This document proposes a novel approach to improve user search results using feedback sessions. It first clusters feedback sessions containing clicked and unclicked URLs using the Fuzzy c-means clustering algorithm. It then generates "pseudo-documents" to represent each feedback session cluster. This reflects the information needs of users within each cluster. It evaluates the performance of restructured search results using a "Classified Average Precision" metric. The key steps are: 1) Clustering feedback sessions with Fuzzy c-means, 2) Creating pseudo-documents for each cluster, 3) Evaluating results using Classified Average Precision. Fuzzy c-means allows URLs to belong to multiple clusters, reflecting uncertain user needs.
Seeds Affinity Propagation Based on Text ClusteringIJRES Journal
The objective is to find among all partitions of the data set, best publishing according to some quality measure. Affinity propagation is a low error, high speed, flexible, and remarkably simple clustering algorithm that may be used in forming teams of participants for business simulations and experiential exercises, and in organizing participant’s preferences for the parameters of simulations. This paper proposes an efficient Affinity Propagation algorithm that guarantees the same clustering result as the original algorithm after convergence. The heart of our approach is (1) to prune unnecessary message exchanges in the iterations and (2) to compute the convergence values of pruned messages after the iterations to determine clusters.
Neural Network Based Context Sensitive Sentiment AnalysisEditor IJCATR
Social media communication is evolving more in these days. Social networking site is being rapidly increased in recent years, which provides platform to connect people all over the world and share their interests. The conversation and the posts available in social media are unstructured in nature. So sentiment analysis will be a challenging work in this platform. These analyses are mostly performed in machine learning techniques which are less accurate than neural network methodologies. This paper is based on sentiment classification using Competitive layer neural networks and classifies the polarity of a given text whether the expressed opinion in the text is positive or negative or neutral. It determines the overall topic of the given text. Context independent sentences and implicit meaning in the text are also considered in polarity classification.
NLP Project: Machine Comprehension Using Attention-Based LSTM Encoder-Decoder...Eugene Nho
Machine comprehension remains a challenging open area of research. While many question answering models have been explored for existing datasets, little work has been done with the newly released MS MARCO dataset, which mirrors the reality much more closely and poses many unique challenges. We explore an end-to-end neural architecture with attention mechanisms for comprehending relevant information and generating text answers for MS MARCO.
Evolving CSP Algorithm in Predicting the Path Loss of Indoor Propagation ModelsEditor IJCATR
Constraint programming is the study of system which is based on constraints. The solution of a constraint satisfaction problem is a set of
variable value assignments, which satisfies all members of the set of constraints in the CSP. In this paper the application of constraint satisfaction
programming is used in predicting the path loss of various indoor propagation models using chronological backtrack algorithm, which is basic
algorithm of CSP. After predicting the path loss at different set of parameters such as frequencies (f), floor attenuation factor (FAF), path loss
coefficient (n), we find the optimum set of parameter frequency (f), floor attenuation factor (FAF), path loss coefficient(n) at which the path loss is
minimum. The Branch and bound algorithm is used to optimize the constraint satisfaction problem.
A Combined Approach for Feature Subset Selection and Size Reduction for High ...IJERA Editor
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.
Modeling Text Independent Speaker Identification with Vector QuantizationTELKOMNIKA JOURNAL
Speaker identification is one of the most important technologies nowadays. Many fields such as
bioinformatics and security are using speaker identification. Also, almost all electronic devices are using
this technology too. Based on number of text, speaker identification divided into text dependent and text
independent. On many fields, text independent is mostly used because number of text is unlimited. So, text
independent is generally more challenging than text dependent. In this research, speaker identification text
independent with Indonesian speaker data was modelled with Vector Quantization (VQ). In this research
VQ with K-Means initialization was used. K-Means clustering also was used to initialize mean and
Hierarchical Agglomerative Clustering was used to identify K value for VQ. The best VQ accuracy was
59.67% when k was 5. According to the result, Indonesian language could be modelled by VQ. This
research can be developed using optimization method for VQ parameters such as Genetic Algorithm or
Particle Swarm Optimization.
Seeds Affinity Propagation Based on Text ClusteringIJRES Journal
The objective is to find among all partitions of the data set, best publishing according to some quality measure. Affinity propagation is a low error, high speed, flexible, and remarkably simple clustering algorithm that may be used in forming teams of participants for business simulations and experiential exercises, and in organizing participant’s preferences for the parameters of simulations. This paper proposes an efficient Affinity Propagation algorithm that guarantees the same clustering result as the original algorithm after convergence. The heart of our approach is (1) to prune unnecessary message exchanges in the iterations and (2) to compute the convergence values of pruned messages after the iterations to determine clusters.
Neural Network Based Context Sensitive Sentiment AnalysisEditor IJCATR
Social media communication is evolving more in these days. Social networking site is being rapidly increased in recent years, which provides platform to connect people all over the world and share their interests. The conversation and the posts available in social media are unstructured in nature. So sentiment analysis will be a challenging work in this platform. These analyses are mostly performed in machine learning techniques which are less accurate than neural network methodologies. This paper is based on sentiment classification using Competitive layer neural networks and classifies the polarity of a given text whether the expressed opinion in the text is positive or negative or neutral. It determines the overall topic of the given text. Context independent sentences and implicit meaning in the text are also considered in polarity classification.
NLP Project: Machine Comprehension Using Attention-Based LSTM Encoder-Decoder...Eugene Nho
Machine comprehension remains a challenging open area of research. While many question answering models have been explored for existing datasets, little work has been done with the newly released MS MARCO dataset, which mirrors the reality much more closely and poses many unique challenges. We explore an end-to-end neural architecture with attention mechanisms for comprehending relevant information and generating text answers for MS MARCO.
Evolving CSP Algorithm in Predicting the Path Loss of Indoor Propagation ModelsEditor IJCATR
Constraint programming is the study of system which is based on constraints. The solution of a constraint satisfaction problem is a set of
variable value assignments, which satisfies all members of the set of constraints in the CSP. In this paper the application of constraint satisfaction
programming is used in predicting the path loss of various indoor propagation models using chronological backtrack algorithm, which is basic
algorithm of CSP. After predicting the path loss at different set of parameters such as frequencies (f), floor attenuation factor (FAF), path loss
coefficient (n), we find the optimum set of parameter frequency (f), floor attenuation factor (FAF), path loss coefficient(n) at which the path loss is
minimum. The Branch and bound algorithm is used to optimize the constraint satisfaction problem.
A Combined Approach for Feature Subset Selection and Size Reduction for High ...IJERA Editor
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.
Modeling Text Independent Speaker Identification with Vector QuantizationTELKOMNIKA JOURNAL
Speaker identification is one of the most important technologies nowadays. Many fields such as
bioinformatics and security are using speaker identification. Also, almost all electronic devices are using
this technology too. Based on number of text, speaker identification divided into text dependent and text
independent. On many fields, text independent is mostly used because number of text is unlimited. So, text
independent is generally more challenging than text dependent. In this research, speaker identification text
independent with Indonesian speaker data was modelled with Vector Quantization (VQ). In this research
VQ with K-Means initialization was used. K-Means clustering also was used to initialize mean and
Hierarchical Agglomerative Clustering was used to identify K value for VQ. The best VQ accuracy was
59.67% when k was 5. According to the result, Indonesian language could be modelled by VQ. This
research can be developed using optimization method for VQ parameters such as Genetic Algorithm or
Particle Swarm Optimization.
