Enviar búsqueda
Cargar
20120140505011
•
0 recomendaciones
•
210 vistas
IAEME Publication
Seguir
Tecnología
Educación
Denunciar
Compartir
Denunciar
Compartir
1 de 11
Descargar ahora
Descargar para leer sin conexión
Recomendados
High dimensional data when processed by using various machine learning and pattern recognition techniques, it undergoes several changes. Dimensionality reduction is one such successfully used pre-processing technique to analyze and represent the high dimensional data that causes several structural changes to occur in the data through the process. The high-dimensional data when used to extract just the target class from among several classes that are spatially scattered then the philosophy of the dimensionality reduction is to find an optimal subset of features either from the original space or from the transformed space using the control set of the target class and then project the input space onto this optimal feature subspace. This paper is an exploratory analysis carried out to study the class properties and the structural properties that are affected due to the target class guided feature subsetting in specific. K-nearest neighbors and minimum spanning tree are employed to study the structural properties, and cluster analysis is applied to understand the target class and other class properties. The experimentation is conducted on the target class derived features on the selected bench mark data sets namely IRIS, AVIRIS Indiana Pine and ROSIS Pavia University data set. Experimentation is also extended to data represented in the optimal principal components obtained by transforming the subset of features and results are also compared
Study of the Class and Structural Changes Caused By Incorporating the Target ...
Study of the Class and Structural Changes Caused By Incorporating the Target ...
ijceronline
Text clustering
Text clustering
KU Leuven
Machine learning for text classification is the underpinning of document cataloging , news filtering, document steering and exemplif ication . In text mining realm, effective feature selection is significant to make the learning task more accurate and competent. One of the traditional lazy text classifier k - Nearest Neighborhood ( k NN) has a major pitfall in calculating the similarity between all the objects in training and testing se t s, there by leads to exaggeration of both computational complexity of the algorithm and massive consumption of main memory . To diminish these shortcomings in viewpoint of a data - mining practitioner a n amalgamati ve technique is proposed in this paper using a novel restructured version of k NN called Augmented k NN (AkNN) and k - Medoids (kMdd) clustering. The proposed work comprises preprocesses on the initial training set by imposing attribute feature selection for reduc tion of high dimensionality, also it detects and excludes the high - fliers samples in t he initial training set and re structure s a constricted training set . The kMdd clustering algorithm generates the cluster centers (as interior objects) for each category and restructures the constricted training set with centroids . This technique is amalgamated with AkNN classifier that was prearranged with text mining similarity measure s. Eventually, s ignifican tweights and ranks were assigned to each object in the new training set based upon the ir accessory towards the object in testing set . Experiments conducted on Reuters - 21578 a UCI benchmark text mining data set , and comparisons with traditional k NN classifier designates the referred method yield spreeminentrecital in b oth clustering and classification
Novel text categorization by amalgamation of augmented k nearest neighbourhoo...
Novel text categorization by amalgamation of augmented k nearest neighbourhoo...
ijcsity
IOSR Journal of Computer Engineering (IOSRJCE)
Different Similarity Measures for Text Classification Using Knn
Different Similarity Measures for Text Classification Using Knn
IOSR Journals
With recent progress in pervasive healthcare, physical activity recognition with wearable body sensors has become an important and challenging area in both research and industrial communities. Here, we address a novel technique for a sensor platform that performs physical activity recognition by leveraging a class specific regularizer term into the dictionary pair learning objective function. The proposed algorithm jointly learns a synthesis dictionary and an analysis dictionary in order to simultaneously perform signal representation and classification once the time-domain features have been extracted. Specifically, the class specific regularizer term ensures that the sparse codes belonging to the same class will be concentrated thereby proving beneficial for the classification stage. In order to develop a more practical approach, we employ a combination of an alternating direction method of multipliers and a l1 − ls minimization method to approximately minimize the objective function. We validate the effectiveness of our proposed model by employing it on two activity recognition problem and an intensity estimation problem, both of which include a large number of physical activities. Experimental results demonstrate that classifiers built in this dictionary learning based framework outperforms state of art algorithms by using simple features, thereby achieving competitive results when compared with classical systems built upon features with prior knowledge
Centralized Class Specific Dictionary Learning for wearable sensors based phy...
Centralized Class Specific Dictionary Learning for wearable sensors based phy...
Sherin Mathews
Many applications of automatic document classification require learning accurately with little training data. The semi-supervised classification technique uses labeled and unlabeled data for training. This technique has shown to be effective in some cases; however, the use of unlabeled data is not always beneficial. On the other hand, the emergence of web technologies has originated the collaborative development of ontologies. In this paper, we propose the use of ontologies in order to improve the accuracy and efficiency of the semi-supervised document classification. We used support vector machines, which is one of the most effective algorithms that have been studied for text. Our algorithm enhances the performance of transductive support vector machines through the use of ontologies. We report experimental results applying our algorithm to three different datasets. Our experiments show an increment of accuracy of 4% on average and up to 20%, in comparison with the traditional semi-supervised model.
USING ONTOLOGIES TO IMPROVE DOCUMENT CLASSIFICATION WITH TRANSDUCTIVE SUPPORT...
USING ONTOLOGIES TO IMPROVE DOCUMENT CLASSIFICATION WITH TRANSDUCTIVE SUPPORT...
IJDKP
Predictive analysis include techniques fromdata mining that analyze current and historical data and make predictions about the future. Predictive analytics is used in actuarial science, financial services, retail, travel, healthcare, insurance, pharmaceuticals, marketing, telecommunications and other fields.Predicting patterns can be considered as a classification problem and combining the different classifiers gives better results. We will study and compare three methods used to combine multiple classifiers. Bayesian networks perform classification based on conditional probability. It is ineffective and easy to interpret as it assumes that the predictors are independent. Tree augmented naïve Bayes (TAN) constructs a maximum weighted spanning tree that maximizes the likelihood of the training data, to perform classification.This tree structure eliminates the independent attribute assumption of naïve Bayesian networks. Behavior-knowledge space method works in two phases and can provide very good performances if large and representative data sets are available.
Comparision of methods for combination of multiple classifiers that predict b...
Comparision of methods for combination of multiple classifiers that predict b...
IJERA Editor
Text mining is a new and exciting research area that tries to solve the information overload problem by using techniques from machine learning, natural language processing (NLP), data mining, information retrieval (IR), and knowledge management. Text mining involves the pre-processing of document collections such as information extraction, term extraction, text categorization, and storage of intermediate representations. The techniques that are used to analyse these intermediate representations such as clustering, distribution analysis, association rules and visualisation of the results.
A systematic study of text mining techniques
A systematic study of text mining techniques
ijnlc
Recomendados
High dimensional data when processed by using various machine learning and pattern recognition techniques, it undergoes several changes. Dimensionality reduction is one such successfully used pre-processing technique to analyze and represent the high dimensional data that causes several structural changes to occur in the data through the process. The high-dimensional data when used to extract just the target class from among several classes that are spatially scattered then the philosophy of the dimensionality reduction is to find an optimal subset of features either from the original space or from the transformed space using the control set of the target class and then project the input space onto this optimal feature subspace. This paper is an exploratory analysis carried out to study the class properties and the structural properties that are affected due to the target class guided feature subsetting in specific. K-nearest neighbors and minimum spanning tree are employed to study the structural properties, and cluster analysis is applied to understand the target class and other class properties. The experimentation is conducted on the target class derived features on the selected bench mark data sets namely IRIS, AVIRIS Indiana Pine and ROSIS Pavia University data set. Experimentation is also extended to data represented in the optimal principal components obtained by transforming the subset of features and results are also compared
Study of the Class and Structural Changes Caused By Incorporating the Target ...
Study of the Class and Structural Changes Caused By Incorporating the Target ...
ijceronline
Text clustering
Text clustering
KU Leuven
Machine learning for text classification is the underpinning of document cataloging , news filtering, document steering and exemplif ication . In text mining realm, effective feature selection is significant to make the learning task more accurate and competent. One of the traditional lazy text classifier k - Nearest Neighborhood ( k NN) has a major pitfall in calculating the similarity between all the objects in training and testing se t s, there by leads to exaggeration of both computational complexity of the algorithm and massive consumption of main memory . To diminish these shortcomings in viewpoint of a data - mining practitioner a n amalgamati ve technique is proposed in this paper using a novel restructured version of k NN called Augmented k NN (AkNN) and k - Medoids (kMdd) clustering. The proposed work comprises preprocesses on the initial training set by imposing attribute feature selection for reduc tion of high dimensionality, also it detects and excludes the high - fliers samples in t he initial training set and re structure s a constricted training set . The kMdd clustering algorithm generates the cluster centers (as interior objects) for each category and restructures the constricted training set with centroids . This technique is amalgamated with AkNN classifier that was prearranged with text mining similarity measure s. Eventually, s ignifican tweights and ranks were assigned to each object in the new training set based upon the ir accessory towards the object in testing set . Experiments conducted on Reuters - 21578 a UCI benchmark text mining data set , and comparisons with traditional k NN classifier designates the referred method yield spreeminentrecital in b oth clustering and classification
Novel text categorization by amalgamation of augmented k nearest neighbourhoo...
Novel text categorization by amalgamation of augmented k nearest neighbourhoo...
ijcsity
IOSR Journal of Computer Engineering (IOSRJCE)
Different Similarity Measures for Text Classification Using Knn
Different Similarity Measures for Text Classification Using Knn
IOSR Journals
With recent progress in pervasive healthcare, physical activity recognition with wearable body sensors has become an important and challenging area in both research and industrial communities. Here, we address a novel technique for a sensor platform that performs physical activity recognition by leveraging a class specific regularizer term into the dictionary pair learning objective function. The proposed algorithm jointly learns a synthesis dictionary and an analysis dictionary in order to simultaneously perform signal representation and classification once the time-domain features have been extracted. Specifically, the class specific regularizer term ensures that the sparse codes belonging to the same class will be concentrated thereby proving beneficial for the classification stage. In order to develop a more practical approach, we employ a combination of an alternating direction method of multipliers and a l1 − ls minimization method to approximately minimize the objective function. We validate the effectiveness of our proposed model by employing it on two activity recognition problem and an intensity estimation problem, both of which include a large number of physical activities. Experimental results demonstrate that classifiers built in this dictionary learning based framework outperforms state of art algorithms by using simple features, thereby achieving competitive results when compared with classical systems built upon features with prior knowledge
Centralized Class Specific Dictionary Learning for wearable sensors based phy...
Centralized Class Specific Dictionary Learning for wearable sensors based phy...
Sherin Mathews
Many applications of automatic document classification require learning accurately with little training data. The semi-supervised classification technique uses labeled and unlabeled data for training. This technique has shown to be effective in some cases; however, the use of unlabeled data is not always beneficial. On the other hand, the emergence of web technologies has originated the collaborative development of ontologies. In this paper, we propose the use of ontologies in order to improve the accuracy and efficiency of the semi-supervised document classification. We used support vector machines, which is one of the most effective algorithms that have been studied for text. Our algorithm enhances the performance of transductive support vector machines through the use of ontologies. We report experimental results applying our algorithm to three different datasets. Our experiments show an increment of accuracy of 4% on average and up to 20%, in comparison with the traditional semi-supervised model.
USING ONTOLOGIES TO IMPROVE DOCUMENT CLASSIFICATION WITH TRANSDUCTIVE SUPPORT...
USING ONTOLOGIES TO IMPROVE DOCUMENT CLASSIFICATION WITH TRANSDUCTIVE SUPPORT...
IJDKP
Predictive analysis include techniques fromdata mining that analyze current and historical data and make predictions about the future. Predictive analytics is used in actuarial science, financial services, retail, travel, healthcare, insurance, pharmaceuticals, marketing, telecommunications and other fields.Predicting patterns can be considered as a classification problem and combining the different classifiers gives better results. We will study and compare three methods used to combine multiple classifiers. Bayesian networks perform classification based on conditional probability. It is ineffective and easy to interpret as it assumes that the predictors are independent. Tree augmented naïve Bayes (TAN) constructs a maximum weighted spanning tree that maximizes the likelihood of the training data, to perform classification.This tree structure eliminates the independent attribute assumption of naïve Bayesian networks. Behavior-knowledge space method works in two phases and can provide very good performances if large and representative data sets are available.
Comparision of methods for combination of multiple classifiers that predict b...
Comparision of methods for combination of multiple classifiers that predict b...
IJERA Editor
Text mining is a new and exciting research area that tries to solve the information overload problem by using techniques from machine learning, natural language processing (NLP), data mining, information retrieval (IR), and knowledge management. Text mining involves the pre-processing of document collections such as information extraction, term extraction, text categorization, and storage of intermediate representations. The techniques that are used to analyse these intermediate representations such as clustering, distribution analysis, association rules and visualisation of the results.
A systematic study of text mining techniques
A systematic study of text mining techniques
ijnlc
With the emergence of XML as de facto format for storing and exchanging information over the Internet, the search for ever more innovative and effective techniques for their querying is a major and current concern of the XML database community. Several studies carried out to help solve this problem are mostly oriented towards the evaluation of so-called exact queries which, unfortunately, are likely (especially in the case of semi-structured documents) to yield abundant results (in the case of vague queries) or empty results (in the case of very precise queries). From the observation that users who make requests are not necessarily interested in all possible solutions, but rather in those that are closest to their needs, an important field of research has been opened on the evaluation of preferences queries. In this paper, we propose an approach for the evaluation of such queries, in case the preferences concern the structure of the document. The solution investigated revolves around the proposal of an evaluation plan in three phases: rewriting-evaluation-merge. The rewriting phase makes it possible to obtain, from a partitioningtransformation operation of the initial query, a hierarchical set of preferences path queries which are holistically evaluated in the second phase by an instrumented version of the algorithm TwigStack. The merge phase is the synthesis of the best results.
