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Search results “Web mining recommender systems with social regularization”
A Novel Recommendation Model Regularized with User Trust and Item Ratings
 
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A Novel Recommendation Model Regularized with User Trust and Item Ratings To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com We propose TrustSVD, a trust-based matrix factorization technique for recommendations. TrustSVD integrates multiple information sources into the recommendation model in order to reduce the data sparsity and cold start problems and their degradation of recommendation performance. An analysis of social trust data from four real-world data sets suggests that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. TrustSVD therefore builds on top of a state-of-the-art recommendation algorithm, SVD++ (which uses the explicit and implicit influence of rated items), by further incorporating both the explicit and implicit influence of trusted and trusting users on the prediction of items for an active user. The proposed technique is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that TrustSVD achieves better accuracy than other ten counterparts recommendation techniques.
Views: 863 jpinfotechprojects
An Approach for Building Efficient and Accurate Social Recommender Systems using Individual Relation
 
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An Approach for Building Efficient and Accurate Social Recommender Systems using Individual Relationship Networks Get the Source Code Link : http://linkshrink.net/7WE5mG
Views: 192 1 Crore Projects
A Novel Recommendation Model Regularized with User Trust and Item Ratings
 
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2016 IEEE Transaction on Knowledge and Data Engineering For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com
Views: 624 manju nath
A Novel Recommendation Model Regularized with User Trust and Item Ratings
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 183 Clickmyproject
A Novel Recommendation Model Regularized with User Trust and Item Ratings
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://myprojectbazaar.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 192 myproject bazaar
Data Mining Projects 2016-2017 | ieee data mining papers 2016
 
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ieee data mining papers 2016 for ME,M.Tech.,M.Phil., Ph.D., B.E, B.Tech., MCA A Novel Recommendation Model Regularized with User Trust and Item Ratings Automatically Mining Facets for Queries from Their Search Results Booster in High Dimensional Data Classification Building an intrusion detection system using a filter-based feature selection algorithm Connecting Social Media to E-Commerce: Cold-Start Product Recommendation Using Microblogging Information Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings Crowdsourcing for Top-K Query Processing over Uncertain Data Cyberbullying Detection based on Semantic-Enhanced Marginalized Denoising Auto-Encoder Domain-Sensitive Recommendation with User-Item Subgroup Analysis Efficient Algorithms for Mining Top-K High Utility Itemsets Efficient Cache-Supported Path Planning on Roads Mining User-Aware Rare Sequential Topic Patterns in Document Streams Nearest Keyword Set Search in Multi-Dimensional Datasets Rating Prediction based on Social Sentiment from Textual Reviews Location Aware Keyword Query Suggestion Based on Document Proximity Using Hashtag Graph-based Topic Model to Connect Semantically-related Words without Co-occurrence in Microblogs Quantifying Political Leaning from Tweets, Retweets, and Retweeters Relevance Feedback Algorithms Inspired By Quantum Detection Sentiment Embeddings with Applications to Sentiment Analysis Top-Down XML Keyword Query Processing TopicSketch: Real-time Bursty Topic Detection from Twitter Top-k Dominating Queries on Incomplete Data Understanding Short Texts through Semantic Enrichment and Hashing To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: www.jpinfotech.org, Blog: www.jpinfotech.blogspot.com
Views: 2461 JPINFOTECH PROJECTS
NASA Trust based recommendation engine
 
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Demo for: Generating Co-authorship Graph Generating Co-authorship sub graph Finding Probability of co-authorship between two authors Performance measures
Views: 200 Pujita Rao
Answering Approximate Queries Over XML Data
 
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Answering Approximate Queries Over XML Data To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: www.jpinfotech.org, Blog: www.jpinfotech.blogspot.com With the increasing popularity of XML for data representations, there is a lot of interest in searching XML data. Due to the structural heterogeneity and textual content’s diversity of XML, it is daunting for users to formulate exact queries and search accurate answers. Therefore, approximate matching is introduced to deal with the difficulty in answering users’ queries, and this matching could be addressed by first relaxing the structure and content of a given query and, then, looking for answers that match the relaxed queries. Ranking and returning the most relevant results of a query have become the most popular paradigm in XML query processing. However, the existing proposals do not adequately take structures into account, and they, therefore, lack the strength to elegantly combine structures with contents to answer the relaxed queries. To address this problem, we first propose a sophisticated framework of query relaxations for supporting approximate queries over XML data. The answers underlying this framework are not compelled to strictly satisfy the given query formulation; instead, they can be founded on properties inferable from the original query. We, then, develop a novel top-k retrieval approach that can smartly generate the most promising answers in an order correlated with the ranking measure. We complement the work with a comprehensive set of experiments to show the effectiveness of our proposed approach in terms of precision and recall metrics.
Views: 216 JPINFOTECH PROJECTS
Domain-Sensitive Recommendation with User-Item Subgroup Analysis
 
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Domain-Sensitive Recommendation with User-Item Subgroup Analysis To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Collaborative Filtering (CF) is one of the most successful recommendation approaches to cope with information overload in the real world. However, typical CF methods equally treat every user and item, and cannot distinguish the variation of user’s interests across different domains. This violates the reality that user’s interests always center on some specific domains, and the users having similar tastes on one domain may have totally different tastes on another domain. Motivated by the observation, in this paper, we propose a novel Domain-sensitive Recommendation (DsRec) algorithm, to make the rating prediction by exploring the user-item subgroup analysis simultaneously, in which a user-item subgroup is deemed as a domain consisting of a subset of items with similar attributes and a subset of users who have interests in these items. The proposed framework of DsRec includes three components: a matrix factorization model for the observed rating reconstruction, a bi-clustering model for the user-item subgroup analysis, and two regularization terms to connect the above two components into a unified formulation. Extensive experiments on Movielens-100K and two real-world product review datasets show that our method achieves the better performance in terms of prediction accuracy criterion over the state-of-the-art methods.
Views: 433 jpinfotechprojects
Domain-Sensitive Recommendation with User-Item Subgroup Analysis
 
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Domain-Sensitive Recommendation with User-Item Subgroup Analysis http://jpinfotech.org/final-year-ieee-projects/2016-ieee-projects/ To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: www.jpinfotech.org, Blog: www.jpinfotech.blogspot.com Collaborative Filtering (CF) is one of the most successful recommendation approaches to cope with information overload in the real world. However, typical CF methods equally treat every user and item, and cannot distinguish the variation of user’s interests across different domains. This violates the reality that user’s interests always center on some specific domains, and the users having similar tastes on one domain may have totally different tastes on another domain. Motivated by the observation, in this paper, we propose a novel Domain-sensitive Recommendation (DsRec) algorithm, to make the rating prediction by exploring the user-item subgroup analysis simultaneously, in which a user-item subgroup is deemed as a domain consisting of a subset of items with similar attributes and a subset of users who have interests in these items. The proposed framework of DsRec includes three components: a matrix factorization model for the observed rating reconstruction, a bi-clustering model for the user-item subgroup analysis, and two regularization terms to connect the above two components into a unified formulation. Extensive experiments on Movielens-100K and two real-world product review datasets show that our method achieves the better performance in terms of prediction accuracy criterion over the state-of-the-art methods.
Views: 182 JPINFOTECH PROJECTS
Optimized Search-and-Compute Circuits and Their Application to Query Evaluation on Encrypted Data
 
