In the present era of globalization and competitive market, cellular manufacturing has become a vital tool for
meeting the challenges of improving productivity, which is the way to sustain growth. Getting best results of
cellular manufacturing depends on the formation of the machine cells and part families. This paper examines
advantages of ART method of cell formation over array based clustering algorithms, namely ROC-2 and DCA.
The comparison and evaluation of the cell formation methods has been carried out in the study. The most
appropriate approach is selected and used to form the cellular manufacturing system. The comparison and
evaluation is done on the basis of performance measure as grouping efficiency and improvements over the
existing cellular manufacturing system is presented.
Application of Neural Network for Cell Formation in Group TechnologyIJMER
Group Technology is a method for increasing productivity of manufacturing quality products.
For improving the flexibility in manufacturing systems, cell formation is the main step in group
technology .Every manufacturing industry faces problem of productivity and their priority is to deliver
product to valuable customer in time. For fulfilling this purpose a proper engineering analysis is needed
which can reduce material handling and wait time. This can be done by cell formation. There are
various techniques which are available for cell formation and discussed by different researchers but
neural network is found the best among them due to its better and fast computation results. Here in this
paper Adaptive Resonance Theory ART1 is analyzed and proven a better way to cope up with the
manufacturing problems.
This document presents a new approach for forming part families and machine cells in a batch-oriented production system. The approach combines a local search heuristic with a genetic algorithm. The genetic algorithm is used to generate initial machine cell sets, while the local search heuristic considers both inter-cell movement and machine utilization to improve the groupings. The heuristic feeds back grouping efficacy results to the genetic algorithm until an optimal solution is found. The goal is to maximize grouping quality and machine utilization within cells, while minimizing inter-cell movement of parts. The approach is tested on a sample manufacturing data set, with results showing higher grouping efficacy than traditional methods.
An Application of Genetic Algorithm for Non-restricted Space and Pre-determin...drboon
The use of a genetic algorithm is presented to solve a facility layout problem in the situation where there is non-restricted space but the ratio of plant length and width is pre-determined. A two-leveled chromosome is constructed. Six rules are established to translate the chromosome to facility design. An approach of solving a facility layout problem is proposed. A numerical example is employed to illustrate the approach.
This document proposes an unsupervised feature selection method that combines weighted principal components analysis with a thresholding algorithm. It begins by introducing weighted principal components, which represent the contribution of each original feature to the principal components. It then proposes a moving range-based thresholding algorithm to identify significant features based on their weighted principal component loadings. The method was evaluated on simulated and real datasets, demonstrating high sensitivity and specificity in identifying significant features, while requiring less computation time than existing methods.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
Control chart pattern recognition using k mica clustering and neural networksISA Interchange
Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of the common types of control chart pattern (CCP). The proposed method includes two main modules: a clustering module and a classifier module. In the clustering module, the input data is first clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and the K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks, and the radial basis function neural networks, are investigated. Using the experimental study, we choose the best classifier in order to recognize the CCPs. Simulation results show that a high recognition accuracy, about 99.65%, is achieved.
Automatic Feature Subset Selection using Genetic Algorithm for Clusteringidescitation
Feature subset selection is a process of selecting a
subset of minimal, relevant features and is a pre processing
technique for a wide variety of applications. High dimensional
data clustering is a challenging task in data mining. Reduced
set of features helps to make the patterns easier to understand.
Reduced set of features are more significant if they are
application specific. Almost all existing feature subset
selection algorithms are not automatic and are not application
specific. This paper made an attempt to find the feature subset
for optimal clusters while clustering. The proposed Automatic
Feature Subset Selection using Genetic Algorithm (AFSGA)
identifies the required features automatically and reduces
the computational cost in determining good clusters. The
performance of AFSGA is tested using public and synthetic
datasets with varying dimensionality. Experimental results
have shown the improved efficacy of the algorithm with optimal
clusters and computational cost.
Enhancing facility layout via ant colony technique (act)Alexander Decker
This document discusses using an ant colony technique called Ant Colony System (ACS) to optimize the facility layout of a manufacturing plant. ACS was inspired by how real ants find food sources and build colonies. The research applies ACS to optimize the positions of machines and routing of parts in an electrical motor factory. Results showed ACS provided a flexible, fast solution that reduced the distance parts traveled in the plant by 500 meters, a reduction of 0.625. ACS is presented as an effective advanced intelligent technique for modifying a production process through facility layout optimization.
Application of Neural Network for Cell Formation in Group TechnologyIJMER
Group Technology is a method for increasing productivity of manufacturing quality products.
For improving the flexibility in manufacturing systems, cell formation is the main step in group
technology .Every manufacturing industry faces problem of productivity and their priority is to deliver
product to valuable customer in time. For fulfilling this purpose a proper engineering analysis is needed
which can reduce material handling and wait time. This can be done by cell formation. There are
various techniques which are available for cell formation and discussed by different researchers but
neural network is found the best among them due to its better and fast computation results. Here in this
paper Adaptive Resonance Theory ART1 is analyzed and proven a better way to cope up with the
manufacturing problems.
