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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
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LPM: A DISTRIBUTED ARCHITECTURE AND ALGORITHMS FOR LOCATION PRIVACY IN LBS
Recent advances in mobile communication and development of sophisticated equipments lead to the wide spread use of Location Based Services (LBS). A major concern for large-scale deployment of LBSs is the potential abuse of their client location data, which may imply sensitive personal information. Protecting location information of the mobile user is challenging because a location itself may reveal user identity. Several schemes have been proposed for location cloaking. In our paper, we propose a generic Enhanced Location Privacy Model (LPM), which describes the concept, the architecture, algorithms and the functionalities for location privacy in LBS. As per the architecture, the system ensures location privacy, without trusting anybody including the peers or LBS servers. The system is fully distributed and evaluation shows its efficiency and high level of privacy with QoS
27
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
Visualization of Relationship between Social Bookmark Users
The document summarizes a study that visualized relationships between users of a social bookmarking service based on the tags they applied to bookmarks. It discusses how social bookmarking data was gathered from a Japanese site including tags and bookmarks. Similarities between users were calculated based on the intersection of tags they used. The relationships were then visualized as a graph with nodes for users and edges for their similarity, represented by differently colored and styled lines. The results helped intuitively show relationships between users based on their interests as reflected in the tags they applied.
Information Filtration
Information filtering is the process of monitoring large amounts of dynamically generated information and identifying the subset of information likely to be of interest to a user based on their information needs. It represents the user's interests and identifies only pieces of information they would find interesting. There are three main categories of information filtering: collaborative filtering which uses recommendations from other users; content-based filtering which uses a comparison between item content and user profiles; and hybrid filtering which combines aspects of collaborative and content-based filtering. Feedback techniques can also be used to continually update and improve filtering.
Context Driven Technique for Document Classification
In this paper we present an innovative hybrid Text
Classification (TC) system that bridges the gap between
statistical and context based techniques. Our algorithm
harnesses contextual information at two stages. First it extracts
a cohesive set of keywords for each category by using lexical
references, implicit context as derived from LSA and wordvicinity
driven semantics. And secondly, each document is
represented by a set of context rich features whose values are
derived by considering both lexical cohesion as well as the extent
of coverage of salient concepts via lexical chaining. After
keywords are extracted, a subset of the input documents is
apportioned as training set. Its members are assigned categories
based on their keyword representation. These labeled
documents are used to train binary SVM classifiers, one for
each category. The remaining documents are supplied to the
trained classifiers in the form of their context-enhanced feature
vectors. Each document is finally ascribed its appropriate
category by an SVM classifier.
ATC full paper format-2014 Social Networks in Telecommunications Asoka Korale...
This summarizes a document describing a novel approach to analyzing social networks in mobile telecommunications by modeling call patterns between subscribers. It identifies leaders and communities by processing call initiation and termination data. Communities are detected using influence diffusion algorithms. Results are presented from a corporate network analyzed, identifying leaders and communities formed around them. The identified leaders are validated using existing centrality measures. The approach allows estimating the degree to which individuals belong to multiple overlapping communities.
The process of communication
The document discusses the process of communication. It defines communication as conveying information such that the message is received and understood. The basic communication process involves a sender, receiver, message, encoding, channel, decoding, and feedback. It identifies key elements of the communication process, including the sender who intends to convey a message, the ideas or content being communicated, encoding the ideas into symbols, selecting a communication channel, the receiver who receives the message, decoding the symbols back into ideas, and feedback to ensure understanding.
Classification-based Retrieval Methods to Enhance Information Discovery on th...
The widespread adoption of the World-Wide Web (the Web) has created challenges both for society as a whole and for the technology used to build and maintain the Web. The ongoing struggle of information retrieval systems is to wade through this vast pile of data and satisfy users by presenting them with information that most adequately it’s their needs. On a societal level, the Web is expanding faster than we can comprehend its implications or develop rules for its use. The ubiquitous use of the Web has raised important social concerns in the areas of privacy, censorship, and access to information. On a technical level, the novelty of the Web and the pace of its growth have created challenges not only in the development of new applications that realize the power of the Web, but also in the technology needed to scale applications to accommodate the resulting large data sets and heavy loads. This thesis presents searching algorithms and hierarchical classification techniques for increasing a search service's understanding of web queries. Existing search services rely solely on a query's occurrence in the document collection to locate relevant documents. They typically do not perform any task or topic-based analysis of queries using other available resources, and do not leverage changes in user query patterns over time. Provided within are a set of techniques and metrics for performing temporal analysis on query logs. Our log analyses are shown to be reasonable and informative, and can be used to detect changing trends and patterns in the query stream, thus providing valuable data to a search service.