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Optimized Search-and-Compute Circuits and Their Application to Query Evaluation on Encrypted Data TO GET THIS PROJECT IN ONLINE OR THROUGH TRAINING SESSIONS CONTACT: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landmark: Opp. To Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690 Email: [email protected], Website: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Private query processing on encrypted databases allows users to obtain data from encrypted databases in such a way that the users’ sensitive data will be protected from exposure. Given an encrypted database, users typically submit queries similar to the following examples: 1) How many employees in an organization make over U.S. $100 000? 2) What is the average age of factory workers suffering from leukemia? Answering the questions requires one to search and then compute over the relevant encrypted data sets in sequence. In this paper, we are interested in efficiently processing queries that require both operations to be performed on fully encrypted databases. One immediate solution is to use several special-purpose encryption schemes simultaneously; however, this approach is associated with a high computational cost for maintaining multiple encryption contexts. Another solution is to use a privacy homomorphic scheme. However, no secure solutions have been developed that satisfy the efficiency requirements. In this paper, we construct a unified framework to efficiently and privately process queries with search and compute operations. For this purpose, the first part of our work involves devising several underlying circuits as primitives for queries on encrypted data. Second, we apply two optimization techniques to improve the efficiency of these circuit primitives. One technique involves exploiting single-instruction-multiple-data (SIMD) techniques to accelerate the basic circuit operations. Unlike general SIMD approaches, our SIMD implementation can be applied even to a single basic operation. The other technique is to use a large integer ring (e.g., Z2t) as a message space rather than a binary field. Even for an integer of k bits with , addition can be performed using degree 1 circuits with lazy carry operations. Finally, we present various experiments performed by varying the considered parameters, such as the query type and the number of tuples.
Views: 238 jpinfotechprojects
Efficient Keyword-aware Representative Travel Route Recommendation
 
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Efficient Keyword-aware Representative Travel Route Recommendation Get the Source Code link : http://linkshrink.net/7BaJua CONTACT US 1 CRORE PROJECTS Door No: 68 & 70,Ground Floor, No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai, Tamin Nadu, INDIA - 600 026 Email id: [email protected] website:1croreprojects.com Phone : +91 97518 00789 / +91 72999 51536
Views: 1324 1 Crore Projects
Location Aware Keyword Query Suggestion Based on Document Proximity
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 460 Clickmyproject
Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks
 
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Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks Huan Zhao (HKUST) Quanming Yao (HKUST) Jianda Li (HKUST) Yangqiu Song (HKUST) Dik Lee (HKUST) Heterogeneous Information Network (HIN) is a natural and general representation of data in modern large commercial recommender systems which involve heterogeneous types of data. HIN based recommenders face two problems: how to represent the high-level semantics of recommendations and how to fuse the heterogeneous information to make recommendations. In this paper, we solve the two problems by first introducing the concept of meta-graph to HIN-based recommendation, and then solving the information fusion problem with a “matrix factorization (MF) + factorization machine (FM)” approach. For the similarities generated by each meta-graph, we perform standard MF to generate latent features for both users and items. With different meta-graph based features, we propose a group lasso regularized FM to automatically learn from the observed ratings to effectively select useful meta-graph based features. Experimental results on two real-world datasets, Amazon and Yelp, show the effectiveness of our approach compared to stateof-the-art FM and other HIN-based recommendation algorithms.
Views: 940 KDD2017 video
Efficient Keyword-Aware Representative Travel Route Recommendation
 
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Efficient Keyword-Aware Representative Travel Route Recommendation IEEE 2017-18 S/W: ANDROID
Efficient Keyword-aware Representative Travel Route Recommendation
 
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Efficient Keyword-aware Representative Travel Route Recommendation To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org With the popularity of social media (e.g., Facebook and Flicker), users can easily share their check-in records and photos during their trips. In view of the huge number of user historical mobility records in social media, we aim to discover travel experiences to facilitate trip planning. When planning a trip, users always have specific preferences regarding their trips. Instead of restricting users to limited query options such as locations, activities or time periods, we consider arbitrary text descriptions as keywords about personalized requirements. Moreover, a diverse and representative set of recommended travel routes is needed. Prior works have elaborated on mining and ranking existing routes from check-in data. To meet the need for automatic trip organization, we claim that more features of Places of Interest (POIs) should be extracted. Therefore, in this paper, we propose an efficient Keyword-aware Representative Travel Route framework that uses knowledge extraction from users’ historical mobility records and social interactions. Explicitly, we have designed a keyword extraction module to classify the POI-related tags, for effective matching with query keywords. We have further designed a route reconstruction algorithm to construct route candidates that fulfill the requirements. To provide befitting query results, we explore Representative Skyline concepts, that is, the Skyline routes which best describe the trade-offs among different POI features. To evaluate the effectiveness and efficiency of the proposed algorithms, we have conducted extensive experiments on real location-based social network datasets, and the experiment results show that our methods do indeed demonstrate good performance compared to state-of-the-art works.
Views: 856 jpinfotechprojects
Local Higher-Order Graph Clustering
 
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Local Higher-Order Graph Clustering Hao Yin (Stanford University) Austin R. Benson (Stanford University) Jure Leskovec (Stanford University) David F. Gleich (Purdue University) Local graph clustering methods aim to find a cluster of nodes by exploring a small region of the graph. These methods are attractive because they enable targeted clustering around a given seed node and are faster than traditional global graph clustering methods because their runtime does not depend on the size of the input graph. However, current local graph partitioning methods are not designed to account for the higher-order structures crucial to the network, nor can they effectively handle directed networks. Here we introduce a new class of local graph clustering methods that address these issues by incorporating higher-order network information captured by small subgraphs, also called network motifs. First, we show how to adapt the approximate personalized PageRank algorithm to find clusters containing a seed node with minimal motif conductance, a generalization of the conductance metric for network motifs. We also generalize existing theory to maintain the properties of fast running time (independent of the size of the graph) and cluster quality (in terms of motif conductance). For community detection tasks on both synthetic and real-world networks, our new framework outperforms the current edge-based personalized PageRank methodology. Second, we develop a theory of node neighborhoods for finding sets that have small motif conductance, where the motif is a clique. We apply these results to the case of finding good seed nodes to use as input to the personalized PageRank algorithm. More on http://www.kdd.org/kdd2017/.
Views: 703 KDD2017 video
Analysis of users’ behaviour in structured e commerce websites
 
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Greetings from ChennaiSunday Systems Pvt Ltd www.chennaisunday.com Our motto is to bridge the knowledge gap between the academics and the industry.We provide project support for all courses include Ph.D,M.Phil, M.E/M.Tech, B.E/B.Tech, MCA/BCA, MBA/BBA, M.SC/B.Sc and etc.We undertake project works of all major universities 1. BIG DATA – MONGODB WITH NOSQL, JAVA WITH ANGULARJS, NODEJS 2. ANDROID , ANDROID WITH JSON AND PHP , CLOUD IMPLEMENTATION 3. DOT NET MVC FOR RAZOR FRAMWORK
Views: 182 Siva Kumar
Personalized Travel Sequence Recommendation on Multi-Source Big Social Media
 