This document presents a new approach for forming part families and machine cells in a batch-oriented production system. The approach combines a local search heuristic with a genetic algorithm. The genetic algorithm is used to generate initial machine cell sets, while the local search heuristic considers both inter-cell movement and machine utilization to improve the groupings. The heuristic feeds back grouping efficacy results to the genetic algorithm until an optimal solution is found. The goal is to maximize grouping quality and machine utilization within cells, while minimizing inter-cell movement of parts. The approach is tested on a sample manufacturing data set, with results showing higher grouping efficacy than traditional methods.
An Application of Genetic Algorithm for Non-restricted Space and Pre-determin...drboon
The use of a genetic algorithm is presented to solve a facility layout problem in the situation where there is non-restricted space but the ratio of plant length and width is pre-determined. A two-leveled chromosome is constructed. Six rules are established to translate the chromosome to facility design. An approach of solving a facility layout problem is proposed. A numerical example is employed to illustrate the approach.
This document proposes an unsupervised feature selection method that combines weighted principal components analysis with a thresholding algorithm. It begins by introducing weighted principal components, which represent the contribution of each original feature to the principal components. It then proposes a moving range-based thresholding algorithm to identify significant features based on their weighted principal component loadings. The method was evaluated on simulated and real datasets, demonstrating high sensitivity and specificity in identifying significant features, while requiring less computation time than existing methods.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
Control chart pattern recognition using k mica clustering and neural networksISA Interchange
Automatic recognition of abnormal patterns in control charts has seen increasing demands nowadays in manufacturing processes. This paper presents a novel hybrid intelligent method (HIM) for recognition of the common types of control chart pattern (CCP). The proposed method includes two main modules: a clustering module and a classifier module. In the clustering module, the input data is first clustered by a new technique. This technique is a suitable combination of the modified imperialist competitive algorithm (MICA) and the K-means algorithm. Then the Euclidean distance of each pattern is computed from the determined clusters. The classifier module determines the membership of the patterns using the computed distance. In this module, several neural networks, such as the multilayer perceptron, probabilistic neural networks, and the radial basis function neural networks, are investigated. Using the experimental study, we choose the best classifier in order to recognize the CCPs. Simulation results show that a high recognition accuracy, about 99.65%, is achieved.
Automatic Feature Subset Selection using Genetic Algorithm for Clusteringidescitation
Feature subset selection is a process of selecting a
subset of minimal, relevant features and is a pre processing
technique for a wide variety of applications. High dimensional
data clustering is a challenging task in data mining. Reduced
set of features helps to make the patterns easier to understand.
Reduced set of features are more significant if they are
application specific. Almost all existing feature subset
selection algorithms are not automatic and are not application
specific. This paper made an attempt to find the feature subset
for optimal clusters while clustering. The proposed Automatic
Feature Subset Selection using Genetic Algorithm (AFSGA)
identifies the required features automatically and reduces
the computational cost in determining good clusters. The
performance of AFSGA is tested using public and synthetic
datasets with varying dimensionality. Experimental results
have shown the improved efficacy of the algorithm with optimal
clusters and computational cost.
Enhancing facility layout via ant colony technique (act)Alexander Decker
This document discusses using an ant colony technique called Ant Colony System (ACS) to optimize the facility layout of a manufacturing plant. ACS was inspired by how real ants find food sources and build colonies. The research applies ACS to optimize the positions of machines and routing of parts in an electrical motor factory. Results showed ACS provided a flexible, fast solution that reduced the distance parts traveled in the plant by 500 meters, a reduction of 0.625. ACS is presented as an effective advanced intelligent technique for modifying a production process through facility layout optimization.
This document discusses using a multi-objective evolutionary algorithm (MOEA) for feature selection in bankruptcy prediction models. The goal is to maximize classifier accuracy while minimizing the number of features. A two-objective problem of minimizing features and maximizing accuracy is analyzed using logistic regression and support vector machines classifiers. The methodology is tested on financial data from 1200 French companies and shown to be an efficient feature selection approach, obtaining best results when optimizing both accuracy and classifier parameters simultaneously.
A Survey and Comparative Study of Filter and Wrapper Feature Selection Techni...theijes
Feature selection is considered as a problem of global combinatorial optimization in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data. However, identification of useful features from hundreds or even thousands of related features is not an easy task. Selecting relevant genes from microarray data becomes even more challenging owing to the high dimensionality of features, multiclass categories involved and the usually small sample size. In order to improve the prediction accuracy and to avoid incomprehensibility due to the number of features different feature selection techniques can be implemented. This survey classifies and analyzes different approaches, aiming to not only provide a comprehensive presentation but also discuss challenges and various performance parameters. The techniques are generally classified into three; filter, wrapper and hybrid.
SURVEY ON CLASSIFICATION ALGORITHMS USING BIG DATASETEditor IJMTER
Data mining environment produces a large amount of data that need to be analyzed.
Using traditional databases and architectures, it has become difficult to process, manage and analyze
patterns. To gain knowledge about the Big Data a proper architecture should be understood.
Classification is an important data mining technique with broad applications to classify the various
kinds of data used in nearly every field of our life. Classification is used to classify the item
according to the features of the item with respect to the predefined set of classes. This paper put a
light on various classification algorithms including j48, C4.5, Naive Bayes using large dataset.
