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TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com
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TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com
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It is A review on a paper titled "Exploiting experts’ knowledge for structure learning of Bayesian networks" published in 2017.
Feature selection, optimization and clustering strategies of text documents
Clustering is one of the most researched areas of data mining applications in the contemporary literature. The need for efficient clustering is observed across wide sectors including consumer segmentation, categorization, shared filtering, document management, and indexing. The research of clustering task is to be performed prior to its adaptation in the text environment. Conventional approaches typically emphasized on the quantitative information where the selected features are numbers. Efforts also have been put forward for achieving efficient clustering in the context of categorical information where the selected features can assume nominal values. This manuscript presents an in-depth analysis of challenges of clustering in the text environment. Further, this paper also details prominent models proposed for clustering along with the pros and cons of each model. In addition, it also focuses on various latest developments in the clustering task in the social network and associated environments.
Ay3313861388
This document presents a general framework for building classifiers and clustering models using hidden topics to deal with short and sparse text data. It analyzes hidden topics from a large universal dataset using LDA. These topics are then used to enrich both the training data and new short text data by combining them with the topic distributions. This helps reduce data sparseness and improves classification and clustering accuracy for short texts like web snippets. The framework is also applied to contextual advertising by matching web pages and ads based on their hidden topic similarity.
What's hot (18)
An efficient concept based mining model for enhancing text clustering(synopsis)
An efficient concept based mining model for enhancing text clustering(synopsis)
An Efficient Semantic Relation Extraction Method For Arabic Texts Based On Si...
An Efficient Semantic Relation Extraction Method For Arabic Texts Based On Si...
Sentence similarity-based-text-summarization-using-clusters
Sentence similarity-based-text-summarization-using-clusters
Taxonomy extraction from automotive natural language requirements using unsup...
Taxonomy extraction from automotive natural language requirements using unsup...
Order out of Chaos: Construction of Knowledge Models from PDF Textbooks
Order out of Chaos: Construction of Knowledge Models from PDF Textbooks
Integrating Textbooks with Smart Interactive Content for Learning Programming
Integrating Textbooks with Smart Interactive Content for Learning Programming
SEMANTICS GRAPH MINING FOR TOPIC DISCOVERY AND WORD ASSOCIATIONS
SEMANTICS GRAPH MINING FOR TOPIC DISCOVERY AND WORD ASSOCIATIONS
MULTILABEL CLASSIFICATION VIA CO-EVOLUTIONARY MULTILABEL HYPERNETWORK
MULTILABEL CLASSIFICATION VIA CO-EVOLUTIONARY MULTILABEL HYPERNETWORK
International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)
Natural Language Processing Through Different Classes of Machine Learning
Natural Language Processing Through Different Classes of Machine Learning
A review on Exploiting experts’ knowledge for structure learning of bayesian ...
A review on Exploiting experts’ knowledge for structure learning of bayesian ...
Feature selection, optimization and clustering strategies of text documents
Feature selection, optimization and clustering strategies of text documents
Similar to 7
AN EFFICIENT APPROACH FOR SEMANTICALLYENHANCED DOCUMENT CLUSTERING BY USING W...
This document presents a new approach to improve document clustering by exploiting the semantic relationships between terms contained in Wikipedia. The approach first maps terms within documents to corresponding Wikipedia concepts. It then calculates the semantic similarity between terms using Wikipedia's link structure. The document vectors are adjusted so that semantically related terms gain more weight. The approach differs from previous work by using a well-known measure of semantic similarity based on Normalized Google Distance, and by applying phrase extraction to more efficiently map terms to Wikipedia concepts. An evaluation on two datasets found the approach improved clustering results over other state-of-the-art methods.
An efficient approach for semantically enhanced document clustering by using ...