HOLISTIC EVALUATION OF XML QUERIES WITH STRUCTURAL PREFERENCES ON AN ANNOTATE...
HOLISTIC EVALUATION OF XML QUERIES WITH STRUCTURAL PREFERENCES ON AN ANNOTATE...
ijseajournal
Clustering of high dimensionality data which can be seen in almost all fields these days is becoming very tedious process. The key disadvantage of high dimensional data which we can pen down is curse of dimensionality. As the magnitude of datasets grows the data points become sparse and density of area becomes less making it difficult to cluster that data which further reduces the performance of traditional algorithms used for clustering. Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pair wise constraints; such constraints are natural for graphs, yet most semi-supervised clustering algorithms are designed for data represented as vectors [2]. In this paper, we unify vector-based and graph-based approaches. We first show that a recently-proposed objective function for semi-supervised clustering based on Hidden Markov Random Fields, with squared Euclidean distance and a certain class of constraint penalty functions, can be expressed as a special case of the global kernel k-means objective [3]. A recent theoretical connection between global kernel k-means and several graph clustering objectives enables us to perform semi-supervised clustering of data. In particular, some methods have been proposed for semi supervised clustering based on pair wise similarity or dissimilarity information. In this paper, we propose a kernel approach for semi supervised clustering and present in detail two special cases of this kernel approach.
A Kernel Approach for Semi-Supervised Clustering Framework for High Dimension...
A Kernel Approach for Semi-Supervised Clustering Framework for High Dimension...
IJCSIS Research Publications
Abstract Text categorization is a process in data mining which assigns predefined categories to free-text documents using machine learning techniques. Any document in the form of text, image, music, etc. can be classified using some categorization techniques. It provides conceptual views of the collected documents and has important applications in the real world. Text based categorization is made use of for document classification with pattern recognition and machine learning. Advantages of a number of classification algorithms have been studied in this paper to classify documents. An example of these algorithms is: Naive Bayes' algorithm, K-Nearest Neighbor, Decision Tree etc. This paper presents a comparative study of advantages and disadvantages of the above mentioned classification algorithm. Keywords: Data Mining, Text Mining, Text Categorization, Machine Learning, Pattern Analysis, Naive Bayes’, KNN, Decision Tree.
Comparative study of classification algorithm for text based categorization
Comparative study of classification algorithm for text based categorization
eSAT Journals
selection of relevant feature from a given set of feature is one of the important issues in the field of data mining as well as classification. In general the dataset may contain a number of features however it is not necessary that the whole set features are important for particular analysis of decision making because the features may share the common information‟s and can also be completely irrelevant to the undergoing processing. This generally happen because of improper selection of features during the dataset formation or because of improper information availability about the observed system. However in both cases the data will contain the features that will just increase the processing burden which may ultimately cause the improper outcome when used for analysis. Because of these reasons some kind of methods are required to detect and remove these features hence in this paper we are presenting an efficient approach for not just removing the unimportant features but also the size of complete dataset size. The proposed algorithm utilizes the information theory to detect the information gain from each feature and minimum span tree to group the similar features with that the fuzzy c-means clustering is used to remove the similar entries from the dataset. Finally the algorithm is tested with SVM classifier using 35 publicly available real-world high-dimensional dataset and the results shows that the presented algorithm not only reduces the feature set and data lengths but also improves the performances of the classifier.
A Combined Approach for Feature Subset Selection and Size Reduction for High ...
A Combined Approach for Feature Subset Selection and Size Reduction for High ...
IJERA Editor
Data Mining- data Preprocessing-discretization
1.8 discretization
1.8 discretization
Krish_ver2
International Journal of Data Mining & Knowledge Management Process (IJDKP)
GCUBE INDEXING
GCUBE INDEXING
IJDKP
www.iosrjournla.org
G0354451
G0354451
iosrjournals
Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.
A new model for iris data set classification based on linear support vector m...
A new model for iris data set classification based on linear support vector m...
IJECEIAES
Abstract— Text categorization is the process of identifying and assigning predefined class to which a document belongs. A wide variety of algorithms are currently available to perform the text categorization. Among them, K-Nearest Neighbor text classifier is the most commonly used one. It is used to test the degree of similarity between documents and k training data, thereby determining the category of test documents. In this paper, an improved K-Nearest Neighbor algorithm for text categorization is proposed. In this method, the text is categorized into different classes based on K-Nearest Neighbor algorithm and constrained one-pass clustering, which provides an effective strategy for categorizing the text. This improves the efficiency of K-Nearest Neighbor algorithm by generating the classification model. The text classification using K-Nearest Neighbor algorithm has a wide variety of text mining applications.
Text Categorization Using Improved K Nearest Neighbor Algorithm
Text Categorization Using Improved K Nearest Neighbor Algorithm
IJTET Journal
IEEE PROJECTS 2015 1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider. It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training. Dot Net DOTNET Project Domain list 2015 1. IEEE based on datamining and knowledge engineering 2. IEEE based on mobile computing 3. IEEE based on networking 4. IEEE based on Image processing 5. IEEE based on Multimedia 6. IEEE based on Network security 7. IEEE based on parallel and distributed systems Java Project Domain list 2015 1. IEEE based on datamining and knowledge engineering 2. IEEE based on mobile computing 3. IEEE based on networking 4. IEEE based on Image processing 5. IEEE based on Multimedia 6. IEEE based on Network security 7. IEEE based on parallel and distributed systems ECE IEEE Projects 2015 1. Matlab project 2. Ns2 project 3. Embedded project 4. Robotics project Eligibility Final Year students of 1. BSc (C.S) 2. BCA/B.E(C.S) 3. B.Tech IT 4. BE (C.S) 5. MSc (C.S) 6. MSc (IT) 7. MCA 8. MS (IT) 9. ME(ALL) 10. BE(ECE)(EEE)(E&I) TECHNOLOGY USED AND FOR TRAINING IN 1. DOT NET 2. C sharp 3. ASP 4. VB 5. SQL SERVER 6. JAVA 7. J2EE 8. STRINGS 9. ORACLE 10. VB dotNET 11. EMBEDDED 12. MAT LAB 13. LAB VIEW 14. Multi Sim CONTACT US 1 CRORE PROJECTS Door No: 214/215,2nd Floor, No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai, Tamin Nadu, INDIA - 600 026 Email id: 1croreprojects@gmail.com website:1croreprojects.com Phone : +91 97518 00789 / +91 72999 51536
Multiview Alignment Hashing for Efficient Image Search
Multiview Alignment Hashing for Efficient Image Search
1crore projects
Among many data clustering approaches available today, mixed data set of numeric and category data poses a significant challenge due to difficulty of an appropriate choice and employment of distance/similarity functions for clustering and its verification. Unsupervised learning models for artificial neural network offers an alternate means for data clustering and analysis. The objective of this study is to highlight an approach and its associated considerations for mixed data set clustering with Adaptive Resonance Theory 2 (ART-2) artificial neural network model and subsequent validation of the clusters with dimensionality reduction using Autoencoder neural network model.
An Approach to Mixed Dataset Clustering and Validation with ART-2 Artificial ...
An Approach to Mixed Dataset Clustering and Validation with ART-2 Artificial ...
Happiest Minds Technologies
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Lx3520322036
Lx3520322036
IJERA Editor
lazy learners
lazy learners and other classication methods
lazy learners and other classication methods
rajshreemuthiah
Updated Machine Learning by Analogy presentation that builds to more advanced methods (TensorFlow, geometry/topology-based methods...) and adds a section on time series methods.
Machine Learning by Analogy II
Machine Learning by Analogy II
Colleen Farrelly
Data partitioning methods are used to partition the data values with similarity. Similarity measures are used to estimate transaction relationships. Hierarchical clustering model produces tree structured results. Partitioned clustering produces results in grid format. Text documents are unstructured data values with high dimensional attributes. Document clustering group ups unlabeled text documents into meaningful clusters. Traditional clustering methods require cluster count (K) for the document grouping process. Clustering accuracy degrades drastically with reference to the unsuitable cluster count. Textual data elements are divided into two types’ discriminative words and nondiscriminative words. Only discriminative words are useful for grouping documents. The involvement of nondiscriminative words confuses the clustering process and leads to poor clustering solution in return. A variation inference algorithm is used to infer the document collection structure and partition of document words at the same time. Dirichlet Process Mixture (DPM) model is used to partition documents. DPM clustering model uses both the data likelihood and the clustering property of the Dirichlet Process (DP). Dirichlet Process Mixture Model for Feature Partition (DPMFP) is used to discover the latent cluster structure based on the DPM model. DPMFP clustering is performed without requiring the number of clusters as input. Document labels are used to estimate the discriminative word identification process. Concept relationships are analyzed with Ontology support. Semantic weight model is used for the document similarity analysis. The system improves the scalability with the support of labels and concept relations for dimensionality reduction process.
Textual Data Partitioning with Relationship and Discriminative Analysis
Textual Data Partitioning with Relationship and Discriminative Analysis
Editor IJMTER
paper tentang metode bayesian
Expandable bayesian
Expandable bayesian
Ahmad Amri
We aim to model an adaptive log file parser. As the content of log files often evolves over time, we established a dynamic statistical model which learns and adapts processing and parsing rules. First, we limit the amount of unstructured text by clustering based on semantics of log file lines. Next, we only take the most relevant cluster into account and focus only on those frequent patterns which lead to the desired output table similar to Vaarandi [10]. Furthermore, we transform the found frequent patterns and the output stating the parsed table into a Hidden Markov Model (HMM). We use this HMM as a specific, however, flexible representation of a pattern for log file parsing to maintain high quality output. After training our model on one system type and applying it to a different system with slightly different log file patterns, we achieve an accuracy over 99.99%
WITH SEMANTICS AND HIDDEN MARKOV MODELS TO AN ADAPTIVE LOG FILE PARSER
WITH SEMANTICS AND HIDDEN MARKOV MODELS TO AN ADAPTIVE LOG FILE PARSER
ijnlc
Electrical, Electronics and Computer Engineering, Information Engineering and Technology, Mechanical, Industrial and Manufacturing Engineering, Automation and Mechatronics Engineering, Material and Chemical Engineering, Civil and Architecture Engineering, Biotechnology and Bio Engineering, Environmental Engineering, Petroleum and Mining Engineering, Marine and Agriculture engineering, Aerospace Engineering.
E1062530
E1062530
IJERD Editor
Overview of common classification techniques in Data Mining: regression and Bayesian models, KNN, decision trees, neural networks.
04 Classification in Data Mining
04 Classification in Data Mining
Valerii Klymchuk
A minimization approach for two level logic synthesis using constrained depth first search
A minimization approach for two level logic synthesis using constrained depth...
A minimization approach for two level logic synthesis using constrained depth...
IAEME Publication
The effect of ethanol extract of leaves of Conyza Dicorides plant on the corrosion inhibition of mild steel in 1M HCl solution was investigated by weight loss and electrochemical polarization techniques at temperature range (25–65 ̊C). The Results obtained showed that the percentage inhibition efficiency increases with the increasing of inhibitor concentration and decreases with the increasing of temperature. At a concentration of 2 g/L, the percentage inhibition efficiency reached about (94.87%) at 25 ̊C. The thermodynamic activation functions of dissolution process and adsorption parameters were calculated and discussed. Adsorption of the additive was found to follow the Langmuir adsorption isotherm.
30120140507002
30120140507002
IAEME Publication
There is an appreciable and adagio impact of mining on surface and groundwater. Rapid development of mining throughout the world with the galloping advances in science and technology is changing the shape of our planet and also hydrological region at micro level. World top quality galaxy granite (Gabbro) occurs in Chimakurthi mandal of Prakasam district of Andhra Pradesh. From the analysis of data pertaining to mining, rainfall and groundwater levels (1999-2010), it is observed that, the rainfall in the mandal shows declining trend over the successive years and more prominent since 2002 onwards. There is no noticeable effect of rainfall on groundwater recharge, because of poor aquifer system
20320140506015
20320140506015
IAEME Publication
Más contenido relacionado
La actualidad más candente
With the emergence of XML as de facto format for storing and exchanging information over the Internet, the search for ever more innovative and effective techniques for their querying is a major and current concern of the XML database community. Several studies carried out to help solve this problem are mostly oriented towards the evaluation of so-called exact queries which, unfortunately, are likely (especially in the case of semi-structured documents) to yield abundant results (in the case of vague queries) or empty results (in the case of very precise queries). From the observation that users who make requests are not necessarily interested in all possible solutions, but rather in those that are closest to their needs, an important field of research has been opened on the evaluation of preferences queries. In this paper, we propose an approach for the evaluation of such queries, in case the preferences concern the structure of the document. The solution investigated revolves around the proposal of an evaluation plan in three phases: rewriting-evaluation-merge. The rewriting phase makes it possible to obtain, from a partitioningtransformation operation of the initial query, a hierarchical set of preferences path queries which are holistically evaluated in the second phase by an instrumented version of the algorithm TwigStack. The merge phase is the synthesis of the best results.
HOLISTIC EVALUATION OF XML QUERIES WITH STRUCTURAL PREFERENCES ON AN ANNOTATE...