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Personalized Travel Sequence Recommendation on Multi-Source Big Social Media Big data increasingly benefit both research and industrial area such as health care, finance service and commercial recommendation. This paper presents a personalized travel sequence recommendation from both travelogues and community contributed photos and the heterogeneous metadata (e.g., tags, geo-location, and date taken) associated with these photos. Unlike most existing travel recommendation approaches, our approach is not only personalized to user's travel interest but also able to recommend a travel sequence rather than individual Points of Interest (POIs). Topical package space including representative tags, the distributions of cost, visiting time and visiting season of each topic, is mined to bridge the vocabulary gap between user travel preference and travel routes. We take advantage of the complementary of two kinds of social media: travelogue and community contributed photos. We map both user's and routes' textual descriptions to the topical package space to get user topical package model and route topical package model (i.e., topical interest, cost, time and season). To recommend personalized POI sequence, first, famous routes are ranked according to the similarity between user package and route package. Then top ranked routes are further optimized by social similar users' travel records. Representative images with viewpoint and seasonal diversity of POIs are shown to offer a more comprehensive impression. We evaluate our recommendation system on a collection of 7 million Flickr images uploaded by 7,387 users and 24,008 travelogues covering 864 travel POIs in nine famous cities, and show its effectiveness. We also contribute a new dataset with more than 200 K photos with heterogeneous metadata in nine famous cities. SIMILAR VIDEOS: https://www.youtube.com/watch?v=AZI6oHAEtU8 https://www.youtube.com/watch?v=o0mT99zKAqA https://www.youtube.com/watch?v=X7jZtTq74WU https://www.youtube.com/watch?v=EO1rgFk07kQ https://www.youtube.com/watch?v=ACtU9aaoh_8 https://www.youtube.com/watch?v=cbZFKV4A0X8 https://www.youtube.com/watch?v=AWcD3pIGJjI https://www.youtube.com/watch?v=0y5w5CbMips https://www.youtube.com/watch?v=rhCtDFPNHCE https://www.youtube.com/watch?v=t41nfgBy8pY https://www.youtube.com/watch?v=LLUlzVlIJOw https://www.youtube.com/watch?v=mSjS4IGyrW0 https://www.youtube.com/watch?v=1TnAqAkxuws https://www.youtube.com/watch?v=nxoUUe8rrtQ https://www.youtube.com/watch?v=XBzwg1EY2SI https://www.youtube.com/watch?v=RRVWWUd9NLk https://www.youtube.com/watch?v=Es0eHDHksiM https://www.youtube.com/watch?v=x5CAAPGuo3g https://www.youtube.com/watch?v=sQKIpfEpQmo https://www.youtube.com/watch?v=hcmrJkwn1T4 https://www.youtube.com/watch?v=cNw3u68a424 https://www.youtube.com/watch?v=6sKfA1vFZBA https://www.youtube.com/watch?v=cFsryGMYxIE For More Videos - https://www.youtube.com/channel/UCR5lsF-lDQu6rVYVJPqNn6Q SOCIAL HANDLES: SCOOP IT- http://www.scoop.it/u/1croreprojects FACEBOOK - https://www.facebook.com/1Croreprojectsieeeprojects/ TWITTER - https://twitter.com/1crore_projects LINKEDIN - https://www.linkedin.com/in/1-crore-projects-ba982a118/ GOOGLE+ - https://plus.google.com/u/0/105783610929019156122 PINTEREST - https://in.pinterest.com/onecroreproject/ BLOG - 1croreprojectz.blogspot.com DOMAIN PROJECTS DOTNET - http://www.1croreprojects.com/dotnet-ieee-project-centers-in-chennai.php JAVA - http://www.1croreprojects.com/java-ieee-projects-chennai.php EMBEDDED - http://www.1croreprojects.com/embedded-systems-ieee-projects-chennai.php MATLAB - http://www.1croreprojects.com/matlab-ieee-projects-chennai.php NS2 - http://www.1croreprojects.com/ns2-ieee-projects-chennai.php VLSI -http://www.1croreprojects.com/vlsi-ieee-projects-chennai.php FOR PROJECTS - http://www.1croreprojects.com/ BUSINESS CONTACT: Email - [email protected] We are always open for all business prospects. You can get in touch which us, using the above mentioned e-mail id and contact number. ABOUT 1CROREPROJECTS: 1Crore Projects is company providing outstanding, cost-effective, effective result authorized on solutions. Our objective is to create solutions that enhance company process and increase come back in most possible time. We started truly to provide solutions to the customers all over the world. We have been effectively in providing solutions for different challenges across a wide range of market and customers propagate across the globe.
Views: 1104 1 Crore Projects
Web Image Re Ranking Using Query Specific Semantic Signatures
 
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1 crore projects offering Final year IEEE 2016- 2017 students project which can be implemented in any language.. SIMILAR VIDEOS: https://www.youtube.com/watch?v=AZI6oHAEtU8 https://www.youtube.com/watch?v=o0mT99zKAqA https://www.youtube.com/watch?v=X7jZtTq74WU https://www.youtube.com/watch?v=EO1rgFk07kQ https://www.youtube.com/watch?v=ACtU9aaoh_8 https://www.youtube.com/watch?v=cbZFKV4A0X8 https://www.youtube.com/watch?v=AWcD3pIGJjI https://www.youtube.com/watch?v=0y5w5CbMips https://www.youtube.com/watch?v=rhCtDFPNHCE https://www.youtube.com/watch?v=t41nfgBy8pY https://www.youtube.com/watch?v=LLUlzVlIJOw https://www.youtube.com/watch?v=mSjS4IGyrW0 https://www.youtube.com/watch?v=1TnAqAkxuws https://www.youtube.com/watch?v=nxoUUe8rrtQ https://www.youtube.com/watch?v=XBzwg1EY2SI https://www.youtube.com/watch?v=RRVWWUd9NLk https://www.youtube.com/watch?v=Es0eHDHksiM https://www.youtube.com/watch?v=x5CAAPGuo3g https://www.youtube.com/watch?v=sQKIpfEpQmo https://www.youtube.com/watch?v=hcmrJkwn1T4 https://www.youtube.com/watch?v=cNw3u68a424 https://www.youtube.com/watch?v=6sKfA1vFZBA https://www.youtube.com/watch?v=cFsryGMYxIE For More Videos - https://www.youtube.com/channel/UCR5lsF-lDQu6rVYVJPqNn6Q SOCIAL HANDLES: SCOOP IT- http://www.scoop.it/u/1croreprojects FACEBOOK - https://www.facebook.com/1Croreprojectsieeeprojects/ TWITTER - https://twitter.com/1crore_projects LINKEDIN - https://www.linkedin.com/in/1-crore-projects-ba982a118/ GOOGLE+ - https://plus.google.com/u/0/105783610929019156122 PINTEREST - https://in.pinterest.com/onecroreproject/ BLOG - 1croreprojectz.blogspot.com DOMAIN PROJECTS DOTNET - http://www.1croreprojects.com/dotnet-ieee-project-centers-in-chennai.php JAVA - http://www.1croreprojects.com/java-ieee-projects-chennai.php EMBEDDED - http://www.1croreprojects.com/embedded-systems-ieee-projects-chennai.php MATLAB - http://www.1croreprojects.com/matlab-ieee-projects-chennai.php NS2 - http://www.1croreprojects.com/ns2-ieee-projects-chennai.php VLSI -http://www.1croreprojects.com/vlsi-ieee-projects-chennai.php FOR PROJECTS - http://www.1croreprojects.com/ BUSINESS CONTACT: Email - [email protected] We are always open for all business prospects. You can get in touch which us, using the above mentioned e-mail id and contact number. ABOUT 1CROREPROJECTS: 1Crore Projects is company providing outstanding, cost-effective, effective result authorized on solutions. Our objective is to create solutions that enhance company process and increase come back in most possible time. We started truly to provide solutions to the customers all over the world. We have been effectively in providing solutions for different challenges across a wide range of market and customers propagate across the globe.
Views: 103 1 Crore Projects
User-Service Rating Prediction by Exploring Social Users’ Rating Behaviors
 