MIM (Mobile Instant Messaging) Classification using Term Frequency-Inverse Do...IJMREMJournal
The focus of the study is based on binary sentiment classification on aspect level to develop a hybrid sentiment
classification framework of WhatsApp MIMs (Mobile Instant Messages). It has been carried out into two phases
i.e. training phase and testing phase. The training phase, 75% data is used for training dataset. Pre-processing
techniques like tokenization, removing stop words, case normalization, removing punctuation and stemming are
applied to acquire cleaner dataset to be used as input. The output is sent to the classifier after applying TF-IDF
for feature weighting. In the second phase, the classifier is trial with 25% testing dataset. Bernoulli’s Naïve
Bayesian classifier which is an improved form of traditional Naïve Bayesian classifier is used to classify
sentiments. There are 417 messages in total where 244 and 173 are classified as positive and negative
respectively. The proposed model has achieved satisfactory results up to 81.73% in comparison to base-line
classification model by getting 12 points higher accuracy i.e. 69.23%.
IDENTIFICATION AND INVESTIGATION OF THE USER SESSION FOR LAN CONNECTIVITY VIA...ijcseit
This paper mainly presents some technical discussions on the identification and analyze of “LAN usersessions”.
The identification of a user-session is non trivial. Classical methods approaches rely on
threshold based mechanisms. Threshold based techniques are very sensitive to the value chosen for the
threshold, which may be difficult to set correctly. Clustering techniques are used to define a novel
methodology to identify LAN user-sessions without requiring an a priori definition of threshold values. We
have defined a clustering based approach in detail, and also we discussed positive and negative of this
approach, and we apply it to real traffic traces. The proposed methodology is applied to artificially
generated traces to evaluate its benefits against traditional threshold based approaches. We also analyzed
the characteristics of user-sessions extracted by the clustering methodology from real traces and study
their statistical properties.
Performance Comparison of Cluster based and Threshold based Algorithms for De...Eswar Publications
In mobile ad-hoc networks (MANET), the movement of the nodes may quickly change the networks topology resulting in the increase of the overhead message in topology maintenance. The nodes communicate with each other by exchanging the hello packet and constructing the neighbor list at each node. MANET is vulnerable to attacks such as black hole attack, gray hole attack, worm hole attack and sybil attack. A black hole attack makes a serious impact on routing, packet delivery ratio, throughput, and end to end delay of packets. In this paper, the performance comparison of clustering based and threshold based algorithms for detection and prevention of
cooperative in MANETs is examined. In this study every node is monitored by its own cluster head (CH), while server (SV) monitors the entire network by channel overhearing method. Server computes the trust value based on sent and receive count of packets of the receiver node. It is implemented using AODV routing protocol in the NS2 simulations. The results are obtained by comparing the performance of clustering based and threshold based methods by varying the concentration of black hole nodes and are analyzed in terms of throughput,
packet delivery ratio. The results demonstrate that the threshold based method outperforms the clustering based method in terms of throughput, packet delivery ratio and end to end delay.
Sentiment analysis focuses on analyzing web documents, especially user-generated content such as product
reviews, to identify opinionated documents, sentences and opinion holders. Most of the time classifiers trained in one
domain do not perform well in another domain. The existing approaches do not detect sentiment and topics
simultaneously. Sentiments may differ with topics. Our proposed model called Joint Sentiment Topic (JST) model to
detect sentiments and topics simultaneously from text. This model is based on Gibbs sampling algorithm. Besides,
unlike supervised approaches to opinion mining which often fail to produce good performance when shifting to other
domains, the semi-supervised nature of JST makes it highly portable to other domains. JST model performs better
when compared to existing supervised approaches.
Conceptual similarity measurement algorithm for domain specific ontology[Zac Darcy
This paper presents the similarity measurement algorithm for domain specific terms collected in the
ontology based data integration system. This similarity measurement algorithm can be used in ontology
mapping and query service of
ontology based data integration sy
stem. In this paper, we focus
o
n the web
query service to apply
this proposed algorithm
. Concepts similarity is important for web query service
because the words in user input query are not
same wholly with the concepts in
ontology. So, we need to
extract the possible concepts that are match or related to the input words with the help of machine readable
dictionary WordNet. Sometimes, we use the generated mapping rules in query generation procedure for
some words that canno
t be
confirmed the similarity of these words
by WordNet. We prove the effect
of this
algorithm with two degree semantic result of web minin
g by generating
the concepts results obtained form
the input query
TEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTIONijistjournal
The user generated content on the web grows rapidly in this emergent information age. The evolutionary changes in technology make use of such information to capture only the user’s essence and finally the useful information are exposed to information seekers. Most of the existing research on text information processing, focuses in the factual domain rather than the opinion domain. In this paper we detect online hotspot forums by computing sentiment analysis for text data available in each forum. This approach analyses the forum text data and computes value for each word of text. The proposed approach combines K-means clustering and Support Vector Machine with PSO (SVM-PSO) classification algorithm that can be used to group the forums into two clusters forming hotspot forums and non-hotspot forums within the current time span. The proposed system accuracy is compared with the other classification algorithms such as Naïve Bayes, Decision tree and SVM. The experiment helps to identify that K-means and SVM-PSO together achieve highly consistent results.
The project re-implements the architecture of the paper Reasoning with Neural Tensor Networks for Knowledge Base Completion in Torch framework, achieving similar accuracy results with an elegant implementation in a modern language.
Below are some links for further details:
https://github.com/agarwal-shubham/Reasoning-Over-Knowledge-Base
http://darsh510.github.io/IREPROJ/
FAST FUZZY FEATURE CLUSTERING FOR TEXT CLASSIFICATION cscpconf
Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text
classification. In this paper, Fast Fuzzy Feature clustering for text classification is proposed. It
is based on the framework proposed by Jung-Yi Jiang, Ren-Jia Liou and Shie-Jue Lee in 2011.
The word in the feature vector of the document is grouped into the cluster in less iteration. The
numbers of iterations required to obtain cluster centers are reduced by transforming clusters
center dimension from n-dimension to 2-dimension. Principle Component Analysis with slit
change is used for dimension reduction. Experimental results show that, this method improve
the performance by significantly reducing the number of iterations required to obtain the cluster
center. The same is being verified with three benchmark datasets
A Survey on Sentiment Categorization of Movie ReviewsEditor IJMTER
Sentiment categorization is a process of mining user generated text content and determine
the sentiment of the users towards that particular thing. It is the approach of detecting the sentiment of
the author in regard to some topics. It also known as sentiment detection, sentiment analysis and opinion
mining. It is very useful for movie production companies that interested in knowing how users feel
about their movies. For example word “excellent” indicates that the review gives positive emotion about
particular movie. The same applies to movies, songs, cars, holiday destinations, Political parties, social
network sites, web blogs, discussion forum and so on. Sentiment categorization can be carried out by
using three approaches. First, Supervised machine learning based text classifier on Naïve Bayes,
Maximum Entropy, SVM, kNN classifier, hidden marcov model. Second, Unsupervised Semantic
Orientation scheme of extracting relevant N-grams of the text and then labelling. Third, SentiWordNet
based publicly available library.
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.
Summarization using ntc approach based on keyword extraction for discussion f...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
STUDY OF DISTANCE MEASUREMENT TECHNIQUES IN CONTEXT TO PREDICTION MODEL OF WE...ijscai
Internet is the boon in modern era as every organization uses it for dissemination of information and ecommerce
related applications. Sometimes people of organization feel delay while accessing internet in
spite of proper bandwidth. Prediction model of web caching and prefetching is an ideal solution of this
delay problem. Prediction model analysing history of internet user from server raw log files and determine
future sequence of web objects and placed all web objects to nearer to the user so access latency could be
reduced to some extent and problem of delay is to be solved. To determine sequence of future web objects,
it is necessary to determine proximity of one web object with other by identifying proper distance metric
technique related to web caching and prefetching. This paper studies different distance metric techniques
and concludes that bio informatics based distance metric techniques are ideal in context to Web Caching
and Web Prefetching
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.
