Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8048-6.ch070
Sharon Moses J., Dhinesh Babu L.D.
Most recommender systems are based on the familiar collaborative filtering algorithm to suggest items. Quite often, collaborative filtering algorithm fails in generating recommendations due to the lack of adequate user information resulting in new user cold start problem. The cold start problem is one among the prevailing issue in recommendation system where the system fails to render recommendations. To overcome the new user cold start issue, demographical information of the user is utilised as the user information source. Among the demographical information, the impact of the user gender is less explored when compared with other information like age, profession, region, etc. In this work, a genetic algorithm-influenced gender-based top-n recommender algorithm is proposed to address the new user cold start problem. The algorithm utilises the evolution concepts of the genetic algorithm to render top-n recommendations to a new user. The evaluation of the proposed algorithm using real world datasets proved that the algorithm has a better efficiency than the state of art approaches.
{"title":"Genetic Algorithm Influenced Top-N Recommender System to Alleviate New User Cold Start Problem","authors":"Sharon Moses J., Dhinesh Babu L.D.","doi":"10.4018/978-1-7998-8048-6.ch070","DOIUrl":"https://doi.org/10.4018/978-1-7998-8048-6.ch070","url":null,"abstract":"Most recommender systems are based on the familiar collaborative filtering algorithm to suggest items. Quite often, collaborative filtering algorithm fails in generating recommendations due to the lack of adequate user information resulting in new user cold start problem. The cold start problem is one among the prevailing issue in recommendation system where the system fails to render recommendations. To overcome the new user cold start issue, demographical information of the user is utilised as the user information source. Among the demographical information, the impact of the user gender is less explored when compared with other information like age, profession, region, etc. In this work, a genetic algorithm-influenced gender-based top-n recommender algorithm is proposed to address the new user cold start problem. The algorithm utilises the evolution concepts of the genetic algorithm to render top-n recommendations to a new user. The evaluation of the proposed algorithm using real world datasets proved that the algorithm has a better efficiency than the state of art approaches.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"16 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":"128410307","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: 10.4018/978-1-5225-3232-3.CH025
Shivlal Mewada, P. Sharma, S. S. Gautam
Fuzzy system was altered from a ‘buzz word' to an important technological area, with various publications in international conferences and transactions. Several Japanese products applying fuzzy logic concepts, such as household appliances and electronic equipment, power engineering, robotics, and optimization have been manufactured. This system is capable to process and learn mathematical data as well as linguistic data. Fuzzy system user linguistic explanations for the variables and linguistic procedures for the I/P-O/P behavior. In this chapter, present the application of fuzzy system with data mining, neural networks, fuzzy automata, and genetic algorithms. It also presents the foundation of fuzzy data Mining, with the fuzzification inference procedure and defuzzification procedure, fuzzy systems and neural networks with feed forward neural network, FNN with it features generalization of Fuzzy Automata, and sixth fuzzy systems and genetic algorithms. The chapter explores a popular fuzzy system model to show complex systems and an application of fuzzy system.
模糊系统从一个“流行语”变成了一个重要的技术领域，在国际会议和交易中有各种出版物。日本已经制造了一些应用模糊逻辑概念的产品，如家用电器和电子设备、电力工程、机器人和优化。该系统既能处理和学习数学数据，也能处理和学习语言数据。模糊系统用户对变量的语言解释和I/P- o /P行为的语言程序。在本章中，介绍了模糊系统在数据挖掘、神经网络、模糊自动机和遗传算法中的应用。介绍了模糊数据挖掘的基础，包括模糊化推理过程和去模糊化过程、模糊系统和神经网络与前馈神经网络、模糊神经网络及其模糊自动机泛化的特点、第六模糊系统和遗传算法。本章探讨了一种流行的模糊系统模型来显示复杂系统和模糊系统的应用。
{"title":"Exploration of Fuzzy System With Applications","authors":"Shivlal Mewada, P. Sharma, S. S. Gautam","doi":"10.4018/978-1-5225-3232-3.CH025","DOIUrl":"https://doi.org/10.4018/978-1-5225-3232-3.CH025","url":null,"abstract":"Fuzzy system was altered from a ‘buzz word' to an important technological area, with various publications in international conferences and transactions. Several Japanese products applying fuzzy logic concepts, such as household appliances and electronic equipment, power engineering, robotics, and optimization have been manufactured. This system is capable to process and learn mathematical data as well as linguistic data. Fuzzy system user linguistic explanations for the variables and linguistic procedures for the I/P-O/P behavior. In this chapter, present the application of fuzzy system with data mining, neural networks, fuzzy automata, and genetic algorithms. It also presents the foundation of fuzzy data Mining, with the fuzzification inference procedure and defuzzification procedure, fuzzy systems and neural networks with feed forward neural network, FNN with it features generalization of Fuzzy Automata, and sixth fuzzy systems and genetic algorithms. The chapter explores a popular fuzzy system model to show complex systems and an application of fuzzy system.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"15 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":"133451274","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: 10.4018/978-1-5225-5832-3.CH005
Manisha Rathee, K. Dilip, Ritu Rathee