Recommended
LPM: A DISTRIBUTED ARCHITECTURE AND ALGORITHMS FOR LOCATION PRIVACY IN LBS
Recent advances in mobile communication and development of sophisticated equipments lead to the wide spread use of Location Based Services (LBS). A major concern for large-scale deployment of LBSs is the potential abuse of their client location data, which may imply sensitive personal information. Protecting location information of the mobile user is challenging because a location itself may reveal user identity. Several schemes have been proposed for location cloaking. In our paper, we propose a generic Enhanced Location Privacy Model (LPM), which describes the concept, the architecture, algorithms and the functionalities for location privacy in LBS. As per the architecture, the system ensures location privacy, without trusting anybody including the peers or LBS servers. The system is fully distributed and evaluation shows its efficiency and high level of privacy with QoS
27
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
Visualization of Relationship between Social Bookmark Users
The document summarizes a study that visualized relationships between users of a social bookmarking service based on the tags they applied to bookmarks. It discusses how social bookmarking data was gathered from a Japanese site including tags and bookmarks. Similarities between users were calculated based on the intersection of tags they used. The relationships were then visualized as a graph with nodes for users and edges for their similarity, represented by differently colored and styled lines. The results helped intuitively show relationships between users based on their interests as reflected in the tags they applied.
Information Filtration
Information filtering is the process of monitoring large amounts of dynamically generated information and identifying the subset of information likely to be of interest to a user based on their information needs. It represents the user's interests and identifies only pieces of information they would find interesting. There are three main categories of information filtering: collaborative filtering which uses recommendations from other users; content-based filtering which uses a comparison between item content and user profiles; and hybrid filtering which combines aspects of collaborative and content-based filtering. Feedback techniques can also be used to continually update and improve filtering.
Context Driven Technique for Document Classification
In this paper we present an innovative hybrid Text
Classification (TC) system that bridges the gap between
statistical and context based techniques. Our algorithm
harnesses contextual information at two stages. First it extracts
a cohesive set of keywords for each category by using lexical
references, implicit context as derived from LSA and wordvicinity
driven semantics. And secondly, each document is
represented by a set of context rich features whose values are
derived by considering both lexical cohesion as well as the extent
of coverage of salient concepts via lexical chaining. After
keywords are extracted, a subset of the input documents is
apportioned as training set. Its members are assigned categories
based on their keyword representation. These labeled
documents are used to train binary SVM classifiers, one for
each category. The remaining documents are supplied to the
trained classifiers in the form of their context-enhanced feature
vectors. Each document is finally ascribed its appropriate
category by an SVM classifier.
ATC full paper format-2014 Social Networks in Telecommunications Asoka Korale...
This summarizes a document describing a novel approach to analyzing social networks in mobile telecommunications by modeling call patterns between subscribers. It identifies leaders and communities by processing call initiation and termination data. Communities are detected using influence diffusion algorithms. Results are presented from a corporate network analyzed, identifying leaders and communities formed around them. The identified leaders are validated using existing centrality measures. The approach allows estimating the degree to which individuals belong to multiple overlapping communities.
The process of communication
The document discusses the process of communication. It defines communication as conveying information such that the message is received and understood. The basic communication process involves a sender, receiver, message, encoding, channel, decoding, and feedback. It identifies key elements of the communication process, including the sender who intends to convey a message, the ideas or content being communicated, encoding the ideas into symbols, selecting a communication channel, the receiver who receives the message, decoding the symbols back into ideas, and feedback to ensure understanding.
Classification-based Retrieval Methods to Enhance Information Discovery on th...
The widespread adoption of the World-Wide Web (the Web) has created challenges both for society as a whole and for the technology used to build and maintain the Web. The ongoing struggle of information retrieval systems is to wade through this vast pile of data and satisfy users by presenting them with information that most adequately it’s their needs. On a societal level, the Web is expanding faster than we can comprehend its implications or develop rules for its use. The ubiquitous use of the Web has raised important social concerns in the areas of privacy, censorship, and access to information. On a technical level, the novelty of the Web and the pace of its growth have created challenges not only in the development of new applications that realize the power of the Web, but also in the technology needed to scale applications to accommodate the resulting large data sets and heavy loads. This thesis presents searching algorithms and hierarchical classification techniques for increasing a search service's understanding of web queries. Existing search services rely solely on a query's occurrence in the document collection to locate relevant documents. They typically do not perform any task or topic-based analysis of queries using other available resources, and do not leverage changes in user query patterns over time. Provided within are a set of techniques and metrics for performing temporal analysis on query logs. Our log analyses are shown to be reasonable and informative, and can be used to detect changing trends and patterns in the query stream, thus providing valuable data to a search service.
Iaetsd hierarchical fuzzy rule based classification
This document discusses a hierarchical fuzzy rule-based classification system using genetic rule selection to filter unwanted messages from online social networks. It aims to improve performance on imbalanced data sets by increasing granularity of fuzzy partitions at class boundaries. The system uses a neural network learning model and genetic algorithm for rule selection to build an accurate and compact fuzzy rule-based model. It analyzes challenges in classifying short texts from social media posts and reviews related work on content-based filtering and policy-based personalization for social networks. The document also discusses issues with imbalanced data sets and proposes oversampling the minority class using SMOTE (Synthetic Minority Over-sampling Technique) as a preprocessing step to address class imbalance problems.
Scaling Down Dimensions and Feature Extraction in Document Repository Classif...