Traditional techniques of document clustering do not consider the semantic relationships between words
when assigning documents to clusters. For instance, if two documents talking about the same topic do that
using different words (which may be synonyms or semantically associated), these techniques may assign
documents to different clusters. Previous research has approached this problem by enriching the document
representation with the background knowledge in an ontology. This paper presents a new approach to
enhance document clustering by exploiting the semantic knowledge contained in Wikipedia. We first map
terms within documents to their corresponding Wikipedia concepts. Then, similarity between each pair of
terms is calculated by using the Wikipedia's link structure. The document’s vector representation is then
adjusted so that terms that are semantically related gain more weight. Our approach differs from related
efforts in two aspects: first, unlink others who built their own methods of measuring similarity through the
Wikipedia categories; our approach uses a similarity measure that is modelled after the Normalized
Google Distance which is a well-known and low-cost method of measuring term similarity. Second, it is
more time efficient as it applies an algorithm for phrase extraction from documents prior to matching terms
with Wikipedia. Our approach was evaluated by being compared with different methods from the state of
the art on two different datasets. Empirical results showed that our approach improved the clustering
results as compared to other approaches.
Improving Text Categorization with Semantic Knowledge in Wikipedia
Text categorization, especially short text categorization, is a difficult and challenging task since the text data is sparse and multidimensional. In traditional text classification methods, document texts are represented with “Bag of Words (BOW)” text representation schema, which is based on word co-occurrence and has many limitations. In this paper, we mapped document texts to Wikipedia concepts and used the Wikipedia-concept-based document representation method to take the place of traditional BOW model for text classification. In order to overcome the weakness of ignoring the semantic relationships among terms in document representation model and utilize rich semantic knowledge in Wikipedia, we constructed a semantic matrix to enrich Wikipedia-concept-based document representation. Experimental evaluation on five real datasets of long and short text shows that our approach outperforms the traditional BOW method.
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans. In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans.In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
A comparative analysis of particle swarm optimization and k means algorithm f...
The volume of digitized text documents on the web have been increasing rapidly. As there is huge collection
of data on the web there is a need for grouping(clustering) the documents into clusters for speedy
information retrieval. Clustering of documents is collection of documents into groups such that the
documents within each group are similar to each other and not to documents of other groups. Quality of
clustering result depends greatly on the representation of text and the clustering algorithm. This paper
presents a comparative analysis of three algorithms namely K-means, Particle swarm Optimization (PSO)
and hybrid PSO+K-means algorithm for clustering of text documents using WordNet. The common way of
representing a text document is bag of terms. The bag of terms representation is often unsatisfactory as it
does not exploit the semantics. In this paper, texts are represented in terms of synsets corresponding to a
word. Bag of terms data representation of text is thus enriched with synonyms from WordNet. K-means,
Particle Swarm Optimization (PSO) and hybrid PSO+K-means algorithms are applied for clustering of
text in Nepali language. Experimental evaluation is performed by using intra cluster similarity and inter
cluster similarity.
Correlation Preserving Indexing Based Text Clustering
This document discusses a correlation preserving indexing (CPI) based text clustering method. CPI aims to find a low dimensional semantic subspace that maximizes correlation between similar documents while minimizing correlation between dissimilar documents. It is different from other methods like LSI and LPI that use Euclidean distance. The document outlines the CPI method and evaluates it on document clustering tasks, showing it doubles the accuracy of previous correlation-based methods. Hierarchical clustering algorithms are also discussed and compared to CPI in terms of evaluation metrics.
Research on ontology based information retrieval techniques
The document summarizes and compares three novel ontology-based information retrieval techniques. It discusses a technique for retrieving information in the domain of Traditional Chinese Medicine that uses an ontology to represent concepts and measures concept similarity to sort search results. It also describes a framework for semantic indexing and querying that uses an ontology and entity-attribute-value model to improve scalability, usability, and retrieval performance for transport systems. Additionally, it outlines a semantic extension retrieval model that uses ontology annotation and semantic extension of queries to address limitations of keyword-based search. The techniques are evaluated based on precision and recall measures to analyze their effectiveness compared to traditional methods.
ONTOLOGY INTEGRATION APPROACHES AND ITS IMPACT ON TEXT CATEGORIZATION
This article will introduce some approaches for improving text categorization models by integrating
previously imported ontologies. From the Reuters Corpus Volume I (RCV1) dataset, some categories very
similar in content and related to telecommunications, Internet and computer areas were selected for models
experiments. Several domain ontologies, covering these areas were built and integrated to categorization
models for their improvements.