IRJET- The Machine Learning: The method of Artificial IntelligenceIRJET Journal
This document discusses machine learning and its role in artificial intelligence. It begins with an abstract that explains machine learning is widely used in artificial intelligence to enable systems to learn and make decisions without being explicitly programmed. It then provides an introduction to machine learning, explaining it allows software to learn from data and improve predictions without being explicitly programmed. The document also discusses related work from other researchers on topics like supervised learning, unsupervised learning, and evaluating different machine learning methods. It describes problems that can occur during the learning process like bias, noise, and pattern recognition. Finally, it provides algorithms for hierarchical clustering and k-means clustering as examples of unsupervised learning methods.
Analysis On Classification Techniques In Mammographic Mass Data SetIJERA Editor
Data mining, the extraction of hidden information from large databases, is to predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. Data-Mining classification techniques deals with determining to which group each data instances are associated with. It can deal with a wide variety of data so that large amount of data can be involved in processing. This paper deals with analysis on various data mining classification techniques such as Decision Tree Induction, Naïve Bayes , k-Nearest Neighbour (KNN) classifiers in mammographic mass dataset.
Data mining or Knowledge discovery (KDD) is
extracting unknown (hidden) and useful knowledge from data.
Data mining is widely used in many areas like retail, sales, ecommerce,
remote sensing, bioinformatics etc. Student’s
performance has become one of the most complex puzzle for
universities and colleges in recent past with the tremendous
growth. In this paper, authors deployed data mining techniques
like classification, association rule, chi-square etc. for knowledge
discovery. For this study, authors have used data set containing
Approx. 180 MCA (post graduate) students results data of 3
colleges. Study found that one can apply data mining
functionalities like Chi-square, Association rule and Lift in
Education and discover areas of improvement.
Applying Genetic Algorithms to Information Retrieval Using Vector Space ModelIJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in 1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used cosine similarity and jaccards to compute similarity between the query and documents, and used two proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study concluded that we might have several improvements when using adaptive genetic algorithms.
Leave one out cross validated Hybrid Model of Genetic Algorithm and Naïve Bay...IJERA Editor
This document presents a new hybrid model for feature selection and classification that combines genetic algorithm and naive Bayes. The proposed method first uses a binary coded genetic algorithm to select a reduced subset of important features from datasets. It then applies a naive Bayes classification method to evaluate the selected features and identify the subset that achieves the highest classification accuracy. The performance of the proposed hybrid model is evaluated on eight datasets and compared to recent publications, finding it achieves satisfactory or higher classification accuracy using fewer features.
Applying genetic algorithms to information retrieval using vector space modelIJCSEA Journal
The document describes a study that applied genetic algorithms to information retrieval using the vector space model. The study used an adaptive genetic algorithm approach with two proposed fitness functions (cosine and Jaccard's), adaptive crossover and mutation probabilities. Experimental results on a test corpus showed improvements in precision and recall compared to traditional approaches, with the proposed cosine fitness function performing best. Precision generally decreased as recall increased. The modifications made to the genetic algorithm and fitness functions led to better weighting of query terms and improved results.
A Threshold fuzzy entropy based feature selection method applied in various b...IJMER
Large amount of data have been stored and manipulated using various database
technologies. Processing all the attributes for the particular means is the difficult task. To avoid such
difficulties, feature selection process is processed.In this paper,we are collect a eight various benchmark
datasets from UCI repository.Feature selection process is carried out using fuzzy entropy based
relevance measure algorithm and follows three selection strategies like Mean selection strategy,Half
selection strategy and Neural network for threshold selection strategy. After the features are selected,
they are evaluated using Radial Basis Function (RBF) network,Stacking,Bagging,AdaBoostM1 and Antminer
classification methodologies.The test results depicts that Neural network for threshold selection
strategy works well in selecting features and Ant-miner methodology works best in bringing out better
accuracy with selected feature than processing with original dataset.The obtained result of this
experiment shows that clearly the Ant-miner is superiority than other classifiers.Thus, this proposed Antminer
algorithm could be a more suitable method for producing good results with fewer features than
the original datasets.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
PREDICTION OF MALIGNANCY IN SUSPECTED THYROID TUMOUR PATIENTS BY THREE DIFFER...cscpconf
This document compares three classification methods - artificial neural networks, decision trees, and logistic regression - for predicting malignancy in thyroid tumor patients using a clinical dataset. It describes each method and applies them to a dataset of 259 thyroid tumor patients. The artificial neural network achieved 98% accuracy on the training set and 92% on the validation set. The decision tree method used 150 cases to build a model and achieved 86% accuracy. Logistic regression analysis resulted in 88% accuracy. The artificial neural network was able to accurately predict malignancy and identified important attributes like multiple nodules and family cancer history.
11.software modules clustering an effective approach for reusabilityAlexander Decker
This document summarizes previous work on using clustering techniques for software module classification and reusability. It discusses hierarchical clustering and non-hierarchical clustering methods. Previous studies have used these techniques for software component classification, identifying reusable software modules, course clustering based on industry needs, mobile phone clustering based on attributes, and customer clustering based on electricity load. The document provides background on clustering analysis and its uses in various domains including software testing, pattern recognition, and software restructuring.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
A New Mathematical Model for Minimization of Exceptional Load in Cellular Man...IJRES Journal
This study is devoted to the cell formation problems in cellular manufacturing systems. Starting point of this study is a paper of Mahdavi et.al. which considers only a few factors of production system. In this research, processing times and the frequencies of the parts are also considered. It is assumed that the load of each machine is known and is the multiplication of the processing times and frequencies. In this case cells are formed to achieve the higher loads inside cells. Also, the proposed model is about the case when alternative technologies are available and the objective is to maximize the loads inside cells. Besides the new model, other main contribution of this study is the computational analysis. The results show that the new model is providing acceptable solution within the logical runtimes.
