Pub Date : 1900-01-01DOI: 10.4018/978-1-5225-3035-0.CH007
Lata Nautiyal, Preeti Shivach, M. Ram
With the advancement in contemporary computational and modeling skills, engineering design completely depends upon on variety of computer modeling and simulation tools to hasten the design cycles and decrease the overall budget. The most difficult design problem will include various design parameters along with the tables. Finding out the design space and ultimate solutions to those problems are still biggest challenges for the area of complex systems. This chapter is all about suggesting the use of Genetic Algorithms to enhance maximum engineering design problems. The chapter recommended that Genetic Algorithms are highly useful to increase the High-Performance Areas for Engineering Design. This chapter is established to use Genetic Algorithms to large number of design areas and delivered a comprehensive conversation on the use, scope and its applications in mechanical engineering.
{"title":"Optimal Designs by Means of Genetic Algorithms","authors":"Lata Nautiyal, Preeti Shivach, M. Ram","doi":"10.4018/978-1-5225-3035-0.CH007","DOIUrl":"https://doi.org/10.4018/978-1-5225-3035-0.CH007","url":null,"abstract":"With the advancement in contemporary computational and modeling skills, engineering design completely depends upon on variety of computer modeling and simulation tools to hasten the design cycles and decrease the overall budget. The most difficult design problem will include various design parameters along with the tables. Finding out the design space and ultimate solutions to those problems are still biggest challenges for the area of complex systems. This chapter is all about suggesting the use of Genetic Algorithms to enhance maximum engineering design problems. The chapter recommended that Genetic Algorithms are highly useful to increase the High-Performance Areas for Engineering Design. This chapter is established to use Genetic Algorithms to large number of design areas and delivered a comprehensive conversation on the use, scope and its applications in mechanical engineering.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"438 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122789419","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 : 1900-01-01DOI: 10.4018/978-1-5225-4769-3.CH012
Gaffari Çelik
Currently, medical diagnosis has a strong relation with the artificial-intelligence-oriented approaches. Because it is practical to employ intelligent mechanisms over some input data-expert knowledge and design effective solution ways, even the biomedical engineering field is interested in taking support from artificial intelligence. If applications in this manner are taken into consideration, we can see that medical diagnoses have a big percentage. In the sense of the explanations, the objective of this chapter is to use genetic algorithm (GA) for diagnosing headache diseases. As a popular and essential technique benefiting from evolutionary mechanisms, GA can deal with many different types of real-world problems. So, it has been chosen as the solution way/algorithm over the headache disease detection problem, which shapes the research framework of the study. The chapter content gives information about the performed diagnosis application and the results.
{"title":"Determining Headache Diseases With Genetic Algorithm","authors":"Gaffari Çelik","doi":"10.4018/978-1-5225-4769-3.CH012","DOIUrl":"https://doi.org/10.4018/978-1-5225-4769-3.CH012","url":null,"abstract":"Currently, medical diagnosis has a strong relation with the artificial-intelligence-oriented approaches. Because it is practical to employ intelligent mechanisms over some input data-expert knowledge and design effective solution ways, even the biomedical engineering field is interested in taking support from artificial intelligence. If applications in this manner are taken into consideration, we can see that medical diagnoses have a big percentage. In the sense of the explanations, the objective of this chapter is to use genetic algorithm (GA) for diagnosing headache diseases. As a popular and essential technique benefiting from evolutionary mechanisms, GA can deal with many different types of real-world problems. So, it has been chosen as the solution way/algorithm over the headache disease detection problem, which shapes the research framework of the study. The chapter content gives information about the performed diagnosis application and the results.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132869769","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 : 1900-01-01DOI: 10.4018/978-1-7998-8048-6.ch037
Dhamodharavadhani S., Rathipriya R.
Regression model (RM) is an important tool for modeling and analyzing data. It is one of the popular predictive modeling techniques which explore the relationship between a dependent (target) and independent (predictor) variables. The variable selection method is used to form a good and effective regression model. Many variable selection methods existing for regression model such as filter method, wrapper method, embedded methods, forward selection method, Backward Elimination methods, stepwise methods, and so on. In this chapter, computational intelligence-based variable selection method is discussed with respect to the regression model in cybersecurity. Generally, these regression models depend on the set of (predictor) variables. Therefore, variable selection methods are used to select the best subset of predictors from the entire set of variables. Genetic algorithm-based quick-reduct method is proposed to extract optimal predictor subset from the given data to form an optimal regression model.
{"title":"Variable Selection Method for Regression Models Using Computational Intelligence Techniques","authors":"Dhamodharavadhani S., Rathipriya R.","doi":"10.4018/978-1-7998-8048-6.ch037","DOIUrl":"https://doi.org/10.4018/978-1-7998-8048-6.ch037","url":null,"abstract":"Regression model (RM) is an important tool for modeling and analyzing data. It is one of the popular predictive modeling techniques which explore the relationship between a dependent (target) and independent (predictor) variables. The variable selection method is used to form a good and effective regression model. Many variable selection methods existing for regression model such as filter method, wrapper method, embedded methods, forward selection method, Backward Elimination methods, stepwise methods, and so on. In this chapter, computational intelligence-based variable selection method is discussed with respect to the regression model in cybersecurity. Generally, these regression models depend on the set of (predictor) variables. Therefore, variable selection methods are used to select the best subset of predictors from the entire set of variables. Genetic algorithm-based quick-reduct method is proposed to extract optimal predictor subset from the given data to form an optimal regression model.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134008179","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 : 1900-01-01DOI: 10.4018/978-1-5225-5091-4.CH001
Adem Guluma Negewo
This chapter provides a literature review of optimization problems in the context of grey system theory, as proposed by various authors. The chapter explains the binary interactive algorithm approach as a problem-solving method for linear programming and quadratic programming problems with uncertainty and a genetic-algorithm-based approach as a second problem-solving scheme for linear programming, quadratic programming, and general nonlinear programming problems with uncertainty. In the chapter, details on the computation procedures involved for solving the aforementioned optimization problems with uncertainty are presented and results from these two approaches are compared and contrasted. Finally, possible future work area in the subject is suggested.
