Pub Date : 2012-10-15DOI: 10.1109/DMO.2012.6329791
Youssef Harrath, J. Kaabi, M. Sassi, M. Ali
In this paper we consider a multiobjective job shop scheduling problem. The machines are subject to availability constraints that are due to preventive maintenance, machine breakdowns or tool replacement. Two optimization criteria were considered; the makespan for the jobs and the total cost for the maintenance activities. The job shop scheduling problem without considering the availability constraints is known to be NP-Hard. Because of the complexity of the problem, we develop a two-phase genetic algorithm based heuristic to solve the addressed problem. A set of pareto optimal solutions is obtained in the first phase containing relatively large number of solutions. This makes difficult the choice of the most suitable solution. For this reason the second phase will filter the obtained set so as to reduce its size. Performance of the proposed heuristic is evaluated through computational experiments on the benchmark of Muth & Thomson mt06 of 6×6 and 10 different sizes benchmarks of Lawrence. The results show that the heuristic gives solutions close to those obtained in the classic job shop scheduling problem.
{"title":"Multiobjective genetic algorithm-based method for job shop scheduling problem: Machines under preventive and corrective maintenance activities","authors":"Youssef Harrath, J. Kaabi, M. Sassi, M. Ali","doi":"10.1109/DMO.2012.6329791","DOIUrl":"https://doi.org/10.1109/DMO.2012.6329791","url":null,"abstract":"In this paper we consider a multiobjective job shop scheduling problem. The machines are subject to availability constraints that are due to preventive maintenance, machine breakdowns or tool replacement. Two optimization criteria were considered; the makespan for the jobs and the total cost for the maintenance activities. The job shop scheduling problem without considering the availability constraints is known to be NP-Hard. Because of the complexity of the problem, we develop a two-phase genetic algorithm based heuristic to solve the addressed problem. A set of pareto optimal solutions is obtained in the first phase containing relatively large number of solutions. This makes difficult the choice of the most suitable solution. For this reason the second phase will filter the obtained set so as to reduce its size. Performance of the proposed heuristic is evaluated through computational experiments on the benchmark of Muth & Thomson mt06 of 6×6 and 10 different sizes benchmarks of Lawrence. The results show that the heuristic gives solutions close to those obtained in the classic job shop scheduling problem.","PeriodicalId":330241,"journal":{"name":"2012 4th Conference on Data Mining and Optimization (DMO)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128044255","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 : 2012-10-15DOI: 10.1109/DMO.2012.6329789
Sharifah Mastura Syed Abdullah
This paper aims to develop an operational methodology for monitoring spatial and temporal changes due to deforestation in Selangor over a 22 year period. The driving forces determining the changes were also analysed. Overall, the results show that the causes of deforestation were the economic factors, namely agriculture intensification, and population dynamics, related to the process of urbanization. However, deforestation statistics shows only a total of 10 percent decrease; it is the degradation of the remaining forest that is the major concern. Knowledge on deforestation and its driving forces in Selangor is very important as it provides the basis for the calculation of the total amount of carbon stock above ground. It also gives insight into the appropriate intervention measures that can be taken to increase carbon stock, thus reducing the release of carbon dioxide emission to the atmosphere.
