Pub Date : 2011-06-28DOI: 10.1109/DMO.2011.5976507
Rabiatul 'Adawiah Mat Noor, Zainal Ahmad
This work attempted on developing soft sensor for prediction of biopolymer molecular weight using neural network as the tool. Molecular weight is a parameter that cannot be measured online whereas it is difficult for most of us to develop and control this particular parameter. Alternatively, the molecular weight is predicted by utilizing inferential estimation method based on neural network model. In this work, temperature of biopolymerization process is used to bring a mutual relation to biopolymer molecular weight. The process involved the development of neural network model for estimation of molecular weight based on various reaction temperatures. In this study, the results are convincing and the soft sensor developed from neural network is really reliable in forecasting the biopolymer molecular weight.
{"title":"Neural network based soft sensor for prediction of biopolycaprolactone molecular weight using bootstrap neural network technique","authors":"Rabiatul 'Adawiah Mat Noor, Zainal Ahmad","doi":"10.1109/DMO.2011.5976507","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976507","url":null,"abstract":"This work attempted on developing soft sensor for prediction of biopolymer molecular weight using neural network as the tool. Molecular weight is a parameter that cannot be measured online whereas it is difficult for most of us to develop and control this particular parameter. Alternatively, the molecular weight is predicted by utilizing inferential estimation method based on neural network model. In this work, temperature of biopolymerization process is used to bring a mutual relation to biopolymer molecular weight. The process involved the development of neural network model for estimation of molecular weight based on various reaction temperatures. In this study, the results are convincing and the soft sensor developed from neural network is really reliable in forecasting the biopolymer molecular weight.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127895851","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976504
Maznan Deraman, Abd Jalil Desa, Z. Othman
Network intrusion detection system (NIDS) commonly attributed to the task to mitigate network and security attacks that has potential to compromise the safety of a network resources and its information. Research in this area mainly focuses to improve the detection method in network traffic flow. Machine learning techniques had been widely used to analyze large datasets including network traffic. In order to develop a sound mechanism for NIDS detection tool, benchmark datasets is required to assist the data mining process. This paper presents the benchmark datasets available publicly for NIDS study such as KDDCup99, IES, pcapr and others. We use some popular machine learning tools to visualize the properties and characteristics of the benchmark datasets.
{"title":"Public domain datasets for optimizing network intrusion and machine learning approaches","authors":"Maznan Deraman, Abd Jalil Desa, Z. Othman","doi":"10.1109/DMO.2011.5976504","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976504","url":null,"abstract":"Network intrusion detection system (NIDS) commonly attributed to the task to mitigate network and security attacks that has potential to compromise the safety of a network resources and its information. Research in this area mainly focuses to improve the detection method in network traffic flow. Machine learning techniques had been widely used to analyze large datasets including network traffic. In order to develop a sound mechanism for NIDS detection tool, benchmark datasets is required to assist the data mining process. This paper presents the benchmark datasets available publicly for NIDS study such as KDDCup99, IES, pcapr and others. We use some popular machine learning tools to visualize the properties and characteristics of the benchmark datasets.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129814934","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976497
Said Fouchal, Murat Ahat, I. Lavallée, M. Bui
We propose in this paper a novel clustering algorithm in ultrametric spaces. It has a computational cost of O(n). This method is based on the ultratriangle inequality property. Using the order induced by an ultrametric in a given space, we demonstrate how we explore quickly data proximities in this space. We present an example of our results and show the efficiency and the consistency of our algorithm compared with another.