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User-Service Rating Prediction by Exploring Social Users’ Rating Behaviors TO GET THIS PROJECT IN ONLINE OR THROUGH TRAINING SESSIONS CONTACT: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landmark: Opp. To Thattanchavady Industrial Estate & Next to VVP Nagar Arch. Landline: (0413) - 4300535 / Mobile: (0)8608600246 / (0)9952649690 Email: [email protected], Website: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com With the boom of social media, it is a very popular trend for people to share what they are doing with friends across various social networking platforms. Nowadays, we have a vast amount of descriptions, comments, and ratings for local services. The information is valuable for new users to judge whether the services meet their requirements before partaking. In this paper, we propose a user-service rating prediction approach by exploring social users’ rating behaviors. In order to predict user-service ratings, we focus on users’ rating behaviors. In our opinion, the rating behavior in recommender system could be embodied in these aspects: 1) when user rated the item, 2) what the rating is, 3) what the item is, 4) what the user interest that we could dig from his/her rating records is, and 5) how the user’s rating behavior diffuses among his/her social friends. Therefore, we propose a concept of the rating schedule to represent users’ daily rating behaviors. In addition, we propose the factor of interpersonal rating behavior diffusion to deep understand users’ rating behaviors. In the proposed user-service rating prediction approach, we fuse four factors—user personal interest (related to user and the item’s topics), interpersonal interest similarity (related to user interest), interpersonal rating behavior similarity (related to users’ rating behavior habits), and interpersonal rating behavior diffusion (related to users’ behavior diffusions)—into a unified matrix-factorized framework. We conduct a series of experiments in the Yelp dataset and Douban Movie dataset. Experimental results show the effectiveness of our approach.
Views: 753 jpinfotechprojects
Mining Weakly Labeled Web Facial Images for Search-Based Face Annotation
 
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To get this project in ONLINE or through TRAINING Sessions, Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com Mining Weakly Labeled Web Facial Images for Search-Based Face Annotation in DOT NET
Views: 608 jpinfotechprojects
User-Centric Similarity Search
 
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User-Centric Similarity Search To get this project in Online or through training sessions Contact: Chennai Office: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai – 83. Landmark: Next to Kotak Mahendra Bank / Bharath Scans. Landline: (044) - 43012642 / Mobile: (0)9952649690 Pondicherry Office: JP INFOTECH, #37, Kamaraj Salai, Thattanchavady, Puducherry – 9. Landline: (0413) - 4204066 / (0)9952649690 Email: [email protected], Mobile: (0)9952649690, Website: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com User preferences play a significant role in market analysis. In the database literature there has been extensive work on query primitives, such as the well known top-k query that can be used for the ranking of products based on the preferences customers have expressed. Still, the fundamental operation that evaluates the similarity between products is typically done ignoring these preferences. Instead products are depicted in a feature space based on their attributes and similarity is computed via traditional distance metrics on that space. In this work we utilize the rankings of the products based on the opinions of their customers in order to map the products in a user-centric space where similarity calculations are performed. We identify important properties of this mapping that result in upper and lower similarity bounds, which in turn permit us to utilize conventional multidimensional indexes on the original product space in order to perform these user-centric similarity computations. We show how interesting similarity calculations that are motivated by the commonly used range and nearest neighbor queries can be performed efficiently, while pruning significant parts of the data set based on the bounds we derive on the user-centric similarity of products.
Views: 513 jpinfotechprojects
Tag Based Image Search by Social Re-ranking
 
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Tag Based Image Search by Social Re-ranking
EMR: A Scalable Graph-based Ranking Model for Content-based Image Retrieval
 
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FINAL YEAR STUDENTS PROJECT www.finalyearstudentsproject.in Opposite to Sripuram Bus Stop, Back of Rajadeepan Jewellers, Tirunelveli. Phone: +91-8903410319 Tamil Nadu India General Information and Enquiries: [email protected] PROJECTS FROM Final Year Students Project 2015 ieee projects, 2015 ieee java projects, 2015 ieee dotnet projects, 2015 ieee android projects, 2015 ieee matlab projects, 2015 ieee embedded projects, 2015 ieee robotics projects, 2015 IEEE EEE PROJECTS, 2015 IEEE POWER ELECTRONICS PROJECTS, ieee 2015 android projects, ieee 2015 java projects, ieee 2015 dotnet projects, 2015 ieee mtech projects, 2015 ieee btech projects, 2015 ieee be projects, ieee 2015 projects for cse, 2015 ieee cse projects, 2015 ieee it projects, 2015 ieee ece projects, 2015 ieee mca projects, 2015 ieee mphil projects, tirunelveli ieee projects, best project centre in tirunelveli, bulk ieee projects, pg embedded systems ieee projects, pg embedded systems ieee projects, latest ieee projects, ieee projects for mtech, ieee projects for btech, ieee projects for mphil, ieee projects for be, ieee projects, student projects, students ieee projects, ieee proejcts india, ms projects, bits pilani ms projects, uk ms projects, ms ieee projects, ieee android real time projects, 2015 mtech projects, 2015 mphil projects, 2015 ieee projects with source code, tirunelveli mtech projects, pg embedded systems ieee projects, ieee projects, 2015 ieee project source code, journal paper publication guidance, conference paper publication guidance, ieee project, free ieee project, ieee projects for students., 2015 ieee omnet++ projects, ieee 2015 oment++ project, innovative ieee projects, latest ieee projects, 2015 latest ieee projects, ieee cloud computing projects, 2015 ieee cloud computing projects, 2015 ieee networking projects, ieee networking projects, 2015 ieee data mining projects, ieee data mining projects, 2015 ieee network security projects, ieee network security projects, 2015 ieee image processing projects, ieee image processing projects, ieee parallel and distributed system projects, ieee information security projects, 2015 wireless networking projects ieee, 2015 ieee web service projects, 2015 ieee soa projects, ieee 2015 vlsi projects, NS2 PROJECTS,NS3 PROJECTS. DOWNLOAD IEEE PROJECTS: 2015 IEEE java projects,2015 ieee Project Titles, 2015 IEEE cse Project Titles, 2015 IEEE NS2 Project Titles, 2015 IEEE dotnet Project Titles. IEEE Software Project Titles, IEEE Embedded System Project Titles, IEEE JavaProject Titles, IEEE DotNET ... IEEE Projects 2015 - 2015 ... Image Processing. IEEE 2015 - 2015 Projects | IEEE Latest Projects 2015 - 2015 | IEEE ECE Projects2015 - 2015, matlab projects, vlsi projects, software projects, embedded. eee projects download, base paper for ieee projects, ieee projects list, ieee projectstitles, ieee projects for cse, ieee projects on networking,ieee projects. Image Processing ieee projects with source code, Image Processing ieee projectsfree download, Image Processing application projects free download. .NET Project Titles, 2015 IEEE C#, C Sharp Project Titles, 2015 IEEE EmbeddedProject Titles, 2015 IEEE NS2 Project Titles, 2015 IEEE Android Project Titles. 2015 IEEE PROJECTS, IEEE PROJECTS FOR CSE 2015, IEEE 2015 PROJECT TITLES, M.TECH. PROJECTS 2015, IEEE 2015 ME PROJECTS.
Views: 90 HARISH G
Efficient Keyword Aware Representative Travel Route Recommendation
 