{"title":"Spatial and temporal analysis of deforestation and forest degradation in Selangor: Implication to carbon stock above ground","authors":"Sharifah Mastura Syed Abdullah","doi":"10.1109/DMO.2012.6329789","DOIUrl":"https://doi.org/10.1109/DMO.2012.6329789","url":null,"abstract":"This paper aims to develop an operational methodology for monitoring spatial and temporal changes due to deforestation in Selangor over a 22 year period. The driving forces determining the changes were also analysed. Overall, the results show that the causes of deforestation were the economic factors, namely agriculture intensification, and population dynamics, related to the process of urbanization. However, deforestation statistics shows only a total of 10 percent decrease; it is the degradation of the remaining forest that is the major concern. Knowledge on deforestation and its driving forces in Selangor is very important as it provides the basis for the calculation of the total amount of carbon stock above ground. It also gives insight into the appropriate intervention measures that can be taken to increase carbon stock, thus reducing the release of carbon dioxide emission to the atmosphere.","PeriodicalId":330241,"journal":{"name":"2012 4th Conference on Data Mining and Optimization (DMO)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115476430","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 : 2012-10-15DOI: 10.1109/DMO.2012.6329798
F. Sia, R. Alfred
The representation of input data set is important for learning task. In data summarization, the representation of the multi-instances stored in non-target tables that have many-to-one relationship with record stored in target table influences the descriptive accuracy of the summarized data. If the summarized data is fed into a classifier as one of the input features, the predictive accuracy of the classifier will also be affected. This paper proposes an evolutionary-based feature construction approach namely Fixed-Length Feature Construction with Substitution (FLFCWS) to address the problem by means of optimizing the feature construction for relational data summarization. This approach allows initial features to be used more than once in constructing newly constructed features. This is performed in order to exploit all possible interactions among attributes which involves an application of genetic algorithm to find a relevant set of features. The constructed features will be used to generate relevant patterns that characterize non-target records associated to the target record as an input representation for data summarization process. Several feature scoring measures are used as fitness function to find the best set of constructed features. The experimental results show that there is an improvement of predictive accuracy for classifying data summarized based on FLFCWS approach which indirectly improves the descriptive accuracy of the summarized data. It shows that FLFCWS approach can generate promising set of constructed features to describe the characteristics of non-target records for data summarization.
{"title":"Evolutionary-based feature construction with substitution for data summarization using DARA","authors":"F. Sia, R. Alfred","doi":"10.1109/DMO.2012.6329798","DOIUrl":"https://doi.org/10.1109/DMO.2012.6329798","url":null,"abstract":"The representation of input data set is important for learning task. In data summarization, the representation of the multi-instances stored in non-target tables that have many-to-one relationship with record stored in target table influences the descriptive accuracy of the summarized data. If the summarized data is fed into a classifier as one of the input features, the predictive accuracy of the classifier will also be affected. This paper proposes an evolutionary-based feature construction approach namely Fixed-Length Feature Construction with Substitution (FLFCWS) to address the problem by means of optimizing the feature construction for relational data summarization. This approach allows initial features to be used more than once in constructing newly constructed features. This is performed in order to exploit all possible interactions among attributes which involves an application of genetic algorithm to find a relevant set of features. The constructed features will be used to generate relevant patterns that characterize non-target records associated to the target record as an input representation for data summarization process. Several feature scoring measures are used as fitness function to find the best set of constructed features. The experimental results show that there is an improvement of predictive accuracy for classifying data summarized based on FLFCWS approach which indirectly improves the descriptive accuracy of the summarized data. It shows that FLFCWS approach can generate promising set of constructed features to describe the characteristics of non-target records for data summarization.","PeriodicalId":330241,"journal":{"name":"2012 4th Conference on Data Mining and Optimization (DMO)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130959261","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 : 2012-10-15DOI: 10.1109/DMO.2012.6329802
M. Imran, R. Hashim, Noor Elaiza Abd Khalid
Particle swarm optimization (PSO) is a stochastic algorithm, used for the optimization problems, proposed by Kennedy [1] in 1995. PSO is a recognized algorithm for optimization problems, but suffers from premature convergence. This paper presents an Opposition-based PSO (OPSO) to accelerate the convergence of PSO and at the same time, avoid early convergence. The proposed OPSO method is coupled with the student T mutation. Results from the experiment performed on the standard benchmark functions show an improvement on the performance of PSO.