DNA fragment assembly (DFA) is one of the most important and challenging problems in computational biology. DFA problem involves reconstruction of target DNA from several hundred (or thousands) of sequenced fragments by identifying the proper orientation and order of fragments. DFA problem is proved to be a NP-Hard combinatorial optimization problem. Metaheuristic techniques have the capability to handle large search spaces and therefore are well suited to deal with such problems. In this chapter, quantum-inspired genetic algorithm-based DNA fragment assembly (QGFA) approach has been proposed to perform the de novo assembly of DNA fragments using overlap-layout-consensus approach. To assess the efficacy of QGFA, it has been compared genetic algorithm, particle swarm optimization, and ant colony optimization-based metaheuristic approaches for solving DFA problem. Experimental results show that QGFA performs comparatively better (in terms of overlap score obtained and number of contigs produced) than other approaches considered herein.
{"title":"DNA Fragment Assembly Using Quantum-Inspired Genetic Algorithm","authors":"Manisha Rathee, K. Dilip, Ritu Rathee","doi":"10.4018/978-1-5225-5832-3.CH005","DOIUrl":"https://doi.org/10.4018/978-1-5225-5832-3.CH005","url":null,"abstract":"DNA fragment assembly (DFA) is one of the most important and challenging problems in computational biology. DFA problem involves reconstruction of target DNA from several hundred (or thousands) of sequenced fragments by identifying the proper orientation and order of fragments. DFA problem is proved to be a NP-Hard combinatorial optimization problem. Metaheuristic techniques have the capability to handle large search spaces and therefore are well suited to deal with such problems. In this chapter, quantum-inspired genetic algorithm-based DNA fragment assembly (QGFA) approach has been proposed to perform the de novo assembly of DNA fragments using overlap-layout-consensus approach. To assess the efficacy of QGFA, it has been compared genetic algorithm, particle swarm optimization, and ant colony optimization-based metaheuristic approaches for solving DFA problem. Experimental results show that QGFA performs comparatively better (in terms of overlap score obtained and number of contigs produced) than other approaches considered herein.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"93 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":"122130790","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: 10.4018/978-1-7998-3222-5.ch010
S. J., D. L D, Santhoshkumar Srinivasan, N. M
Most recommender systems are based on the familiar collaborative filtering algorithm to suggest items. Quite often, collaborative filtering algorithm fails in generating recommendations due to the lack of adequate user information resulting in new user cold start problem. Cold start problem is one of the prevailing issues in recommendation system where the system fails to render recommendation. To overcome the new user cold start issue, demographical information of the user is utilised as the user information source. Among the demographical information, the impact of user gender is less explored when compared with other information like age, profession, region, etc. In this chapter, genetic algorithm influenced gender-based top-n recommender algorithm is proposed to address the new user cold start problem. The algorithm utilises the evolution concepts of genetic algorithm to render top-n recommendations to a new user. The evaluation of the proposed algorithm using real world datasets proved that the algorithm has a better efficiency than the state-of-art approaches.
{"title":"Genetic Algorithm-Influenced Top-N Recommender System to Alleviate the New User Cold Start Problem","authors":"S. J., D. L D, Santhoshkumar Srinivasan, N. M","doi":"10.4018/978-1-7998-3222-5.ch010","DOIUrl":"https://doi.org/10.4018/978-1-7998-3222-5.ch010","url":null,"abstract":"Most recommender systems are based on the familiar collaborative filtering algorithm to suggest items. Quite often, collaborative filtering algorithm fails in generating recommendations due to the lack of adequate user information resulting in new user cold start problem. Cold start problem is one of the prevailing issues in recommendation system where the system fails to render recommendation. To overcome the new user cold start issue, demographical information of the user is utilised as the user information source. Among the demographical information, the impact of user gender is less explored when compared with other information like age, profession, region, etc. In this chapter, genetic algorithm influenced gender-based top-n recommender algorithm is proposed to address the new user cold start problem. The algorithm utilises the evolution concepts of genetic algorithm to render top-n recommendations to a new user. The evaluation of the proposed algorithm using real world datasets proved that the algorithm has a better efficiency than the state-of-art approaches.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"25 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":"125375003","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: 10.4018/978-1-5225-2128-0.CH011
S. Aote, M. Raghuwanshi
To solve the problems of optimization, various methods are provided in different domain. Evolutionary computing (EC) is one of the methods to solve these problems. Mostly used EC techniques are available like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). These techniques have different working structure but the inner working structure is same. Different names and formulae are given for different task but ultimately all do the same. Here we tried to find out the similarities among these techniques and give the working structure in each step. All the steps are provided with proper example and code written in MATLAB, for better understanding. Here we started our discussion with introduction about optimization and solution to optimization problems by PSO, GA and DE. Finally, we have given brief comparison of these.