HOLISTIC EVALUATION OF XML QUERIES WITH STRUCTURAL PREFERENCES ON AN ANNOTATE...
ijseajournal
Clustering of high dimensionality data which can be seen in almost all fields these days is becoming very tedious process. The key disadvantage of high dimensional data which we can pen down is curse of dimensionality. As the magnitude of datasets grows the data points become sparse and density of area becomes less making it difficult to cluster that data which further reduces the performance of traditional algorithms used for clustering. Semi-supervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pair wise constraints; such constraints are natural for graphs, yet most semi-supervised clustering algorithms are designed for data represented as vectors [2]. In this paper, we unify vector-based and graph-based approaches. We first show that a recently-proposed objective function for semi-supervised clustering based on Hidden Markov Random Fields, with squared Euclidean distance and a certain class of constraint penalty functions, can be expressed as a special case of the global kernel k-means objective [3]. A recent theoretical connection between global kernel k-means and several graph clustering objectives enables us to perform semi-supervised clustering of data. In particular, some methods have been proposed for semi supervised clustering based on pair wise similarity or dissimilarity information. In this paper, we propose a kernel approach for semi supervised clustering and present in detail two special cases of this kernel approach.
A Kernel Approach for Semi-Supervised Clustering Framework for High Dimension...
A Kernel Approach for Semi-Supervised Clustering Framework for High Dimension...
IJCSIS Research Publications
Abstract Text categorization is a process in data mining which assigns predefined categories to free-text documents using machine learning techniques. Any document in the form of text, image, music, etc. can be classified using some categorization techniques. It provides conceptual views of the collected documents and has important applications in the real world. Text based categorization is made use of for document classification with pattern recognition and machine learning. Advantages of a number of classification algorithms have been studied in this paper to classify documents. An example of these algorithms is: Naive Bayes' algorithm, K-Nearest Neighbor, Decision Tree etc. This paper presents a comparative study of advantages and disadvantages of the above mentioned classification algorithm. Keywords: Data Mining, Text Mining, Text Categorization, Machine Learning, Pattern Analysis, Naive Bayes’, KNN, Decision Tree.
Comparative study of classification algorithm for text based categorization
Comparative study of classification algorithm for text based categorization
eSAT Journals
selection of relevant feature from a given set of feature is one of the important issues in the field of data mining as well as classification. In general the dataset may contain a number of features however it is not necessary that the whole set features are important for particular analysis of decision making because the features may share the common information‟s and can also be completely irrelevant to the undergoing processing. This generally happen because of improper selection of features during the dataset formation or because of improper information availability about the observed system. However in both cases the data will contain the features that will just increase the processing burden which may ultimately cause the improper outcome when used for analysis. Because of these reasons some kind of methods are required to detect and remove these features hence in this paper we are presenting an efficient approach for not just removing the unimportant features but also the size of complete dataset size. The proposed algorithm utilizes the information theory to detect the information gain from each feature and minimum span tree to group the similar features with that the fuzzy c-means clustering is used to remove the similar entries from the dataset. Finally the algorithm is tested with SVM classifier using 35 publicly available real-world high-dimensional dataset and the results shows that the presented algorithm not only reduces the feature set and data lengths but also improves the performances of the classifier.
A Combined Approach for Feature Subset Selection and Size Reduction for High ...
A Combined Approach for Feature Subset Selection and Size Reduction for High ...
IJERA Editor
Data Mining- data Preprocessing-discretization
1.8 discretization
1.8 discretization
Krish_ver2
International Journal of Data Mining & Knowledge Management Process (IJDKP)
GCUBE INDEXING
GCUBE INDEXING
IJDKP
www.iosrjournla.org
G0354451
G0354451
iosrjournals
Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.
A new model for iris data set classification based on linear support vector m...
A new model for iris data set classification based on linear support vector m...
IJECEIAES
Abstract— Text categorization is the process of identifying and assigning predefined class to which a document belongs. A wide variety of algorithms are currently available to perform the text categorization. Among them, K-Nearest Neighbor text classifier is the most commonly used one. It is used to test the degree of similarity between documents and k training data, thereby determining the category of test documents. In this paper, an improved K-Nearest Neighbor algorithm for text categorization is proposed. In this method, the text is categorized into different classes based on K-Nearest Neighbor algorithm and constrained one-pass clustering, which provides an effective strategy for categorizing the text. This improves the efficiency of K-Nearest Neighbor algorithm by generating the classification model. The text classification using K-Nearest Neighbor algorithm has a wide variety of text mining applications.
Text Categorization Using Improved K Nearest Neighbor Algorithm
Text Categorization Using Improved K Nearest Neighbor Algorithm
IJTET Journal
IEEE PROJECTS 2015 1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider. It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training. Dot Net DOTNET Project Domain list 2015 1. IEEE based on datamining and knowledge engineering 2. IEEE based on mobile computing 3. IEEE based on networking 4. IEEE based on Image processing 5. IEEE based on Multimedia 6. IEEE based on Network security 7. IEEE based on parallel and distributed systems Java Project Domain list 2015 1. IEEE based on datamining and knowledge engineering 2. IEEE based on mobile computing 3. IEEE based on networking 4. IEEE based on Image processing 5. IEEE based on Multimedia 6. IEEE based on Network security 7. IEEE based on parallel and distributed systems ECE IEEE Projects 2015 1. Matlab project 2. Ns2 project 3. Embedded project 4. Robotics project Eligibility Final Year students of 1. BSc (C.S) 2. BCA/B.E(C.S) 3. B.Tech IT 4. BE (C.S) 5. MSc (C.S) 6. MSc (IT) 7. MCA 8. MS (IT) 9. ME(ALL) 10. BE(ECE)(EEE)(E&I) TECHNOLOGY USED AND FOR TRAINING IN 1. DOT NET 2. C sharp 3. ASP 4. VB 5. SQL SERVER 6. JAVA 7. J2EE 8. STRINGS 9. ORACLE 10. VB dotNET 11. EMBEDDED 12. MAT LAB 13. LAB VIEW 14. Multi Sim CONTACT US 1 CRORE PROJECTS Door No: 214/215,2nd Floor, No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai, Tamin Nadu, INDIA - 600 026 Email id: 1croreprojects@gmail.com website:1croreprojects.com Phone : +91 97518 00789 / +91 72999 51536
Multiview Alignment Hashing for Efficient Image Search
Multiview Alignment Hashing for Efficient Image Search
1crore projects
Among many data clustering approaches available today, mixed data set of numeric and category data poses a significant challenge due to difficulty of an appropriate choice and employment of distance/similarity functions for clustering and its verification. Unsupervised learning models for artificial neural network offers an alternate means for data clustering and analysis. The objective of this study is to highlight an approach and its associated considerations for mixed data set clustering with Adaptive Resonance Theory 2 (ART-2) artificial neural network model and subsequent validation of the clusters with dimensionality reduction using Autoencoder neural network model.
An Approach to Mixed Dataset Clustering and Validation with ART-2 Artificial ...
An Approach to Mixed Dataset Clustering and Validation with ART-2 Artificial ...
Happiest Minds Technologies
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Lx3520322036
Lx3520322036
IJERA Editor
lazy learners
lazy learners and other classication methods
lazy learners and other classication methods
rajshreemuthiah
Updated Machine Learning by Analogy presentation that builds to more advanced methods (TensorFlow, geometry/topology-based methods...) and adds a section on time series methods.
Machine Learning by Analogy II
Machine Learning by Analogy II
Colleen Farrelly
Data partitioning methods are used to partition the data values with similarity. Similarity measures are used to estimate transaction relationships. Hierarchical clustering model produces tree structured results. Partitioned clustering produces results in grid format. Text documents are unstructured data values with high dimensional attributes. Document clustering group ups unlabeled text documents into meaningful clusters. Traditional clustering methods require cluster count (K) for the document grouping process. Clustering accuracy degrades drastically with reference to the unsuitable cluster count. Textual data elements are divided into two types’ discriminative words and nondiscriminative words. Only discriminative words are useful for grouping documents. The involvement of nondiscriminative words confuses the clustering process and leads to poor clustering solution in return. A variation inference algorithm is used to infer the document collection structure and partition of document words at the same time. Dirichlet Process Mixture (DPM) model is used to partition documents. DPM clustering model uses both the data likelihood and the clustering property of the Dirichlet Process (DP). Dirichlet Process Mixture Model for Feature Partition (DPMFP) is used to discover the latent cluster structure based on the DPM model. DPMFP clustering is performed without requiring the number of clusters as input. Document labels are used to estimate the discriminative word identification process. Concept relationships are analyzed with Ontology support. Semantic weight model is used for the document similarity analysis. The system improves the scalability with the support of labels and concept relations for dimensionality reduction process.
Textual Data Partitioning with Relationship and Discriminative Analysis
Textual Data Partitioning with Relationship and Discriminative Analysis
Editor IJMTER
paper tentang metode bayesian
Expandable bayesian
Expandable bayesian
Ahmad Amri
We aim to model an adaptive log file parser. As the content of log files often evolves over time, we established a dynamic statistical model which learns and adapts processing and parsing rules. First, we limit the amount of unstructured text by clustering based on semantics of log file lines. Next, we only take the most relevant cluster into account and focus only on those frequent patterns which lead to the desired output table similar to Vaarandi [10]. Furthermore, we transform the found frequent patterns and the output stating the parsed table into a Hidden Markov Model (HMM). We use this HMM as a specific, however, flexible representation of a pattern for log file parsing to maintain high quality output. After training our model on one system type and applying it to a different system with slightly different log file patterns, we achieve an accuracy over 99.99%
WITH SEMANTICS AND HIDDEN MARKOV MODELS TO AN ADAPTIVE LOG FILE PARSER
WITH SEMANTICS AND HIDDEN MARKOV MODELS TO AN ADAPTIVE LOG FILE PARSER
ijnlc
Electrical, Electronics and Computer Engineering, Information Engineering and Technology, Mechanical, Industrial and Manufacturing Engineering, Automation and Mechatronics Engineering, Material and Chemical Engineering, Civil and Architecture Engineering, Biotechnology and Bio Engineering, Environmental Engineering, Petroleum and Mining Engineering, Marine and Agriculture engineering, Aerospace Engineering.
E1062530
E1062530
IJERD Editor
Overview of common classification techniques in Data Mining: regression and Bayesian models, KNN, decision trees, neural networks.
04 Classification in Data Mining
04 Classification in Data Mining
Valerii Klymchuk
La actualidad más candente
(19)
HOLISTIC EVALUATION OF XML QUERIES WITH STRUCTURAL PREFERENCES ON AN ANNOTATE...
HOLISTIC EVALUATION OF XML QUERIES WITH STRUCTURAL PREFERENCES ON AN ANNOTATE...
A Kernel Approach for Semi-Supervised Clustering Framework for High Dimension...
A Kernel Approach for Semi-Supervised Clustering Framework for High Dimension...
Comparative study of classification algorithm for text based categorization
Comparative study of classification algorithm for text based categorization
A Combined Approach for Feature Subset Selection and Size Reduction for High ...
A Combined Approach for Feature Subset Selection and Size Reduction for High ...
1.8 discretization
1.8 discretization
GCUBE INDEXING
GCUBE INDEXING
G0354451
G0354451
A new model for iris data set classification based on linear support vector m...
A new model for iris data set classification based on linear support vector m...
Text Categorization Using Improved K Nearest Neighbor Algorithm
Text Categorization Using Improved K Nearest Neighbor Algorithm
Multiview Alignment Hashing for Efficient Image Search
Multiview Alignment Hashing for Efficient Image Search
An Approach to Mixed Dataset Clustering and Validation with ART-2 Artificial ...
An Approach to Mixed Dataset Clustering and Validation with ART-2 Artificial ...
Lx3520322036
Lx3520322036
lazy learners and other classication methods
lazy learners and other classication methods
Machine Learning by Analogy II
Machine Learning by Analogy II
Textual Data Partitioning with Relationship and Discriminative Analysis
Textual Data Partitioning with Relationship and Discriminative Analysis
Expandable bayesian
Expandable bayesian
WITH SEMANTICS AND HIDDEN MARKOV MODELS TO AN ADAPTIVE LOG FILE PARSER
WITH SEMANTICS AND HIDDEN MARKOV MODELS TO AN ADAPTIVE LOG FILE PARSER
E1062530
E1062530
04 Classification in Data Mining
04 Classification in Data Mining
Destacado
A minimization approach for two level logic synthesis using constrained depth first search
A minimization approach for two level logic synthesis using constrained depth...
A minimization approach for two level logic synthesis using constrained depth...
IAEME Publication
The effect of ethanol extract of leaves of Conyza Dicorides plant on the corrosion inhibition of mild steel in 1M HCl solution was investigated by weight loss and electrochemical polarization techniques at temperature range (25–65 ̊C). The Results obtained showed that the percentage inhibition efficiency increases with the increasing of inhibitor concentration and decreases with the increasing of temperature. At a concentration of 2 g/L, the percentage inhibition efficiency reached about (94.87%) at 25 ̊C. The thermodynamic activation functions of dissolution process and adsorption parameters were calculated and discussed. Adsorption of the additive was found to follow the Langmuir adsorption isotherm.