{"title":"An O(N) clustering method on ultrametric data","authors":"Said Fouchal, Murat Ahat, I. Lavallée, M. Bui","doi":"10.1109/DMO.2011.5976497","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976497","url":null,"abstract":"We propose in this paper a novel clustering algorithm in ultrametric spaces. It has a computational cost of O(n). This method is based on the ultratriangle inequality property. Using the order induced by an ultrametric in a given space, we demonstrate how we explore quickly data proximities in this space. We present an example of our results and show the efficiency and the consistency of our algorithm compared with another.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122219992","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976510
A. Singh, Kumar Shubhankar, Vikram Pudi
In this paper we propose an efficient method to rank the research papers from various fields of research published in various conferences over the years. This ranking method is based on citation network. The importance of a research paper is captured well by the peer vote, which in this case is the research paper being cited in other research papers. Using a modified version of the PageRank algorithm, we rank the research papers, assigning each of them an authoritative score. Using the scores of the research papers calculated by above mentioned method, we formulate scores for conferences and authors and rank them as well. We have introduced a new metric in the algorithm which takes into account the time factor in ranking the research papers to reduce the bias against the recent papers which get less time for being studied and consequently cited by the researchers as compared to the older papers. Often a researcher is more interested in finding the top conferences in a particular year rather than the overall conference ranking. Considering the year of publication of the papers, in addition to the paper scores we also calculated the year-wise score of each conference by slight improvisation of the above mentioned algorithm.
{"title":"An efficient algorithm for ranking research papers based on citation network","authors":"A. Singh, Kumar Shubhankar, Vikram Pudi","doi":"10.1109/DMO.2011.5976510","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976510","url":null,"abstract":"In this paper we propose an efficient method to rank the research papers from various fields of research published in various conferences over the years. This ranking method is based on citation network. The importance of a research paper is captured well by the peer vote, which in this case is the research paper being cited in other research papers. Using a modified version of the PageRank algorithm, we rank the research papers, assigning each of them an authoritative score. Using the scores of the research papers calculated by above mentioned method, we formulate scores for conferences and authors and rank them as well. We have introduced a new metric in the algorithm which takes into account the time factor in ranking the research papers to reduce the bias against the recent papers which get less time for being studied and consequently cited by the researchers as compared to the older papers. Often a researcher is more interested in finding the top conferences in a particular year rather than the overall conference ranking. Considering the year of publication of the papers, in addition to the paper scores we also calculated the year-wise score of each conference by slight improvisation of the above mentioned algorithm.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123788949","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976539
Ehsan Toreini, M. Mehrnejad
Data clustering has been studied for a long time and every day trends are proposed for better outcomes in this field. One of the latest trends in this area is the application of Particle Swarm Optimization (PSO) in clustering which has good potential for improvements. In this paper, we consider a new fitness function for our PSO-based clustering method and compared it with the previous ones. Experimental results show that our method has better outcomes than the other ones.
{"title":"Clustering data with Particle Swarm Optimization using a new fitness","authors":"Ehsan Toreini, M. Mehrnejad","doi":"10.1109/DMO.2011.5976539","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976539","url":null,"abstract":"Data clustering has been studied for a long time and every day trends are proposed for better outcomes in this field. One of the latest trends in this area is the application of Particle Swarm Optimization (PSO) in clustering which has good potential for improvements. In this paper, we consider a new fitness function for our PSO-based clustering method and compared it with the previous ones. Experimental results show that our method has better outcomes than the other ones.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130030988","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976518
A. A. Ibrahim, A. Mohamed, H. Shareef, S. Ghoshal
In the modern industry, most of the equipment use semiconductor devices and microprocessors which are sensitive against power disturbances. Among power disturbances, voltage sags are considered as the most frequent type of disturbances in the field and their impact on sensitive loads is severe. However, to assess voltage sags, installation of power quality monitors (PQM) at all system buses is not economical. Thus, this study is carried out to develop a power quality monitor positioning algorithm to find the optimal number and placement of PQMs in both transmission and distribution systems. In this approach, first, the concept of topological monitor reach area is introduced. Then the binary particle swarm optimization hybridized with artificial immune system is used to solve multi-objective function in finding the optimal placement of PQMs. The proposed algorithm has been implemented and tested on the IEEE 30-bus and the 69-bus test systems to show the effectiveness of the proposed method for both transmission and distribution systems.