{"title":"Mathematical Optimization by Using Particle Swarm Optimization, Genetic Algorithm, and Differential Evolution and Its Similarities","authors":"S. Aote, M. Raghuwanshi","doi":"10.4018/978-1-5225-2128-0.CH011","DOIUrl":"https://doi.org/10.4018/978-1-5225-2128-0.CH011","url":null,"abstract":"To solve the problems of optimization, various methods are provided in different domain. Evolutionary computing (EC) is one of the methods to solve these problems. Mostly used EC techniques are available like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). These techniques have different working structure but the inner working structure is same. Different names and formulae are given for different task but ultimately all do the same. Here we tried to find out the similarities among these techniques and give the working structure in each step. All the steps are provided with proper example and code written in MATLAB, for better understanding. Here we started our discussion with introduction about optimization and solution to optimization problems by PSO, GA and DE. Finally, we have given brief comparison of these.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"7 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":"130795176","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.ch019
Riham Moharam, E. Morsy, Ismail A. Ismail
The t-spanner problem is a popular combinatorial optimization problem and has different applications in communication networks and distributed systems. This chapter considers the problem of constructing a t-spanner subgraph H in a given undirected edge-weighted graph G in the sense that the distance between every pair of vertices in H is at most t times the shortest distance between the two vertices in G. The value of t, called the stretch factor, quantifies the quality of the distance approximation of the corresponding t-spanner subgraph. This chapter studies two variations of the problem, the Minimum t-Spanner Subgraph (MtSS) and the Minimum Maximum Stretch Spanning Tree(MMST). Given a value for the stretch factor t, the MtSS problem asks to find the t-spanner subgraph of the minimum total weight in G. The MMST problem looks for a tree T in G that minimizes the maximum distance between all pairs of vertices in V (i.e., minimizing the stretch factor of the constructed tree). It is easy to conclude from the literatures that the above problems are NP-hard. This chapter presents genetic algorithms that returns a high quality solution for those two problems.
{"title":"T-Spanner Problem","authors":"Riham Moharam, E. Morsy, Ismail A. Ismail","doi":"10.4018/978-1-7998-8048-6.ch019","DOIUrl":"https://doi.org/10.4018/978-1-7998-8048-6.ch019","url":null,"abstract":"The t-spanner problem is a popular combinatorial optimization problem and has different applications in communication networks and distributed systems. This chapter considers the problem of constructing a t-spanner subgraph H in a given undirected edge-weighted graph G in the sense that the distance between every pair of vertices in H is at most t times the shortest distance between the two vertices in G. The value of t, called the stretch factor, quantifies the quality of the distance approximation of the corresponding t-spanner subgraph. This chapter studies two variations of the problem, the Minimum t-Spanner Subgraph (MtSS) and the Minimum Maximum Stretch Spanning Tree(MMST). Given a value for the stretch factor t, the MtSS problem asks to find the t-spanner subgraph of the minimum total weight in G. The MMST problem looks for a tree T in G that minimizes the maximum distance between all pairs of vertices in V (i.e., minimizing the stretch factor of the constructed tree). It is easy to conclude from the literatures that the above problems are NP-hard. This chapter presents genetic algorithms that returns a high quality solution for those two problems.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"1 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":"129139450","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.ch016
Anuj Chandila, S. Tiwari, K. Mishra, Akash Punhani
This article describes how optimization is a process of finding out the best solutions among all available solutions for a problem. Many randomized algorithms have been designed to identify optimal solutions in optimization problems. Among these algorithms evolutionary programming, evolutionary strategy, genetic algorithm, particle swarm optimization and genetic programming are widely accepted for the optimization problems. Although a number of randomized algorithms are available in literature for solving optimization problems yet their design objectives are same. Each algorithm has been designed to meet certain goals like minimizing total number of fitness evaluations to capture nearly optimal solutions, to capture diverse optimal solutions in multimodal solutions when needed and also to avoid the local optimal solution in multi modal problems. This article discusses a novel optimization algorithm named as Environmental Adaption Method (EAM) foable 3r solving the optimization problems. EAM is designed to reduce the overall processing time for retrieving optimal solution of the problem, to improve the quality of solutions and particularly to avoid being trapped in local optima. The results of the proposed algorithm are compared with the latest version of existing algorithms such as particle swarm optimization (PSO-TVAC), and differential evolution (SADE) on benchmark functions and the proposed algorithm proves its effectiveness over the existing algorithms in all the taken cases.