-In this study a comprehensive evaluation of two
supervised feature selection methods for dimensionality
reduction is performed - Latent Semantic Indexing (LSI) and
Principal Component Analysis (PCA). This is gauged against
unsupervised techniques like fuzzy feature clustering using
hard fuzzy C-means (FCM) . The main objective of the study is
to estimate the relative efficiency of two supervised techniques
against unsupervised fuzzy techniques while reducing the
feature space. It is found that clustering using FCM leads to
better accuracy in classifying documents in the face of
evolutionary algorithms like LSI and PCA. Results show that
the clustering of features improves the accuracy of document
classification
A Review: Text Classification on Social Media Data
This document provides a review of different classifiers used for text classification on social media data. It discusses how social media data is often unstructured and contains users' opinions and sentiments. Various machine learning algorithms can be used to classify this social media text data, extracting meaningful information. The document focuses on describing Naive Bayes classifiers, which are commonly used for text classification tasks. It explains how Naive Bayes classifiers work by calculating the posterior probability that a document belongs to a certain class, based on applying Bayes' theorem with an independence assumption between features.
InternshipProject
This document discusses using vague language to present quantitative data in a way that is appropriate to the level of uncertainty or inconsistency in the data. It argues that expressing information at a suitable level of precision makes it more accessible and relevant to users. A probabilistic approach to vagueness is proposed, which utilizes uncertainty measures on propositions defined over three-valued truth models to capture blurred category boundaries and borderline cases. The project aims to develop an implementable model of imprecise assertion strategies to derive posterior probabilities over descriptive terms based on data and assertability conventions.
QUERY EXPANSION WITH ENRICHED USER PROFILES FOR PERSONALIZED SEARCH UTILIZING...
The document proposes two novel techniques for personalized query expansion using folksonomy data. It introduces a model that constructs enriched user profiles by integrating word embeddings with topic models from user annotations and an external corpus. The first technique selects expansion terms using topical weights-enhanced word embeddings. The second calculates topical relevance between the query and user profile terms. An evaluation shows the approaches outperform existing non-personalized and personalized query expansion methods.
IRJET- Quantify Mutually Dependent Privacy Risks with Locality Data
This document discusses how co-location information shared on social networks can threaten users' location privacy by enabling more accurate localization of users' locations over time. It formalizes the problem of quantifying privacy risks from co-location data and location information, and proposes optimal and approximate localization attack algorithms to incorporate co-location data. Experimental evaluations on mobility trace data show that considering a single friend's co-locations can decrease a user's median location privacy by up to 62%. Differential privacy perspectives are also discussed. The study aims to quantify the effect of co-location information on location privacy risks.
security enhanced content sharing in social io t a directed hypergraph based ...
The document proposes a secure content sharing scheme for social IoT that leverages users' social trust to avoid attacks from untrusted users. It models the relationships between users, content, and social groups as a graph to dynamically extract social trust over time. A hierarchical game model is formulated to optimize user pairing and channel selection as sub-problems. Specifically, user pairing is modeled as a matching game and channel selection as a directed hypergraph game. An algorithm is designed to find the optimal Nash equilibrium for these sub-games and thereby the global optimum. Simulation results show the proposed scheme can enhance security without sacrificing quality of experience.
USPatents
This document lists 30 US patents related to search technologies, television content searching, personalized search, and modeling disk input/output throughput. The patents cover methods for conducting searches across and within multiple documents using reduced text input and incremental searching. They also include interfaces for selecting content based on user relationships and conversation state information in interactive systems.
Data Mining: Graph mining and social network analysis
Graph mining analyzes structured data like social networks and the web through graph search algorithms. It aims to find frequent subgraphs using Apriori-based or pattern growth approaches. Social networks exhibit characteristics like densification and heavy-tailed degree distributions. Link mining analyzes heterogeneous, multi-relational social network data through tasks like link prediction and group detection, facing challenges of logical vs statistical dependencies and collective classification. Multi-relational data mining searches for patterns across multiple database tables, including multi-relational clustering that utilizes information across relations.
An Investigation of Data Privacy and Utility Using Machine Learning as a Gauge
Kato Mivule is a doctoral candidate at Bowie State University researching data privacy and utility preservation using machine learning classifiers. His talk will discuss investigating the optimal balance between data privacy and user needs using his classification error gauge model, which employs machine learning to measure utility based on classification error. He will also present the SIED framework, which outlines the specifications, implementation, evaluation, and dissemination phases of the data privacy process. The talk will take place on April 10, 2014 from 3:30-4:45 PM in room 210 of the CSB building at Bowie State University.
INTELLIGENT INFORMATION RETRIEVAL WITHIN DIGITAL LIBRARY USING DOMAIN ONTOLOGY
A digital library is a type of information retrieval (IR) system. The existing information retrieval
methodologies generally have problems on keyword-searching. We proposed a model to solve
the problem by using concept-based approach (ontology) and metadata case base. This model
consists of identifying domain concepts in user’s query and applying expansion to them. The
system aims at contributing to an improved relevance of results retrieved from digital libraries
by proposing a conceptual query expansion for intelligent concept-based retrieval. We need to
import the concept of ontology, making use of its advantage of abundant semantics and
standard concept. Domain specific ontology can be used to improve information retrieval from
traditional level based on keyword to the lay based on knowledge (or concept) and change the
process of retrieval from traditional keyword matching to semantics matching. One approach is
query expansion techniques using domain ontology and the other would be introducing a case
based similarity measure for metadata information retrieval using Case Based Reasoning
(CBR) approach. Results show improvements over classic method, query expansion using
general purpose ontology and a number of other approaches.