{"title":"Optimal power quality monitor placement in power systems based on particle swarm optimization and artificial immune system","authors":"A. A. Ibrahim, A. Mohamed, H. Shareef, S. Ghoshal","doi":"10.1109/DMO.2011.5976518","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976518","url":null,"abstract":"In the modern industry, most of the equipment use semiconductor devices and microprocessors which are sensitive against power disturbances. Among power disturbances, voltage sags are considered as the most frequent type of disturbances in the field and their impact on sensitive loads is severe. However, to assess voltage sags, installation of power quality monitors (PQM) at all system buses is not economical. Thus, this study is carried out to develop a power quality monitor positioning algorithm to find the optimal number and placement of PQMs in both transmission and distribution systems. In this approach, first, the concept of topological monitor reach area is introduced. Then the binary particle swarm optimization hybridized with artificial immune system is used to solve multi-objective function in finding the optimal placement of PQMs. The proposed algorithm has been implemented and tested on the IEEE 30-bus and the 69-bus test systems to show the effectiveness of the proposed method for both transmission and distribution systems.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130088512","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976511
Kumar Shubankar, A. Singh, Vikram Pudi
In this paper we introduce a novel and efficient approach to detect topics in a large corpus of research papers. With rapidly growing size of academic literature, the problem of topic detection has become a very challenging task. We present a unique approach that uses closed frequent keyword-set to form topics. Our approach also provides a natural method to cluster the research papers into hierarchical, overlapping clusters using topic as similarity measure. To rank the research papers in the topic cluster, we devise a modified PageRank algorithm that assigns an authoritative score to each research paper by considering the sub-graph in which the research paper appears. We test our algorithms on the DBLP dataset and experimentally show that our algorithms are fast, effective and scalable.
{"title":"A frequent keyword-set based algorithm for topic modeling and clustering of research papers","authors":"Kumar Shubankar, A. Singh, Vikram Pudi","doi":"10.1109/DMO.2011.5976511","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976511","url":null,"abstract":"In this paper we introduce a novel and efficient approach to detect topics in a large corpus of research papers. With rapidly growing size of academic literature, the problem of topic detection has become a very challenging task. We present a unique approach that uses closed frequent keyword-set to form topics. Our approach also provides a natural method to cluster the research papers into hierarchical, overlapping clusters using topic as similarity measure. To rank the research papers in the topic cluster, we devise a modified PageRank algorithm that assigns an authoritative score to each research paper by considering the sub-graph in which the research paper appears. We test our algorithms on the DBLP dataset and experimentally show that our algorithms are fast, effective and scalable.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114786266","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976540
F. Ismail, Noor Elaiza Abd Khalid, Nordin Abu Bakar, Ropandi Mamat
The shortage of rubber wood (RW) supply has increased the demand to reduce its composition in the Medium Density Fiberboard (MDF). Oil palm biomass such as empty fruit bunch (EFB) has been proven to be an excellent substitute to RW. An accurate percentage combination of RW and EFB will produce a high quality MDF. An MDF needs to be tested in terms of mechanical and physical properties so that it meets the required standard. These tests are costly for they involve high amount of resources. The aim of this research is to optimize the properties of MDF so that quality-testing procedures can be reduced. A prediction model will be used to make predictions on the MDF properties. A stepwise multiple linear regression selects the predictor variables to be used as inputs to the input nodes. With these variables, the multilayer perceptron neural network with various training criteria will train the data and finally produce the prediction. The results produced have shown that some of the property tests can be omitted.