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.
MIM (Mobile Instant Messaging) Classification using Term Frequency-Inverse Do...IJMREMJournal
The focus of the study is based on binary sentiment classification on aspect level to develop a hybrid sentiment
classification framework of WhatsApp MIMs (Mobile Instant Messages). It has been carried out into two phases
i.e. training phase and testing phase. The training phase, 75% data is used for training dataset. Pre-processing
techniques like tokenization, removing stop words, case normalization, removing punctuation and stemming are
applied to acquire cleaner dataset to be used as input. The output is sent to the classifier after applying TF-IDF
for feature weighting. In the second phase, the classifier is trial with 25% testing dataset. Bernoulli’s Naïve
Bayesian classifier which is an improved form of traditional Naïve Bayesian classifier is used to classify
sentiments. There are 417 messages in total where 244 and 173 are classified as positive and negative
respectively. The proposed model has achieved satisfactory results up to 81.73% in comparison to base-line
classification model by getting 12 points higher accuracy i.e. 69.23%.
IDENTIFICATION AND INVESTIGATION OF THE USER SESSION FOR LAN CONNECTIVITY VIA...ijcseit
This paper mainly presents some technical discussions on the identification and analyze of “LAN usersessions”.
The identification of a user-session is non trivial. Classical methods approaches rely on
threshold based mechanisms. Threshold based techniques are very sensitive to the value chosen for the
threshold, which may be difficult to set correctly. Clustering techniques are used to define a novel
methodology to identify LAN user-sessions without requiring an a priori definition of threshold values. We
have defined a clustering based approach in detail, and also we discussed positive and negative of this
approach, and we apply it to real traffic traces. The proposed methodology is applied to artificially
generated traces to evaluate its benefits against traditional threshold based approaches. We also analyzed
the characteristics of user-sessions extracted by the clustering methodology from real traces and study
their statistical properties.
Performance Comparison of Cluster based and Threshold based Algorithms for De...Eswar Publications
In mobile ad-hoc networks (MANET), the movement of the nodes may quickly change the networks topology resulting in the increase of the overhead message in topology maintenance. The nodes communicate with each other by exchanging the hello packet and constructing the neighbor list at each node. MANET is vulnerable to attacks such as black hole attack, gray hole attack, worm hole attack and sybil attack. A black hole attack makes a serious impact on routing, packet delivery ratio, throughput, and end to end delay of packets. In this paper, the performance comparison of clustering based and threshold based algorithms for detection and prevention of
cooperative in MANETs is examined. In this study every node is monitored by its own cluster head (CH), while server (SV) monitors the entire network by channel overhearing method. Server computes the trust value based on sent and receive count of packets of the receiver node. It is implemented using AODV routing protocol in the NS2 simulations. The results are obtained by comparing the performance of clustering based and threshold based methods by varying the concentration of black hole nodes and are analyzed in terms of throughput,
packet delivery ratio. The results demonstrate that the threshold based method outperforms the clustering based method in terms of throughput, packet delivery ratio and end to end delay.
Sentiment analysis focuses on analyzing web documents, especially user-generated content such as product
reviews, to identify opinionated documents, sentences and opinion holders. Most of the time classifiers trained in one
domain do not perform well in another domain. The existing approaches do not detect sentiment and topics
simultaneously. Sentiments may differ with topics. Our proposed model called Joint Sentiment Topic (JST) model to
detect sentiments and topics simultaneously from text. This model is based on Gibbs sampling algorithm. Besides,
unlike supervised approaches to opinion mining which often fail to produce good performance when shifting to other
domains, the semi-supervised nature of JST makes it highly portable to other domains. JST model performs better
when compared to existing supervised approaches.
Conceptual similarity measurement algorithm for domain specific ontology[Zac Darcy
This paper presents the similarity measurement algorithm for domain specific terms collected in the
ontology based data integration system. This similarity measurement algorithm can be used in ontology
mapping and query service of
ontology based data integration sy
stem. In this paper, we focus
o
n the web
query service to apply
this proposed algorithm
. Concepts similarity is important for web query service
because the words in user input query are not
same wholly with the concepts in
ontology. So, we need to
extract the possible concepts that are match or related to the input words with the help of machine readable
dictionary WordNet. Sometimes, we use the generated mapping rules in query generation procedure for
some words that canno
t be
confirmed the similarity of these words
by WordNet. We prove the effect
of this
algorithm with two degree semantic result of web minin
g by generating
the concepts results obtained form
the input query
TEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTIONijistjournal
The user generated content on the web grows rapidly in this emergent information age. The evolutionary changes in technology make use of such information to capture only the user’s essence and finally the useful information are exposed to information seekers. Most of the existing research on text information processing, focuses in the factual domain rather than the opinion domain. In this paper we detect online hotspot forums by computing sentiment analysis for text data available in each forum. This approach analyses the forum text data and computes value for each word of text. The proposed approach combines K-means clustering and Support Vector Machine with PSO (SVM-PSO) classification algorithm that can be used to group the forums into two clusters forming hotspot forums and non-hotspot forums within the current time span. The proposed system accuracy is compared with the other classification algorithms such as Naïve Bayes, Decision tree and SVM. The experiment helps to identify that K-means and SVM-PSO together achieve highly consistent results.
The project re-implements the architecture of the paper Reasoning with Neural Tensor Networks for Knowledge Base Completion in Torch framework, achieving similar accuracy results with an elegant implementation in a modern language.
Below are some links for further details:
https://github.com/agarwal-shubham/Reasoning-Over-Knowledge-Base
http://darsh510.github.io/IREPROJ/
FAST FUZZY FEATURE CLUSTERING FOR TEXT CLASSIFICATION cscpconf
Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text
classification. In this paper, Fast Fuzzy Feature clustering for text classification is proposed. It
is based on the framework proposed by Jung-Yi Jiang, Ren-Jia Liou and Shie-Jue Lee in 2011.
The word in the feature vector of the document is grouped into the cluster in less iteration. The
numbers of iterations required to obtain cluster centers are reduced by transforming clusters
center dimension from n-dimension to 2-dimension. Principle Component Analysis with slit
change is used for dimension reduction. Experimental results show that, this method improve
the performance by significantly reducing the number of iterations required to obtain the cluster
center. The same is being verified with three benchmark datasets
A Survey on Sentiment Categorization of Movie ReviewsEditor IJMTER
Sentiment categorization is a process of mining user generated text content and determine
the sentiment of the users towards that particular thing. It is the approach of detecting the sentiment of
the author in regard to some topics. It also known as sentiment detection, sentiment analysis and opinion
mining. It is very useful for movie production companies that interested in knowing how users feel
about their movies. For example word “excellent” indicates that the review gives positive emotion about
particular movie. The same applies to movies, songs, cars, holiday destinations, Political parties, social
network sites, web blogs, discussion forum and so on. Sentiment categorization can be carried out by
using three approaches. First, Supervised machine learning based text classifier on Naïve Bayes,
Maximum Entropy, SVM, kNN classifier, hidden marcov model. Second, Unsupervised Semantic
Orientation scheme of extracting relevant N-grams of the text and then labelling. Third, SentiWordNet
based publicly available library.
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.
Summarization using ntc approach based on keyword extraction for discussion f...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
STUDY OF DISTANCE MEASUREMENT TECHNIQUES IN CONTEXT TO PREDICTION MODEL OF WE...ijscai
Internet is the boon in modern era as every organization uses it for dissemination of information and ecommerce
related applications. Sometimes people of organization feel delay while accessing internet in
spite of proper bandwidth. Prediction model of web caching and prefetching is an ideal solution of this
delay problem. Prediction model analysing history of internet user from server raw log files and determine
future sequence of web objects and placed all web objects to nearer to the user so access latency could be
reduced to some extent and problem of delay is to be solved. To determine sequence of future web objects,
it is necessary to determine proximity of one web object with other by identifying proper distance metric
technique related to web caching and prefetching. This paper studies different distance metric techniques
and concludes that bio informatics based distance metric techniques are ideal in context to Web Caching
and Web Prefetching
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.