Digital Research Conference 2013 - Framework to assess the quality of Twitter...
Presentation at the DIgital Research Conference, on 10 Sep 2013. Jan and I present an approach to assessing the quality of Twitter data for (business) research, and illustrate its application with a paper looking at the role of Social Media in customer service.
The Revolution Of Cloud Computing
Here are the key points about using content-based filtering techniques:
- Content-based filtering relies on analyzing the content or description of items to recommend items similar to what the user has liked in the past. It looks for patterns and regularities in item attributes/descriptions to distinguish highly rated items.
- The item content/descriptions are analyzed automatically by extracting information from sources like web pages, or entered manually from product databases.
- It focuses on objective attributes about items that can be extracted algorithmically, like text analysis of documents.
- However, personal preferences and what makes an item appealing are often subjective qualities not easily extracted algorithmically, like writing style or taste.
- So while content-based filtering can
5
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
5
This document discusses outsourcing similarity searches on metric data assets to cloud computing services while preserving data privacy. The data is transformed before being provided to the service provider to enable similarity queries on the transformed data. Techniques are presented that offer trade-offs between query cost and accuracy, and are extended to provide an intuitive privacy guarantee. Empirical studies demonstrate the techniques can perform efficient and accurate similarity queries while maintaining data privacy.
Semantic web personalization
This document provides a survey of semantic web personalization techniques. It begins by defining semantic web personalization and its advantages over traditional web personalization. It then classifies semantic web personalization approaches into several categories, including ontology-based, context-based, and hybrid recommendation systems. For each category, it provides examples of approaches and compares their methods and steps for personalization. The goal of the survey is to analyze and compare different techniques used for personalization in the semantic web.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
This document summarizes a research paper on developing user profiles from search engine queries to enable personalized search results. It discusses how current search engines generally return the same results regardless of individual user interests. The paper proposes methods to construct user profiles capturing both positive and negative preferences from search histories and click-through data. Experimental results showed profiles including both preferences performed best by improving query clustering and separating similar vs. dissimilar queries. Future work aims to use profiles for collaborative filtering and predicting new query intents.
Identical Users in Different Social Media Provides Uniform Network Structure ...
The primary point of this venture is secure the client login and information sharing among the interpersonal organizations like Gmail, Face book and furthermore find unknown client utilizing this systems. On the off chance that the first client not accessible in the systems, but rather their companions or mysterious client knows their login points of interest implies conceivable to abuse their talks. In this venture we need to defeat the mysterious client utilizing the system without unique client information. Unapproved client utilizing the login to talk, share pictures or recordings and so on. This is the issue to be overcome in this venture .That implies client initially enlist their subtle elements with one secured question and reply. Since the unknown client can erase their talk or information. In this by utilizing the secured questions we need to recuperate the unapproved client talk history or imparting subtle elements to their IP address or MAC address. So in this venture they have discovered an approach to keep the mysterious clients abuse the first client login points.
48
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
IRJET- Cross System User Modeling and Personalization on the Social Web
The document discusses cross-system user modeling and personalization on social media networks. It proposes the Friend Relationship-Based User Identification (FRUI) algorithm to identify identical users across different social media platforms based on their friendship networks. FRUI calculates a matching score for candidate user pairs and only high scoring pairs are considered matches. Two proposals are introduced to improve the efficiency of the algorithm. Experimental results show FRUI performs better than existing network structure-based methods. The real-world friendship network is highly individual, so using friendship structure to analyze cross-platform social media is more accurate.
Al26234241
This document summarizes an article from the International Journal of Engineering Research and Applications titled "Explicit User Profiles for Semantic Web Search Using XML" by C. Srinvas.
The article discusses how to build explicit user profiles using XML to personalize semantic web searches. It covers collecting user data, inferring user profiles, and representing profiles. The key approach is to define an ontology of topics and annotate concepts with interest scores that are continuously updated based on user behavior using spreading activation methods. This builds ontological user profiles that represent user interests to improve search accuracy.
M045067275
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.
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Iaetsd hierarchical fuzzy rule based classification
This document discusses a hierarchical fuzzy rule-based classification system using genetic rule selection to filter unwanted messages from online social networks. It aims to improve performance on imbalanced data sets by increasing granularity of fuzzy partitions at class boundaries. The system uses a neural network learning model and genetic algorithm for rule selection to build an accurate and compact fuzzy rule-based model. It analyzes challenges in classifying short texts from social media posts and reviews related work on content-based filtering and policy-based personalization for social networks. The document also discusses issues with imbalanced data sets and proposes oversampling the minority class using SMOTE (Synthetic Minority Over-sampling Technique) as a preprocessing step to address class imbalance problems.
Scaling Down Dimensions and Feature Extraction in Document Repository Classif...