Classification of medical datasets using back propagation neural network powe...IJECEIAES
The classification is a one of the most indispensable domains in the data mining and machine learning. The classification process has a good reputation in the area of diseases diagnosis by computer systems where the progress in smart technologies of computer can be invested in diagnosing various diseases based on data of real patients documented in databases. The paper introduced a methodology for diagnosing a set of diseases including two types of cancer (breast cancer and lung), two datasets for diabetes and heart attack. Back Propagation Neural Network plays the role of classifier. The performance of neural net is enhanced by using the genetic algorithm which provides the classifier with the optimal features to raise the classification rate to the highest possible. The system showed high efficiency in dealing with databases differs from each other in size, number of features and nature of the data and this is what the results illustrated, where the ratio of the classification reached to 100% in most datasets).
Predicting performance of classification algorithmsIAEME Publication
This paper presents a performance comparison study of common classification algorithms using three datasets from the UCI machine learning repository. The algorithms evaluated include Naive Bayes, SMO, KStar, AdaBoostM1, JRip, OneR, PART, J48, LMT, and Random Tree. Each algorithm is evaluated based on accuracy and training time using the WEKA machine learning tool. The goal is to determine which algorithms perform best for a given dataset and identify the optimal size of training data needed.
This document presents a study that compares the performance of 10 classification algorithms (Naive Bayes, SMO, KStar, AdaBoostM1, JRip, OneR, PART, J48, LMT, Random Tree) using 3 datasets from the UCI Machine Learning Repository (German credit data, ionosphere data, vote data). The algorithms are tested using the WEKA machine learning tool. The results show that Random Tree and LMT generally have the best predictive performance across the different testing modes and datasets, with Random Tree achieving the highest accuracy on the German credit and vote datasets, and LMT performing best on the ionosphere data.
This document discusses using attribute reduction to increase the efficiency of credit card fraud detection using decision trees. It analyzes a credit card transaction dataset containing attributes like credit usage, employment status, and purpose. Attribute statistics show some attributes have a single dominant value. The paper performs tests removing these attributes and finds the correctly classified instances increases from 70.5% to 72.9%, showing attribute reduction improves efficiency. By removing unnecessary attributes that don't contribute useful information, decision trees can more accurately classify transactions as fraudulent or genuine.
Latest ieee 2016 projects titles in big data@ trichyranjith kumar
IEEE Final Year Projects for M.E/M.TECH-CSE,VLSI,COMMUNICATION SYSTEM,B.E-CSE/IT from any domain & Technologies.For more detail contact:-DreamWeb TechnoSolutions@7200021403/04, 73/5 3rd floor,Kamatchi cmplx,SALAI ROADThillai nagar 1st cross,Trichy
Project center in trichy @ieee 2016 17 titles for java and dotnetranjith kumar
IEEE Final Year Projects for M.E/M.TECH-CSE,VLSI,COMMUNICATION SYSTEM,B.E-CSE/IT from any domain & Technologies.For more detail contact:-DreamWeb TechnoSolutions@7200021403/04, 73/5 3rd floor,Kamatchi cmplx,SALAI ROADThillai nagar 1st cross,Trichy
Project center in trichy @ieee 2016 17 titles for java and dotnetranjith kumar
IEEE Final Year Projects for M.E/M.TECH-CSE,VLSI,COMMUNICATION SYSTEM,B.E-CSE/IT from any domain & Technologies.For more detail contact:-DreamWeb TechnoSolutions@7200021403/04, 73/5 3rd floor,Kamatchi cmplx,SALAI ROADThillai nagar 1st cross,Trichy
This document discusses using a multi-objective evolutionary algorithm (MOEA) for feature selection in bankruptcy prediction models. The goal is to maximize classifier accuracy while minimizing the number of features. A two-objective problem of minimizing features and maximizing accuracy is analyzed using logistic regression and support vector machines classifiers. The methodology is tested on financial data from 1200 French companies and shown to be an efficient feature selection approach, obtaining best results when optimizing both accuracy and classifier parameters simultaneously.
A Survey and Comparative Study of Filter and Wrapper Feature Selection Techni...theijes
Feature selection is considered as a problem of global combinatorial optimization in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data. However, identification of useful features from hundreds or even thousands of related features is not an easy task. Selecting relevant genes from microarray data becomes even more challenging owing to the high dimensionality of features, multiclass categories involved and the usually small sample size. In order to improve the prediction accuracy and to avoid incomprehensibility due to the number of features different feature selection techniques can be implemented. This survey classifies and analyzes different approaches, aiming to not only provide a comprehensive presentation but also discuss challenges and various performance parameters. The techniques are generally classified into three; filter, wrapper and hybrid.
SURVEY ON CLASSIFICATION ALGORITHMS USING BIG DATASETEditor IJMTER
Data mining environment produces a large amount of data that need to be analyzed.