O NTOLOGY B ASED D OCUMENT C LUSTERING U SING M AP R EDUCE
Nowadays, document clustering is considered as a da
ta intensive task due to the dramatic, fast increas
e in
the number of available documents. Nevertheless, th
e features that represent those documents are also
too
large. The most common method for representing docu
ments is the vector space model, which represents
document features as a bag of words and does not re
present semantic relations between words. In this
paper we introduce a distributed implementation for
the bisecting k-means using MapReduce programming
model. The aim behind our proposed implementation i
s to solve the problem of clustering intensive data
documents. In addition, we propose integrating the
WordNet ontology with bisecting k-means in order to
utilize the semantic relations between words to enh
ance document clustering results. Our presented
experimental results show that using lexical catego
ries for nouns only enhances internal evaluation
measures of document clustering; and decreases the
documents features from thousands to tens features.
Our experiments were conducted using Amazon ElasticMapReduce to deploy the Bisecting k-means
algorithm
A survey on phrase structure learning methods for text classification
Text classification is a task of automatic classification of text into one of the predefined categories. The
problem of text classification has been widely studied in different communities like natural language
processing, data mining and information retrieval. Text classification is an important constituent in many
information management tasks like topic identification, spam filtering, email routing, language
identification, genre classification, readability assessment etc. The performance of text classification
improves notably when phrase patterns are used. The use of phrase patterns helps in capturing non-local
behaviours and thus helps in the improvement of text classification task. Phrase structure extraction is the
first step to continue with the phrase pattern identification. In this survey, detailed study of phrase structure
learning methods have been carried out. This will enable future work in several NLP tasks, which uses
syntactic information from phrase structure like grammar checkers, question answering, information
extraction, machine translation, text classification. The paper also provides different levels of classification
and detailed comparison of the phrase structure learning methods.
LARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDING
Applying natural language processing-related algorithms is currently a popular project in legal
applications, for instance, document classification of legal documents, contract review and machine
translation. Using the above machine learning algorithms, all need to encode the words in the document in
the form of vectors. The word embedding model is a modern distributed word representation approach and
the most common unsupervised word encoding method. It facilitates subjecting other algorithms and
subsequently performing the downstream tasks of natural language processing vis-à-vis. The most common
and practical approach of accuracy evaluation with the word embedding model uses a benchmark set with
linguistic rules or the relationship between words to perform analogy reasoning via algebraic calculation.
This paper proposes establishing a 1,256 Legal Analogical Reasoning Questions Set (LARQS) from the
2,388 Chinese Codex corpus using five kinds of legal relations, which are then used to evaluate the
accuracy of the Chinese word embedding model. Moreover, we discovered that legal relations might be
ubiquitous in the word embedding model.
LARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDING
This document describes the development of a new legal word embedding evaluation dataset for Chinese called LARQS (Legal Analogical Reasoning Questions Set). It was created using a corpus of 2,388 Chinese legal documents and contains 1,256 questions evaluating 5 categories of legal relationships. The document discusses word embedding and existing evaluation benchmarks. It then describes how LARQS was created by legal experts and its potential usefulness compared to general-purpose benchmarks for evaluating legal-domain word embeddings.
IRJET- Short-Text Semantic Similarity using Glove Word Embedding
The document describes a study that uses GloVe word embeddings to measure semantic similarity between short texts. GloVe is an unsupervised learning algorithm for obtaining vector representations of words. The study trains GloVe word embeddings on a large corpus, then uses the embeddings to encode short texts and calculate their semantic similarity, comparing the accuracy to methods that use Word2Vec embeddings. It aims to show that GloVe embeddings may provide better performance for short text semantic similarity tasks.
Co-Clustering For Cross-Domain Text Classification
Abstract: Traditional approaches for document classification need data which is labelled for the construction reliable classifiers which are even accurate. Unfortunately, data which is already labelled are rarely available, and often too costly to obtain. For the given learning task for which data which is trained is unavailable, abundant labelled data may be there for a different and related domain. One would like to use the related labelled data as auxiliary information to accomplish the classification task in the target domain. Recently, the paradigm of transfer learning has been introduced to enable effective learning strategies when auxiliary data obey a different probability distribution. A co-clustering based classification algorithm has been previously proposed to tackle cross-domain text classification. In this work, we extend the idea underlying this approach by making the latent semantic relationship between the two domains explicit. This goal is achieved with the use of Wikipedia. As a result, the pathway that allows propagating labels between the two domains not only captures common words, but also semantic concepts based on the content of documents. We empirically demonstrate the efficacy of our semantic-based approach to cross-domain classification using a variety of real data.Keywords: Classification, Clustering, Cross-domain Text Classification, Co-clustering, Labelled data, Traditional Approaches.