30120140507002
30120140507002
IAEME Publication
There is an appreciable and adagio impact of mining on surface and groundwater. Rapid development of mining throughout the world with the galloping advances in science and technology is changing the shape of our planet and also hydrological region at micro level. World top quality galaxy granite (Gabbro) occurs in Chimakurthi mandal of Prakasam district of Andhra Pradesh. From the analysis of data pertaining to mining, rainfall and groundwater levels (1999-2010), it is observed that, the rainfall in the mandal shows declining trend over the successive years and more prominent since 2002 onwards. There is no noticeable effect of rainfall on groundwater recharge, because of poor aquifer system
20320140506015
20320140506015
IAEME Publication
Utilization of ict in r & d institutions libraries in chennai a pilot study
Utilization of ict in r & d institutions libraries in chennai a pilot study
Utilization of ict in r & d institutions libraries in chennai a pilot study
IAEME Publication
Optimization of surface roughness in high speed end milling operation using
Optimization of surface roughness in high speed end milling operation using
IAEME Publication
Benefits of fdi in indain retail sector and customer perception of organized r
Benefits of fdi in indain retail sector and customer perception of organized r
IAEME Publication
Comparison of fuzzy neural clustering based outlier detection techniques
Comparison of fuzzy neural clustering based outlier detection techniques
IAEME Publication
Notes de cours. Version PowerPoint portant sur le 3e cours (Le phénomène Twitter). Légère mise à jour apportée le 21 septembre 2010
Twitter: le phénomène (II)
Twitter: le phénomène (II)
Patrice Leroux
acordao 1/3
Acordao 1.3
Acordao 1.3
Marcia Pereira
C:\Documents And Settings\Admin\Mis Documentos\Power Points\2010\Sala Delfine...
C:\Documents And Settings\Admin\Mis Documentos\Power Points\2010\Sala Delfine...
martinyomar
De les accions del Govern del president Montilla detallem: * Tolerància zero als incendis * Més habitatges de protecció oficial També accions específiques territorials: * Alt Empordà * Alt Urgell * Gironès * Segrià * Vallès Oriental
Butlletí n.58. Acció de Govern
Butlletí n.58. Acció de Govern
socialistes_ cat
Liturgia 10 vida liturgica
Liturgia 10 vida liturgica
clasesteologia
Matrimonio 07 Transmision De La Vida
Matrimonio 07 Transmision De La Vida
clasesteologia
305. Zakon Vitestva
305. Zakon Vitestva
Dino dino
Apuntamentos e Boletín de xercicios 1º eso Ciencias da Natureza Xacobo de Toro
Tema 1. Exercicios de masa, volume e densidade con lectura 2
Tema 1. Exercicios de masa, volume e densidade con lectura 2
Consellería de Educación, Universidade e Formación Profesional. Xunta de Galicia
De Andrés García Vilariño
Coresma 1 a. mt 4, 1 11.
Coresma 1 a. mt 4, 1 11.
Olga López Míguez
patrologia-tema15
patrologia-tema15
clasesteologia
Estudiantes financiero corregidos septiembre 24 2014
Estudiantes financiero corregidos septiembre 24 2014
proyectosdecorazon
Tradución ao galego dunha presentación do prof. Tomás Pérez Molina, onde presenta as principais características da pintura gótica, a través da obra de Giotto
A pintura gótica
A pintura gótica
profesor historia
EL AUTISMO
EL AUTISMO
guestf563ed
Destacado
(20)
A minimization approach for two level logic synthesis using constrained depth...
A minimization approach for two level logic synthesis using constrained depth...
30120140507002
30120140507002
20320140506015
20320140506015
Utilization of ict in r & d institutions libraries in chennai a pilot study
Utilization of ict in r & d institutions libraries in chennai a pilot study
Optimization of surface roughness in high speed end milling operation using
Optimization of surface roughness in high speed end milling operation using
Benefits of fdi in indain retail sector and customer perception of organized r
Benefits of fdi in indain retail sector and customer perception of organized r
Comparison of fuzzy neural clustering based outlier detection techniques
Comparison of fuzzy neural clustering based outlier detection techniques
Twitter: le phénomène (II)
Twitter: le phénomène (II)
Acordao 1.3
Acordao 1.3
C:\Documents And Settings\Admin\Mis Documentos\Power Points\2010\Sala Delfine...
C:\Documents And Settings\Admin\Mis Documentos\Power Points\2010\Sala Delfine...
Butlletí n.58. Acció de Govern
Butlletí n.58. Acció de Govern
Liturgia 10 vida liturgica
Liturgia 10 vida liturgica
Matrimonio 07 Transmision De La Vida
Matrimonio 07 Transmision De La Vida
305. Zakon Vitestva
305. Zakon Vitestva
Tema 1. Exercicios de masa, volume e densidade con lectura 2
Tema 1. Exercicios de masa, volume e densidade con lectura 2
Coresma 1 a. mt 4, 1 11.
Coresma 1 a. mt 4, 1 11.
patrologia-tema15
patrologia-tema15
Estudiantes financiero corregidos septiembre 24 2014
Estudiantes financiero corregidos septiembre 24 2014
A pintura gótica
A pintura gótica
EL AUTISMO
EL AUTISMO
Similar a 20120140505011
Volume 17, Issue 2, Ver. V (Mar – Apr. 2015)
J017256674
J017256674
IOSR Journals
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Supervised WSD Using Master- Slave Voting Technique
Supervised WSD Using Master- Slave Voting Technique
iosrjce
For Details, Contact TSYS Academic Projects. Ph: 9841103123, 044-42607879 Website: http://www.tsys.co.in/ Mail Id: tsysglobalsolutions2014@gmail.com.
IEEE Datamining 2016 Title and Abstract
IEEE Datamining 2016 Title and Abstract
tsysglobalsolutions
Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression. They belong to a family of generalized linear classifiers. In other terms, SVM is a classification and regression prediction tool that uses machine learning theory to maximize predictive accuracy. In this article, the discussion about linear and non-linear SVM classifiers with their functions and parameters is investigated. Due to the equality type of constraints in the formulation, the solution follows from solving a set of linear equations. Besides this, if the under-consideration problem is in the form of a non-linear case, then the problem must convert into linear separable form with the help of kernel trick and solve it according to the methods. Some important algorithms related to sentimental work are also presented in this paper. Generalization of the formulation of linear and non-linear SVMs is also open in this article. In the final section of this paper, the different modified sections of SVM are discussed which are modified by different research for different purposes.
Generalization of linear and non-linear support vector machine in multiple fi...
Generalization of linear and non-linear support vector machine in multiple fi...
CSITiaesprime
In the data mining field the classification of data stream creates many problems. The challenges faces in the data stream are infinite length, concept drift, concept evaluation and feature evolution. Most of the existing system focuses on the only first two challenges. We propose a framework in which each classifier is prepared with the novel class detector for addressing the two challenges concept drift and concept evaluation and for addressing the feature evolution feature set homogeneous technique is proposed. We improved the novel class detection module by building it more adaptive to evolving the stream. SVM based feature extraction for RBF kernel method is also proposed for detecting the novel class from the steaming data. By using the concept of permutation and combination RBF kernel extracts the features and find out the relation between them. This improves the novel class detect technique and provide more accuracy for classifying the data
Novel Class Detection Using RBF SVM Kernel from Feature Evolving Data Streams
Novel Class Detection Using RBF SVM Kernel from Feature Evolving Data Streams
irjes
Medical data mining has great deal for exploring new knowledge from large amount of data. Classification is one of the important data mining techniques for classification of data. In this research work, we have used various data mining based classification techniques for classification of cancer diseases patient or not. We applied the Breast Cancer-Wisconsin (Original) data set into different data mining techniques and compared the accuracy of models with two different data partitions. BayesNet achieved highest accuracy as 97.13% in case of 10-fold data partitions. We have also applied the info gain feature selection technique on BayesNet and Support Vector Machine (SVM) and achieved best accuracy 97.28% accuracy with BayesNet in case of 6 feature subset.
Classification of Breast Cancer Diseases using Data Mining Techniques
Classification of Breast Cancer Diseases using Data Mining Techniques
inventionjournals
Currently, the support vector machine (SVM) regarded as one of supervised machine learning algorithm that provides analysis of data for classification and regression. This technique is implemented in many fields such as bioinformatics, face recognition, text and hypertext categorization, generalized predictive control and many other different areas. The performance of SVM is affected by some parameters, which are used in the training phase, and the settings of parameters can have a profound impact on the resulting engine’s implementation. This paper investigated the SVM performance based on value of gamma parameter with used kernels. It studied the impact of gamma value on (SVM) efficiency classifier using different kernels on various datasets descriptions. SVM classifier has been implemented by using Python. The kernel functions that have been investigated are polynomials, radial based function (RBF) and sigmoid. UC irvine machine learning repository is the source of all the used datasets. Generally, the results show uneven effect on the classification accuracy of three kernels on used datasets. The changing of the gamma value taking on consideration the used dataset influences polynomial and sigmoid kernels. While the performance of RBF kernel function is more stable with different values of gamma as its accuracy is slightly changed.
The effect of gamma value on support vector machine performance with differen...
The effect of gamma value on support vector machine performance with differen...
IJECEIAES
Constructing a classification model is important in machine learning for a particular task. A classification process involves assigning objects into predefined groups or classes based on a number of observed attributes related to those objects. Artificial neural network is one of the classification algorithms which, can be used in many application areas. This paper investigates the potential of applying the feed forward neural network architecture for the classification of medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and applied to feed forward neural network to enhance the learning process and the network learning is validated in terms of convergence rate and classification accuracy. In this paper, MBDE algorithm with various migration policies is proposed for classification problems using medical diagnosis.
Medical diagnosis classification
Medical diagnosis classification
csandit
Constructing a classification model is important in machine learning for a particular task. A classification process involves assigning objects into predefined groups or classes based on a number of observed attributes related to those objects. Artificial neural network is one of the classification algorithms which, can be used in many application areas. This paper investigates the potential of applying the feed forward neural network architecture for the classification of medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and applied to feed forward neural network to enhance the learning process and the network learning is validated in terms of convergence rate and classification accuracy. In this paper, MBDE algorithm with various migration policies is proposed for classification problems using medical diagnosis.
MEDICAL DIAGNOSIS CLASSIFICATION USING MIGRATION BASED DIFFERENTIAL EVOLUTION...
MEDICAL DIAGNOSIS CLASSIFICATION USING MIGRATION BASED DIFFERENTIAL EVOLUTION...
cscpconf
Identification of Personality is a complex process. To ease this process, a model is developed using cursive handwriting. Area based, width based and height based thresholds are set for only character selection, word selection and line selection. The rest is considered as noise. Followed by feature vector construction. Slope feature using slope calculation, shape features and edge detection done using Sobel filter and direction histogram is considered. Based on the direction of handwriting the analysis was done. Writing which rises to the right shows optimism and cheerfulness. Sagging to the right shows physical or mental weariness. The lines which are straight, reveals over-control to compensate for an inner fear of loss of control.The analysis was done using single line and multiple lines. Simple techniques have provided good results. The results using single line were 95% and multiple lines were 91%.The classification is done using SVM classifier.
SVM Based Identification of Psychological Personality Using Handwritten Text
SVM Based Identification of Psychological Personality Using Handwritten Text
IJERA Editor
The first year of an engineering student was important to take proper academic planning. All subjects in the first year were essential for an engineering basis. Student performance prediction helped academics improve their performance better. Students checked performance by themselves. If they were aware that their performance are low, then they could make some improvement for their better performance. This research focused on combining the oversampling minority class data with various kinds of classifier models. Oversampling techniques were SMOTE, BorderlineSMOTE, SVMSMOTE, and ADASYN and four classifiers were applied using MLP, gradient boosting, AdaBoost and random forest in this research. The results represented that Borderline-SMOTE gave the best result for minority class prediction with several classifiers.
Oversampling technique in student performance classification from engineering...
Oversampling technique in student performance classification from engineering...
IJECEIAES
Decision-making typically needs the mechanisms to compromise among opposing norms. Once multiple objectives square measure is concerned of machine learning, a vital step is to check the weights of individual objectives to the system-level performance. Determinant, the weights of multi-objectives is associate in analysis method, associated it's been typically treated as a drawback. However, our preliminary investigation has shown that existing methodologies in managing the weights of multi-objectives have some obvious limitations like the determination of weights is treated as one drawback, a result supporting such associate improvement is limited, if associated it will even be unreliable, once knowledge concerning multiple objectives is incomplete like an integrity caused by poor data. The constraints of weights are also mentioned. Variable weights square measure is natural in decision-making processes. Here, we'd like to develop a scientific methodology in determinant variable weights of multi-objectives. The roles of weights in a creative multi-objective decision-making or machine-learning of square measure analyzed, and therefore the weights square measure determined with the help of a standard neural network.
A Formal Machine Learning or Multi Objective Decision Making System for Deter...
A Formal Machine Learning or Multi Objective Decision Making System for Deter...
Editor IJCATR
Analysis of Co-occurrence problem with MapReduce
Big Data Processing using a AWS Dataset
Big Data Processing using a AWS Dataset
Vishva Abeyrathne
Similarity join is most important technique to involve many applications such as data integration, record linkage and pattern recognition. Here we introduce new algorithm for similarity join with edit distance constraints. Currently extracting overlapping grams from string and consider only string that share certain gram as candidate. Now we propose extracting non-overlapping substring or chunk from string. Chunk scheme based on tail-restricted chunk boundary dictionary (CBD). This approach integrated existing approach for calculating similarity with several new filters unique to chunk based method. Greedy algorithm automatically select good chunking scheme from given data set. Then show the result our method occupies less space and faster performance to compute the value
Vchunk join an efficient algorithm for edit similarity joins
Vchunk join an efficient algorithm for edit similarity joins
Vijay Koushik
50120140504015
50120140504015
IAEME Publication
228-SE3001_2
228-SE3001_2
Boshra Albayaty
This paper proposes and optimizes a two-term cost function consisting of a sparseness term and a generalized v-fold cross-validation term by a new adaptive particle swarm optimization (APSO). APSO updates its parameters adaptively based on a dynamic feedback from the success rate of the each particle’s personal best. Since the proposed cost function is based on the choosing fewer numbers of support vectors, the complexity of SVM models decreased while the accuracy remains in an acceptable range. Therefore, the testing time decreases and makes SVM more applicable for practical applications in real data sets. A comparative study on data sets of UCI database is performed between the proposed cost function and conventional cost function to demonstrate the effectiveness of the proposed cost function.