Using traditional databases and architectures, it has become difficult to process, manage and analyze
patterns. To gain knowledge about the Big Data a proper architecture should be understood.
Classification is an important data mining technique with broad applications to classify the various
kinds of data used in nearly every field of our life. Classification is used to classify the item
according to the features of the item with respect to the predefined set of classes. This paper put a
light on various classification algorithms including j48, C4.5, Naive Bayes using large dataset.
IRJET- The Machine Learning: The method of Artificial IntelligenceIRJET Journal
This document discusses machine learning and its role in artificial intelligence. It begins with an abstract that explains machine learning is widely used in artificial intelligence to enable systems to learn and make decisions without being explicitly programmed. It then provides an introduction to machine learning, explaining it allows software to learn from data and improve predictions without being explicitly programmed. The document also discusses related work from other researchers on topics like supervised learning, unsupervised learning, and evaluating different machine learning methods. It describes problems that can occur during the learning process like bias, noise, and pattern recognition. Finally, it provides algorithms for hierarchical clustering and k-means clustering as examples of unsupervised learning methods.
Analysis On Classification Techniques In Mammographic Mass Data SetIJERA Editor
Data mining, the extraction of hidden information from large databases, is to predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. Data-Mining classification techniques deals with determining to which group each data instances are associated with. It can deal with a wide variety of data so that large amount of data can be involved in processing. This paper deals with analysis on various data mining classification techniques such as Decision Tree Induction, Naïve Bayes , k-Nearest Neighbour (KNN) classifiers in mammographic mass dataset.
Data mining or Knowledge discovery (KDD) is
extracting unknown (hidden) and useful knowledge from data.
Data mining is widely used in many areas like retail, sales, ecommerce,
remote sensing, bioinformatics etc. Student’s
performance has become one of the most complex puzzle for
universities and colleges in recent past with the tremendous
growth. In this paper, authors deployed data mining techniques
like classification, association rule, chi-square etc. for knowledge
discovery. For this study, authors have used data set containing
Approx. 180 MCA (post graduate) students results data of 3
colleges. Study found that one can apply data mining
functionalities like Chi-square, Association rule and Lift in
Education and discover areas of improvement.
Applying Genetic Algorithms to Information Retrieval Using Vector Space ModelIJCSEA Journal
Genetic algorithms are usually used in information retrieval systems (IRs) to enhance the information retrieval process, and to increase the efficiency of the optimal information retrieval in order to meet the users’ needs and help them find what they want exactly among the growing numbers of available information. The improvement of adaptive genetic algorithms helps to retrieve the information needed by the user accurately, reduces the retrieved relevant files and excludes irrelevant files. In this study, the researcher explored the problems embedded in this process, attempted to find solutions such as the way of choosing mutation probability and fitness function, and chose Cranfield English Corpus test collection on mathematics. Such collection was conducted by Cyrial Cleverdon and used at the University of Cranfield in 1960 containing 1400 documents, and 225 queries for simulation purposes. The researcher also used cosine similarity and jaccards to compute similarity between the query and documents, and used two proposed adaptive fitness function, mutation operators as well as adaptive crossover. The process aimed at evaluating the effectiveness of results according to the measures of precision and recall. Finally, the study concluded that we might have several improvements when using adaptive genetic algorithms.
Leave one out cross validated Hybrid Model of Genetic Algorithm and Naïve Bay...IJERA Editor
This document presents a new hybrid model for feature selection and classification that combines genetic algorithm and naive Bayes. The proposed method first uses a binary coded genetic algorithm to select a reduced subset of important features from datasets. It then applies a naive Bayes classification method to evaluate the selected features and identify the subset that achieves the highest classification accuracy. The performance of the proposed hybrid model is evaluated on eight datasets and compared to recent publications, finding it achieves satisfactory or higher classification accuracy using fewer features.
Applying genetic algorithms to information retrieval using vector space modelIJCSEA Journal
The document describes a study that applied genetic algorithms to information retrieval using the vector space model. The study used an adaptive genetic algorithm approach with two proposed fitness functions (cosine and Jaccard's), adaptive crossover and mutation probabilities. Experimental results on a test corpus showed improvements in precision and recall compared to traditional approaches, with the proposed cosine fitness function performing best. Precision generally decreased as recall increased. The modifications made to the genetic algorithm and fitness functions led to better weighting of query terms and improved results.
A Threshold fuzzy entropy based feature selection method applied in various b...IJMER
Large amount of data have been stored and manipulated using various database
technologies. Processing all the attributes for the particular means is the difficult task. To avoid such
difficulties, feature selection process is processed.In this paper,we are collect a eight various benchmark
datasets from UCI repository.Feature selection process is carried out using fuzzy entropy based
relevance measure algorithm and follows three selection strategies like Mean selection strategy,Half
selection strategy and Neural network for threshold selection strategy. After the features are selected,
they are evaluated using Radial Basis Function (RBF) network,Stacking,Bagging,AdaBoostM1 and Antminer
classification methodologies.The test results depicts that Neural network for threshold selection
strategy works well in selecting features and Ant-miner methodology works best in bringing out better
accuracy with selected feature than processing with original dataset.The obtained result of this
experiment shows that clearly the Ant-miner is superiority than other classifiers.Thus, this proposed Antminer
algorithm could be a more suitable method for producing good results with fewer features than
the original datasets.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
PREDICTION OF MALIGNANCY IN SUSPECTED THYROID TUMOUR PATIENTS BY THREE DIFFER...cscpconf
This document compares three classification methods - artificial neural networks, decision trees, and logistic regression - for predicting malignancy in thyroid tumor patients using a clinical dataset. It describes each method and applies them to a dataset of 259 thyroid tumor patients. The artificial neural network achieved 98% accuracy on the training set and 92% on the validation set. The decision tree method used 150 cases to build a model and achieved 86% accuracy. Logistic regression analysis resulted in 88% accuracy. The artificial neural network was able to accurately predict malignancy and identified important attributes like multiple nodules and family cancer history.