-In this study a comprehensive evaluation of two
supervised feature selection methods for dimensionality
reduction is performed - Latent Semantic Indexing (LSI) and
Principal Component Analysis (PCA). This is gauged against
unsupervised techniques like fuzzy feature clustering using
hard fuzzy C-means (FCM) . The main objective of the study is
to estimate the relative efficiency of two supervised techniques
against unsupervised fuzzy techniques while reducing the
feature space. It is found that clustering using FCM leads to
better accuracy in classifying documents in the face of
evolutionary algorithms like LSI and PCA. Results show that
the clustering of features improves the accuracy of document
classification
A Review: Text Classification on Social Media Data
This document provides a review of different classifiers used for text classification on social media data. It discusses how social media data is often unstructured and contains users' opinions and sentiments. Various machine learning algorithms can be used to classify this social media text data, extracting meaningful information. The document focuses on describing Naive Bayes classifiers, which are commonly used for text classification tasks. It explains how Naive Bayes classifiers work by calculating the posterior probability that a document belongs to a certain class, based on applying Bayes' theorem with an independence assumption between features.
InternshipProject
This document discusses using vague language to present quantitative data in a way that is appropriate to the level of uncertainty or inconsistency in the data. It argues that expressing information at a suitable level of precision makes it more accessible and relevant to users. A probabilistic approach to vagueness is proposed, which utilizes uncertainty measures on propositions defined over three-valued truth models to capture blurred category boundaries and borderline cases. The project aims to develop an implementable model of imprecise assertion strategies to derive posterior probabilities over descriptive terms based on data and assertability conventions.
QUERY EXPANSION WITH ENRICHED USER PROFILES FOR PERSONALIZED SEARCH UTILIZING...
The document proposes two novel techniques for personalized query expansion using folksonomy data. It introduces a model that constructs enriched user profiles by integrating word embeddings with topic models from user annotations and an external corpus. The first technique selects expansion terms using topical weights-enhanced word embeddings. The second calculates topical relevance between the query and user profile terms. An evaluation shows the approaches outperform existing non-personalized and personalized query expansion methods.
IRJET- Quantify Mutually Dependent Privacy Risks with Locality Data
This document discusses how co-location information shared on social networks can threaten users' location privacy by enabling more accurate localization of users' locations over time. It formalizes the problem of quantifying privacy risks from co-location data and location information, and proposes optimal and approximate localization attack algorithms to incorporate co-location data. Experimental evaluations on mobility trace data show that considering a single friend's co-locations can decrease a user's median location privacy by up to 62%. Differential privacy perspectives are also discussed. The study aims to quantify the effect of co-location information on location privacy risks.
security enhanced content sharing in social io t a directed hypergraph based ...
The document proposes a secure content sharing scheme for social IoT that leverages users' social trust to avoid attacks from untrusted users. It models the relationships between users, content, and social groups as a graph to dynamically extract social trust over time. A hierarchical game model is formulated to optimize user pairing and channel selection as sub-problems. Specifically, user pairing is modeled as a matching game and channel selection as a directed hypergraph game. An algorithm is designed to find the optimal Nash equilibrium for these sub-games and thereby the global optimum. Simulation results show the proposed scheme can enhance security without sacrificing quality of experience.
USPatents
This document lists 30 US patents related to search technologies, television content searching, personalized search, and modeling disk input/output throughput. The patents cover methods for conducting searches across and within multiple documents using reduced text input and incremental searching. They also include interfaces for selecting content based on user relationships and conversation state information in interactive systems.
Data Mining: Graph mining and social network analysis
Graph mining analyzes structured data like social networks and the web through graph search algorithms. It aims to find frequent subgraphs using Apriori-based or pattern growth approaches. Social networks exhibit characteristics like densification and heavy-tailed degree distributions. Link mining analyzes heterogeneous, multi-relational social network data through tasks like link prediction and group detection, facing challenges of logical vs statistical dependencies and collective classification. Multi-relational data mining searches for patterns across multiple database tables, including multi-relational clustering that utilizes information across relations.
An Investigation of Data Privacy and Utility Using Machine Learning as a Gauge
Kato Mivule is a doctoral candidate at Bowie State University researching data privacy and utility preservation using machine learning classifiers. His talk will discuss investigating the optimal balance between data privacy and user needs using his classification error gauge model, which employs machine learning to measure utility based on classification error. He will also present the SIED framework, which outlines the specifications, implementation, evaluation, and dissemination phases of the data privacy process. The talk will take place on April 10, 2014 from 3:30-4:45 PM in room 210 of the CSB building at Bowie State University.
INTELLIGENT INFORMATION RETRIEVAL WITHIN DIGITAL LIBRARY USING DOMAIN ONTOLOGY
A digital library is a type of information retrieval (IR) system. The existing information retrieval
methodologies generally have problems on keyword-searching. We proposed a model to solve
the problem by using concept-based approach (ontology) and metadata case base. This model
consists of identifying domain concepts in user’s query and applying expansion to them. The
system aims at contributing to an improved relevance of results retrieved from digital libraries
by proposing a conceptual query expansion for intelligent concept-based retrieval. We need to
import the concept of ontology, making use of its advantage of abundant semantics and
standard concept. Domain specific ontology can be used to improve information retrieval from
traditional level based on keyword to the lay based on knowledge (or concept) and change the
process of retrieval from traditional keyword matching to semantics matching. One approach is
query expansion techniques using domain ontology and the other would be introducing a case
based similarity measure for metadata information retrieval using Case Based Reasoning
(CBR) approach. Results show improvements over classic method, query expansion using
general purpose ontology and a number of other approaches.