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.
Unplanned startup launch: Product Hunt vs Fast Company vs Gizmodo. Source eff...Alessandro Marchesini
We shared our landing page in a few Facebook groups to test our startup idea and gain feedbacks. In the next days we ended up on Product Hunt, Fast Company and Gizmodo. We weren't ready for it and ... we'd like to share with you what we learned during the unplanned launch of Earlyclaim.com
Now knowledge pre-processing, model and reasoning issues, power metrics, quality
issues, post-processing of discovered structures, visualization, and on-line change is best challenge.
In this paper Neural Network based forecasting of stock prices of selected sectors under Bombay
Stock Exchange show that neural networks have the power to predict prices albeit the volatility in the
markets[9]. The motivation for the development of neural network technology stemmed from the
desire to develop an artificial system that could perform “intelligent" tasks similar to those performed
by the human brain. Artificial Neural Networks are being counted as the wave of the future in
computing. They are indeed self-learning mechanisms which don’t require the traditional skills of a
programmer. Back propagation is one of the approaches to implement concept of neural networks.
Back propagation is a form of supervised learning for multi-layer nets. Error data at the output layer
is back propagated to earlier ones, allowing incoming weights to these layers to be updated. It is most
often used as training algorithm in current neural network applications. In this paper, we apply data
mining technology to stock market in order to research the trend of price; it aims to predict the future
trend of the stock market and the fluctuation of price. This paper points out the shortage that exists in
current traditional statistical analysis in the stock, then makes use of BP neural network algorithm to
predict the stock market by establishing a three-tier structure of the neural network, namely input
layer, hidden layer and output layer. Finally, we get a better predictive model to improve forecast
accuracy
Impact and Dynamics of Centralization in Transportation Cost of Cement Bag’s ...IJMER
The goal of many research efforts cognate to supply chain management is to propose
mechanisms to reduce operational costs. Inventory holding and conveyance costs are regarded as the
most paramount operational costs in inventory management. Many researches in supply chain
management only consider the inventory cost as a criterion to decide replenishment policy. In the
replenishment process, in juxtaposition of the inventory cost, the conveyance cost is a major cost factor
which affects the shipment size. Thus in this research work the conveyance cost is additionally considered
to minimize the inventory cost.
Two models are studied: when the retailers make decisions independently i.e. Decentralized decision
model and when the retailers are branches of the same firm i.e. Centralized decision model to determine
the best solution to minimize costs.
Evaluation of Tensile and Flexural Properties of Polymer Matrix CompositesIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Education set for collecting and visualizing data using sensor system based ...IJMER
This article presents the issues of the wireless sensor measuring systems design which might
be used in education process of computer science faculty. The work shows the integration of a simple
measuring system, data management system, visual system and the hardware. Education set is designed
to consolidate knowledge in many fields of computer science and the interdependence between them, as
programming techniques, database, Web server, communications protocols, software and hardware.
Presented measuring sensor system consists of a number of measurement nodes, whose role is to
provide information about certain desirable characteristics, warning against natural hazards or
violation of the physical safety. An important part of the sensor system is a measuring subsystem and
the collecting measurement data subsystem. The article presents the temperature measurement sensor
system concepts and measurement data storage and visualization methods
Scalable Rough C-Means clustering using Firefly algorithm..................................................................1
Abhilash Namdev and B.K. Tripathy
Significance of Embedded Systems to IoT................................................................................................. 15
P. R. S. M. Lakshmi, P. Lakshmi Narayanamma and K. Santhi Sri
Cognitive Abilities, Information Literacy Knowledge and Retrieval Skills of Undergraduates: A
Comparison of Public and Private Universities in Nigeria ........................................................................ 24
Janet O. Adekannbi and Testimony Morenike Oluwayinka
Risk Assessment in Constructing Horseshoe Vault Tunnels using Fuzzy Technique................................ 48
Erfan Shafaghat and Mostafa Yousefi Rad
Evaluating the Adoption of Deductive Database Technology in Augmenting Criminal Intelligence in
Zimbabwe: Case of Zimbabwe Republic Police......................................................................................... 68
Mahlangu Gilbert, Furusa Samuel Simbarashe, Chikonye Musafare and Mugoniwa Beauty
Analysis of Petrol Pumps Reachability in Anand District of Gujarat ....................................................... 77
Nidhi Arora
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Abstract Learning Analytics by nature relies on computational information processing activities intended to extract from raw data some interesting aspects that can be used to obtain insights into the behaviors of learners, the design of learning experiences, etc. There is a large variety of computational techniques that can be employed, all with interesting properties, but it is the interpretation of their results that really forms the core of the analytics process. As a rising subject, data mining and business intelligence are playing an increasingly important role in the decision support activity of every walk of life. The Variance Rover System (VRS) mainly focused on the large data sets obtained from online web visiting and categorizing this into clusters according some similarity and the process of predicting customer behavior and selecting actions to influence that behavior to benefit the company, so as to take optimized and beneficial decisions of business expansion. Keywords: Analytics, Business intelligence, Clustering, Data Mining, Standard K-means, Optimized K-means
A Novel Approach for Travel Package Recommendation Using Probabilistic Matrix...IJSRD
Recent years have witnessed an increased interest on recommendation system. Classification techniques are supervised that has classified data item into predefined class. An existing system unsupervised constraints are automatically derived from two hidden Tourist area season topic (TAST) for tourist in travel group. It used to an alternating TRAST model are unique characteristic for the travel data and cocktail.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Efficient Filtering Algorithms for Location- Aware Publish/subscribeIJSRD
Location-based services have been mostly used in many systems. preceding systems uses a pull model or user-initiated model, where a user arrival a query to a server which gives response with location-aware answers. To offer outcomes to users with fast responses, a push model or server-initiated model is flattering an important computing model in the next-generation location-based services. In the push model, subscribers arrive spatio-textual subscriptions to fastening their curiosities, and publishers send spatio-textual messages. It is used for a high-performance location-aware publish/subscribe system to send publishers’ messages to valid subscribers. In this paper, we find the exploration happenstances that start in manipulative a location-aware publish/subscribe system. We recommend an R-tree based index by merging textual descriptions into R-tree nodes. We design efficient filtering algorithms and effective pruning techniques to accomplish high performance. This method can support likewise conjunctive queries and ranking queries.
With the surge in modern research focus towards Pervasive Computing, lot of techniques and challenges
needs to be addressed so as to effectively create smart spaces and achieve miniaturization. In the process of
scaling down to compact devices, the real things to ponder upon are the Information Retrieval challenges.
In this work, we discuss the aspects of multimedia which makes information access challenging. An
Example Pattern Recognition scenario is presented and the mathematical techniques that can be used to
model uncertainty are also presented for developing a system that can sense, compute and communicate in
a way that can make human life easy with smart objects assisting from around his surroundings.
Dynamic thresholding on speech segmentationeSAT Journals
Abstract Word is the preferred and natural unit of speech, because word units have well defined acoustic representation. This paper presents several dynamic thresholding approaches for segmenting continuous Bangla speech sentences into words/sub-words. We have proposed three efficient methods for speech segmentation: two of them are usually used in pattern classification (i.e., k-means and FCM clustering) and one of them is used in image segmentation (i.e., Otsu’s thresholding method). We also used new approaches blocking black area and boundary detection techniques to properly detect word boundaries in continuous speech and label the entire speech sentence into a sequence of words/sub-words. K-Means and FCM clustering methods produce better segmentation results than that of Otsu’s Method. All the algorithms and methods used in this research are implemented in MATLAB and the proposed system achieved the average segmentation accuracy of 94% approximately. Keywords: Blocking Black Area, Clustering, Dynamic Thresholding, Fuzzy Logic and Speech Segmentation.