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Greetings from ChennaiSunday Systems Pvt Ltd www.chennaisunday.com Our motto is to bridge the knowledge gap between the academics and the industry.We provide project support for all courses include Ph.D,M.Phil, M.E/M.Tech, B.E/B.Tech, MCA/BCA, MBA/BBA, M.SC/B.Sc and etc.We undertake project works of all major universities 1. BIG DATA – MONGODB WITH NOSQL, JAVA WITH ANGULARJS, NODEJS 2. ANDROID , ANDROID WITH JSON AND PHP , CLOUD IMPLEMENTATION 3. DOT NET MVC FOR RAZOR FRAMWORK
Views: 135 Siva Kumar
Text Mining the Contributors to Rail Accidents
 
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2016 IEEE Transaction on Knowledge and Data Engineering For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com 2016 and 2017 IEEE @ TMKS Infotech,Bangalore
Views: 394 manju nath
Top k Dominating Queries on Incomplete Data
 
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2016 IEEE Transaction on Knowledge and Data Engineering For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com
Views: 994 manju nath
Query Planning for Continuous Aggregation Queries over a Network of Data Aggregators
 
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Continuous queries are used to monitor changes to time varying data and to provide results useful for online decision making. Typically a user desires to obtain the value of some aggregation function over distributed data items, for example, to know value of portfolio for a client; or the AVG of temperatures sensed by a set of sensors. In these queries a client specifies a coherency requirement as part of the query. We present a low-cost, scalable technique to answer continuous aggregation queries using a network of aggregators of dynamic data items. In such a network of data aggregators, each data aggregator serves a set of data items at specific coherencies. Just as various fragments of a dynamic webpage are served by one or more nodes of a content distribution network, our technique involves decomposing a client query into subqueries and executing subqueries on judiciously chosen data aggregators with their individual subquery incoherency bounds. We provide a technique for getting the optimal set of subqueries with their incoherency bounds which satisfies client query's coherency requirement with least number of refresh messages sent from aggregators to the client. For estimating the number of refresh messages, we build a query cost model which can be used to estimate the number of messages required to satisfy the client specified incoherency bound. Performance results using real-world traces show that our cost-based query planning leads to queries being executed using less than one third the number of messages required by existing schemes.
Views: 231 Renown Technologies
Protecting Your Right Attribute based Keyword Search with Finegrained Owner
 
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Protecting your right: Attribute-based keyword search with fine-grained owner-enforced search authorization in the cloud Search over encrypted data is a critically important enabling technique in cloud computing, where encryption-before-outsourcing is a fundamental solution to protecting user data privacy in the untrusted cloud server environment. Many secure search schemes have been focusing on the single-contributor scenario, where the outsourced dataset or the secure searchable index of the dataset are encrypted and managed by a single owner, typically based on symmetric cryptography. In this paper, we focus on a different yet more challenging scenario where the outsourced dataset can be contributed from multiple owners and are searchable by multiple users, i.e. multi-user multi-contributor case. Inspired by attribute-based encryption (ABE), we present the first attribute-based keyword search scheme with efficient user revocation (ABKS-UR) that enables scalable fine-grained (i.e. file-level) search authorization. Our scheme allows multiple owners to encrypt and outsource their data to the cloud server independently. Users can generate their own search capabilities without relying on an always online trusted authority. Fine-grained search authorization is also implemented by the owner-enforced access policy on the index of each file. Further, by incorporating proxy re-encryption and lazy re-encryption techniques, we are able to delegate heavy system update workload during user revocation to the resourceful semi-trusted cloud server. We formalize the security definition and prove the proposed ABKS-UR scheme selectively secure against chosen-keyword attack. Finally, performance evaluation shows the efficiency of our scheme. SIMILAR VIDEOS: https://www.youtube.com/watch?v=AZI6oHAEtU8 https://www.youtube.com/watch?v=o0mT99zKAqA https://www.youtube.com/watch?v=X7jZtTq74WU https://www.youtube.com/watch?v=EO1rgFk07kQ https://www.youtube.com/watch?v=ACtU9aaoh_8 https://www.youtube.com/watch?v=cbZFKV4A0X8 https://www.youtube.com/watch?v=AWcD3pIGJjI https://www.youtube.com/watch?v=0y5w5CbMips https://www.youtube.com/watch?v=rhCtDFPNHCE https://www.youtube.com/watch?v=t41nfgBy8pY https://www.youtube.com/watch?v=LLUlzVlIJOw https://www.youtube.com/watch?v=mSjS4IGyrW0 https://www.youtube.com/watch?v=1TnAqAkxuws https://www.youtube.com/watch?v=nxoUUe8rrtQ https://www.youtube.com/watch?v=XBzwg1EY2SI https://www.youtube.com/watch?v=RRVWWUd9NLk https://www.youtube.com/watch?v=Es0eHDHksiM https://www.youtube.com/watch?v=x5CAAPGuo3g https://www.youtube.com/watch?v=sQKIpfEpQmo https://www.youtube.com/watch?v=hcmrJkwn1T4 https://www.youtube.com/watch?v=cNw3u68a424 https://www.youtube.com/watch?v=6sKfA1vFZBA https://www.youtube.com/watch?v=cFsryGMYxIE For More Videos - https://www.youtube.com/channel/UCR5lsF-lDQu6rVYVJPqNn6Q SOCIAL HANDLES: SCOOP IT- http://www.scoop.it/u/1croreprojects FACEBOOK - https://www.facebook.com/1Croreprojectsieeeprojects/ TWITTER - https://twitter.com/1crore_projects LINKEDIN - https://www.linkedin.com/in/1-crore-projects-ba982a118/ GOOGLE+ - https://plus.google.com/u/0/105783610929019156122 PINTEREST - https://in.pinterest.com/onecroreproject/ BLOG - 1croreprojectz.blogspot.com DOMAIN PROJECTS DOTNET - http://www.1croreprojects.com/dotnet-ieee-project-centers-in-chennai.php JAVA - http://www.1croreprojects.com/java-ieee-projects-chennai.php EMBEDDED - http://www.1croreprojects.com/embedded-systems-ieee-projects-chennai.php MATLAB - http://www.1croreprojects.com/matlab-ieee-projects-chennai.php NS2 - http://www.1croreprojects.com/ns2-ieee-projects-chennai.php VLSI -http://www.1croreprojects.com/vlsi-ieee-projects-chennai.php FOR PROJECTS - http://www.1croreprojects.com/ BUSINESS CONTACT: Email - [email protected] We are always open for all business prospects. You can get in touch which us, using the above mentioned e-mail id and contact number. ABOUT 1CROREPROJECTS: 1Crore Projects is company providing outstanding, cost-effective, effective result authorized on solutions. Our objective is to create solutions that enhance company process and increase come back in most possible time. We started truly to provide solutions to the customers all over the world. We have been effectively in providing solutions for different challenges across a wide range of market and customers propagate across the globe.
Views: 375 1 Crore Projects
Inverted Linear Quadtree Efficient Top K Spatial Keyword Search
 