{"title":"Environmental Adaption Method","authors":"Anuj Chandila, S. Tiwari, K. Mishra, Akash Punhani","doi":"10.4018/978-1-7998-8048-6.ch016","DOIUrl":"https://doi.org/10.4018/978-1-7998-8048-6.ch016","url":null,"abstract":"This article describes how optimization is a process of finding out the best solutions among all available solutions for a problem. Many randomized algorithms have been designed to identify optimal solutions in optimization problems. Among these algorithms evolutionary programming, evolutionary strategy, genetic algorithm, particle swarm optimization and genetic programming are widely accepted for the optimization problems. Although a number of randomized algorithms are available in literature for solving optimization problems yet their design objectives are same. Each algorithm has been designed to meet certain goals like minimizing total number of fitness evaluations to capture nearly optimal solutions, to capture diverse optimal solutions in multimodal solutions when needed and also to avoid the local optimal solution in multi modal problems. This article discusses a novel optimization algorithm named as Environmental Adaption Method (EAM) foable 3r solving the optimization problems. EAM is designed to reduce the overall processing time for retrieving optimal solution of the problem, to improve the quality of solutions and particularly to avoid being trapped in local optima. The results of the proposed algorithm are compared with the latest version of existing algorithms such as particle swarm optimization (PSO-TVAC), and differential evolution (SADE) on benchmark functions and the proposed algorithm proves its effectiveness over the existing algorithms in all the taken cases.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"18 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":"126963038","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.ch030
Shouvik Chakraborty, Sankhadeep Chatterjee, A. Ashour, Kalyani Mali, N. Dey
Biomedical imaging is considered main procedure to acquire valuable physical information about the human body and some other biological species. It produces specialized images of different parts of the biological species for clinical analysis. It assimilates various specialized domains including nuclear medicine, radiological imaging, Positron emission tomography (PET), and microscopy. From the early discovery of X-rays, progress in biomedical imaging continued resulting in highly sophisticated medical imaging modalities, such as magnetic resonance imaging (MRI), ultrasound, Computed Tomography (CT), and lungs monitoring. These biomedical imaging techniques assist physicians for faster and accurate analysis and treatment. The present chapter discussed the impact of intelligent computing methods for biomedical image analysis and healthcare. Different Artificial Intelligence (AI) based automated biomedical image analysis are considered. Different approaches are discussed including the AI ability to resolve various medical imaging problems. It also introduced the popular AI procedures that employed to solve some special problems in medicine. Artificial Neural Network (ANN) and support vector machine (SVM) are active to classify different types of images from various imaging modalities. Different diagnostic analysis, such as mammogram analysis, MRI brain image analysis, CT images, PET images, and bone/retinal analysis using ANN, feed-forward back propagation ANN, probabilistic ANN, and extreme learning machine continuously. Various optimization techniques of ant colony optimization (ACO), genetic algorithm (GA), particle swarm optimization (PSO) and other bio-inspired procedures are also frequently conducted for feature extraction/selection and classification. The advantages and disadvantages of some AI approaches are discussed in the present chapter along with some suggested future research perspectives.