{"title":"Opposition based Particle Swarm Optimization with student T mutation (OSTPSO)","authors":"M. Imran, R. Hashim, Noor Elaiza Abd Khalid","doi":"10.1109/DMO.2012.6329802","DOIUrl":"https://doi.org/10.1109/DMO.2012.6329802","url":null,"abstract":"Particle swarm optimization (PSO) is a stochastic algorithm, used for the optimization problems, proposed by Kennedy [1] in 1995. PSO is a recognized algorithm for optimization problems, but suffers from premature convergence. This paper presents an Opposition-based PSO (OPSO) to accelerate the convergence of PSO and at the same time, avoid early convergence. The proposed OPSO method is coupled with the student T mutation. Results from the experiment performed on the standard benchmark functions show an improvement on the performance of PSO.","PeriodicalId":330241,"journal":{"name":"2012 4th Conference on Data Mining and Optimization (DMO)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133652187","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 : 2012-10-15DOI: 10.1109/DMO.2012.6329797
Zaid Abdi Alkareem, Ibrahim Venkat, M. Al-Betar, A. Khader
Population based metaheuristic algorithms have been providing efficient solutions to the problems posed by various domains including image processing. In this contribution we address the problem of image enhancement with a specific focus on preserving the edges inherent in images with the aid of a musically inspired harmony search based metaheuristic algorithm. We demonstrate the significance of our proposed intuitive approach which combines efficient techniques from the image processing domain as well as from the optimization domain. Pertaining to the problem under consideration, further we compare our results with the state-of-the-art histogram equalization approach.
{"title":"Edge preserving image enhancement via harmony search algorithm","authors":"Zaid Abdi Alkareem, Ibrahim Venkat, M. Al-Betar, A. Khader","doi":"10.1109/DMO.2012.6329797","DOIUrl":"https://doi.org/10.1109/DMO.2012.6329797","url":null,"abstract":"Population based metaheuristic algorithms have been providing efficient solutions to the problems posed by various domains including image processing. In this contribution we address the problem of image enhancement with a specific focus on preserving the edges inherent in images with the aid of a musically inspired harmony search based metaheuristic algorithm. We demonstrate the significance of our proposed intuitive approach which combines efficient techniques from the image processing domain as well as from the optimization domain. Pertaining to the problem under consideration, further we compare our results with the state-of-the-art histogram equalization approach.","PeriodicalId":330241,"journal":{"name":"2012 4th Conference on Data Mining and Optimization (DMO)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123851923","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 : 2012-10-15DOI: 10.1109/DMO.2012.6329807
R. Aziz, M. Ayob, Z. Othman
The basic idea of the Variable Neighborhood Search (VNS) algorithm is to systematically explore the neighborhood of current solution using a set of predefined neighborhood structures. Since different problem instances have different landscape and complexity, the choice of which neighborhood structure to be applied is a challenging task. Different neighborhood structures may lead to different solution space. Therefore, this work proposes a learning mechanism in a Variable Neighborhood Search (VNS), refer to hereafter as a Variable Neighborhood Guided Search (VNGS). Its effectiveness is illustrated by solving a course timetabling problems. The learning mechanism memorizes which neighborhood structure could effectively solve a specific soft constraint violations and used it to guide the selection of neighborhood structure to enhance the quality of a best solution. The performance of the VNGS is tested over Socha course timetabling dataset. Results demonstrate that the performance of the VNGS is comparable with the results of the other VNS variants and outperformed others in some instances. This demonstrates the effectiveness of applying a learning mechanism in a VNS algorithm.
{"title":"The effect of learning mechanism in Variables Neighborhood Search","authors":"R. Aziz, M. Ayob, Z. Othman","doi":"10.1109/DMO.2012.6329807","DOIUrl":"https://doi.org/10.1109/DMO.2012.6329807","url":null,"abstract":"The basic idea of the Variable Neighborhood Search (VNS) algorithm is to systematically explore the neighborhood of current solution using a set of predefined neighborhood structures. Since different problem instances have different landscape and complexity, the choice of which neighborhood structure to be applied is a challenging task. Different neighborhood structures may lead to different solution space. Therefore, this work proposes a learning mechanism in a Variable Neighborhood Search (VNS), refer to hereafter as a Variable Neighborhood Guided Search (VNGS). Its effectiveness is illustrated by solving a course timetabling problems. The learning mechanism memorizes which neighborhood structure could effectively solve a specific soft constraint violations and used it to guide the selection of neighborhood structure to enhance the quality of a best solution. The performance of the VNGS is tested over Socha course timetabling dataset. Results demonstrate that the performance of the VNGS is comparable with the results of the other VNS variants and outperformed others in some instances. This demonstrates the effectiveness of applying a learning mechanism in a VNS algorithm.","PeriodicalId":330241,"journal":{"name":"2012 4th Conference on Data Mining and Optimization (DMO)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123198640","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 : 2012-10-15DOI: 10.1109/DMO.2012.6329810
Lay-Ki Soon, Yee-Ern Ku, Sang Ho Lee
URL signature was proposed to be implemented in web crawling, aiming to avoid processing duplicated web pages for further web crawling. In this paper, we present our performance study on an open source web crawler - WebSPHINX, in which we have embedded URL signature. The experimental result indicates that URL signature is able to reduce the processing of duplicated web pages significantly for further web crawling at a negligible cost compared to the one without URL signature.