Digital Research Conference 2013 - Framework to assess the quality of Twitter...
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Similar to 56
The Revolution Of Cloud Computing
Here are the key points about using content-based filtering techniques:
- Content-based filtering relies on analyzing the content or description of items to recommend items similar to what the user has liked in the past. It looks for patterns and regularities in item attributes/descriptions to distinguish highly rated items.
- The item content/descriptions are analyzed automatically by extracting information from sources like web pages, or entered manually from product databases.
- It focuses on objective attributes about items that can be extracted algorithmically, like text analysis of documents.
- However, personal preferences and what makes an item appealing are often subjective qualities not easily extracted algorithmically, like writing style or taste.
- So while content-based filtering can
5
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
5
This document discusses outsourcing similarity searches on metric data assets to cloud computing services while preserving data privacy. The data is transformed before being provided to the service provider to enable similarity queries on the transformed data. Techniques are presented that offer trade-offs between query cost and accuracy, and are extended to provide an intuitive privacy guarantee. Empirical studies demonstrate the techniques can perform efficient and accurate similarity queries while maintaining data privacy.
Semantic web personalization
This document provides a survey of semantic web personalization techniques. It begins by defining semantic web personalization and its advantages over traditional web personalization. It then classifies semantic web personalization approaches into several categories, including ontology-based, context-based, and hybrid recommendation systems. For each category, it provides examples of approaches and compares their methods and steps for personalization. The goal of the survey is to analyze and compare different techniques used for personalization in the semantic web.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
This document summarizes a research paper on developing user profiles from search engine queries to enable personalized search results. It discusses how current search engines generally return the same results regardless of individual user interests. The paper proposes methods to construct user profiles capturing both positive and negative preferences from search histories and click-through data. Experimental results showed profiles including both preferences performed best by improving query clustering and separating similar vs. dissimilar queries. Future work aims to use profiles for collaborative filtering and predicting new query intents.
Identical Users in Different Social Media Provides Uniform Network Structure ...
The primary point of this venture is secure the client login and information sharing among the interpersonal organizations like Gmail, Face book and furthermore find unknown client utilizing this systems. On the off chance that the first client not accessible in the systems, but rather their companions or mysterious client knows their login points of interest implies conceivable to abuse their talks. In this venture we need to defeat the mysterious client utilizing the system without unique client information. Unapproved client utilizing the login to talk, share pictures or recordings and so on. This is the issue to be overcome in this venture .That implies client initially enlist their subtle elements with one secured question and reply. Since the unknown client can erase their talk or information. In this by utilizing the secured questions we need to recuperate the unapproved client talk history or imparting subtle elements to their IP address or MAC address. So in this venture they have discovered an approach to keep the mysterious clients abuse the first client login points.
48
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
IRJET- Cross System User Modeling and Personalization on the Social Web
The document discusses cross-system user modeling and personalization on social media networks. It proposes the Friend Relationship-Based User Identification (FRUI) algorithm to identify identical users across different social media platforms based on their friendship networks. FRUI calculates a matching score for candidate user pairs and only high scoring pairs are considered matches. Two proposals are introduced to improve the efficiency of the algorithm. Experimental results show FRUI performs better than existing network structure-based methods. The real-world friendship network is highly individual, so using friendship structure to analyze cross-platform social media is more accurate.
Al26234241
This document summarizes an article from the International Journal of Engineering Research and Applications titled "Explicit User Profiles for Semantic Web Search Using XML" by C. Srinvas.
The article discusses how to build explicit user profiles using XML to personalize semantic web searches. It covers collecting user data, inferring user profiles, and representing profiles. The key approach is to define an ontology of topics and annotate concepts with interest scores that are continuously updated based on user behavior using spreading activation methods. This builds ontological user profiles that represent user interests to improve search accuracy.
M045067275
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.
Building a collective experience
This paper describes the Collective Experience Engine (CEE), a system for indexing Experiential-
Knowledge about Web knowledge-sources (websites), and performing relative-experience calculations
between participants of the CEE. The CEE provides an in-browser interface to query the collective
experience of others participating in the CEE. This interface accepts a list of URLs, to which the CEE adds
additional information based on the Queryee's previously indexed Experiential-Knowledge. The core of the
CEE is its Experiential-Context Conversation (ECConversation) functionality, whereby an collection of a
person’s Web Experiential-Knowledge can be utilized to allow a real-world conversation-like exchange of
information to take place, including adjusting information-flow based on the Queryee's experiential
background and knowledge, and providing additional experientially-related knowledge integrated into the
answer from multiple selected 'experience donors'. A relative-experience calculation ensures that
information is retrieved only from relative-experts, to ensure sufficient additional information exists, but
that such information isn't too advanced for the Queryee to process. This paper gives an overview of the
CEE, and the underlying algorithms and data structures, and describes a system architecture and
implementation details.
Achieving Privacy in Publishing Search logs
The document discusses algorithms for publishing search logs while preserving user privacy. It analyzes a search log using an algorithm that produces three types of outputs: query counts, a query-action graph showing query-result click counts, and a query-reformulation graph showing query suggestions clicked. The algorithm adds noise to query counts before publishing to achieve differential privacy. It aims to provide useful aggregated information for applications like search improvement while preventing re-identification of individual user data in the search log.