{"title":"A Survey on Grey Optimization","authors":"Adem Guluma Negewo","doi":"10.4018/978-1-5225-5091-4.CH001","DOIUrl":"https://doi.org/10.4018/978-1-5225-5091-4.CH001","url":null,"abstract":"This chapter provides a literature review of optimization problems in the context of grey system theory, as proposed by various authors. The chapter explains the binary interactive algorithm approach as a problem-solving method for linear programming and quadratic programming problems with uncertainty and a genetic-algorithm-based approach as a second problem-solving scheme for linear programming, quadratic programming, and general nonlinear programming problems with uncertainty. In the chapter, details on the computation procedures involved for solving the aforementioned optimization problems with uncertainty are presented and results from these two approaches are compared and contrasted. Finally, possible future work area in the subject is suggested.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132210955","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 : 1900-01-01DOI: 10.4018/978-1-5225-3531-7.CH007
Amit Kumar, B. K. Sarkar
Research in medical data prediction has become an important classification problem due to its domain specificity, voluminous, and class imbalanced nature. In this chapter, four well-known nature-inspired algorithms, namely genetic algorithms (GA), genetic programming (GP), particle swarm optimization (PSO), and ant colony optimization (ACO), are used for feature selection in order to enhance the classification performances of medical data using Bayesian classifier. Naïve Bayes is most widely used Bayesian classifier in automatic medical diagnostic tools. In total, 12 real-world medical domain data sets are selected from the University of California, Irvine (UCI repository) for conducting the experiment. The experimental results demonstrate that nature-inspired Bayesian model plays an effective role in undertaking medical data prediction.
{"title":"Performance Analysis of Nature-Inspired Algorithms-Based Bayesian Prediction Models for Medical Data Sets","authors":"Amit Kumar, B. K. Sarkar","doi":"10.4018/978-1-5225-3531-7.CH007","DOIUrl":"https://doi.org/10.4018/978-1-5225-3531-7.CH007","url":null,"abstract":"Research in medical data prediction has become an important classification problem due to its domain specificity, voluminous, and class imbalanced nature. In this chapter, four well-known nature-inspired algorithms, namely genetic algorithms (GA), genetic programming (GP), particle swarm optimization (PSO), and ant colony optimization (ACO), are used for feature selection in order to enhance the classification performances of medical data using Bayesian classifier. Naïve Bayes is most widely used Bayesian classifier in automatic medical diagnostic tools. In total, 12 real-world medical domain data sets are selected from the University of California, Irvine (UCI repository) for conducting the experiment. The experimental results demonstrate that nature-inspired Bayesian model plays an effective role in undertaking medical data prediction.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125302082","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 : 1900-01-01DOI: 10.4018/978-1-7998-8048-6.ch045
Shashwati Mishra, M. Panda
Thresholding is one of the important steps in image analysis process and used extensively in different image processing techniques. Medical image segmentation plays a very important role in surgery planning, identification of tumours, diagnosis of organs, etc. In this article, a novel approach for medical image segmentation is proposed using a hybrid technique of genetic algorithm and fuzzy logic. Fuzzy logic can handle uncertain and imprecise information. Genetic algorithms help in global optimization, gives good results in noisy environments and supports multi-objective optimization. Gaussian, trapezoidal and triangular membership functions are used separately for calculating the entropy and finding the fitness value. CPU time, Root Mean Square Error, sensitivity, specificity, and accuracy are calculated using the three membership functions separately at threshold levels 2, 3, 4, 5, 7 and 9. MRI images are considered for applying the proposed method and the results are analysed. The experimental results obtained prove the effectiveness and efficiency of the proposed method.
{"title":"Medical Image Thresholding Using Genetic Algorithm and Fuzzy Membership Functions","authors":"Shashwati Mishra, M. Panda","doi":"10.4018/978-1-7998-8048-6.ch045","DOIUrl":"https://doi.org/10.4018/978-1-7998-8048-6.ch045","url":null,"abstract":"Thresholding is one of the important steps in image analysis process and used extensively in different image processing techniques. Medical image segmentation plays a very important role in surgery planning, identification of tumours, diagnosis of organs, etc. In this article, a novel approach for medical image segmentation is proposed using a hybrid technique of genetic algorithm and fuzzy logic. Fuzzy logic can handle uncertain and imprecise information. Genetic algorithms help in global optimization, gives good results in noisy environments and supports multi-objective optimization. Gaussian, trapezoidal and triangular membership functions are used separately for calculating the entropy and finding the fitness value. CPU time, Root Mean Square Error, sensitivity, specificity, and accuracy are calculated using the three membership functions separately at threshold levels 2, 3, 4, 5, 7 and 9. MRI images are considered for applying the proposed method and the results are analysed. The experimental results obtained prove the effectiveness and efficiency of the proposed method.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133144949","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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