{"title":"Web crawler with URL signature — A performance study","authors":"Lay-Ki Soon, Yee-Ern Ku, Sang Ho Lee","doi":"10.1109/DMO.2012.6329810","DOIUrl":"https://doi.org/10.1109/DMO.2012.6329810","url":null,"abstract":"URL signature was proposed to be implemented in web crawling, aiming to avoid processing duplicated web pages for further web crawling. In this paper, we present our performance study on an open source web crawler - WebSPHINX, in which we have embedded URL signature. The experimental result indicates that URL signature is able to reduce the processing of duplicated web pages significantly for further web crawling at a negligible cost compared to the one without URL signature.","PeriodicalId":330241,"journal":{"name":"2012 4th Conference on Data Mining and Optimization (DMO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129239531","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 : 2012-10-15DOI: 10.1109/DMO.2012.6329793
N. Husin, N. Mustapha, M. N. Sulaiman, R. Yaakob
Prediction of dengue outbreak becomes crucial in Malaysia because this infectious disease remains one of the main health issues in the country. Malaysia has a good surveillance system but there have been insufficient findings on suitable model to predict future outbreaks. While there are previous studies on dengue prediction models in Malaysia, unfortunately some of these models still have constraints in finding good parameter with high accuracy. The aim of this paper is to design a more promising model for predicting dengue outbreak by using a hybrid model based on genetic algorithm for the determination of weight in neural network model. Several model architectures are designed and the parameters are adjusted to achieve optimal prediction performance. Sample data that covers dengue and rainfall data of five districts in Selangor collected from State Health Department of Selangor (SHD) and Malaysian Meteorological Department is used as a case study to evaluate the proposed model. However, due to incomplete collection of real data, a sample data with similar behavior was created for the purpose of preliminary experiment. The result shows that the hybrid model produces the better prediction compared to standalone models.
{"title":"A hybrid model using genetic algorithm and neural network for predicting dengue outbreak","authors":"N. Husin, N. Mustapha, M. N. Sulaiman, R. Yaakob","doi":"10.1109/DMO.2012.6329793","DOIUrl":"https://doi.org/10.1109/DMO.2012.6329793","url":null,"abstract":"Prediction of dengue outbreak becomes crucial in Malaysia because this infectious disease remains one of the main health issues in the country. Malaysia has a good surveillance system but there have been insufficient findings on suitable model to predict future outbreaks. While there are previous studies on dengue prediction models in Malaysia, unfortunately some of these models still have constraints in finding good parameter with high accuracy. The aim of this paper is to design a more promising model for predicting dengue outbreak by using a hybrid model based on genetic algorithm for the determination of weight in neural network model. Several model architectures are designed and the parameters are adjusted to achieve optimal prediction performance. Sample data that covers dengue and rainfall data of five districts in Selangor collected from State Health Department of Selangor (SHD) and Malaysian Meteorological Department is used as a case study to evaluate the proposed model. However, due to incomplete collection of real data, a sample data with similar behavior was created for the purpose of preliminary experiment. The result shows that the hybrid model produces the better prediction compared to standalone models.","PeriodicalId":330241,"journal":{"name":"2012 4th Conference on Data Mining and Optimization (DMO)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124640262","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 : 2012-10-15DOI: 10.1109/DMO.2012.6329792
Abbas Madraky, Z. Othman, Abdul Razak Hamdan
Spatio-Temporal data is related to many of the issues around us such as satellite images, weather maps, transportation systems and so on. Furthermore, this information is commonly not static and can change over the time. Therefore the nature of this kind of data are huge, analysing data is a complex task. This research aims to propose an intermediate data model that can represented suitable for Spatio-Temporal data and performing data mining task easily while facing problem in frequently changing the data. In order to propose suitable data model, this research also investigate the analytical parameters, the structure and its specifications for Spatio-Temporal data. The concept of proposed data model is inspired from the nature of hair which has specific properties and its growth over the time. In order to have better looking and quality, the data is needed to maintain over the time such as combing, cutting, colouring, covering, cleaning etc. The proposed data model is represented by using mathematical model and later developed the data model tools. The data model is developed based on the existing relational and object-oriented models. This paper deals with the problems of available Spatio-Temporal data models for utilizing data mining technology and defines a new model based on analytical attributes and functions.