Avoiding Anonymous Users in Multiple Social Media Networks (SMN)
Abstract: The main aim of this project is secure the user login and data sharing among the social networks like Gmail, Facebook and also find anonymous user using this networks. If the original user not available in the networks, but their friends or anonymous user knows their login details means possible to misuse their chats. In this project we have to overcome the anonymous user using the network without original user knowledge. Unauthorized user using the login to chat, share images or videos etc This is the problem to be overcome in this project .That means user first register their details with one secured question and answer. Because the anonymous user can delete their chat or data In this by using the secured questions we have to recover the unauthorized user chat history or sharing details with their IP address or MAC address. So in this project they have found out a way to prevent the anonymous users misuse the original user login details.
MUTATION AND CROSSOVER ISSUES FOR OSN PRIVACY
Abstract: Privacy is one of the friction points that emerge when communications get mediated in Online Social Networks (OSNs). Different communities of computer science researchers have framed the ‘OSN privacy problem’ as one of surveillance, institutional or social privacy. In this article, first we provide an introduction to the surveillance and social privacy perspectives emphasizing the narratives that inform them, as well as their assumptions and goals. This paper mainly addresses visitors events (population) on an users account and updates the account holders log information. And thus the evolutionary aspects of Surveillance are reflected in User's Log, this needs the implementation of Genetic Algorithm. Further, this requires a bridge module between every interaction between the user and social network server. This paper implements mutation aspects through Genetic Algorithm by differing users into Guests and Friends, and identifies and Cross Over issues of a guest Clicking Friend of a friend.
MUTATION AND CROSSOVER ISSUES FOR OSN PRIVACY
Abstract: Privacy is one of the friction points that emerge when communications get mediated in Online Social Networks (OSNs). Different communities of computer science researchers have framed the ‘OSN privacy problem’ as one of surveillance, institutional or social privacy. In this article, first we provide an introduction to the surveillance and social privacy perspectives emphasizing the narratives that inform them, as well as their assumptions and goals. This paper mainly addresses visitors events (population) on an users account and updates the account holders log information. And thus the evolutionary aspects of Surveillance are reflected in User's Log, this needs the implementation of Genetic Algorithm. Further, this requires a bridge module between every interaction between the user and social network server. This paper implements mutation aspects through Genetic Algorithm by differing users into Guests and Friends, and identifies and Cross Over issues of a guest Clicking Friend of a friend.Title: MUTATION AND CROSSOVER ISSUES FOR OSN PRIVACY
Author: C. Narasimham, Jacob
International Journal of Recent Research in Mathematics Computer Science and Information Technology
ISSN: 2350-1022
Paper Publications
Unification Algorithm in Hefty Iterative Multi-tier Classifiers for Gigantic ...
Dr.G.Anandharaj1, Dr.P.Srimanchari2
1Associate Professor and Head, Department of Computer Science
Adhiparasakthi College of Arts and Science (Autonomous), Kalavai, Vellore (Dt) -632506
2 Assistant Professor and Head, Department of Computer Applications
Erode Arts and Science College (Autonomous), Erode (Dt) - 638001
ABSTRACT
In unpredictable increase in mobile apps, more and more threats migrate from outmoded PC client to mobile device. Compared
with traditional windows Intel alliance in PC, Android alliance dominates in Mobile Internet, the apps replace the PC client
software as the foremost target of hateful usage. In this paper, to improve the confidence status of recent mobile apps, we
propose a methodology to estimate mobile apps based on cloud computing platform and data mining. Compared with
traditional method, such as permission pattern based method, combines the dynamic and static analysis methods to
comprehensively evaluate an Android applications The Internet of Things (IoT) indicates a worldwide network of
interconnected items uniquely addressable, via standard communication protocols. Accordingly, preparing us for the
forthcoming invasion of things, a tool called data fusion can be used to manipulate and manage such data in order to improve
progression efficiency and provide advanced intelligence. In this paper, we propose an efficient multidimensional fusion
algorithm for IoT data based on partitioning. Finally, the attribute reduction and rule extraction methods are used to obtain the
synthesis results. By means of proving a few theorems and simulation, the correctness and effectiveness of this algorithm is
illustrated. This paper introduces and investigates large iterative multitier ensemble (LIME) classifiers specifically tailored for
big data. These classifiers are very hefty, but are quite easy to generate and use. They can be so large that it makes sense to use
them only for big data. Our experiments compare LIME classifiers with various vile classifiers and standard ordinary ensemble
Meta classifiers. The results obtained demonstrate that LIME classifiers can significantly increase the accuracy of
classifications. LIME classifiers made better than the base classifiers and standard ensemble Meta classifiers.
Keywords: LIME classifiers, ensemble Meta classifiers, Internet of Things, Big data
Contextual model of recommending resources on an academic networking portal
Artificial Intelligence techniques have been instrumental in helping users to handle the large
amount of information on the Internet. The idea of recommendation systems, custom search
engines, and intelligent software has been widely accepted among users who seek assistance in
searching, sorting, classifying, filtering and sharing this vast quantity of information. In this
paper, we present a contextual model of recommendation engine which keeping in mind the
context and activities of a user, recommends resources in an academic networking portal. The
proposed method uses the implicit method of feedback and the concepts relationship hierarchy
to determine the similarity between a user and the resources in the portal. The proposed
algorithm has been tested on an academic networking portal and the results are convincing.