{"title":"Optimizing oil palm fiberboard properties using neural network","authors":"F. Ismail, Noor Elaiza Abd Khalid, Nordin Abu Bakar, Ropandi Mamat","doi":"10.1109/DMO.2011.5976540","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976540","url":null,"abstract":"The shortage of rubber wood (RW) supply has increased the demand to reduce its composition in the Medium Density Fiberboard (MDF). Oil palm biomass such as empty fruit bunch (EFB) has been proven to be an excellent substitute to RW. An accurate percentage combination of RW and EFB will produce a high quality MDF. An MDF needs to be tested in terms of mechanical and physical properties so that it meets the required standard. These tests are costly for they involve high amount of resources. The aim of this research is to optimize the properties of MDF so that quality-testing procedures can be reduced. A prediction model will be used to make predictions on the MDF properties. A stepwise multiple linear regression selects the predictor variables to be used as inputs to the input nodes. With these variables, the multilayer perceptron neural network with various training criteria will train the data and finally produce the prediction. The results produced have shown that some of the property tests can be omitted.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131948575","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976531
Majid Abdolrazzagh Nezhad, S. Abdullah
Most of the methods to solve job-shop scheduling problem (JSSP) are population-based and one of the strategies to reduce the time to reach the optimal solution is to produce an initial population that firstly has suitable distribution on space solution, secondly some of its points settle nearby to the optimal solution and lastly generate it in the shortest possible time. But since JSSP is one of the most difficult NP-complete problems and its space solution is complex, most of the previous researchers have preferred to utilize random methods or priority rules for producing initial population. In this paper, by mapping each schedule to a unique sequence of jobs on machines matrix (SJM), we have proposed the novel concept of plates, and have redefined and adapted concepts of tail and head path and have designed evaluator functions between SJM matrix and its corresponding schedule aiming at identifying gaps in the obtained schedule, we have proposed three novel initialization procedures. The proposed procedures have been run on 73 benchmark datasets and their results have been compared with some existing initialization procedures and even some approximation algorithms for solving JSSP. Based on this comparison, we have seen the proposed procedures have the significant advantage both in the quality-generated points and in the time producing them. The more interesting point in the implementation of proposed procedures on some datasets is that we see the best known solution in the produced initial population.
{"title":"Robust start for population-based algorithms solving job-shop scheduling problems","authors":"Majid Abdolrazzagh Nezhad, S. Abdullah","doi":"10.1109/DMO.2011.5976531","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976531","url":null,"abstract":"Most of the methods to solve job-shop scheduling problem (JSSP) are population-based and one of the strategies to reduce the time to reach the optimal solution is to produce an initial population that firstly has suitable distribution on space solution, secondly some of its points settle nearby to the optimal solution and lastly generate it in the shortest possible time. But since JSSP is one of the most difficult NP-complete problems and its space solution is complex, most of the previous researchers have preferred to utilize random methods or priority rules for producing initial population. In this paper, by mapping each schedule to a unique sequence of jobs on machines matrix (SJM), we have proposed the novel concept of plates, and have redefined and adapted concepts of tail and head path and have designed evaluator functions between SJM matrix and its corresponding schedule aiming at identifying gaps in the obtained schedule, we have proposed three novel initialization procedures. The proposed procedures have been run on 73 benchmark datasets and their results have been compared with some existing initialization procedures and even some approximation algorithms for solving JSSP. Based on this comparison, we have seen the proposed procedures have the significant advantage both in the quality-generated points and in the time producing them. The more interesting point in the implementation of proposed procedures on some datasets is that we see the best known solution in the produced initial population.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133183651","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 : 2011-06-28DOI: 10.1109/DMO.2011.5976514
M.N.I. Sulaiman, Y. Choo, K. Chong
This work presents an approach based on the ant colony optimization technique to address the assembly line balancing problem. An improved ant colony optimization with look forward ant is proposed to solve the simple assembly line balancing problem of type 1 (SALBP-1). The proposed algorithm introduces an approach to dynamically assign the value of priority rule or heuristic information during the task selection phase by allowing the ant to look forward its direct successors during the consideration in selecting a task to be assigned into a workstation. The proposed algorithm is tested and compared with literature data sets and the result from the proposed algorithm shows competitive performance against them.
{"title":"Ant colony optimization with look forward ant in solving assembly line balancing problem","authors":"M.N.I. Sulaiman, Y. Choo, K. Chong","doi":"10.1109/DMO.2011.5976514","DOIUrl":"https://doi.org/10.1109/DMO.2011.5976514","url":null,"abstract":"This work presents an approach based on the ant colony optimization technique to address the assembly line balancing problem. An improved ant colony optimization with look forward ant is proposed to solve the simple assembly line balancing problem of type 1 (SALBP-1). The proposed algorithm introduces an approach to dynamically assign the value of priority rule or heuristic information during the task selection phase by allowing the ant to look forward its direct successors during the consideration in selecting a task to be assigned into a workstation. The proposed algorithm is tested and compared with literature data sets and the result from the proposed algorithm shows competitive performance against them.","PeriodicalId":436393,"journal":{"name":"2011 3rd Conference on Data Mining and Optimization (DMO)","volume":"2007 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125570512","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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