Similar to A Novel Approach for User Search Results Using Feedback Sessions (20)
A Study on Translucent Concrete Product and Its Properties by Using Optical F...IJMER
- Translucent concrete is a concrete based material with light-transferring properties,
obtained due to embedded light optical elements like Optical fibers used in concrete. Light is conducted
through the concrete from one end to the other. This results into a certain light pattern on the other
surface, depending on the fiber structure. Optical fibers transmit light so effectively that there is
virtually no loss of light conducted through the fibers. This paper deals with the modeling of such
translucent or transparent concrete blocks and panel and their usage and also the advantages it brings
in the field. The main purpose is to use sunlight as a light source to reduce the power consumption of
illumination and to use the optical fiber to sense the stress of structures and also use this concrete as an
architectural purpose of the building
Developing Cost Effective Automation for Cotton Seed DelintingIJMER
A low cost automation system for removal of lint from cottonseed is to be designed and
developed. The setup consists of stainless steel drum with stirrer in which cottonseeds having lint is mixed
with concentrated sulphuric acid. So lint will get burn. This lint free cottonseed treated with lime water to
neutralize acidic nature. After water washing this cottonseeds are used for agriculter purpose
Study & Testing Of Bio-Composite Material Based On Munja FibreIJMER
The incorporation of natural fibres such as munja fiber composites has gained
increasing applications both in many areas of Engineering and Technology. The aim of this study is to
evaluate mechanical properties such as flexural and tensile properties of reinforced epoxy composites.
This is mainly due to their applicable benefits as they are light weight and offer low cost compared to
synthetic fibre composites. Munja fibres recently have been a substitute material in many weight-critical
applications in areas such as aerospace, automotive and other high demanding industrial sectors. In
this study, natural munja fibre composites and munja/fibreglass hybrid composites were fabricated by a
combination of hand lay-up and cold-press methods. A new variety in munja fibre is the present work
the main aim of the work is to extract the neat fibre and is characterized for its flexural characteristics.
The composites are fabricated by reinforcing untreated and treated fibre and are tested for their
mechanical, properties strictly as per ASTM procedures.
Hybrid Engine (Stirling Engine + IC Engine + Electric Motor)IJMER
Hybrid engine is a combination of Stirling engine, IC engine and Electric motor. All these 3 are
connected together to a single shaft. The power source of the Stirling engine will be a Solar Panel. The aim of
this is to run the automobile using a Hybrid engine
Fabrication & Characterization of Bio Composite Materials Based On Sunnhemp F...IJMER
The present day technology demands eco-friendly developments. In this era the
composite material are playing a vital roal in different field of Engineering .The composite materials
are using as a principle materials. Nowaday the composite materials are utilizing as a important
component of engineering field .Where as the importance of the applications of composites is well
known, but thrust on the use of natural fibres in it for reinforcement has been given priority for some
times. But changing from synthetic fibres to natural fibres provides only half green-composites. A
partial green composite will be achieved if the matrix component is also eco-friendly. Keeping this in
view, a detailed literature surveyed has been carried out through various issues of the Journals
related to this field. The material systems used are sunnhemp fibres. Some epoxy and hardener has
been also added for stability and drying of the bio-composites. Various graphs and bar-charts are
super-imposed on each other for comparison among themselves and Graphs is plotted on MAT LAB
and ORIGIN 6.0 software. To determining tensile strengths, Various properties for different biocomposites
have been compared among themselves. Comparison of the behaviour of bio-composites of
this work has been also compare with other works. The bio-composites developed in this work are
likely to get applications in fall ceilings, partitions, bio-degradable packagings, automotive interiors,
sports things (e.g. rackets, nets, etc.), toys etc.
Geochemistry and Genesis of Kammatturu Iron Ores of Devagiri Formation, Sandu...IJMER
The Greenstone belts of Karnataka are enriched in BIFs in Dharwar craton, where Iron
formations are confined to the basin shelf, clearly separated from the deeper-water iron formation that
accumulated at the basin margin and flanking the marine basin. Geochemical data procured in terms of
major, trace and REE are plotted in various diagrams to interpret the genesis of BIFs. Al2O3, Fe2O3 (T),
TiO2, CaO, and SiO2 abundances and ratios show a wide variation. Ni, Co, Zr, Sc, V, Rb, Sr, U, Th,
ΣREE, La, Ce and Eu anomalies and their binary relationships indicate that wherever the terrigenous
component has increased, the concentration of elements of felsic such as Zr and Hf has gone up. Elevated
concentrations of Ni, Co and Sc are contributed by chlorite and other components characteristic of basic
volcanic debris. The data suggest that these formations were generated by chemical and clastic
sedimentary processes on a shallow shelf. During transgression, chemical precipitation took place at the
sediment-water interface, whereas at the time of regression. Iron ore formed with sedimentary structures
and textures in Kammatturu area, in a setting where the water column was oxygenated.
Experimental Investigation on Characteristic Study of the Carbon Steel C45 in...IJMER
In this paper, the mechanical characteristics of C45 medium carbon steel are investigated
under various working conditions. The main characteristic to be studied on this paper is impact toughness
of the material with different configurations and the experiment were carried out on charpy impact testing
equipment. This study reveals the ability of the material to absorb energy up to failure for various
specimen configurations under different heat treated conditions and the corresponding results were
compared with the analysis outcome
Non linear analysis of Robot Gun Support Structure using Equivalent Dynamic A...IJMER
Robot guns are being increasingly employed in automotive manufacturing to replace
risky jobs and also to increase productivity. Using a single robot for a single operation proves to be
expensive. Hence for cost optimization, multiple guns are mounted on a single robot and multiple
operations are performed. Robot Gun structure is an efficient way in which multiple welds can be done
simultaneously. However mounting several weld guns on a single structure induces a variety of
dynamic loads, especially during movement of the robot arm as it maneuvers to reach the weld
locations. The primary idea employed in this paper, is to model those dynamic loads as equivalent G
force loads in FEA. This approach will be on the conservative side, and will be saving time and
subsequently cost efficient. The approach of the paper is towards creating a standard operating
procedure when it comes to analysis of such structures, with emphasis on deploying various technical
aspects of FEA such as Non Linear Geometry, Multipoint Constraint Contact Algorithm, Multizone
meshing .
Static Analysis of Go-Kart Chassis by Analytical and Solid Works SimulationIJMER
This paper aims to do modelling, simulation and performing the static analysis of a go
kart chassis consisting of Circular beams. Modelling, simulations and analysis are performed using 3-D
modelling software i.e. Solid Works and ANSYS according to the rulebook provided by Indian Society of
New Era Engineers (ISNEE) for National Go Kart Championship (NGKC-14).The maximum deflection is
determined by performing static analysis. Computed results are then compared to analytical calculation,
where it is found that the location of maximum deflection agrees well with theoretical approximation but
varies on magnitude aspect.