{"title":"Hair data model: A new data model for Spatio-Temporal data mining","authors":"Abbas Madraky, Z. Othman, Abdul Razak Hamdan","doi":"10.1109/DMO.2012.6329792","DOIUrl":"https://doi.org/10.1109/DMO.2012.6329792","url":null,"abstract":"Spatio-Temporal data is related to many of the issues around us such as satellite images, weather maps, transportation systems and so on. Furthermore, this information is commonly not static and can change over the time. Therefore the nature of this kind of data are huge, analysing data is a complex task. This research aims to propose an intermediate data model that can represented suitable for Spatio-Temporal data and performing data mining task easily while facing problem in frequently changing the data. In order to propose suitable data model, this research also investigate the analytical parameters, the structure and its specifications for Spatio-Temporal data. The concept of proposed data model is inspired from the nature of hair which has specific properties and its growth over the time. In order to have better looking and quality, the data is needed to maintain over the time such as combing, cutting, colouring, covering, cleaning etc. The proposed data model is represented by using mathematical model and later developed the data model tools. The data model is developed based on the existing relational and object-oriented models. This paper deals with the problems of available Spatio-Temporal data models for utilizing data mining technology and defines a new model based on analytical attributes and functions.","PeriodicalId":330241,"journal":{"name":"2012 4th Conference on Data Mining and Optimization (DMO)","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122924738","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 : 2012-10-15DOI: 10.1109/DMO.2012.6329803
M. Adnan, A. Zain, H. Haron
Rule-based reasoning and fuzzy logic are used to develop a model to predict the surface roughness value of milling process. The process parameters considered in this study are cutting speed, feed rate, and radial rake angle, each has five linguistic values. The fuzzy rule-based model is developed using MATLAB fuzzy logic toolbox. Nine linguistic values and twenty four IF-THEN rules are created for model development. Predicted result of the proposed model has been compared to the experimental result, and it gave a good agreement with the correlation 0.9845. The differences between experimental result and predicted result have been proven with estimation error value 0.0008. The best predicted value of surface roughness using the fuzzy rule-based is located at combination of High cutting speed, VeryLow feed rate, and High radial rake angle.
{"title":"Fuzzy rule-based for predicting machining performance for SNTR carbide in milling titanium alloy (Ti-6Al-4v)","authors":"M. Adnan, A. Zain, H. Haron","doi":"10.1109/DMO.2012.6329803","DOIUrl":"https://doi.org/10.1109/DMO.2012.6329803","url":null,"abstract":"Rule-based reasoning and fuzzy logic are used to develop a model to predict the surface roughness value of milling process. The process parameters considered in this study are cutting speed, feed rate, and radial rake angle, each has five linguistic values. The fuzzy rule-based model is developed using MATLAB fuzzy logic toolbox. Nine linguistic values and twenty four IF-THEN rules are created for model development. Predicted result of the proposed model has been compared to the experimental result, and it gave a good agreement with the correlation 0.9845. The differences between experimental result and predicted result have been proven with estimation error value 0.0008. The best predicted value of surface roughness using the fuzzy rule-based is located at combination of High cutting speed, VeryLow feed rate, and High radial rake angle.","PeriodicalId":330241,"journal":{"name":"2012 4th Conference on Data Mining and Optimization (DMO)","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116170136","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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