CONTEXTUAL MODEL OF RECOMMENDING RESOURCES ON AN ACADEMIC NETWORKING PORTAL
Artificial Intelligence techniques have been instrumental in helping users to handle the large amount of information on the Internet. The idea of recommendation systems, custom search engines, and intelligent software has been widely accepted among users who seek assistance insearching, sorting, classifying, filtering and sharing this vast quantity of information. In thispaper, we present a contextual model of recommendation engine which keeping in mind the context and activities of a user, recommends resources in an academic networking portal. Theproposed method uses the implicit method of feedback and the concepts relationship hierarchy to determine the similarity between a user and the resources in the portal. The proposed algorithm has been tested on an academic networking portal and the results are convincing
International Journal of Engineering Research and Development (IJERD)
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journal of engineering, online Submission
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IJCER (www.ijceronline.com) International Journal of computational Engineerin...
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Identical Users in Different Social Media Provides Uniform Network Structure ...
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IRJET- Cross System User Modeling and Personalization on the Social Web
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Avoiding Anonymous Users in Multiple Social Media Networks (SMN)
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Unification Algorithm in Hefty Iterative Multi-tier Classifiers for Gigantic ...
Contextual model of recommending resources on an academic networking portal
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CONTEXTUAL MODEL OF RECOMMENDING RESOURCES ON AN ACADEMIC NETWORKING PORTAL
CONTEXTUAL MODEL OF RECOMMENDING RESOURCES ON AN ACADEMIC NETWORKING PORTAL
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)
More from ieeeprojectsvadapalani
59
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
58
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
57
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
55
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
54
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
53
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
52
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
51
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
50
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
49
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
47
This document presents a spam zombie detection system called SPOT that monitors outgoing messages from a network. SPOT uses a statistical tool called Sequential Probability Ratio Test to detect compromised machines involved in spamming. When evaluated on a two-month email trace from a large campus network, SPOT identified 132 of 440 internal IP addresses as compromised, with 126 being confirmed and 16 likely compromised. SPOT missed only 7 compromised machines and outperformed detection algorithms based on spam message counts.
46
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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
45
This document discusses a new mechanism for improving the trustworthiness of a host system and its data by ensuring the correct origin or provenance of critical system information. It defines data provenance integrity as preventing the source of data from being spoofed or tampered with. It then describes a cryptographic provenance verification approach for enforcing this at the kernel level, with two applications - keystroke integrity verification using a Trusted Computing Platform to prevent forged key events, and a lightweight framework for restricting outbound malware traffic to help identify network activities of stealthy malware.
44
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
43
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
42
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
40
The document proposes a dynamic trust computation model called "SecuredTrust" for evaluating trust in multi-agent systems. It aims to address issues with existing trust/reputation models, including their inability to properly evaluate trust of agents with unpredictable malicious behavior or provide quick response to behavioral changes. The model also seeks to distribute workload evenly among service-providing agents. The paper analyzes factors for evaluating agent trust, proposes a quantitative trust measurement model, and a novel load-balancing algorithm based on defined factors. Simulation results show the model can effectively handle strategic changes in malicious agents and efficiently distribute workloads compared to other models.
39
This document discusses extending and aggregating attack graph-based security metrics to more accurately assess network security. It proposes a new suite of complimentary metrics to overcome the shortcomings of existing metrics like shortest path, number of paths, and mean path length. Specifically, it suggests an algorithm to combine these metrics to better determine which of two attack graphs corresponds to the most secure network configuration in many cases.
38
The document discusses a solution called Assured Digital Signing (ADS) that aims to enhance the trustworthiness of digital signatures. ADS takes advantage of trusted computing and virtualization technologies to examine not only a signature's cryptographic validity, but also its system security validity by ensuring the private signing key and signing function are secure, even if the signing application and operating system kernel are compromised. The modular design of ADS makes it application-transparent and hypervisor-independent. The document reports on an implementation of ADS using the Xen hypervisor to demonstrate feasibility.
37
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
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- 1. Impulse Technologies Beacons U to World of technology 044-42133143, 98401 03301,9841091117 ieeeprojects@yahoo.com www.impulse.net.in Query Profile Obfuscation by Means of Optimal Query Exchange between Users Abstract— We address the problem of query profile obfuscation by means of partial query exchanges between two users, in order for their profiles of interest to appear distorted to the information provider (database, search engine, etc.). We illustrate a methodology to reach mutual privacy gain, that is, a situation where both users increase their own privacy protection through collaboration in query exchange. To this end, our approach starts with a mathematical formulation, involving the modeling of the users' apparent profiles as probability distributions over categories of interest, and the measure of their privacy as the corresponding Shannon entropy. The question of which query categories to exchange translates into finding optimization variables representing exchange policies, for various optimization objectives based on those entropies, possibly under exchange traffic constraints. Your Own Ideas or Any project from any company can be Implemented at Better price (All Projects can be done in Java or DotNet whichever the student wants) 1