Knowledge sharing is done through various knowledge sharing forums which requires multiple logins through multiple browser instances. Here a single Multi- Forum knowledge sharing concept is introduced which requires only one login session which makes user to connect multiple forums and display the data in a single browser window. Also few optimization techniques are introduced here to speed up the access time using cloud computing architecture.
{"title":"Optimization of knowledge sharing through Multi-Forum using cloud computing architecture","authors":"M. Sriram, Srivatsan Sankaran","doi":"10.1117/12.920151","DOIUrl":"https://doi.org/10.1117/12.920151","url":null,"abstract":"Knowledge sharing is done through various knowledge sharing forums which requires multiple logins through multiple browser instances. Here a single Multi- Forum knowledge sharing concept is introduced which requires only one login session which makes user to connect multiple forums and display the data in a single browser window. Also few optimization techniques are introduced here to speed up the access time using cloud computing architecture.","PeriodicalId":363714,"journal":{"name":"2011 International Conference on Advanced Computer Science and Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122594064","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 : 2011-12-01DOI: 10.6084/M9.FIGSHARE.1609661.V1
Agus Widodo, Mohamad Ivan Fanani, I. Budi
Improving the accuracy of prediction on future values based on the past and current observations has been pursued by enhancing the prediction's methods, combining those methods or performing data pre-processing. In this paper, another approach is taken, namely by increasing the number of input in the dataset. This approach would be useful especially for a shorter time series data. By filling the in-between values in the time series, the number of training set can be increased, thus increasing the generalization capability of the predictor. The algorithm used to make prediction is Neural Network as it is widely used in literature for time series tasks. For comparison, Support Vector Regression is also employed. The dataset used in the experiment is the frequency of USPTO's patents and PubMed's scientific publications on the field of health, namely on Apnea, Arrhythmia, and Sleep Stages. Another time series data designated for NN3 Competition in the field of transportation is also used for benchmarking. The experimental result shows that the prediction performance can be significantly increased by filling in-between data in the time series. Furthermore, the use of detrend and deseasonalization which separates the data into trend, seasonal and stationary time series also improve the prediction performance both on original and filled dataset. The optimal number of increase on the dataset in this experiment is about five times of the length of original dataset.
{"title":"Enriching time series datasets using Nonparametric kernel regression to improve forecasting accuracy","authors":"Agus Widodo, Mohamad Ivan Fanani, I. Budi","doi":"10.6084/M9.FIGSHARE.1609661.V1","DOIUrl":"https://doi.org/10.6084/M9.FIGSHARE.1609661.V1","url":null,"abstract":"Improving the accuracy of prediction on future values based on the past and current observations has been pursued by enhancing the prediction's methods, combining those methods or performing data pre-processing. In this paper, another approach is taken, namely by increasing the number of input in the dataset. This approach would be useful especially for a shorter time series data. By filling the in-between values in the time series, the number of training set can be increased, thus increasing the generalization capability of the predictor. The algorithm used to make prediction is Neural Network as it is widely used in literature for time series tasks. For comparison, Support Vector Regression is also employed. The dataset used in the experiment is the frequency of USPTO's patents and PubMed's scientific publications on the field of health, namely on Apnea, Arrhythmia, and Sleep Stages. Another time series data designated for NN3 Competition in the field of transportation is also used for benchmarking. The experimental result shows that the prediction performance can be significantly increased by filling in-between data in the time series. Furthermore, the use of detrend and deseasonalization which separates the data into trend, seasonal and stationary time series also improve the prediction performance both on original and filled dataset. The optimal number of increase on the dataset in this experiment is about five times of the length of original dataset.","PeriodicalId":363714,"journal":{"name":"2011 International Conference on Advanced Computer Science and Information Systems","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129036094","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 : 2011-12-01DOI: 10.1007/978-3-642-27951-5_32
Hanquan Huang
{"title":"A normalization method of converting online handwritten Chinese character to Stroke-Segment-Mesh Glyph","authors":"Hanquan Huang","doi":"10.1007/978-3-642-27951-5_32","DOIUrl":"https://doi.org/10.1007/978-3-642-27951-5_32","url":null,"abstract":"","PeriodicalId":363714,"journal":{"name":"2011 International Conference on Advanced Computer Science and Information Systems","volume":"2015 25","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114087025","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}
Model simplification is a methodology to reduce size and complexity of models, e.g. by moving some of the details away from the model and into the model implementation code. This paper talks about supporting Petri net model simplification in a new tool for modeling and simulation of discrete event dynamic systems. Firstly, this paper presents a brief introduction to model abstraction and model simplification. Secondly, this paper presents a brief introduction to the new tool known as GPenSIM. Thirdly, through a case study, this work shows how model simplification can be done in GPenSIM and also how effective or useful model simplification can be.
{"title":"Model simplification in Petri net models","authors":"R. Davidrajuh","doi":"10.1109/EMS.2011.91","DOIUrl":"https://doi.org/10.1109/EMS.2011.91","url":null,"abstract":"Model simplification is a methodology to reduce size and complexity of models, e.g. by moving some of the details away from the model and into the model implementation code. This paper talks about supporting Petri net model simplification in a new tool for modeling and simulation of discrete event dynamic systems. Firstly, this paper presents a brief introduction to model abstraction and model simplification. Secondly, this paper presents a brief introduction to the new tool known as GPenSIM. Thirdly, through a case study, this work shows how model simplification can be done in GPenSIM and also how effective or useful model simplification can be.","PeriodicalId":363714,"journal":{"name":"2011 International Conference on Advanced Computer Science and Information Systems","volume":"13 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115839445","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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