{"title":"Intelligent Computing in Medical Imaging","authors":"Shouvik Chakraborty, Sankhadeep Chatterjee, A. Ashour, Kalyani Mali, N. Dey","doi":"10.4018/978-1-7998-8048-6.ch030","DOIUrl":"https://doi.org/10.4018/978-1-7998-8048-6.ch030","url":null,"abstract":"Biomedical imaging is considered main procedure to acquire valuable physical information about the human body and some other biological species. It produces specialized images of different parts of the biological species for clinical analysis. It assimilates various specialized domains including nuclear medicine, radiological imaging, Positron emission tomography (PET), and microscopy. From the early discovery of X-rays, progress in biomedical imaging continued resulting in highly sophisticated medical imaging modalities, such as magnetic resonance imaging (MRI), ultrasound, Computed Tomography (CT), and lungs monitoring. These biomedical imaging techniques assist physicians for faster and accurate analysis and treatment. The present chapter discussed the impact of intelligent computing methods for biomedical image analysis and healthcare. Different Artificial Intelligence (AI) based automated biomedical image analysis are considered. Different approaches are discussed including the AI ability to resolve various medical imaging problems. It also introduced the popular AI procedures that employed to solve some special problems in medicine. Artificial Neural Network (ANN) and support vector machine (SVM) are active to classify different types of images from various imaging modalities. Different diagnostic analysis, such as mammogram analysis, MRI brain image analysis, CT images, PET images, and bone/retinal analysis using ANN, feed-forward back propagation ANN, probabilistic ANN, and extreme learning machine continuously. Various optimization techniques of ant colony optimization (ACO), genetic algorithm (GA), particle swarm optimization (PSO) and other bio-inspired procedures are also frequently conducted for feature extraction/selection and classification. The advantages and disadvantages of some AI approaches are discussed in the present chapter along with some suggested future research perspectives.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"28 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":"117192901","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: 10.4018/978-1-5225-2857-9.CH023
P. Samui, R. Viswanathan, J. Jagan, P. Kurup
This study adopts four modeling techniques Ordinary Kriging(OK), Generalized Regression Neural Network (GRNN), Genetic Programming(GP) and Minimax Probability Machine Regression(MPMR) for prediction of rock depth(d) at Chennai(India). Latitude (Lx) and Longitude(Ly) have been used as inputs of the models. A semivariogram has been constructed for developing the OK model. The developed GP gives equation for prediction of d at any point in Chennai. A comparison of four modeling techniques has been carried out. The performance of MPMR is slightly better than the other models. The developed models give the spatial variability of rock depth at Chennai.
{"title":"Determination of Spatial Variability of Rock Depth of Chennai","authors":"P. Samui, R. Viswanathan, J. Jagan, P. Kurup","doi":"10.4018/978-1-5225-2857-9.CH023","DOIUrl":"https://doi.org/10.4018/978-1-5225-2857-9.CH023","url":null,"abstract":"This study adopts four modeling techniques Ordinary Kriging(OK), Generalized Regression Neural Network (GRNN), Genetic Programming(GP) and Minimax Probability Machine Regression(MPMR) for prediction of rock depth(d) at Chennai(India). Latitude (Lx) and Longitude(Ly) have been used as inputs of the models. A semivariogram has been constructed for developing the OK model. The developed GP gives equation for prediction of d at any point in Chennai. A comparison of four modeling techniques has been carried out. The performance of MPMR is slightly better than the other models. The developed models give the spatial variability of rock depth at Chennai.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"114 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":"132751440","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: 10.4018/978-1-7998-0268-6.ch002
Şahin İnanç, Arzu Eren Şenaras
The nurse scheduling problem (NSP) is the problem involving allocating the monthly shifts (day and night shifts, holidays, and so on) for nurses under various constraints. Generally, the NSP has a lot of constraints. As a result, it needs a lot of knowledge and experience to make the scheduling table with its constraints, and it has been made by the head nurse or the authority in the hospitals. This allocation of the shifts gives a lot of burden (time and efforts) to them, and it has been growing the demand for the automatic nurse scheduling system. This chapter aims to develop a genetic algorithm application for the Nurse Scheduling Problem (NSP). The application will be developed using Microsoft Visual Studio in C# programming language.
{"title":"Solving Nurse Scheduling Problem via Genetic Algorithm in Home Healthcare","authors":"Şahin İnanç, Arzu Eren Şenaras","doi":"10.4018/978-1-7998-0268-6.ch002","DOIUrl":"https://doi.org/10.4018/978-1-7998-0268-6.ch002","url":null,"abstract":"The nurse scheduling problem (NSP) is the problem involving allocating the monthly shifts (day and night shifts, holidays, and so on) for nurses under various constraints. Generally, the NSP has a lot of constraints. As a result, it needs a lot of knowledge and experience to make the scheduling table with its constraints, and it has been made by the head nurse or the authority in the hospitals. This allocation of the shifts gives a lot of burden (time and efforts) to them, and it has been growing the demand for the automatic nurse scheduling system. This chapter aims to develop a genetic algorithm application for the Nurse Scheduling Problem (NSP). The application will be developed using Microsoft Visual Studio in C# programming language.","PeriodicalId":345892,"journal":{"name":"Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms","volume":"14 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":"122852904","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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