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IEEE PROJECTS 2016 - 2017 1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider. It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training. SIMILAR VIDEOS: https://www.youtube.com/watch?v=AZI6oHAEtU8 https://www.youtube.com/watch?v=o0mT99zKAqA https://www.youtube.com/watch?v=X7jZtTq74WU https://www.youtube.com/watch?v=EO1rgFk07kQ https://www.youtube.com/watch?v=ACtU9aaoh_8 https://www.youtube.com/watch?v=cbZFKV4A0X8 https://www.youtube.com/watch?v=AWcD3pIGJjI https://www.youtube.com/watch?v=0y5w5CbMips https://www.youtube.com/watch?v=rhCtDFPNHCE https://www.youtube.com/watch?v=t41nfgBy8pY https://www.youtube.com/watch?v=LLUlzVlIJOw https://www.youtube.com/watch?v=mSjS4IGyrW0 https://www.youtube.com/watch?v=1TnAqAkxuws https://www.youtube.com/watch?v=nxoUUe8rrtQ https://www.youtube.com/watch?v=XBzwg1EY2SI https://www.youtube.com/watch?v=RRVWWUd9NLk https://www.youtube.com/watch?v=Es0eHDHksiM https://www.youtube.com/watch?v=x5CAAPGuo3g https://www.youtube.com/watch?v=sQKIpfEpQmo https://www.youtube.com/watch?v=hcmrJkwn1T4 https://www.youtube.com/watch?v=cNw3u68a424 https://www.youtube.com/watch?v=6sKfA1vFZBA https://www.youtube.com/watch?v=cFsryGMYxIE For More Videos - https://www.youtube.com/channel/UCR5lsF-lDQu6rVYVJPqNn6Q SOCIAL HANDLES: SCOOP IT- http://www.scoop.it/u/1croreprojects FACEBOOK - https://www.facebook.com/1Croreprojectsieeeprojects/ TWITTER - https://twitter.com/1crore_projects LINKEDIN - https://www.linkedin.com/in/1-crore-projects-ba982a118/ GOOGLE+ - https://plus.google.com/u/0/105783610929019156122 PINTEREST - https://in.pinterest.com/onecroreproject/ BLOG - 1croreprojectz.blogspot.com DOMAIN PROJECTS DOTNET - http://www.1croreprojects.com/dotnet-ieee-project-centers-in-chennai.php JAVA - http://www.1croreprojects.com/java-ieee-projects-chennai.php EMBEDDED - http://www.1croreprojects.com/embedded-systems-ieee-projects-chennai.php MATLAB - http://www.1croreprojects.com/matlab-ieee-projects-chennai.php NS2 - http://www.1croreprojects.com/ns2-ieee-projects-chennai.php VLSI -http://www.1croreprojects.com/vlsi-ieee-projects-chennai.php FOR PROJECTS - http://www.1croreprojects.com/ BUSINESS CONTACT: Email - [email protected] We are always open for all business prospects. You can get in touch which us, using the above mentioned e-mail id and contact number. ABOUT 1CROREPROJECTS: 1Crore Projects is company providing outstanding, cost-effective, effective result authorized on solutions. Our objective is to create solutions that enhance company process and increase come back in most possible time. We started truly to provide solutions to the customers all over the world. We have been effectively in providing solutions for different challenges across a wide range of market and customers propagate across the globe.
Views: 327 1 Crore Projects
A Locality Sensitive Low-Rank Model for Image Tag Completion
 
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A Locality Sensitive Low-Rank Model for Image Tag Completion To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com Many visual applications have benefited from the outburst of web images, yet the imprecise and incomplete tags arbitrarily provided by users, as the thorn of the rose, may hamper the performance of retrieval or indexing systems relying on such data. In this paper, we propose a novel locality sensitive low-rank model for image tag completion, which approximates the global nonlinear model with a collection of local linear models. To effectively infuse the idea of locality sensitivity, a simple and effective pre-processing module is designed to learn suitable representation for data partition, and a global consensus regularizer is introduced to mitigate the risk of over fitting. Meanwhile, low-rank matrix factorization is employed as local models, where the local geometry structures are preserved for the low-dimensional representation of both tags and samples. Extensive empirical evaluations conducted on three datasets demonstrate the effectiveness and efficiency of the proposed method, where our method outperforms pervious ones by a large margin.
Views: 235 jpinfotechprojects
A Probabilistic Misbehavior Detection Scheme towards Efficient Trust Establishment in DTN
 
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To get this project in ONLINE or through TRAINING Sessions, Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com A Probabilistic Misbehavior Detection Scheme towards Efficient Trust Establishment in Delay-tolerant Networks Malicious and selfish behaviors represent a serious threat against routing in Delay/Disruption Tolerant Networks (DTNs). Due to the unique network characteristics, designing a misbehavior detection scheme in DTN is regarded as a great challenge. In this paper, we propose iTrust, a probabilistic misbehavior detection scheme, for secure DTN routing towards efficient trust establishment. The basic idea of iTrust is introducing a periodically available Trusted Authority (TA) to judge the node’s behavior based on the collected routing evidences and probabilistically checking. We model iTrust as the Inspection Game and use game theoretical analysis to demonstrate that, by setting an appropriate investigation probability, TA could ensure the security of DTN routing at a reduced cost. To further improve the efficiency of the proposed scheme, we correlate detection probability with a node’s reputation, which allows a dynamic detection probability determined by the trust of the users. The extensive analysis and simulation results show that the proposed scheme substantiates the effectiveness and efficiency of the proposed scheme.
Views: 322 jpinfotechprojects
Differentiated Virtual Passwords, Secret Little Functions, and Codebooks for Protecting Users
 