A parsimonious SVM model selection criterion for classification of real-world ...
A parsimonious SVM model selection criterion for classification of real-world ...
o_almasi
Abc
An efficient-classification-model-for-unstructured-text-document
An efficient-classification-model-for-unstructured-text-document
SaleihGero
Athifah procedia technology_2013
Athifah procedia technology_2013
Nong Tiun
Data Streams are sequential set of data records. When data appears at highest speed and constantly, so predicting the class accordingly to the time is very essential. Currently Ensemble modeling techniques are growing speedily in Classification of Data Stream. Ensemble learning will be accepted since its benefit to manage huge amount of data stream, means it will manage the data in a large size and also it will be able to manage concept drifting. Prior learning, mostly focused on accuracy of ensemble model, prediction efficiency has not considered much since existing ensemble model predicts in linear time, which is enough for small applications and accessible models workings on integrating some of the classifier. Although real time application has huge amount of data stream so we required base classifier to recognize dissimilar model and make a high grade ensemble model. To fix these challenges we developed Ensemble tree which is height balanced tree indexing structure of base classifier for quick prediction on data streams by ensemble modeling techniques. Ensemble Tree manages ensembles as geodatabases and it utilizes R tree similar to structure to achieve sub linear time complexity
Novel Ensemble Tree for Fast Prediction on Data Streams
Novel Ensemble Tree for Fast Prediction on Data Streams
IJERA Editor
Similar a 20120140505011
(20)
J017256674
J017256674
Supervised WSD Using Master- Slave Voting Technique
Supervised WSD Using Master- Slave Voting Technique
IEEE Datamining 2016 Title and Abstract
IEEE Datamining 2016 Title and Abstract
Generalization of linear and non-linear support vector machine in multiple fi...
Generalization of linear and non-linear support vector machine in multiple fi...
Novel Class Detection Using RBF SVM Kernel from Feature Evolving Data Streams
Novel Class Detection Using RBF SVM Kernel from Feature Evolving Data Streams
Classification of Breast Cancer Diseases using Data Mining Techniques
Classification of Breast Cancer Diseases using Data Mining Techniques
The effect of gamma value on support vector machine performance with differen...
The effect of gamma value on support vector machine performance with differen...
Medical diagnosis classification
Medical diagnosis classification
MEDICAL DIAGNOSIS CLASSIFICATION USING MIGRATION BASED DIFFERENTIAL EVOLUTION...
MEDICAL DIAGNOSIS CLASSIFICATION USING MIGRATION BASED DIFFERENTIAL EVOLUTION...
SVM Based Identification of Psychological Personality Using Handwritten Text
SVM Based Identification of Psychological Personality Using Handwritten Text
Oversampling technique in student performance classification from engineering...
Oversampling technique in student performance classification from engineering...
A Formal Machine Learning or Multi Objective Decision Making System for Deter...
A Formal Machine Learning or Multi Objective Decision Making System for Deter...
Big Data Processing using a AWS Dataset
Big Data Processing using a AWS Dataset
Vchunk join an efficient algorithm for edit similarity joins
Vchunk join an efficient algorithm for edit similarity joins
50120140504015
50120140504015
228-SE3001_2
228-SE3001_2
A parsimonious SVM model selection criterion for classification of real-world ...
A parsimonious SVM model selection criterion for classification of real-world ...
An efficient-classification-model-for-unstructured-text-document
An efficient-classification-model-for-unstructured-text-document
Athifah procedia technology_2013
Athifah procedia technology_2013
Novel Ensemble Tree for Fast Prediction on Data Streams
Novel Ensemble Tree for Fast Prediction on Data Streams
Más de IAEME Publication
Submission Deadline: 30th September 2022 Acceptance Notification: Within Three Days’ time period Online Publication: Within 24 Hrs. time Period Expected Date of Dispatch of Printed Journal: 5th October 2022
IAEME_Publication_Call_for_Paper_September_2022.pdf
IAEME_Publication_Call_for_Paper_September_2022.pdf
IAEME Publication
White layer thickness (WLT) formed and surface roughness in wire electric discharge turning (WEDT) of tungsten carbide composite has been made to model through response surface methodology (RSM). A Taguchi’s standard Design of experiments involving five input variables with three levels has been employed to establish a mathematical model between input parameters and responses. Percentage of cobalt content, spindle speed, Pulse on-time, wire feed and pulse off-time were changed during the experimental tests based on the Taguchi’s orthogonal array L27 (3^13). Analysis of variance (ANOVA) revealed that the mathematical models obtained can adequately describe performance within the parameters of the factors considered. There was a good agreement between the experimental and predicted values in this study.
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...
IAEME Publication
The study explores the reasons for a transgender to become entrepreneurs. In this study transgender entrepreneur was taken as independent variable and reasons to become as dependent variable. Data were collected through a structured questionnaire containing a five point Likert Scale. The study examined the data of 30 transgender entrepreneurs in Salem Municipal Corporation of Tamil Nadu State, India. Simple Random sampling technique was used. Garrett Ranking Technique (Percentile Position, Mean Scores) was used as the analysis for the present study to identify the top 13 stimulus factors for establishment of trans entrepreneurial venture. Economic advancement of a nation is governed upon the upshot of a resolute entrepreneurial doings. The conception of entrepreneurship has stretched and materialized to the socially deflated uncharted sections of transgender community. Presently transgenders have smashed their stereotypes and are making recent headlines of achievements in various fields of our Indian society. The trans-community is gradually being observed in a new light and has been trying to achieve prospective growth in entrepreneurship. The findings of the research revealed that the optimistic changes are taking place to change affirmative societal outlook of the transgender for entrepreneurial ventureship. It also laid emphasis on other transgenders to renovate their traditional living. The paper also highlights that legislators, supervisory body should endorse an impartial canons and reforms in Tamil Nadu Transgender Welfare Board Association.
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURS
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURS
IAEME Publication
Since ages gender difference is always a debatable theme whether caused by nature, evolution or environment. The birth of a transgender is dreadful not only for the child but also for their parents. The pain of living in the wrong physique and treated as second class victimized citizen is outrageous and fully harboured with vicious baseless negative scruples. For so long, social exclusion had perpetuated inequality and deprivation experiencing ingrained malign stigma and besieged victims of crime or violence across their life spans. They are pushed into the murky way of life with a source of eternal disgust, bereft sexual potency and perennial fear. Although they are highly visible but very little is known about them. The common public needs to comprehend the ravaged arrogance on these insensitive souls and assist in integrating them into the mainstream by offering equal opportunity, treat with humanity and respect their dignity. Entrepreneurship in the current age is endorsing the gender fairness movement. Unstable careers and economic inadequacy had inclined one of the gender variant people called Transgender to become entrepreneurs. These tiny budding entrepreneurs resulted in economic transition by means of employment, free from the clutches of stereotype jobs, raised standard of living and handful of financial empowerment. Besides all these inhibitions, they were able to witness a platform for skill set development that ignited them to enter into entrepreneurial domain. This paper epitomizes skill sets involved in trans-entrepreneurs of Thoothukudi Municipal Corporation of Tamil Nadu State and is a groundbreaking determination to sightsee various skills incorporated and the impact on entrepreneurship.
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURS
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURS
IAEME Publication
The banking and financial services industries are experiencing increased technology penetration. Among them, the banking industry has made technological advancements to better serve the general populace. The economy focused on transforming the banking sector's system into a cashless, paperless, and faceless one. The researcher wants to evaluate the user's intention for utilising a mobile banking application. The study also examines the variables affecting the user's behaviour intention when selecting specific applications for financial transactions. The researcher employed a well-structured questionnaire and a descriptive study methodology to gather the respondents' primary data utilising the snowball sampling technique. The study includes variables like performance expectations, effort expectations, social impact, enabling circumstances, and perceived risk. Each of the aforementioned variables has a major impact on how users utilise mobile banking applications. The outcome will assist the service provider in comprehending the user's history with mobile banking applications.
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONS
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONS
IAEME Publication
Technology upgradation in banking sector took the economy to view that payment mode towards online transactions using mobile applications. This system enabled connectivity between banks, Merchant and user in a convenient mode. there are various applications used for online transactions such as Google pay, Paytm, freecharge, mobikiwi, oxygen, phonepe and so on and it also includes mobile banking applications. The study aimed at evaluating the predilection of the user in adopting digital transaction. The study is descriptive in nature. The researcher used random sample techniques to collect the data. The findings reveal that mobile applications differ with the quality of service rendered by Gpay and Phonepe. The researcher suggest the Phonepe application should focus on implementing the application should be user friendly interface and Gpay on motivating the users to feel the importance of request for money and modes of payments in the application.
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONS
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONS
IAEME Publication
The prototype of a voice-based ATM for visually impaired using Arduino is to help people who are blind. This uses RFID cards which contain users fingerprint encrypted on it and interacts with the users through voice commands. ATM operates when sensor detects the presence of one person in the cabin. After scanning the RFID card, it will ask to select the mode like –normal or blind. User can select the respective mode through voice input, if blind mode is selected the balance check or cash withdraw can be done through voice input. Normal mode procedure is same as the existing ATM.
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINO
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINO
IAEME Publication
There is increasing acceptability of emotional intelligence as a major factor in personality assessment and effective human resource management. Emotional intelligence as the ability to build capacity, empathize, co-operate, motivate and develop others cannot be divorced from both effective performance and human resource management systems. The human person is crucial in defining organizational leadership and fortunes in terms of challenges and opportunities and walking across both multinational and bilateral relationships. The growing complexity of the business world requires a great deal of self-confidence, integrity, communication, conflict and diversity management to keep the global enterprise within the paths of productivity and sustainability. Using the exploratory research design and 255 participants the result of this original study indicates strong positive correlation between emotional intelligence and effective human resource management. The paper offers suggestions on further studies between emotional intelligence and human capital development and recommends for conflict management as an integral part of effective human resource management.
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...
IAEME Publication
Our life journey, in general, is closely defined by the way we understand the meaning of why we coexist and deal with its challenges. As we develop the "inspiration economy", we could say that nearly all of the challenges we have faced are opportunities that help us to discover the rest of our journey. In this note paper, we explore how being faced with the opportunity of being a close carer for an aging parent with dementia brought intangible discoveries that changed our insight of the meaning of the rest of our life journey.
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMY
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMY
IAEME Publication
The main objective of this study is to analyze the impact of aspects of Organizational Culture on the Effectiveness of the Performance Management System (PMS) in the Health Care Organization at Thanjavur. Organizational Culture and PMS play a crucial role in present-day organizations in achieving their objectives. PMS needs employees’ cooperation to achieve its intended objectives. Employees' cooperation depends upon the organization’s culture. The present study uses exploratory research to examine the relationship between the Organization's culture and the Effectiveness of the Performance Management System. The study uses a Structured Questionnaire to collect the primary data. For this study, Thirty-six non-clinical employees were selected from twelve randomly selected Health Care organizations at Thanjavur. Thirty-two fully completed questionnaires were received.
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...
IAEME Publication
Living in 21st century in itself reminds all of us the necessity of police and its administration. As more and more we are entering into the modern society and culture, the more we require the services of the so called ‘Khaki Worthy’ men i.e., the police personnel. Whether we talk of Indian police or the other nation’s police, they all have the same recognition as they have in India. But as already mentioned, their services and requirements are different after the like 26th November, 2008 incidents, where they without saving their own lives has sacrificed themselves without any hitch and without caring about their respective family members and wards. In other words, they are like our heroes and mentors who can guide us from the darkness of fear, militancy, corruption and other dark sides of life and so on. Now the question arises, if Gandhi would have been alive today, what would have been his reaction/opinion to the police and its functioning? Would he have some thing different in his mind now what he had been in his mind before the partition or would he be going to start some Satyagraha in the form of some improvement in the functioning of the police administration? Really these questions or rather night mares can come to any one’s mind, when there is too much confusion is prevailing in our minds, when there is too much corruption in the society and when the polices working is also in the questioning because of one or the other case throughout the India. It is matter of great concern that we have to thing over our administration and our practical approach because the police personals are also like us, they are part and parcel of our society and among one of us, so why we all are pin pointing towards them.
GANDHI ON NON-VIOLENT POLICE
GANDHI ON NON-VIOLENT POLICE
IAEME Publication
The goal of this study was to see how talent management affected employee retention in the selected IT organizations in Chennai. The fundamental issue was the difficulty to attract, hire, and retain talented personnel who perform well and the gap between supply and demand of talent acquisition and retaining them within the firms. The study's main goals were to determine the impact of talent management on employee retention in IT companies in Chennai, investigate talent management strategies that IT companies could use to improve talent acquisition, performance management, career planning and formulate retention strategies that the IT firms could use. The respondents were given a structured close-ended questionnaire with the 5 Point Likert Scale as part of the study's quantitative research design. The target population consisted of 289 IT professionals. The questionnaires were distributed and collected by the researcher directly. The Statistical Package for Social Sciences (SPSS) was used to collect and analyse the questionnaire responses. Hypotheses that were formulated for the various areas of the study were tested using a variety of statistical tests. The key findings of the study suggested that talent management had an impact on employee retention. The studies also found that there is a clear link between the implementation of talent management and retention measures. Management should provide enough training and development for employees, clarify job responsibilities, provide adequate remuneration packages, and recognise employees for exceptional performance.