Title: Co-Clustering For Cross-Domain Text Classification
Author: Rayala Venkat, Mahanthi Kasaragadda
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
DOCUMENT SUMMARIZATION IN KANNADA USING KEYWORD EXTRACTION
The internet has caused a humongous growth in the amount of data available to the common
man. Summaries of documents can help find the right information and are particularly effective
when the document base is very large. Keywords are closely associated to a document as they
reflect the document's content and act as indexes for the given document. In this work, we
present a method to produce extractive summaries of documents in the Kannada language. The
algorithm extracts key words from pre-categorized Kannada documents collected from online
resources. We combine GSS (Galavotti, Sebastiani, Simi) coefficients and IDF (Inverse
Document Frequency) methods along with TF (Term Frequency) for extracting key words and
later use these for summarization. In the current implementation a document from a given category is selected from our database and depending on the number of sentences given by theuser, a summary is generated.
Document Classification Using KNN with Fuzzy Bags of Word Representation
Abstract — Text classification is used to classify the documents depending on the words, phrases and word combinations according to the declared syntaxes. There are many applications that are using text classification such as artificial intelligence, to maintain the data according to the category and in many other. Some keywords which are called topics are selected to classify the given document. Using these Topics the main idea of the document can be identified. Selecting the Topics is an important task to classify the document according to the category. In this proposed system keywords are extracted from documents using TF-IDF and Word Net. TF-IDF algorithm is mainly used to select the important words by which document can be classified. Word Net is mainly used to find similarity between these candidate words. The words which are having the maximum similarity are considered as Topics(keywords). In this experiment we used TF-IDF model to find the similar words so that to classify the document. Decision tree algorithm gives the better accuracy for text classification when compared to other algorithms fuzzy system to classify text written in natural language according to topic. It is necessary to use a fuzzy classifier for this task, due to the fact that a given text can cover several topics with different degrees. In this context, traditional classifiers are inappropriate, as they attempt to sort each text in a single class in a winner-takes-all fashion. The classifier we proposeautomatically learns its fuzzy rules from training examples. We have applied it to classify news articles, and the results we obtained are promising. The dimensionality of a vector is very important in text classification. We can decrease this dimensionality by using clustering based on fuzzy logic. Depending on the similarity we can classify the document and thus they can be formed into clusters according to their Topics. After formation of clusters one can easily access the documents and save the documents very easily. In this we can find the similarity and summarize the words called Topics which can be used to classify the Documents.
F017243241
This document proposes using an enhanced suffix tree approach to measure semantic similarity between multiple documents. It involves preprocessing documents by removing stop words, special characters, and converting to lowercase. Phrases are extracted and used to construct a suffix tree, where internal nodes represent phrases shared across documents. Term frequency-inverse document frequency (tf-idf) is used to calculate weights for internal nodes. Cosine, Dice, and Hellinger similarity measures are then applied to calculate pairwise similarities between documents based on the weighted internal nodes. The approach aims to efficiently and accurately measure semantic similarity between documents.
An Enhanced Suffix Tree Approach to Measure Semantic Similarity between Multi...
1) The document discusses an approach to measure semantic similarity between multiple documents using an enhanced suffix tree. It involves preprocessing documents, constructing a suffix tree with documents' phrases as edges, calculating weights of shared nodes using TF-IDF, and applying cosine, dice, and hellinger similarity measures to determine pairwise document similarities.
2) The approach first preprocesses documents by removing stop words, special characters, and converting to lowercase. A suffix tree is constructed with documents' phrases as edges. Shared nodes in the tree represent common phrases between documents.
