the country. As a developing country, Indonesia has several health problems. First, Indonesia has a high value of mortality caused by heart and cardio vascular diseases. One of the major cause is the lack of medical checkup especially for heart monitoring. It is caused by limited number of medical instrumentation e.g. ECG in hospital and public health center. The supporting factor is the small number of cardiologist in Indonesia. There are 365 cardiologists across the country, which is a very small number compared to the 200 million of Indonesia population. Furthermore, they are not distributed evenly in all provinces, but only centered in Jakarta and other capital cities. Therefore, it is difficult for residents to get appropriate heart monitoring. Second, the mortality rate of mother and baby during delivery of the baby in Indonesia is also high. One way to solve this problem is to devise a system where the health clinics in rural areas can perform fetal biometry detection before consulting the results to the expert physicians from other areas. The proposed system will be equipped with algorithms for automatic fetal detection and biometry measurement. By the end of this development, we have several results, the first is a classifier to automatic heartbeat disease prediction with accuracy more than 95%, the second is compression method based on wavelet decompositon, and the third is detection and approximation a fetus in an ultrasound image with hit rate more than 93%.
{"title":"Developing smart telehealth system in Indonesia: Progress and challenge","authors":"W. Jatmiko, M. A. Ma'sum, S. M. Isa, E. Imah, R. Rahmatullah, B. Wiweko","doi":"10.1109/ICACSIS.2015.7415199","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415199","url":null,"abstract":"Indonesia is developing country with high population. There are more than 200 million residents living in the country. As a developing country, Indonesia has several health problems. First, Indonesia has a high value of mortality caused by heart and cardio vascular diseases. One of the major cause is the lack of medical checkup especially for heart monitoring. It is caused by limited number of medical instrumentation e.g. ECG in hospital and public health center. The supporting factor is the small number of cardiologist in Indonesia. There are 365 cardiologists across the country, which is a very small number compared to the 200 million of Indonesia population. Furthermore, they are not distributed evenly in all provinces, but only centered in Jakarta and other capital cities. Therefore, it is difficult for residents to get appropriate heart monitoring. Second, the mortality rate of mother and baby during delivery of the baby in Indonesia is also high. One way to solve this problem is to devise a system where the health clinics in rural areas can perform fetal biometry detection before consulting the results to the expert physicians from other areas. The proposed system will be equipped with algorithms for automatic fetal detection and biometry measurement. By the end of this development, we have several results, the first is a classifier to automatic heartbeat disease prediction with accuracy more than 95%, the second is compression method based on wavelet decompositon, and the third is detection and approximation a fetus in an ultrasound image with hit rate more than 93%.","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":"114168060","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.2015.7415154
A. G. Salman, Bayu Kanigoro, Y. Heryadi
Weather forecasting has gained attention many researchers from various research communities due to its effect to the global human life. The emerging deep learning techniques in the last decade coupled with the wide availability of massive weather observation data and the advent of information and computer technology have motivated many researches to explore hidden hierarchical pattern in the large volume of weather dataset for weather forecasting. This study investigates deep learning techniques for weather forecasting. In particular, this study will compare prediction performance of Recurrence Neural Network (RNN), Conditional Restricted Boltzmann Machine (CRBM), and Convolutional Network (CN) models. Those models are tested using weather dataset provided by BMKG (Indonesian Agency for Meteorology, Climatology, and Geophysics) which are collected from a number of weather stations in Aceh area from 1973 to 2009 and El-Nino Southern Oscilation (ENSO) data set provided by International Institution such as National Weather Service Center for Environmental Prediction Climate (NOAA). Forecasting accuracy of each model is evaluated using Frobenius norm. The result of this study expected to contribute to weather forecasting for wide application domains including flight navigation to agriculture and tourism.
{"title":"Weather forecasting using deep learning techniques","authors":"A. G. Salman, Bayu Kanigoro, Y. Heryadi","doi":"10.1109/ICACSIS.2015.7415154","DOIUrl":"https://doi.org/10.1109/ICACSIS.2015.7415154","url":null,"abstract":"Weather forecasting has gained attention many researchers from various research communities due to its effect to the global human life. The emerging deep learning techniques in the last decade coupled with the wide availability of massive weather observation data and the advent of information and computer technology have motivated many researches to explore hidden hierarchical pattern in the large volume of weather dataset for weather forecasting. This study investigates deep learning techniques for weather forecasting. In particular, this study will compare prediction performance of Recurrence Neural Network (RNN), Conditional Restricted Boltzmann Machine (CRBM), and Convolutional Network (CN) models. Those models are tested using weather dataset provided by BMKG (Indonesian Agency for Meteorology, Climatology, and Geophysics) which are collected from a number of weather stations in Aceh area from 1973 to 2009 and El-Nino Southern Oscilation (ENSO) data set provided by International Institution such as National Weather Service Center for Environmental Prediction Climate (NOAA). Forecasting accuracy of each model is evaluated using Frobenius norm. The result of this study expected to contribute to weather forecasting for wide application domains including flight navigation to agriculture and tourism.","PeriodicalId":325539,"journal":{"name":"2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS)","volume":"190 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":"131911583","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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