In récent year various vehicle introduced in market but due to limitation in
carbon émission and BS Séries limitd speed availability vehicle in the market and causing of
environnent pollution over few year There is need to decrease dependancy on fuel vehicle.
bicycle is to be modified for optional in the future To implement new technique using change in
pedal assembly and variable speed gearbox such as planetary gear optimise speed of vehicle
with variable speed ratio.To increase the efficiency of bicycle for confortable drive and to
reduce torque appli éd on bicycle. we introduced epicyclic gear box in which transmission done
throgh Chain Drive (i.e. Sprocket )to rear wheel with help of Epicyclical gear Box to give
number of différent Speed during driving.To reduce torque requirent in the cycle with change in
the pedal mechanism
Integration of Struts & Spring & Hibernate for Enterprise ApplicationsIJMER
The proposal of this paper is to present Spring Framework which is widely used in
developing enterprise applications. Considering the current state where applications are developed using
the EJB model, Spring Framework assert that ordinary java beans(POJO) can be utilize with minimal
modifications. This modular framework can be used to develop the application faster and can reduce
complexity. This paper will highlight the design overview of Spring Framework along with its features that
have made the framework useful. The integration of multiple frameworks for an E-commerce system has
also been addressed in this paper. This paper also proposes structure for a website based on integration of
Spring, Hibernate and Struts Framework.
Microcontroller Based Automatic Sprinkler Irrigation SystemIJMER
Microcontroller based Automatic Sprinkler System is a new concept of using
intelligence power of embedded technology in the sprinkler irrigation work. Designed system replaces
the conventional manual work involved in sprinkler irrigation to automatic process. Using this system a
farmer is protected against adverse inhuman weather conditions, tedious work of changing over of
sprinkler water pipe lines & risk of accident due to high pressure in the water pipe line. Overall
sprinkler irrigation work is transformed in to a comfortableautomatic work. This system provides
flexibility & accuracy in respect of time set for the operation of a sprinkler water pipe lines. In present
work the author has designed and developed an automatic sprinkler irrigation system which is
controlled and monitored by a microcontroller interfaced with solenoid valves.
On some locally closed sets and spaces in Ideal Topological SpacesIJMER
In this paper we introduce and characterize some new generalized locally closed sets
known as
δ
ˆ
s-locally closed sets and spaces are known as
δ
ˆ
s-normal space and
δ
ˆ
s-connected space and
discussed some of their properties
Intrusion Detection and Forensics based on decision tree and Association rule...IJMER
This paper present an approach based on the combination of, two techniques using
decision tree and Association rule mining for Probe attack detection. This approach proves to be
better than the traditional approach of generating rules for fuzzy expert system by clustering methods.
Association rule mining for selecting the best attributes together and decision tree for identifying the
best parameters together to create the rules for fuzzy expert system. After that rules for fuzzy expert
system are generated using association rule mining and decision trees. Decision trees is generated for
dataset and to find the basic parameters for creating the membership functions of fuzzy inference
system. Membership functions are generated for the probe attack. Based on these rules we have
created the fuzzy inference system that is used as an input to neuro-fuzzy system. Fuzzy inference
system is loaded to neuro-fuzzy toolbox as an input and the final ANFIS structure is generated for
outcome of neuro-fuzzy approach. The experiments and evaluations of the proposed method were
done with NSL-KDD intrusion detection dataset. As the experimental results, the proposed approach
based on the combination of, two techniques using decision tree and Association rule mining
efficiently detected probe attacks. Experimental results shows better results for detecting intrusions as
compared to others existing methods
Natural Language Ambiguity and its Effect on Machine LearningIJMER
"Natural language processing" here refers to the use and ability of systems to process
sentences in a natural language such as English, rather than in a specialized artificial computer
language such as C++. The systems of real interest here are digital computers of the type we think of as
personal computers and mainframes. Of course humans can process natural languages, but for us the
question is whether digital computers can or ever will process natural languages. We have tried to
explore in depth and break down the types of ambiguities persistent throughout the natural languages
and provide an answer to the question “How it affects the machine translation process and thereby
machine learning as whole?” .
Today in era of software industry there is no perfect software framework available for
analysis and software development. Currently there are enormous number of software development
process exists which can be implemented to stabilize the process of developing a software system. But no
perfect system is recognized till yet which can help software developers for opting of best software
development process. This paper present the framework of skillful system combined with Likert scale. With
the help of Likert scale we define a rule based model and delegate some mass score to every process and
develop one tool name as MuxSet which will help the software developers to select an appropriate
development process that may enhance the probability of system success.
Material Parameter and Effect of Thermal Load on Functionally Graded CylindersIJMER
The present study investigates the creep in a thick-walled composite cylinders made
up of aluminum/aluminum alloy matrix and reinforced with silicon carbide particles. The distribution
of SiCp is assumed to be either uniform or decreasing linearly from the inner to the outer radius of
the cylinder. The creep behavior of the cylinder has been described by threshold stress based creep
law with a stress exponent of 5. The composite cylinders are subjected to internal pressure which is
applied gradually and steady state condition of stress is assumed. The creep parameters required to
be used in creep law, are extracted by conducting regression analysis on the available experimental
results. The mathematical models have been developed to describe steady state creep in the composite
cylinder by using von-Mises criterion. Regression analysis is used to obtain the creep parameters
required in the study. The basic equilibrium equation of the cylinder and other constitutive equations
have been solved to obtain creep stresses in the cylinder. The effect of varying particle size, particle
content and temperature on the stresses in the composite cylinder has been analyzed. The study
revealed that the stress distributions in the cylinder do not vary significantly for various combinations
of particle size, particle content and operating temperature except for slight variation observed for
varying particle content. Functionally Graded Materials (FGMs) emerged and led to the development
of superior heat resistant materials.
Energy Audit is the systematic process for finding out the energy conservation
opportunities in industrial processes. The project carried out studies on various energy conservation
measures application in areas like lighting, motors, compressors, transformer, ventilation system etc.
In this investigation, studied the technical aspects of the various measures along with its cost benefit
analysis.
Investigation found that major areas of energy conservation are-
1. Energy efficient lighting schemes.
2. Use of electronic ballast instead of copper ballast.
3. Use of wind ventilators for ventilation.
4. Use of VFD for compressor.
5. Transparent roofing sheets to reduce energy consumption.
So Energy Audit is the only perfect & analyzed way of meeting the Industrial Energy Conservation.
An Implementation of I2C Slave Interface using Verilog HDLIJMER
The focus of this paper is on implementation of Inter Integrated Circuit (I2C) protocol
following slave module for no data loss. In this paper, the principle and the operation of I2C bus protocol
will be introduced. It follows the I2C specification to provide device addressing, read/write operation and
an acknowledgement. The programmable nature of device provide users with the flexibility of configuring
the I2C slave device to any legal slave address to avoid the slave address collision on an I2C bus with
multiple slave devices. This paper demonstrates how I2C Master controller transmits and receives data to
and from the Slave with proper synchronization.
The module is designed in Verilog and simulated in ModelSim. The design is also synthesized in Xilinx
XST 14.1. This module acts as a slave for the microprocessor which can be customized for no data loss.
Discrete Model of Two Predators competing for One PreyIJMER
This paper investigates the dynamical behavior of a discrete model of one prey two
predator systems. The equilibrium points and their stability are analyzed. Time series plots are obtained
for different sets of parameter values. Also bifurcation diagrams are plotted to show dynamical behavior
of the system in selected range of growth parameter
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
A Novel Approach for User Search Results Using Feedback Sessions
1. International
OPEN ACCESS Journal
Of Modern Engineering Research (IJMER)
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 4 | Iss. 5| May. 2014 | 70 |
A Novel Approach for User Search Results Using Feedback
Sessions
Miss. T.Yogeshwari1
, Mr. S. Balamurugan2
1, 2
(PG Student, Assistant Professor, Sri Manakula Vinayagar Engineering College, Pondicherry-605106)
I. Introduction
It is a novel approach for user search result with their feedback session. First, we have to cluster the
feedback session by using Fuzzy c-means algorithm. Second, a novel optimization method to map feedback
sessions to pseudo-documents which can efficiently reflect user information needs. Third, evaluate the
CAP of restructured web search results.Generally, data mining (sometimes called data or knowledge discovery)
is the process of analyzing data from different perspectives and summarizing it into useful information -
information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of
analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles,
categorize it, and summarize the relationships identified. Technically, data mining is the process of finding
correlations or patterns among dozens of fields in large relational databases.