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Differentiated Virtual Passwords, Secret Little Functions, and Codebooks for Protecting Users From Password Theft To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: www.jpinfotech.org, Blog: www.jpinfotech.blogspot.com In this paper, we discuss how to prevent users’ passwords from being stolen by adversaries in online environments and automated teller machines. We propose differentiated virtual password mechanisms in which a user has the freedom to choose a virtual password scheme ranging from weak security to strong security, where a virtual password requires a small amount of human computing to secure users’ passwords. The tradeoff is that the stronger the scheme, the more complex the scheme may be. Among the schemes, we have a default method (i.e., traditional password scheme), system recommended functions, user-specified functions, user-specified programs, and so on. A function/program is used to implement the virtual password concept with a tradeoff of security for complexity requiring a small amount of human computing. We further propose several functions to serve as system recommended functions and provide a security analysis. For user-specified functions, we adopt secret little functions in which security is enhanced by hiding secret functions/algorithms.
Views: 257 jpinfotechprojects
Understanding Short Texts through Semantic Enrichment and Hashing
 
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Understanding Short Texts through Semantic Enrichment and Hashing Abstract: Clustering short texts (such as news titles) by their meaning is a challenging task. The semantic hashing approach encodes the meaning of a text into a compact binary code. Thus, to tell if two texts have similar meanings, we only need to check if they have similar codes. The encoding is created by a deep neural network, which is trained on texts represented by word-count vectors (bag-of-word representation). Unfortunately, for short texts such as search queries, tweets, or news titles, such representations are insufficient to capture the underlying semantics. To cluster short texts by their meanings, we propose to add more semantic signals to short texts. Specifically, for each term in a short text, we obtain its concepts and co-occurring terms from a probabilistic knowledge base to enrich the short text. Furthermore, we introduce a simplified deep learning network consisting of a 3-layer stacked auto-encoders for semantic hashing. Comprehensive experiments show that, with more semantic signals, our simplified deep learning model is able to capture the semantics of short texts, which enables a variety of applications including short text retrieval, classification, and general purpose text processing. SIMILAR VIDEOS: https://www.youtube.com/watch?v=AZI6oHAEtU8 https://www.youtube.com/watch?v=o0mT99zKAqA https://www.youtube.com/watch?v=X7jZtTq74WU https://www.youtube.com/watch?v=EO1rgFk07kQ https://www.youtube.com/watch?v=ACtU9aaoh_8 https://www.youtube.com/watch?v=cbZFKV4A0X8 https://www.youtube.com/watch?v=AWcD3pIGJjI https://www.youtube.com/watch?v=0y5w5CbMips https://www.youtube.com/watch?v=rhCtDFPNHCE https://www.youtube.com/watch?v=t41nfgBy8pY https://www.youtube.com/watch?v=LLUlzVlIJOw https://www.youtube.com/watch?v=mSjS4IGyrW0 https://www.youtube.com/watch?v=1TnAqAkxuws https://www.youtube.com/watch?v=nxoUUe8rrtQ https://www.youtube.com/watch?v=XBzwg1EY2SI https://www.youtube.com/watch?v=RRVWWUd9NLk https://www.youtube.com/watch?v=Es0eHDHksiM https://www.youtube.com/watch?v=x5CAAPGuo3g https://www.youtube.com/watch?v=sQKIpfEpQmo https://www.youtube.com/watch?v=hcmrJkwn1T4 https://www.youtube.com/watch?v=cNw3u68a424 https://www.youtube.com/watch?v=6sKfA1vFZBA https://www.youtube.com/watch?v=cFsryGMYxIE For More Videos - https://www.youtube.com/channel/UCR5lsF-lDQu6rVYVJPqNn6Q SOCIAL HANDLES: SCOOP IT- http://www.scoop.it/u/1croreprojects FACEBOOK - https://www.facebook.com/1Croreprojectsieeeprojects/ TWITTER - https://twitter.com/1crore_projects LINKEDIN - https://www.linkedin.com/in/1-crore-projects-ba982a118/ GOOGLE+ - https://plus.google.com/u/0/105783610929019156122 PINTEREST - https://in.pinterest.com/onecroreproject/ BLOG - 1croreprojectz.blogspot.com DOMAIN PROJECTS DOTNET - http://www.1croreprojects.com/dotnet-ieee-project-centers-in-chennai.php JAVA - http://www.1croreprojects.com/java-ieee-projects-chennai.php EMBEDDED - http://www.1croreprojects.com/embedded-systems-ieee-projects-chennai.php MATLAB - http://www.1croreprojects.com/matlab-ieee-projects-chennai.php NS2 - http://www.1croreprojects.com/ns2-ieee-projects-chennai.php VLSI -http://www.1croreprojects.com/vlsi-ieee-projects-chennai.php FOR PROJECTS - http://www.1croreprojects.com/ BUSINESS CONTACT: Email - [email protected] We are always open for all business prospects. You can get in touch which us, using the above mentioned e-mail id and contact number. ABOUT 1CROREPROJECTS: 1Crore Projects is company providing outstanding, cost-effective, effective result authorized on solutions. Our objective is to create solutions that enhance company process and increase come back in most possible time. We started truly to provide solutions to the customers all over the world. We have been effectively in providing solutions for different challenges across a wide range of market and customers propagate across the globe.
Views: 1070 1 Crore Projects
Top-Down XML Keyword Query Processing
 
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1 Crore Projects providing ieee project 2016 - 2017 an innovative, interactive, latest, the best project training and excellent final year projects in all over the world. IEEE PROJECTS 2016 guide you to build a strong in various technologies, domains which good suits for your project concepts.BE , ME , MCA , BSC , BCA ... SIMILAR VIDEOS: https://www.youtube.com/watch?v=AZI6oHAEtU8 https://www.youtube.com/watch?v=o0mT99zKAqA https://www.youtube.com/watch?v=X7jZtTq74WU https://www.youtube.com/watch?v=EO1rgFk07kQ https://www.youtube.com/watch?v=ACtU9aaoh_8 https://www.youtube.com/watch?v=cbZFKV4A0X8 https://www.youtube.com/watch?v=AWcD3pIGJjI https://www.youtube.com/watch?v=0y5w5CbMips https://www.youtube.com/watch?v=rhCtDFPNHCE https://www.youtube.com/watch?v=t41nfgBy8pY https://www.youtube.com/watch?v=LLUlzVlIJOw https://www.youtube.com/watch?v=mSjS4IGyrW0 https://www.youtube.com/watch?v=1TnAqAkxuws https://www.youtube.com/watch?v=nxoUUe8rrtQ https://www.youtube.com/watch?v=XBzwg1EY2SI https://www.youtube.com/watch?v=RRVWWUd9NLk https://www.youtube.com/watch?v=Es0eHDHksiM https://www.youtube.com/watch?v=x5CAAPGuo3g https://www.youtube.com/watch?v=sQKIpfEpQmo https://www.youtube.com/watch?v=hcmrJkwn1T4 https://www.youtube.com/watch?v=cNw3u68a424 https://www.youtube.com/watch?v=6sKfA1vFZBA https://www.youtube.com/watch?v=cFsryGMYxIE For More Videos - https://www.youtube.com/channel/UCR5lsF-lDQu6rVYVJPqNn6Q SOCIAL HANDLES: SCOOP IT- http://www.scoop.it/u/1croreprojects FACEBOOK - https://www.facebook.com/1Croreprojectsieeeprojects/ TWITTER - https://twitter.com/1crore_projects LINKEDIN - https://www.linkedin.com/in/1-crore-projects-ba982a118/ GOOGLE+ - https://plus.google.com/u/0/105783610929019156122 PINTEREST - https://in.pinterest.com/onecroreproject/ BLOG - 1croreprojectz.blogspot.com DOMAIN PROJECTS DOTNET - http://www.1croreprojects.com/dotnet-ieee-project-centers-in-chennai.php JAVA - http://www.1croreprojects.com/java-ieee-projects-chennai.php EMBEDDED - http://www.1croreprojects.com/embedded-systems-ieee-projects-chennai.php MATLAB - http://www.1croreprojects.com/matlab-ieee-projects-chennai.php NS2 - http://www.1croreprojects.com/ns2-ieee-projects-chennai.php VLSI -http://www.1croreprojects.com/vlsi-ieee-projects-chennai.php FOR PROJECTS - http://www.1croreprojects.com/ BUSINESS CONTACT: Email - [email protected] We are always open for all business prospects. You can get in touch which us, using the above mentioned e-mail id and contact number. ABOUT 1CROREPROJECTS: 1Crore Projects is company providing outstanding, cost-effective, effective result authorized on solutions. Our objective is to create solutions that enhance company process and increase come back in most possible time. We started truly to provide solutions to the customers all over the world. We have been effectively in providing solutions for different challenges across a wide range of market and customers propagate across the globe.
Views: 574 1 Crore Projects
FRoDO: Fraud Resilient Device for Off-line micro-payments
 