11.software modules clustering an effective approach for reusabilityAlexander Decker
This document summarizes previous work on using clustering techniques for software module classification and reusability. It discusses hierarchical clustering and non-hierarchical clustering methods. Previous studies have used these techniques for software component classification, identifying reusable software modules, course clustering based on industry needs, mobile phone clustering based on attributes, and customer clustering based on electricity load. The document provides background on clustering analysis and its uses in various domains including software testing, pattern recognition, and software restructuring.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
A New Mathematical Model for Minimization of Exceptional Load in Cellular Man...IJRES Journal
This study is devoted to the cell formation problems in cellular manufacturing systems. Starting point of this study is a paper of Mahdavi et.al. which considers only a few factors of production system. In this research, processing times and the frequencies of the parts are also considered. It is assumed that the load of each machine is known and is the multiplication of the processing times and frequencies. In this case cells are formed to achieve the higher loads inside cells. Also, the proposed model is about the case when alternative technologies are available and the objective is to maximize the loads inside cells. Besides the new model, other main contribution of this study is the computational analysis. The results show that the new model is providing acceptable solution within the logical runtimes.
Classification of medical datasets using back propagation neural network powe...IJECEIAES
The classification is a one of the most indispensable domains in the data mining and machine learning. The classification process has a good reputation in the area of diseases diagnosis by computer systems where the progress in smart technologies of computer can be invested in diagnosing various diseases based on data of real patients documented in databases. The paper introduced a methodology for diagnosing a set of diseases including two types of cancer (breast cancer and lung), two datasets for diabetes and heart attack. Back Propagation Neural Network plays the role of classifier. The performance of neural net is enhanced by using the genetic algorithm which provides the classifier with the optimal features to raise the classification rate to the highest possible. The system showed high efficiency in dealing with databases differs from each other in size, number of features and nature of the data and this is what the results illustrated, where the ratio of the classification reached to 100% in most datasets).
Predicting performance of classification algorithmsIAEME Publication
This paper presents a performance comparison study of common classification algorithms using three datasets from the UCI machine learning repository. The algorithms evaluated include Naive Bayes, SMO, KStar, AdaBoostM1, JRip, OneR, PART, J48, LMT, and Random Tree. Each algorithm is evaluated based on accuracy and training time using the WEKA machine learning tool. The goal is to determine which algorithms perform best for a given dataset and identify the optimal size of training data needed.
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Comparison of Cell formation techniques in Cellular manufacturing using three cell formation algorithms
1. Prabhat Kumar Giri Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 6, Issue 1, (Part - 5) January 2016, pp.98-101
www.ijera.com 98 | P a g e
Comparison of Cell formation techniques in Cellular
manufacturing using three cell formation algorithms
Prabhat Kumar Giri1
, Dr.S. K. Moulick2
1
(Research scholar ,Dr. C.V. Raman University, Bilaspur(C.G.),India)
2
(Department of Mechanical Engineering, BIT-Durg, India)
ABSTRACT
In the present era of globalization and competitive market, cellular manufacturing has become a vital tool for
meeting the challenges of improving productivity, which is the way to sustain growth. Getting best results of
cellular manufacturing depends on the formation of the machine cells and part families. This paper examines
advantages of ART method of cell formation over array based clustering algorithms, namely ROC-2 and DCA.
The comparison and evaluation of the cell formation methods has been carried out in the study. The most
appropriate approach is selected and used to form the cellular manufacturing system. The comparison and
evaluation is done on the basis of performance measure as grouping efficiency and improvements over the
existing cellular manufacturing system is presented.
Keywords - Neural Network, ART Model, Group Technology
I. INTRODUCTION
Group Technology is a manufacturing
philosophy in which similar parts are identified.
Machines on which these parts are to be processed
are grouped together to form a GT cell. The purpose
of GT cell is that the Cellular manufacturing system
is result of implementation of GT to the production.
The number of benefit has been achieved by
implementation of CMS, like material handling, cost
reduction; work in process inventory reduction, set-
up time reduction, and equipment cost reduction,
direct/indirect labor cost reduction, improvement
of quality, improvement in space utilization and
employees satisfaction etc.
Formation of part families and machine cells is
the key step towards the design of cellular
manufacturing system (CMS). The input data are
derived from route sheet. These data are in the form
of zero-one matrices. The rows represent the
machines and columns represent parts. Elements of
the matrix ‘aij’ will be ‘1’ if the jth component is
processed on ith machine. If it is not ‘aij’ will be
zero. The output is obtained in the form of block
diagonal structure. Each block represents a machine
cell and a part family. Number of research work has
been done in the last decades for cell formation. The
researchers have proposed number of algorithms for
cell formation using production flow analysis. In this
paper it is presented that ART algorithm is found
better over array based cell formation techniques.