Data mining is the process of choosing, discovering, and exhibiting huge volumes of data to
determine unknown patterns or associations useful to the data analyst. The objectives of data mining can
be classified into two tasks: description and prediction. While the purpose of description is to mine
understandable forms and relations from data, the goal of prediction is to forecast one or more variables of
interest.
Clustering is the most important concept used here. Clustering analyzes data objects without consulting
a known class label. The objects are grouped or clustered based on the principle of maximizing the intra
class similarity and minimizing the inter class similarity. Apriori algorithm is a methodology of association
rule of data mining, is used to findout the frequently used URL.
II. Feedback Session
The proposed feedback session consists of both clicked and unclicked URLs and ends with the last
URL that was clicked in a single session. It is motivated that before the last click, all the URLs have been
scanned and evaluated by users. The clicked URLs tell what users require and the unclicked URLs reflect what
users do not care about. It is more efficient to analyze the feedback sessions than to analyze the search results or
clicked URLs directly.
First, we are extracting the titles and snippets of the returned URLs appearing in the feedback session.
Each URL in a feedback session is represented by a small text paragraph that consists of its title and
snippet.Each URL‟s title and snippet are represented by a Term Frequency-Inverse Document Frequency (TF-
IDF) vector, respectively, as in
Tui=[tw1;tw2;...;twn]T
;
Sui=[sw1;sw2;...;swn]T
;
Abstract: In present scenario user search results using Fuzzy c-means algorithm focuses queries are
submitted to search engines to represent the information needs of users. The proposed feedbacks
sessions are clustered by data are bound to each cluster by means of a membership function. Feedback
sessions are constructed from user click-through logs and can efficiently reflect the information
needs of users. Pseudo-documents are generated to better understand the clustered feedbacks. Fuzzy
C-means clustering algorithm is used to cluster the feedbacks. Clustering the feedbacks can effectively
reflect the user needs. Fuzzy c-means algorithm uses the reciprocal of distances to decide the cluster
centers. Ranking model is used to provide ranks to the URL based on the user search
feedbacks. Evaluate the performance using “Classified Average Precision (CAP)” for user search
results.
Keywords: Fuzzy c means algorithm, member function, feedback sessions, pseudo documents,
classified average precision.
2. A Novel Approach For User Search Results Using Feedback Sessions
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 4 | Iss. 5| May. 2014 | 71 |
whereTuiandSui are the TF-IDF vectors of the URL‟s title and snippet, respectively. ui means theith URL in the
feedback session. And wj(j=1;2;...;n) is the jth
term appearing in the enriched URLs.twj and swj represent the TF-
IDF value of the jth
term in the URL‟s title and snippet, respectively.
The distributions of different user search goals can be obtained conveniently after feedback
sessions are clustered. A novel optimization method isused to combine the enriched URLs in a feedback
session to form a pseudo-document, which can effectively reflect the information need of a user. We infer
the user goals by clustering, feedback sessions are proposed. Clustering the feedbacks can effectively reflect the
user needs.
III. Forming Pseudo Document
We propose an optimization method to combineboth clicked and unclicked URLs in the feedback
session. Let Ffs be the feature representation of a feedback session and ffs (w) is the value for the term w. Let
Fucm(m=1;2,...;M) and Fucl(l=1;2, ...;L) be the feature representations of the clicked and unclicked URLs in this
feedbacksession, respectively. Let fucm (w) andfucl(w)be the values for the term win the vectors. We want to
obtain such a Ffs that the sum of the distances between Ffs and each Fucm is minimized and the sum of the
distances betweenFfs and each Fucl is maximized.
Ffs= ffs(w1);ffs(w2);...ffs(wn)T
;
Ffs(w)=arg min ffs(w)∑ffs(w)-fucm(w) 2
- λ2
∑ffs(w)-fucl(w) 2
; ffs(w)€Ic
Let Ic be the interval µ fuc(w)- σ fuc(w), µ fuc(w) + σ fuc(w) and Iucl be the interval µ fucl(w)- σ
fucl(w), µ fucl(w)+ σ fucl(w) ,where µ fuc(w) and σ fuc(w) represent the mean and mean square error of
fuc (w)respectively, and µ fucl(w)and σ fucl(w) represent the mean and mean square error of
fucl(w),respectively.Even if people skip some unclicked URLs because of duplication. Each dimension
of Ffs indicates the importance of a term in this feedback session. Ffs is the pseudo-document that we
want to introduce. It reflects what users desire and what they do not care about. It can be used to
approximate the goal texts in user mind.
User
User click log
database
Feedback
session
Fuzzy c means
clustering
Pseudo
documents
User search goals
Restructured user
search resultsCAP
Optimizing
the number of
clusters
3. A Novel Approach For User Search Results Using Feedback Sessions
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 4 | Iss. 5| May. 2014 | 72 |
IV. Fuzzy C Means Algorithm
In Fuzzy clustering (also referred to as soft clustering), data elements can belong to more than one
cluster, and associated with each element is a set of membership levels. These indicate the strength of the
association between that data element and a particular cluster. Fuzzy clustering is a process of assigning these
membership levels, and then using them to assign data elements to one or more clusters.In many situations,
fuzzy clustering is more natural than hard clustering. Objects on the boundaries between several classes are not
forced to fully belong to one of the classes, but rather are assigned membership degrees between 0 and 1
indicating their partial membership
The Fuzzy C-Means algorithm (FCM) is used in the areas like computational geometry, data
compression and vector quantization, pattern recognition and pattern classification. Fuzzy C-Mean (FCM) is
an unsupervised clustering algorithm that has been applied to wide range of problems involving feature analysis,
clustering and classifier design.
The main features of that algorithm were the (i) use of a fuzzy local similarity measure, (ii) shielding of
the algorithm from noise-related hypersensitivities.FCM clustering techniques are based on fuzzy behavior and
they provide a technique which is natural for producing a clustering where membership weights have a natural
interpretation but not probabilistic at all.In fuzzy clustering, every point has a degree of belonging to clusters, as
in fuzzy logic, rather than belonging completely too just one cluster. Thus, points on the edge of a cluster may
be in the cluster to a lesser degree than points in the center of cluster.
FCM clustering which constitute theoldest component of software computing, are really suitable for
handling the issues related to understand ability of patterns, incomplete/noisy data, mixed media
information, human interaction and it can provide approximate solutions faster.
FCM has a wide domain of applications such as agricultural engineering, astronomy, chemistry,
geology, image analysis, medical diagnosis, shape analysis, andtarget recognition.More the data is near to the
cluster center more is its membership towards the particular cluster center. The basic idea of fuzzy c-means is to
find a fuzzy pseudo-partition to minimize the cost function.Fuzzy c-means has been a very important tool for
image processing in clustering objects in an image. In the 70's, mathematicians introduced the spatial term into
the FCM algorithm to improve the accuracy of clustering under noise.Fuzzy c-means algorithm uses the
reciprocal of distances to decide the cluster centers.
This algorithm works by assigning membership to each data point corresponding to each cluster center
on the basis of distance between the cluster center and the data point. More the data is near to the cluster center
more is its membership towards the particular cluster center. Clearly, summation of membership of each data
point should be equal to one. After each iteration membership and cluster centers are updated according to the
formula. The FCM algorithm converges to a local minimum of the c-means functional. Hence, different
initializations may lead to different results.The minimization of the c-means functional represents a nonlinear
optimization problem that can be solved by using a variety of methods, including iterative minimization,
simulated annealing or genetic algorithms.