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...
IAEME Publication
Globally, Millions of dollars were spent by the organizations for employing skilled Information Technology (IT) professionals. It is costly to replace unskilled employees with IT professionals possessing technical skills and competencies that aid in interconnecting the business processes. The organization’s employment tactics were forced to alter by globalization along with technological innovations as they consistently diminish to remain lean, outsource to concentrate on core competencies along with restructuring/reallocate personnel to gather efficiency. As other jobs, organizations or professions have become reasonably more appropriate in a shifting employment landscape, the above alterations trigger both involuntary as well as voluntary turnover. The employee view on jobs is also afflicted by the COVID-19 pandemic along with the employee-driven labour market. So, having effective strategies is necessary to tackle the withdrawal rate of employees. By associating Emotional Intelligence (EI) along with Talent Management (TM) in the IT industry, the rise in attrition rate was analyzed in this study. Only 303 respondents were collected out of 350 participants to whom questionnaires were distributed. From the employees of IT organizations located in Bangalore (India), the data were congregated. A simple random sampling methodology was employed to congregate data as of the respondents. Generating the hypothesis along with testing is eventuated. The effect of EI and TM along with regression analysis between TM and EI was analyzed. The outcomes indicated that employee and Organizational Performance (OP) were elevated by effective EI along with TM.
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...
IAEME Publication
By implementing talent management strategy, organizations would have the option to retain their skilled professionals while additionally working on their overall performance. It is the course of appropriately utilizing the ideal individuals, setting them up for future top positions, exploring and dealing with their performance, and holding them back from leaving the organization. It is employee performance that determines the success of every organization. The firm quickly obtains an upper hand over its rivals in the event that its employees having particular skills that cannot be duplicated by the competitors. Thus, firms are centred on creating successful talent management practices and processes to deal with the unique human resources. Firms are additionally endeavouring to keep their top/key staff since on the off chance that they leave; the whole store of information leaves the firm's hands. The study's objective was to determine the impact of talent management on organizational performance among the selected IT organizations in Chennai. The study recommends that talent management limitedly affects performance. On the off chance that this talent is appropriately management and implemented properly, organizations might benefit as much as possible from their maintained assets to support development and productivity, both monetarily and non-monetarily.
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...
IAEME Publication
Banking regulations act of India, 1949 defines banking as “acceptance of deposits for the purpose of lending or investment from the public, repayment on demand or otherwise and withdrawable through cheques, drafts order or otherwise”, the major participants of the Indian financial system are commercial banks, the financial institution encompassing term lending institutions. Investments institutions, specialized financial institution and the state level development banks, non banking financial companies (NBFC) and other market intermediaries such has the stock brokers and money lenders are among the oldest of the certain variants of NBFC and the oldest market participants. The asset quality of banks is one of the most important indicators of their financial health. The Indian banking sector has been facing severe problems of increasing Non- Performing Assets (NPAs). The NPAs growth directly and indirectly affects the quality of assets and profitability of banks. It also shows the efficiency of banks credit risk management and the recovery effectiveness. NPA do not generate any income, whereas, the bank is required to make provisions for such as assets that why is a double edge weapon. This paper outlines the concept of quality of bank loans of different types like Housing, Agriculture and MSME loans in state Haryana of selected public and private sector banks. This study is highlighting problems associated with the role of commercial bank in financing Small and Medium Scale Enterprises (SME). The overall objective of the research was to assess the effect of the financing provisions existing for the setting up and operations of MSMEs in the country and to generate recommendations for more robust financing mechanisms for successful operation of the MSMEs, in turn understanding the impact of MSME loans on financial institutions due to NPA. There are many research conducted on the topic of Non- Performing Assets (NPA) Management, concerning particular bank, comparative study of public and private banks etc. In this paper the researcher is considering the aggregate data of selected public sector and private sector banks and attempts to compare the NPA of Housing, Agriculture and MSME loans in state Haryana of public and private sector banks. The tools used in the study are average and Anova test and variance. The findings reveal that NPA is common problem for both public and private sector banks and is associated with all types of loans either that is housing loans, agriculture loans and loans to SMES. NPAs of both public and private sector banks show the increasing trend. In 2010-11 GNPA of public and private sector were at same level it was 2% but after 2010-11 it increased in many fold and at present there is GNPA in some more than 15%. It shows the dark area of Indian banking sector.
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...
IAEME Publication
An experiment conducted in this study found that BaSO4 changed Nylon 6's mechanical properties. By changing the weight ratios, BaSO4 was used to make Nylon 6. This Researcher looked into how hard Nylon-6/BaSO4 composites are and how well they wear. Experiments were done based on Taguchi design L9. Nylon-6/BaSO4 composites can be tested for their hardness number using a Rockwell hardness testing apparatus. On Nylon/BaSO4, the wear behavior was measured by a wear monitor, pinon-disc friction by varying reinforcement, sliding speed, and sliding distance, and the microstructure of the crack surfaces was observed by SEM. This study provides significant contributions to ultimate strength by increasing BaSO4 content up to 16% in the composites, and sliding speed contributes 72.45% to the wear rate
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...
IAEME Publication
The majority of the population in India lives in villages. The village is the back bone of the country. Village or rural industries play an important role in the national economy, particularly in the rural development. Developing the rural economy is one of the key indicators towards a country’s success. Whether it be the need to look after the welfare of the farmers or invest in rural infrastructure, Governments have to ensure that rural development isn’t compromised. The economic development of our country largely depends on the progress of rural areas and the standard of living of rural masses. Village or rural industries play an important role in the national economy, particularly in the rural development. Rural entrepreneurship is based on stimulating local entrepreneurial talent and the subsequent growth of indigenous enterprises. It recognizes opportunity in the rural areas and accelerates a unique blend of resources either inside or outside of agriculture. Rural entrepreneurship brings an economic value to the rural sector by creating new methods of production, new markets, new products and generate employment opportunities thereby ensuring continuous rural development. Social Entrepreneurship has the direct and primary objective of serving the society along with the earning profits. So, social entrepreneurship is different from the economic entrepreneurship as its basic objective is not to earn profits but for providing innovative solutions to meet the society needs which are not taken care by majority of the entrepreneurs as they are in the business for profit making as a sole objective. So, the Social Entrepreneurs have the huge growth potential particularly in the developing countries like India where we have huge societal disparities in terms of the financial positions of the population. Still 22 percent of the Indian population is below the poverty line and also there is disparity among the rural & urban population in terms of families living under BPL. 25.7 percent of the rural population & 13.7 percent of the urban population is under BPL which clearly shows the disparity of the poor people in the rural and urban areas. The need to develop social entrepreneurship in agriculture is dictated by a large number of social problems. Such problems include low living standards, unemployment, and social tension. The reasons that led to the emergence of the practice of social entrepreneurship are the above factors. The research problem lays upon disclosing the importance of role of social entrepreneurship in rural development of India. The paper the tendencies of social entrepreneurship in India, to present successful examples of such business for providing recommendations how to improve situation in rural areas in terms of social entrepreneurship development. Indian government has made some steps towards development of social enterprises, social entrepreneurship, and social in- novation, but a lot remains to be improved.
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...
IAEME Publication
Distribution system is a critical link between the electric power distributor and the consumers. Most of the distribution networks commonly used by the electric utility is the radial distribution network. However in this type of network, it has technical issues such as enormous power losses which affect the quality of the supply. Nowadays, the introduction of Distributed Generation (DG) units in the system help improve and support the voltage profile of the network as well as the performance of the system components through power loss mitigation. In this study network reconfiguration was done using two meta-heuristic algorithms Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA) to enhance power quality and voltage profile in the system when simultaneously applied with the DG units. Backward/Forward Sweep Method was used in the load flow analysis and simulated using the MATLAB program. Five cases were considered in the Reconfiguration based on the contribution of DG units. The proposed method was tested using IEEE 33 bus system. Based on the results, there was a voltage profile improvement in the system from 0.9038 p.u. to 0.9594 p.u.. The integration of DG in the network also reduced power losses from 210.98 kW to 69.3963 kW. Simulated results are drawn to show the performance of each case.
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...
IAEME Publication
Manufacturing industries have witnessed an outburst in productivity. For productivity improvement manufacturing industries are taking various initiatives by using lean tools and techniques. However, in different manufacturing industries, frugal approach is applied in product design and services as a tool for improvement. Frugal approach contributed to prove less is more and seems indirectly contributing to improve productivity. Hence, there is need to understand status of frugal approach application in manufacturing industries. All manufacturing industries are trying hard and putting continuous efforts for competitive existence. For productivity improvements, manufacturing industries are coming up with different effective and efficient solutions in manufacturing processes and operations. To overcome current challenges, manufacturing industries have started using frugal approach in product design and services. For this study, methodology adopted with both primary and secondary sources of data. For primary source interview and observation technique is used and for secondary source review has done based on available literatures in website, printed magazines, manual etc. An attempt has made for understanding application of frugal approach with the study of manufacturing industry project. Manufacturing industry selected for this project study is Mahindra and Mahindra Ltd. This paper will help researcher to find the connections between the two concepts productivity improvement and frugal approach. This paper will help to understand significance of frugal approach for productivity improvement in manufacturing industry. This will also help to understand current scenario of frugal approach in manufacturing industry. In manufacturing industries various process are involved to deliver the final product. In the process of converting input in to output through manufacturing process productivity plays very critical role. Hence this study will help to evolve status of frugal approach in productivity improvement programme. The notion of frugal can be viewed as an approach towards productivity improvement in manufacturing industries.
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...
IAEME Publication
In this paper, we investigated a queuing model of fuzzy environment-based a multiple channel queuing model (M/M/C) ( /FCFS) and study its performance under realistic conditions. It applies a nonagonal fuzzy number to analyse the relevant performance of a multiple channel queuing model (M/M/C) ( /FCFS). Based on the sub interval average ranking method for nonagonal fuzzy number, we convert fuzzy number to crisp one. Numerical results reveal that the efficiency of this method. Intuitively, the fuzzy environment adapts well to a multiple channel queuing models (M/M/C) ( /FCFS) are very well.
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENT
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENT
IAEME Publication
Más de IAEME Publication
(20)
IAEME_Publication_Call_for_Paper_September_2022.pdf
IAEME_Publication_Call_for_Paper_September_2022.pdf
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURS
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURS
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURS
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURS
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONS
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONS
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONS
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONS
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINO
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINO
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMY
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMY
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...
GANDHI ON NON-VIOLENT POLICE
GANDHI ON NON-VIOLENT POLICE
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENT
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENT
Último
Intelligent Gimbal FINAL PAPER
Intelligent Gimbal FINAL PAPER Engineering.pdf
Intelligent Gimbal FINAL PAPER Engineering.pdf
Anthony Lucente
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other? Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024
Tobias Schneck
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
"Impact of front-end architecture on development cost", Viktor Turskyi
"Impact of front-end architecture on development cost", Viktor Turskyi
Fwdays
Generative AI architecture, at its core, revolves around the concept of machines being able to generate content autonomously, mimicking human-like creativity and decision-making processes. Unlike traditional AI systems that rely on predefined rules and data inputs, generative AI leverages deep learning techniques to produce new, original outputs based on patterns and examples it has learned from vast datasets. This capability opens up a multitude of possibilities across various domains within an enterprise.
The architecture of Generative AI for enterprises.pdf
The architecture of Generative AI for enterprises.pdf
alexjohnson7307
Keynote at the 21st European Semantic Web Conference
Knowledge engineering: from people to machines and back
Knowledge engineering: from people to machines and back
Elena Simperl
ScyllaDB has the potential to deliver impressive performance and scalability. The better you understand how it works, the more you can squeeze out of it. But before you squeeze, make sure you know what to monitor! Watch our experienced Postgres developer work through monitoring and performance strategies that help him understand what mistakes he’s made moving to NoSQL. And learn with him as our database performance expert offers friendly guidance on how to use monitoring and performance tuning to get his sample Rust application on the right track. This webinar focuses on using monitoring and performance tuning to discover and correct mistakes that commonly occur when developers move from SQL to NoSQL. For example: - Common issues getting up and running with the monitoring stack - Using the CQL optimizations dashboard - Common issues causing high latency in a node - Common issues causing replica imbalance - What a healthy system looks like in terms of memory - Key metrics to keep an eye on This isn’t “Death-by-Powerpoint.” We’ll walk through problems encountered while migrating a real application from Postgres to ScyllaDB – and try to fix them live as well.
Optimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through Observability
ScyllaDB
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to: Create a campaign using Mailchimp with merge tags/fields Send an interactive Slack channel message (using buttons) Have the message received by managers and peers along with a test email for review But there’s more: In a second workflow supporting the same use case, you’ll see: Your campaign sent to target colleagues for approval If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team But—if the “Reject” button is pushed, colleagues will be alerted via Slack message Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors. And... Speakers: Akshay Agnihotri, Product Manager Charlie Greenberg, Host
Connector Corner: Automate dynamic content and events by pushing a button
Connector Corner: Automate dynamic content and events by pushing a button
DianaGray10
The presentation underscores the strategic advantage of treating design systems not just as technical assets but as vital business components that require thoughtful management, robust planning, and strategic alignment with organizational goals. Key Points Covered: - Understanding Design Systems as Business Entities: Conceptualizing design systems as internal business entities can streamline their integration and evolution within a company. - Adoption and Expansion: Elaborating on the importance of tactical adoption across organizational structures, enhancing product suites to cater to user needs and broadening scope to mobile and content authoring solutions. - Data-Driven Development: Utilizing data insights for component development ensures that resources are allocated to create valuable, widely used features. - Financial Modeling for Design Systems: Developing sustainable funding models is crucial for long-term support and success of design systems. - Promoting Internal Buy-In: Stressing on strategies for promoting design systems within the organization to increase engagement and investment from internal stakeholders.
A Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System Strategy
UXDXConf
The New York Times continues to lead in user-centered design by innovating and adapting to enhance both user engagement and understanding, aligning product experiences with its journalistic mission. This presentation discusses innovative strategies in user experience at The New York Times, focusing on subscriber experiences and storytelling. Key Points Covered: - Mission-Driven Design: Emphasizing the Times' mission to "seek the truth and help people understand the world," the design team prioritizes clarity and engagement to support high-quality journalism. - UX Design Principles: The team follows five core UX tenets—clarity, time optimization, craftsmanship, accessibility, and trust—to maintain a strong focus on user-centric design. - Innovative Design Strategies: Product Feature Advancement, Editorial Expression, Long-term Visioning - Integrating Diverse Content: Examples include the successful integration of popular games like Wordle, which not only entertain but also attract and retain a diverse user base.
Transforming The New York Times: Empowering Evolution through UX
Transforming The New York Times: Empowering Evolution through UX
UXDXConf
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx
Abida Shariff
Intrigued by why some of the world's largest companies (Netflix, Google, Cisco, Twitter, Uber etc) are using gRPC? In this demo based talk we delve into the world of gRPC in .Net, what it does and why we should use it. We compare the interface with both Rest and graphQL. We will show you how to implement grpc server-side in .net and in the web. Finally, I will show you how the tooling helps you deliver powerful interfaces and interact with them quickly and simply.
Demystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John Staveley
John Staveley
We're living the AI revolution and Salesforce is adapting and bring new value to their customers. Einstein products are evolving rapidly and navigating their limitations, language support, and use cases can be challenging. Let's make review of what Einstein product are available currently, what are the capabilities and what can be used for in CEE region and how Rossie.ai can help to learn Salesforce speak Czech. We will explore the Einstein roadmap and I will make a short live demo (based on your vote) of some Einstein feature.
AI revolution and Salesforce, Jiří Karpíšek
AI revolution and Salesforce, Jiří Karpíšek
CzechDreamin
PLAI is the Italian Accelerator igniting the growth of innovative Startups and nurturing a community of talents in the Generative AI field.
PLAI - Acceleration Program for Generative A.I. Startups
PLAI - Acceleration Program for Generative A.I. Startups
Stefano
This presentation discusses the complexities of aligning teams and ensuring consistent product experiences across various platforms, proposing Server Driven UI (SDUI) as a solution. Key Points Covered: - The challenge of maintaining consistency in product experiences across web and app interfaces, highlighted by discrepancies in user experience features like comment sections. - Introduction of Server Driven UI (SDUI) to manage uniformity and streamline updates across different platforms. - The importance of adapting design systems to accommodate SDUI, ensuring uniform naming conventions, and component functionalities. - Technical discussions on overcoming framework differences and the operational load on developers due to continuous OS updates.
Server-Driven User Interface (SDUI) at Priceline
Server-Driven User Interface (SDUI) at Priceline
UXDXConf
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application. In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics. Length: 30 minutes Session Overview ------------------------------------------- During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana: - What out-of-the-box solutions are available for real-time monitoring JMeter tests? - What are the benefits of integrating InfluxDB and Grafana into the load testing stack? - Which features are provided by Grafana? - Demonstration of InfluxDB and Grafana using a practice web application To view the webinar recording, go to: https://www.rttsweb.com/jmeter-integration-webinar
JMeter webinar - integration with InfluxDB and Grafana
JMeter webinar - integration with InfluxDB and Grafana
RTTS
In today's presentation, we'll explore Security Onion, a powerful open-source platform designed to fortify your network security. Security Onion, much like its namesake vegetable, peels back the layers of your network traffic, enabling you to identify and address potential threats. We'll delve into its functionalities, core components, and the advantages it brings to your cybersecurity posture.
Enterprise Security Monitoring, And Log Management.
Enterprise Security Monitoring, And Log Management.
Boni Yeamin
New customer? New industry? New cloud? New team? A lot to handle! How to ensure the success of the project? Start it well! I've created the 3 areas of focus at the beginning of the project that helped me in multiple roles (BA, PO, and Consultant). Learn from real-world experiences and discover how these insights can empower you to deliver unparalleled value to your customers right from the project's start.
Powerful Start- the Key to Project Success, Barbara Laskowska
Powerful Start- the Key to Project Success, Barbara Laskowska
CzechDreamin
The standard Salesforce Approval process can be limiting in many ways, especially in complex scenarios. What if there was a way to implement very flexible approvals where one can use Apex code to make data updates in unrelated records, dynamically generate next steps details, and compute assignees on the fly? And still use UI-based configurations to implement concrete approval processes. In this session, we will share ideas behind such a solution and show a few lines of code to get you started.
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
CzechDreamin
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring. Learn about: • The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks. • Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective. • Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification. • Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process. Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality
Inflectra
Brief Introduction to Generative AI and LLM in particular. Overview of the market, and usages of LLMs. What's it like to train and build a model. Retrieval Augmented Generation 101, explained for non savvies, and a perspective of what are the moving parts making it complex
AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101
vincent683379
Último
(20)
Intelligent Gimbal FINAL PAPER Engineering.pdf
Intelligent Gimbal FINAL PAPER Engineering.pdf
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024
"Impact of front-end architecture on development cost", Viktor Turskyi
"Impact of front-end architecture on development cost", Viktor Turskyi
The architecture of Generative AI for enterprises.pdf
The architecture of Generative AI for enterprises.pdf
Knowledge engineering: from people to machines and back
Knowledge engineering: from people to machines and back
Optimizing NoSQL Performance Through Observability
Optimizing NoSQL Performance Through Observability
Connector Corner: Automate dynamic content and events by pushing a button
Connector Corner: Automate dynamic content and events by pushing a button
A Business-Centric Approach to Design System Strategy
A Business-Centric Approach to Design System Strategy
Transforming The New York Times: Empowering Evolution through UX
Transforming The New York Times: Empowering Evolution through UX
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx
IOS-PENTESTING-BEGINNERS-PRACTICAL-GUIDE-.pptx
Demystifying gRPC in .Net by John Staveley
Demystifying gRPC in .Net by John Staveley
AI revolution and Salesforce, Jiří Karpíšek
AI revolution and Salesforce, Jiří Karpíšek
PLAI - Acceleration Program for Generative A.I. Startups
PLAI - Acceleration Program for Generative A.I. Startups
Server-Driven User Interface (SDUI) at Priceline
Server-Driven User Interface (SDUI) at Priceline
JMeter webinar - integration with InfluxDB and Grafana
JMeter webinar - integration with InfluxDB and Grafana
Enterprise Security Monitoring, And Log Management.
Enterprise Security Monitoring, And Log Management.
Powerful Start- the Key to Project Success, Barbara Laskowska
Powerful Start- the Key to Project Success, Barbara Laskowska
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
Custom Approval Process: A New Perspective, Pavel Hrbacek & Anindya Halder
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality
AI presentation and introduction - Retrieval Augmented Generation RAG 101
AI presentation and introduction - Retrieval Augmented Generation RAG 101
20120140505011
1.
International Journal of
Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 91-101 © IAEME 91 EXPERIMENTAL EVALUATION OF DIFFERENT CLASSIFICATION TECHNIQUES FOR WEB PAGE CLASSIFICATION Ms. Rutu Joshi1 , Priyank Thakkar2 Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad ABSTRACT Classification of web pages is essential for improving the quality of web search, focused crawling, development of web directories like Yahoo, ODP etc. This paper compares various classification techniques for the task of web page classification. The classification techniques compared include k-Nearest Neighbours (KNN), Naive Bayes (NB), Support Vector Machine (SVM), Classification and Regression Trees (CART), Random Forest (RF) and Particle Swarm Optimization (PSO).Impact of using different representations of web pages is also studied. The different representations of the web pages that are used comprise Boolean, bag-of-words and Term Frequency and Inverse Document Frequency (TFIDF). Experiments are performed using WebKB and R8 data sets. Accuracy and F-measure are used as the evaluation measures. Impact of feature selectionon the accuracy of the classifier is moreover demonstrated. Keywords: Classification and Regression Trees (CART), K-Nearest Neighbours (KNN), Naive Bayes (NB), Particle Swarm Optimization (PSO), Random Forest, Support Vector Machine (SVM), Web Page Classification. 1. INTRODUCTION The internet consists of millions of web pages corresponding to each and every search word which provides highly useful information. Search engines help users retrieve web pages related to a keyword but searching those innumerable pages is tedious. Also, web pages are dynamic and volatile in nature. There is no unique format for the web pages. Some web pages may be unstructured (text), some pages may be semi structured (HTML pages) and some pages may be structured (database). This heterogeneous format on the web presents additional challenges for classification. Hence it is important for us to find a technique which accurately classifies web pages and provide only the most INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) ISSN 0976 - 6480 (Print) ISSN 0976 - 6499 (Online) Volume 5, Issue 5, May (2014), pp. 91-101 © IAEME: www.iaeme.com/ijaret.asp Journal Impact Factor (2014): 7.8273 (Calculated by GISI) www.jifactor.com IJARET © I A E M E
2.
International Journal of
Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 91-101 © IAEME 92 relevant web pages. Classification is a data mining technique which predicts pre-defined classes for data sets. Classification is a supervised learning technique. Here, the classifier is learnt using the training data set. The trained classifier then assigns class labels to the testing data set. In Web Page classification, web pages are assigned to pre-defined classes mainly according to their content [1]. The rest of the paper is organized as follows. Section 2 focuses on related work. Section 3 discusses different classifiers for web page classification. Implementation methodology is described in Section 4. The paper finally ends with conclusions as section 5. 2. RELATED WORK Classification of web pages using various techniques was extensively studied. Four classification techniques namely decision trees, k-nearest neighbour, SVM and naive Bayes were discussed in [4]. The paper focussed on obtaining accurate system results. When decision tree gave accurate result, Bayesian network did not and vice versa due to their different operational profiles. Since many methods of web page classification were proposed, no clear conclusion about the best method was obtained. In [5],the best result was obtained with SVM employing linear kernel function (followed by method of k-nearest neighbours) and term frequency (TF) document model using feature selection by mutual information score. Here, special attention was paid while treating short documents, which frequently occurred on the web. In [6], authors concentrated on the effects of using context features (text, title and anchor words) in web page classification using SVM classifiers. Experiment showed that SVM technique gave very good result on the WebKB data set even using the text components only. Also, the accuracy of classification improved significantly when context features consisting of title components and anchor words were used. But elimination of anchor words could not render consistently good classification results for all the classes of data set. The performance of SVM was compared using four different kernel functions in [7]. Experimental results showed that out of these four kernel functions, Analysis of Variance (ANOVA) kernel function yielded the best result. Thereafter, Latent Semantic Analysis SVM (LSA-SVM), Weighted Vote SVM (WVSVM) and Back Propagation Neural Network (BPN) were also compared. From the experimental results it was concluded that WVSVM could classify accurately even with a small data set. Even if the smaller category had less training data, WVSVM could still classify those web pages with acceptable accuracy. Whereas in [9], even with a small data set LS-SVM yielded better accuracy with faster speed and reduced runtime of the algorithm. PSO produced more accurate classification models than associative classifiers in [10]. PSO was used for classifying multidimensional real data set in [11], where the parameters were tuned in such a way that it gave the best result. PSO, KNN, Naive Bayes and Decision Tree classification techniques were applied on Reuter-21578 and TREC-APdata set and the results were compared in [12]. The experimental results indicated that PSO yielded much better performance than other conventional algorithms. Three different fitness functions were used in [13] on different data sets. PSO was compared with nine other classification techniques, like Multi-Layer Perceptron Artificial Neural Network (MLP), Bayes Network, Naive Bayes Tree etc. Here, PSO was in fourth position, quite close to its predecessors. Also, PSO seemed effective for two class problem but contrasting results were obtained for more than two classes. Hence, no clear conclusion was inferred.
3.
International Journal of
Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 91-101 © IAEME 93 3. CLASSIFICATION METHODS The following classifiers are used in this paper for the task of web page classification. 3.1 KNN Classifier K-Nearest Neighbour is a lazy learning method [2]. Here, the training data set is not used to train the classifier. For a test instance say ,ݐ the KNN method compares ݐ with the training data set to find the k most similar training instances. It then returns the class which represents the maximum of the ݇instances of the data set. Normally ݇ ൌ 1 is not opted for classification due to noise and other anomalies in the data set. Hence,݇ ൌ 3 is chosen forKNN classification in this study. 3.2 Naive Bayes Classifier Naive Bayes classifier is based on Bayes’ theorem. Here, classification is considered as estimating the posterior probabilities of class C for test instance X. PሺC|Xሻ= PሺX|CሻPሺCሻ PሺXሻ PሺX|Cሻ=P(x1, xଶ, … , ݔ|ܥሻ ൌ ෑ ܲሺݔ ୀଵ |ܥሻ Where,ܲሺܺ|ܥሻ is the posterior probability of class given attribute, ܲሺܺ|ܥሻ is the probability of predictor given class,ܲሺܥሻ is the prior probability of class and ܲሺܺሻ is the prior probability of predictor. Here, for each class, probability is calculated. Thereafter, the class which has the highest probability is assigned to the test instance. Based on how the web pages are represented, appropriate distribution is fitted to the data. Gaussian distribution is fitted when web pages are represented by means of TFIDF scores of the terms. For Boolean representation, multivariate Bernoulli distribution and for bag-of-words representation, multinomial distribution is fitted to the data. 3.3 Support Vector Machine (SVM) SVM is one of the most popular classification method. SVM uses supervised learning technique and can be used for both classification and regression. In general, linear SVMs are used for binary classification. For more than two classes, SVM network. To build a classifier, SVM finds a maximum margin hyper plane ݂ሺݔሻ ൌ ݓ כ ݔ ܾ. Thereafter, an input vector sayݔ is assigned to the positive class, if ݂ሺݔሻ ൌ 0, and to the negative class otherwise. In essence, SVM finds a hyper planeݓ כ ݔ ܾ ൌ 0 that separates positive and negative training examples. This hyper plane is called the decision boundary or decision surface [2]. The main objective function here is to maximize hyper plane’s margin between positive and negative data points. If the data set is noisy, linear SVM is not be able to find a solution. In this case, soft margin SVMs are used. Also, if the data set cannot be separated linearly, kernel functions are used. The kernel function transforms the original space to a higher dimensional space so that a linear decision boundary can be formed in the transformed space to accurately separate positive and negative examples. Here the transformed space is called the feature space. Kernel functions can be polynomial functions, linear kernels etc. There are various methods to find the separating hyper plane. The “Least Square (LS)” method finds solution by solving a set of linear equations. The “Sequential Minimal Optimization (SMO)” method breaks a problem into 2D sub-problems that may be solved analytically, eliminating the need of a numerical optimization algorithms.