3) Node weights are calculated using TF-IDF, with higher weights given to rarer phrases. Several similarity measures (cosine, dice, hellinger) are then applied
A Novel Approach for Keyword extraction in learning objects using text mining
Keyword extraction, concept finding are in learning objects is very important subject in today’s eLearning environment. Keywords are subset of words that contains the useful information about the content of the document. Keyword extraction is a process that is used to get the important keywords from documents. In this proposed System Decision tree algorithm is used for feature selection process using wordnet dictionary. WordNet is a lexical database of English which is used to find similarity from the candidate words. The words having highest similarity are taken as keywords.
Similar to 7 (20)
AN EFFICIENT APPROACH FOR SEMANTICALLYENHANCED DOCUMENT CLUSTERING BY USING W...
AN EFFICIENT APPROACH FOR SEMANTICALLYENHANCED DOCUMENT CLUSTERING BY USING W...
An efficient approach for semantically enhanced document clustering by using ...
An efficient approach for semantically enhanced document clustering by using ...
Improving Text Categorization with Semantic Knowledge in Wikipedia
Improving Text Categorization with Semantic Knowledge in Wikipedia
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
A comparative analysis of particle swarm optimization and k means algorithm f...
A comparative analysis of particle swarm optimization and k means algorithm f...
Correlation Preserving Indexing Based Text Clustering
Correlation Preserving Indexing Based Text Clustering
Research on ontology based information retrieval techniques
Research on ontology based information retrieval techniques
ONTOLOGY INTEGRATION APPROACHES AND ITS IMPACT ON TEXT CATEGORIZATION
ONTOLOGY INTEGRATION APPROACHES AND ITS IMPACT ON TEXT CATEGORIZATION
O NTOLOGY B ASED D OCUMENT C LUSTERING U SING M AP R EDUCE
O NTOLOGY B ASED D OCUMENT C LUSTERING U SING M AP R EDUCE
A survey on phrase structure learning methods for text classification
A survey on phrase structure learning methods for text classification
LARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDING
LARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDING
LARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDING
LARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDING
IRJET- Short-Text Semantic Similarity using Glove Word Embedding
IRJET- Short-Text Semantic Similarity using Glove Word Embedding
Co-Clustering For Cross-Domain Text Classification
Co-Clustering For Cross-Domain Text Classification
DOCUMENT SUMMARIZATION IN KANNADA USING KEYWORD EXTRACTION
DOCUMENT SUMMARIZATION IN KANNADA USING KEYWORD EXTRACTION
Document Classification Using KNN with Fuzzy Bags of Word Representation
Document Classification Using KNN with Fuzzy Bags of Word Representation
An Enhanced Suffix Tree Approach to Measure Semantic Similarity between Multi...
An Enhanced Suffix Tree Approach to Measure Semantic Similarity between Multi...
A Novel Approach for Keyword extraction in learning objects using text mining
A Novel Approach for Keyword extraction in learning objects using text mining
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18
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
17
This document discusses efficient rendezvous algorithms for wireless sensor networks with mobile base stations. It proposes an approach where select sensor nodes act as rendezvous points, buffering and aggregating data from other sensors. These rendezvous points then transfer the collected data to the base station when it arrives, combining the advantages of controlled mobility and in-network caching. Algorithms are presented for rendezvous design with mobile base stations having variable or fixed tracks. Both theoretical analysis and simulations validate that this approach can achieve a good balance between energy savings and reduced data collection latency in the network.
16
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
15
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
25
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
24
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
23
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
22
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
21
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
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20
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
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Chennai-26.
www.impulse.net.in
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19
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
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Chennai-26.
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18
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
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17
This document discusses preventing private information inference attacks on social networks. It explores how released social networking data could be used to predict undisclosed private information about individuals, such as their political affiliation or sexual orientation. It then describes three sanitization techniques that could be used to decrease the effectiveness of such attacks. An experiment is conducted applying these techniques to a Facebook dataset to attempt to discover sensitive attributes through collective inference and show that the sanitization methods decrease the effectiveness of local and relational classification algorithms.
16
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N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
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15
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
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Chennai-26.
www.impulse.net.in
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14
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
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Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
13
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
12
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
11
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
10
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
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You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
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Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
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