The Algorithm Fuzzy C-Means (FCM) is a method of clustering which allows one piece of data
to belong to two or more clusters. This method is frequently used in pattern recognition. It is based on
minimization of the following objective function:
,
where m is any real number greater than 1, uij is the degree of membership of xi in the cluster j, xi is the ith of d-
dimensional measured data, cj is the d-dimension center of the cluster, and ||*|| is any norm expressing the
similarity between any measured data and the center.
Time complexity of FCM is O (ndc2
i).
V. Clustering Pseudo-Documents Using Fuzzy C Means Algorithm
Each feedback session is represented bypseudo-document and the feature representation of the pseudo-
document is Ffs. We cluster pseudo-documents by Fuzzy c-means clustering which is simple and effective Fuzzy
partitioning is carried out through an iterative optimization of the objective function shown above, with the
update of membership uij and the cluster centers cj by
4. A Novel Approach For User Search Results Using Feedback Sessions
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 4 | Iss. 5| May. 2014 | 73 |
N
vj= ∑uij
m
. xi
i-1
N
∑uij
m
,
i-1
˅j=1, 2 ….c. This iteration will stop when
maxij uij
(k+1)
- uij
(k)
< ᶓ
whereis a termination criterion between 0 and 1, whereas k is the iteration steps. This procedure converges to a
local minimum or a saddle point of Jm.
FCM clustering is an iterative process. The process stops when the maximum number of iterations
is reached, or when
the objective function improvement between two consecutive iterations is less than the minimum amount
of improvement specified.
5.1 STEPS
1) Randomly select „c‟ cluster centers.
2) calculate the fuzzy membership 'µij' using:
3) compute the fuzzy centers 'vj' using:
N
vj= ∑ uij
m
. xi
i-1
N
∑ uij
m
i-1
˅j=1,2 ….c.
4) Repeat step 2) and 3) until the minimum „J‟ value is achieved or ||U(k+1)
- U (k)
||< β.
Where,
k‟ is the iteration step.
β‟ is the termination criterion between [0, 1].
„U = (µij)n*c‟ is the fuzzy membership matrix.
„J‟ is the objective function.
5. A Novel Approach For User Search Results Using Feedback Sessions
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 4 | Iss. 5| May. 2014 | 74 |
FCM is also called as Fuzzy ISODATA. FCM employs fuzzy partitioning such that a data point can belong to
all groups which different membership grades between 0 and 1.
5.2 Parameters of the FCM algorithm
Before using the FCM algorithm, the following parameters must be specified:
the number of clusters, c,
the fuzziness exponent, m,
The termination tolerance, ε.
norm-inducing matrix, A
Norm inducing matrixes are 3 types. They are
Euclidean norm
diagonal norm
Mahalanobis norm
After clustering all the pseudo-documents, each cluster can be considered as one user search goal.
VI. Evaluating Cap (Classified Average Precision)
CAP (classified Average Precision) is used toevaluate the performance of user search goal inference
based on restructuring web search results. A possible evaluation criterion is the average precision (AP) which
evaluates according to user implicit feedbacks. AP is the average of precisions computed at the point of each
relevant document in the ranked sequence,
N
AP= 1 N+
∑ rel(r) Rr r ,
r=1
Where N+
is the number of relevant (or clicked) documents in the retrieved ones, r is the rank, N is the total
number of retrieved documents, rel() is a binary function on the relevance of a given rank, and Rr is the number
of relevant retrieved documents of rank r or less. “Voted AP (VAP)”
which is the AP of the class including more clicks namely votes. There should be a riskto avoid classifying
search results into too many classes by error. We propose the risk as follows
Risk = ∑m
i;j=1(i<j) dij
C2
m
It calculates the normalized number of clicked URL pairs that are not in the same class, where m is the number
of the clicked URLs. If the pair of the ith
clicked URL and the jth
clicked URL are not categorized into one class,
dij will be 1; otherwise, it will be 0. C2
m=m(m-1) 2 is the total number of the clicked URL pairs.
We can further extend VAP by introducing the above Risk and propose a new criterion “Classified
AP,” as shown below
CAP = VAP × (1-Risk)ᵞ
is used to adjust the influence of Risk on CAP, which can be learned from
training data.
VII. Conclusion
In this paper, a novel approach has been proposed to user search results for a query by clustering its
feedback sessions represented by pseudo-documents. Clustering feedback sessions are more efficient than
clustering search results or clicked URL‟s directly. A new criterion called classified average Precision is used to
evaluate the performance of restructured web search results. In this paper , we used Fuzzy c means clustering
which constitute the oldest component of software computing, are really suitable for handling the issues
related to understand ability of patterns, incomplete/noisy data, mixed media information, human
interaction and it can provide approximate solutions faster. The execution time of FCM clustering
algorithm for arbitrary data points depends only on the number of clusters and not on the data points. The
distance between data points and some shape of the distribution, has the effect on the performance and behavior
of the algorithm. Gives best result for overlapped data set and comparatively better then k-means algorithm.
REFRENCES
[1] Gayathri A, Nandhakumar C, Gokulavani M, Santhamani V,” Inferring User Goals Using Customer Feedback and
Analyzing Customer Behavior”, International Journal of Computer Applications Technology and Research Volume
3– Issue 2, 125 - 129, 2014.
[2] T. Velmurugan, T.Santhanam,” Implementation of Fuzzy C-Means Clustering Algorithm for Arbitrary Data Points”,
International Conference OnSystemics, Cybernetics And Informatics
6. A Novel Approach For User Search Results Using Feedback Sessions
| IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 4 | Iss. 5| May. 2014 | 75 |
[3] ZhengLu,HongyuanZha, XiaokangYang,Weiyao Lin, and ZhaohuiZheng,” A New Algorithm for Inferring User
Search Goals with Feedback Sessions”,IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,
VOL. 25, NO. 3 MARCH 2013.
[4] SoumiGhosh , Sanjay Kumar Dubey , “Comparative Analysis of K-Means and Fuzzy C Means Algorithms”,
((IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 4, No.4, 2013.
[5] “A New Algorithm for Clustering Search Results” GIANSALVATOREMECCA, SALVATORERAUNICH,
ALESSANDROP APPALARDO Dipartimento di Matematica e InformaticaUniversitàdella Basilicata Potenza –
Italy.
[6] T. Joachims, “Evaluating Retrieval Performance Using Clickthrough Data,” Text Mining, J. Franke, G.
Nakhaeizadeh, and I. Renz, eds., pp. 79-96, Physica/Springer Verlag, 2003..
[7] J.-R Wen, J.-Y Nie, and H.-J Zhang, “Clustering User Queries of a Search Engine,” Proc. Tenth Int‟l Conf.
World Wide Web (WWW ‟01), pp. 162-168, 2001.
[8] D.Kavitha, K.M.Subramanian, Dr.K.Venkatachalam,“SURVEY ON INFERRING USER SEARCH GOAL USING
FEEDBACK SESSION”, International Journal of Advanced Research in Computer Engineering & Technology
(IJARCET)Volume 2, Issue 12, December 2013.
[9] D. Shen, J. Sun, Q. Yang, and Z. Chen, “Building bridges for web query classification,” in Proceedings of the 29th
annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2006, pp.
131–138.
[10] Charudatt Mane, PallaviKulkarni,” A Novel Approach to Discover User Search Goals Using Clickthrough Data”,
Charudatt Mane et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (1)
, 2014, 20-24.
[11] X. Wang and C.-X Zhai, “Learn from Web Search Logs toOrganize Search Results,” Proc. 30th Ann. Int‟l ACM
SIGIR Conf. Research and Development in Information Retrieval (SIGIR ‟07), pp. 87-94, 2007.