4.
International Journal of
Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 91-101 © IAEME 94 3.4 Classification and Regression Tree (CART) CART was first developed by Breiman et. al [8]. CART is a non-parametric decision tree learning technique that produces either classification or regression trees, depending on whether the dependent variable is categorical or numeric, respectively. In CART, leaves represent class labels, while branches represent conditions that will lead to any of the class labels i.e. leaves. The decision tree consists of linear combination of features that help in determining a class for test data set. CART uses historical data to construct decision trees which thereafter classify new data set. In order to use CART for categorizing the instances, it is necessary to know number of classes a priori. To perform classification/regression for a test instance, follow the decisions in the tree from the root node to the leaf node. The leaf node predicts the result for the test instance. Classification trees give nominal responses such as 'true' or 'false'. Regression trees give numeric responses. 3.5 Random Forest Random Forest consists of an ensemble of decision trees that may be used for either classification or regression. To train each tree, different subsets of the training data set (probably 2/3rd ) are selected. To predict class labels of an ensemble of trees for testing data set, Random Forest takes an average of predictions from individual trees. For estimating the prediction error, predictions are computed for each tree on its out-of-bag observations (those observations that were not used to train the trees). Thereafter these predictions are averaged over the entire ensemble for each observation and then compared with the true value of this observation. Here, an ensemble of 50 trees is considered for classifying web pages using random forest. 3.6 Particle Swarm Optimization (PSO) The PSO is a population-based stochastic optimization method first proposed by Kennedy and Eberhart [3] in 1995. It is simple as well as efficient in global search. In PSO, each particle represents a possible solution. PSO finds optimal solution using this swarm of particles. PSO Algorithm is of two types: global best (gbest) PSO and local best (lbest) PSO. In gbest PSO, the neighbourhood of the particle is the entire swarm while in lbest PSO, a particle may have social or geographical neighbourhood. The PSO algorithm starts with initializing the position and velocity of each particle. The function that is to be optimized for the PSO algorithm is called the fitness function. For each iteration, the velocity of the particles is updated by considering the previous velocity along with the personal best and global best position. Vij(t+1) = Vij(t) + C1 * R1 ( Pib(t) - Xij(t) ) + C2 * R2 (Pigb(t) – Xij(t) ) where,ܸሺݐሻ is the velocity at iteration ,ݐ ܥଵ and ܥଶ are acceleration constants, ܴଵ and ܴଶ are random values in the range ሾ0,1ሿ, ܲሻሺݐሻ is the personal best position of particle for iteration ,ݐ ܺሺݐሻ is the position of particle for iteration ݐ and ܲሺݐሻ is the global best position of particle.The personal best position is calculated by comparing the fitness of all the previous positions of the particle and selecting the position with the best fitness value. The global best position of the particle is obtained by selecting the personal best position of particle having the best fitness value. The position of the particle is updated using the new velocity and older position of the particle. Xij(t+1) = Xij(t) + Vij(t) These iterations are repeated until the algorithm satisfies the stopping criteria. The stopping criteria may be no of iterations or when the motion of the particles ceases. The algorithm renders the position of the particle having the best fitness value.
5.
International Journal of
Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 91-101 © IAEME 95 Selection of appropriate parameters is essential for the algorithm to render best results. For distinguishing web pages, hyperplane is used. So, in this study, initially fifty particles are used for PSO which are the hyperplanes obtained from SVMs. The initial velocity for all particles is zero and the value for ܥଵ, ܥଶ is 2 and 0.8 respectively. The algorithm is iterated 10 times. Fig 4.1: PSO Algorithm 4. IMPLEMENTATION METHODOLOGY 4.1 Performance Parameters F-measure is used to measure the performance of the algorithms. F-measure is a measure that combines precision and recall. It is the harmonic mean of precision and recall. It is also known as F1 measure as precision and recall are evenly weighted. F-measure is used for better visualization. F െ measure ൌ 2 כ Precision כ Recall Precision Recall Here, Precision (also called positive predictive value) is the ratio of true positives elements and the total number of elements that are predicted as positives (regardless of whether they are positive or not). Precision ൌ True Positive True Positive False Positive
6.
International Journal of
Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 91-101 © IAEME 96 Here, Recall (also known as sensitivity) is the ratio of true positives elements and the total no of elements that are actually positive. Recall ൌ True Positive True Positive False Negative 4.2 Data sets Two data sets are used for experimentations. 1) WebKB data set It consists of 4 classes: Project (Training 335, Testing 166 webpages), Course(Training 620, Testing 306 web pages), Faculty(Training 745, Testing 371 web pages) and Student(Training 1085 Testing 540 web pages). The web pages in the data set are represented by means of 7771 terms appearing in these web pages. 2) R8 data set of Reuters-21578 It consists of 8 classes: acq(Training 1596, Testing 696 web pages),crude(Training 253, Testing 121 web pages), earn(Training 2840, Testing 1083 web pages), grain(Training 41, Testing 10 web pages), interest(Training 190, Testing 81, web pages), money-fx(Training 206, Testing 87 web pages), ship(Training 108, Testing 36 web pages) and trade(Training 251, Testing 75 web pages). The web pages in this data set are represented by means of 17386 terms appearing in the web pages of this data set. 4.3 Pre-Processing of Web Pages Pre-processing of web pages is necessary to improve subsequent classification process. First of all, all the terms are converted to lower case. Each word in the document is extracted and the stop words are removed from the data set. The Boolean, TFIDF and bag-of-words representations are then obtained. Boolean representation consists of zeroes and ones, zero indicating the absence of the word in the web page while one indicating the presence of the word in the web page. In bag-of-words representation, number of times, the specific word appears in the web page is used as the value of the feature corresponding to that word. In TFIDF representation, feature values are in terms of TFIDF scores of the words. 4.4 Feature Selection Feature selection focuses on removing redundant or irrelevant attributes. Redundant features are those which provide no more information than the currently selected features, and irrelevant features provide no useful information in any context. Feature selection reduces the set of terms to be used in classification, thus improving both efficiency and accuracy. In this paper, feature selection is done using Information Gain [14]. Information Gain helps us determine which attributes in a given training set are most useful for discriminating between classes. It tells us how important a given attribute of the feature is. ݊݅ܽܩ݂݊ܫሺ,ݏݏ݈ܽܥ ݁ݐݑܾ݅ݎݐݐܣሻ ൌ ܪሺݏݏ݈ܽܥሻ െ ܪሺ݁ݐݑܾ݅ݎݐݐܣ/ݏݏ݈ܽܥሻ Where,ܪ stands for Entropy.Entropy measures the level of impurity in a group. ݕݎݐ݊ܧ ൌ െ݈݃ଶ
7.
International Journal of
Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 91-101 © IAEME 97 4.5 Results and Discussions All the classification methods discussed in section 3 are applied on the three different representations of web page collection of both the data set. Number of features are varied to see the impact on the performance of the classifiers. Figure 4.1: Impact of feature selection on F-measure (WebKB data set, Boolean representation) Figure 4.2: Impact of feature selection on F-measure (WebKB data set, TFIDF representation) Figures 4.1 to 4.3 show the impact of feature selection on f-measure for Boolean, TFIDF and bag-of-words representation respectively for WebKB dataset. Similar results for R8 data set are depicted in Figures 4.4 to 4.6. It can be seen that a classifier learnt using appropriate number of
8.
International Journal of
Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 91-101 © IAEME 98 features improves the performance. It is also evident that LS-SVM is most sensitive to the number of features. Figure 4.3: Impact of feature selection on F-measure (WebKB data set, bag-of-words representation) Figure 4.4: Impact of feature selection on F-measure (R8 data set, Boolean representation) Figures 4.7 to 4.9 depict the best performance of each of the classification techniques for both the data set. Number of features when each of the classification techniques has performed the best, along with the best performance, is also shown in this figures.
9.
International Journal of
Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 91-101 © IAEME 99 Figure 4.5: Impact of feature selection on F-measure (R8 data set, TFIDF representation) Figure 4.6: Impact of feature selection on F-measure (R8 data set, bag-of-words representation) It can be seen that, random forest achieves the best results for both the data sets. However, the best results are obtained for bag-of-words representation in case of R8 data set while in case of WebKB data set, best results are achieved for TFIDF representation. 5. CONCLUSIONS This paper addresses the task of classifying web pages using various classification techniques. Performance of KNN, NB, SVM, CART, RF and PSO is compared for different possible representation of web pages. Among all the methods, Random Forest (RF) gives best overall result. Results also demonstrate that the performance of the classifier is affected by the representation used.
10.
International Journal of
Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 91-101 © IAEME 100 Figure 4.7: Best F-measure (Boolean representation) Figure 4.8: Best F-measure (TFIDF representation) Figure 4.9: Best F-measure (Bag-of-words representation) It can be seen from the results that different classification techniques perform best for different representation of the web pages. This implies that there is no single representation which works best for all the classification techniques. One should select the representation based on the techniques to be used. Impact of feature selection is also studied in the paper and results show that selecting right number of features definitely improves the performance of the classifier.
11.
International Journal of
Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 5, May (2014), pp. 91-101 © IAEME 101 REFERENCES [1] Thair Nu Phyu, “Survey of Classification Techniques in Data Mining”, Proceedings of the International MultiConference of Engineers and Computer Scientists, vol. I, 2009. [2] Bing Liu, Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications), Springer-Verlag New York, Inc., Secaucus, NJ, 2006. [3] R.C.Eberhart, J.Kennedy, “A new optimizer using particle swarm theory”, Proceedings of the 6th Symposium MicroMachine and Human Science, IEEE Press, Los Alamitos, CA, October 1995, pp. 39-43. [4] M. A. Nayak, “A comparative study of web page classification techniques," GIT Journal of Engineering and Technology, vol. 6, 2013. [5] J. Materna, “Automatic web page classification," 2008. [6] W.-K. N. Aixin Sun, Ee-Peng Lim, “Web classification using support vector machine." Proceedings of the fourth international workshop on Web information and data management - WIDM '02, 2002. [7] R.-C. Chen and C.-H. Hsieh, “Web page classification based on a support vector machine using a weighted vote schema," Expert Systems with Applications, vol. 31, 2006 [8] Breiman, L. and Friedman, J. H. and Olshen, R. A. and Stone, "Classification and Regression Trees", 1984. [9] L.-b. X. Yong Zhang, Bin Fan, “Web page classification based-on a least square support vector machine with latent semantic analysis," Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008. [10] D. Radha Damodaram, “Phishing website detection and optimization using particle swarm optimization technique," 2011. [11] A. K. J. Sarita Mahapatra and B. Naik, “Performance evaluation of pso based classifier for classification of multidimensional data with variation of pso parameters in knowledge discovery database," vol. 34, 2011. [12] D. Z. Ziqiang Wang, Qingzhou Zhang, “A pso-based web document classification algorithm," Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD2007), 2007. [13] E. T. De Falco, A. Della Cioppa, “Facing classification problems with particles warm optimization," Applied Soft Computing, vol. 7, 2007. [14] Jiawei Han, Micheline Kamber and Jian Pei, “Data mining: concepts and techniques”, Morgan Kaufmann, 2006. [15] R. Manickam, D. Boominath and V. Bhuvaneswari, “An Analysis of Data Mining: Past, Present and Future”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 1, 2012, pp. 1 - 9, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [16] Sandip S. Patil and Asha P. Chaudhari, “Classification of Emotions from Text using SVM Based Opinion Mining”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 1, 2012, pp. 330 - 338, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [17] Prof. Sindhu P Menon and Dr. Nagaratna P Hegde, “Research on Classification Algorithms and its Impact on Web Mining”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 4, 2013, pp. 495 - 504, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [18] Priyank Thakkar, Samir Kariya and K Kotecha, “Web Page Clustering using Cemetery Organization Behavior of Ants”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 5, Issue 1, 2014, pp. 7 - 17, ISSN Print: 0976-6480, ISSN Online: 0976-6499. [19] Alamelu Mangai J, Santhosh Kumar V and Sugumaran V, “Recent Research in Web Page Classification – A Review”, International Journal of Computer Engineering & Technology (IJCET), Volume 1, Issue 1, 2010, pp. 112 - 122, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.
Descargar ahora