II. LITERATURE SURVEY
Survey of literature has been carried out to
identify the findings and directions given by
researchers. The contribution and directions of
selected research work reported in the literature have
been presented below:
The problem was originally identified by Murthy
and Srinivasan [1]. They used simulated annealing
(SA) and heuristics algorithms (HA) for fractional
cell formation. In other research, Srinivasan and
Zimmers [2] used a neighborhood search algorithm
for fractional cell formation.
The architecture of the ART1 is based on the
idea of adaptive resonant feedback between two
layers of nodes, as developed by Grossberg [3]. The
ART1 Model described in Carpenter and Grossberg
[4] was designed to cluster binary input patterns.
Dagli and Huggahalli [5] and Chen and Park [6] also
modified the ART1 in their works to improve its
performance in GT cell formation. But their
modifications are not suitable for fractional cell
formation. Miin-Shen Yang and Jenn- Hwai Yang [7]
proposed a modified ART1 neural learning
algorithm. In modified ART1, the vigilance
parameter can be simply estimated by the data so that
it is more efficient and reliable than Dagli and
Huggahalli’s method for selecting a vigilance value.
M. Murugan and Selladurai[8] proposed an Art
Modified Single Linkage Clustering approach (ART-
MOD-SLC) to solve cell formation problems in
Cellular Manufacturing. In this study, an ART1
network is integrated with Modified Single Linkage
Clustering (MOD-SLC) to solve cell formation
problems. The Percentage of Exceptional Elements
(PE), Machine Utilization (MU), Grouping
Efficiency (GE) and Grouping Efficacy (GC) are
considered as performance measures. This proposed
heuristic ART1 Modified Single Linkage Clustering
(ART-MOD-SLC) first constructs a cell formation
RESEARCH ARTICLE OPEN ACCESS
2. Prabhat Kumar Giri Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 6, Issue 1, (Part - 5) January 2016, pp.98-101
www.ijera.com 99 | P a g e
using an ART1 and then refines the solution using
Modified Single Linkage Clustering (MOD-SLC)
heuristic. ART1 Modified Single Linkage Clustering
has been applied to most popular examples in the
literature including a real time manufacturing data.
According to P. Venkumar and A. Noorul Haq [9] the
GT cell formation by any known algorithm/heuristics
results in much intercell movement known as
exceptional elements. In such cases, fractional cell
formation using reminder cells can be adopted
successfully to minimize the number of exceptional
elements. The fractional cell formation problem is
solved using modified adaptive resonance theory1
network (ART1). The input to the modified ART1 is
machine-part incidence matrix comprising of the
binary digits 0 and 1. This method is applied to the
known benchmarked problems found in the literature
and it is found to be equal or superior to other
algorithms in terms of minimizing the number of the
exceptional elements. The relative merits of using
this method with respect to other known
algorithms/heuristics in terms of computational speed
and consistency are presented. Yong Yina and
Kazuhiko Yasudab[10] gave a comprehensive
overview and discussion for similarity coefficients
developed to date for use in solving the cell
formation (CF) problem. Despite previous studies
indicated that similarity coefficients based method
(SCM) is more flexible than other CF methods, none
of the studies has explained the reason why SCM is
more flexible. They tried to explain the reason
explicitly. They also developed a taxonomy to
clarify the definition and usage of various similarity
coefficients in designing CM systems. Existing
similarity (dissimilarity) coefficients developed so far
are mapped onto the taxonomy. Additionally,
production information based similarity coefficients
are discussed and a historical evolution of these
similarity coefficients is outlined. Finally,
recommendations for future research are suggested.
Chang-Chun Tsai and Chung-ying Leewe [11]
presented a multi-functional MP (mathematical
programming) model that incorporates the merits of
related CF (Cell Formation) models based on the
systematic study of MP models. The proposed model
can offer the suitable modules that include the
different objective functions and constraints for user
to solve the related problem. In addition, analysis
results demonstrate that the proposed model’s
performance to outperform the other related models.
Jose Fernando Goncalves and Mauricio G.C.
Resende [12] presented a new approach for obtaining
machine cells and product families. The approach
combines a local search heuristic with a genetic
algorithm. Computational experience with the
algorithm on a set of group technology problems
available in the literature is also presented the
approach produced solutions with a grouping efficacy
that is at least as good as any results previously
reported in literature and improved the grouping
efficacy for 59% of the problems.
III. METHODOLOGY
Proposed methodology uses the Adaptive
Resonance Theory (ART) neural network to solve the
cell formation problem in group technology (GT).
The advantage of using an ART network over the
other conventional methods, like ROC (Rank order
clustering) and DCA (Direct clustering Analysis) are
the fast computation and outstanding ability to handle
large-scale industrial problems.
A. Rank order clustering2 (ROC-2)
ROC-2 was developed by King and Nakoranchai
(1982) to overcome the limitations of ROC. ROC-2
can identify block diagonal structure (of machine part
incidence matrix) very quickly. Therefore it is found
practicable to apply in an interactive manner even for
large matrices.
Algorithm:
Step 1 Start from the last column, move the rows
with positive entries to the top of the matrix.
Step 2 Repeat step 1 for all the columns.
Step 3 Start from the last row, move the columns
with positive entries to the left of the matrix.
Step 4 Repeat step 3 for all rows.
Step 5 Compare the matrix with the previous result.
If the matrices are different go to step 1 otherwise go
to Step 6.
Step6 Print the final machine-component incidence
matrix.
B. Direct clustering analysis (DCA):
In this method, the initial matrix is rearranged
according to the row and column assignments. After
rearrangement the rows and columns are rearranged
to form the clustered part- machine incidence matrix.
Algorithm:
Step 1 The row and column ranks are found by
adding their corresponding positive entries.
Step 2 The matrix is rearranged according to the
ranks.
Step 3 Start from the first row, move the columns
with positive entries to the left of the matrix
Step 4 Repeat the step 3 for all the rows.
Step 5 Start from the first column, move the rows
with positive entries to the top.
Step 6 Repeat the step 5 for all the columns.
Step 7 Compare the matrix with the previous result. If
the matrices are different go to step 3 otherwise go to
step 8.
Step 8 Print the final machine component incidence
matrix
3. Prabhat Kumar Giri Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 6, Issue 1, (Part - 5) January 2016, pp.98-101
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C. Adaptive Resonance Theory(ART) :
An artificial neural network is built on a number
of simple processing elements called neurons. These
neurons are often recognized into a sequence of
layers. All layers of the network are linked by
weights, which are adapted using a learning
algorithm. The structure of a neural network could be
characterized by interconnection architecture among
neurons, the activation function for conversion of
input into outputs, and the learning algorithm.
Algorithm:
Step1 : Define the number of neurons in the input
layer Nin and number of neurons in the output layer
Nout and select a value for vigilance parameter, ρ
Nin = the number of columns (parts ) of machine-part
incidence matrix.
Nout = the maximum expected number of machine
cells.
Step 2 : Enable all the output units and initialize top
down weights Wt
and bottom up weights Wb
Wt
ij = 1 = tij(0)
1 1
(0)
1 1
b
ij ij
in
W b
N N
Wt
ij = top down weight from neuron j in the
output layer to neuron i in the input layer.
b
ijW Bottom –up weight from neuron i in the
input to neuron j in the output layer.
Step3 : Present a machine vector X to input layer , X
consist of zero/one element ix .
Step4: compute machining scores for all the enabled
output nodes
b
j ij i
i
Net W x
Where Netj is the output of neuron j in the output
layer
Step5: Select a node with the largest value of
matching score as best matching exemplar let this
node be j’. In the event of a tie, the unit on the left is
selected
, max jj j
Net net
Step6: Vigilance test (i,e test of similarity with best
matching exemplar)
Compute the following:
i
i
X x (norm of vector X)
' '. .t t
j ij iW X W x
Let X = New pattern and Y= exemplar
So the Euclidean distance =
2
i ix y
If
2
i ix y ρ, go to step 8, else go to step 7.
Step7: Disable best exemplar temporarily
Since the vector X does not belong to cluster
j’, the output of node j’ selected in step 5 is
temporarily disabled and removed from future
competitions; go to step4.
Step8: Adapt best matching exemplar
'
'
'
.
0.5 .
t
ij ib
ij t
ij i
W x
W
W x
Step9: Enable any node s disabled in step7 and go
to step3.
D. Measure of Performance
To measure the efficiency of the group grouping
efficiency is considered as measuring parameter
represented by η,
1 21q q
Where
1
1
d
k
r r
r
e
M N
0
2
1
1 k
r r
r
e
mn M N
m = Number of machines (rows)
n = Number of parts (columns)
Mr = Number of machines in the r-th cell
Nr = Number of parts in the r-th family
ed = Number of 1’s within the machine /parts group
e0 = Number of 1’s outside the machine/parts group
k = Number of clusters
= Grouping efficiency
q = Weighting factor (0< q <1)
Grouping efficiency (GE) ranges from 0 to 1. A
GE with a value closed to 1.0 means that the solution
matrix has a perfect structure. In this paper the
solutions are evaluated in terms of GE and
Exceptional Element (EE).
IV. TEST PROBLEMS
To check the efficiency and working of proposed
methodology, few test problems are generated
randomly
Problem No. Matrix Size Minimum Threshold
1 20x15 0.9
2 20x15 0.5
3 20x15 0.6
4 20x15 0.7
4. Prabhat Kumar Giri Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 6, Issue 1, (Part - 5) January 2016, pp.98-101
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The results obtained are given in table below.
V. CONCLUSION
The neural network based on adaptive resonance
theory (ART) can be effectively used for machine-
part cell formation using the information from route
sheet of parts. The industries seeking to reframe their
existing facilities to cellular layout can derive
maximum benefit from the proposed methodology.
Usually the implementation of GT is a continuous
process. Different methods may be found more useful
or can give better results for different kind of
products. The neural network can effectively execute
the dynamic characteristic of GT implementation.
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Pro
ble
m
No.
Calculated
efficiency
(%)by ART
algorithm
Calculated
efficiency(%)
by DCA
algorithm
Calculated
efficiency
(%)by ROC
algorithm
1 72.00 63.23 67.00
2 66.15 52.00 62.24
3 66.00 61.08 65.14
4 61.50 58.25 59.28