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FRoDO: Fraud Resilient Device for Off-line micro-payments 2016-2017 MICANS INFOTECH offers Projects in CSE ,IT, EEE, ECE, MECH , MCA. MPHIL , BSC, in various domains JAVA ,PHP, DOT NET , ANDROID , MATLAB , NS2 , EMBEDDED , VLSI , APPLICATION PROJECTS , IEEE PROJECTS. CALL : +91 90036 28940 +91 94435 11725 [email protected] WWW.MICANSINFOTECH.COM COMPANY PROJECTS, INTERNSHIP TRAINING, MECHANICAL PROJECTS, ANSYS PROJECTS, CAD PROJECTS, CAE PROJECTS, DESIGN PROJECTS, CIVIL PROJECTS, IEEE MCA PROJECTS, IEEE M.TECH PROJECTS, IEEE PROJECTS, IEEE PROJECTS IN PONDY, IEEE PROJECTS, EMBEDDED PROJECTS, ECE PROJECTS PONDICHERRY, DIPLOMA PROJECTS, FABRICATION PROJECTS, IEEE PROJECTS CSE, IEEE PROJECTS CHENNAI, IEEE PROJECTS CUDDALORE, IEEE PROJECTS IN PONDICHERRY, PROJECT DEVELOPMENT CENTRE
Dictionary Based Secure Provenance Compression for Wireless Sensor Networks
 
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Dictionary Based Secure Provenance Compression for Wireless Sensor Networks To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: www.jpinfotech.org, Blog: www.jpinfotech.blogspot.com Due to energy and bandwidth limitations of wireless sensor networks (WSNs), it is crucial that data provenance for these networks be as compact as possible. Even if lossy compression techniques are used for encoding provenance information, the size of the provenance increases with the number of nodes traversed by the network packets. To address such issues, we propose a dictionary based provenance scheme. In our approach, each sensor node in the network stores a packet path dictionary. With the support of this dictionary, a path index instead of the path itself is enclosed with each packet. Since the packet path index is a code word of a dictionary, its size is independent of the number of nodes present in the packet’s path. Furthermore, as our scheme binds the packet and its provenance through an AM-FM sketch and uses a secure packet sequence number generation technique, it can defend against most of the known provenance attacks. Through simulation and experimental results, we show that our scheme outperforms other compact provenance schemes with respect to provenance size, robustness, and energy consumption.
Views: 191 jpinfotechprojects
A Locality Sensitive Low Rank Model for Image Tag Completion
 
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2016 IEEE Transaction on Image Processing For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com 2016 and 2017 IEEE @ TMKS Infotech,Bangalore
Views: 138 manju nath
A Comprehensive Study on Willingness Maximization for Social Activity Planning
 
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A Comprehensive Study on Willingness Maximization for Social Activity Planning with
Efficient Ranking on Entity Graphs with Personalized Relationships
 
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ChennaiSunday Systems Pvt.Ltd We are ready to provide guidance to successfully complete your projects and also download the abstract, base paper from our website IEEE 2014 Java Projects: http://www.chennaisunday.com/projectsNew.php?id=1&catName=IEEE_2014-2015_Java_Projects IEEE 2014 Dotnet Projects: http://www.chennaisunday.com/projectsNew.php?id=20&catName=IEEE_2014-2015_DotNet_Projects Output Videos: https://www.youtube.com/channel/UCCpF34pmRlZbAsbkareU8_g/videos IEEE 2013 Java Projects: http://www.chennaisunday.com/projectsNew.php?id=2&catName=IEEE_2013-2014_Java_Projects IEEE 2013 Dotnet Projects: http://www.chennaisunday.com/projectsNew.php?id=3&catName=IEEE_2013-2014_Dotnet_Projects Output Videos: https://www.youtube.com/channel/UCpo4sL0gR8MFTOwGBCDqeFQ/videos IEEE 2012 Java Projects: http://www.chennaisunday.com/projectsNew.php?id=26&catName=IEEE_2012-2013_Java_Projects Output Videos: https://www.youtube.com/user/siva6351/videos IEEE 2012 Dotnet Projects: http://www.chennaisunday.com/projectsNew.php?id=28&catName=IEEE_2012-2013_Dotnet_Projects Output Videos: https://www.youtube.com/channel/UC4nV8PIFppB4r2wF5N4ipqA/videos IEEE 2011 Java Projects: http://chennaisunday.com/projectsNew.php?id=29&catName=IEEE_2011-2012_Java_Project IEEE 2011 Dotnet Projects: http://chennaisunday.com/projectsNew.php?id=33&catName=IEEE_2011-2012_Dotnet_Projects Output Videos: https://www.youtube.com/channel/UCtmBGO0q5XZ5UsMW0oDhZ-A/videos Application Projects: http://www.chennaisunday.com/software -- *Contact * * P.Sivakumar MCA Director Chennai Sunday Systems Pvt Ltd Phone No: 09566137117 No: 1,15th Street Vel Flats Ashok Nagar Chennai-83 Landmark R3 Police Station Signal (Via 19th Street) URL: www.chennaisunday.com
Views: 313 siva kumar
Conjunctive Keyword Search With Designated Tester and Timing Enabled Proxy Re-Encryption Function
 
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Conjunctive Keyword Search With Designated Tester and Timing Enabled Proxy Re-Encryption Function for E-Health Clouds To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com An electronic health (e-health) record system is a novel application that will bring great convenience in healthcare. The privacy and security of the sensitive personal information are the major concerns of the users, which could hinder further development and widely adoption of the systems. The searchable encryption (SE) scheme is a technology to incorporate security protection and favorable operability functions together, which can play an important role in the e-health record system. In this paper, we introduce a novel cryptographic primitive named as conjunctive keyword search with designated tester and timing enabled proxy reencryption function (Re-dtPECK), which is a kind of a time-dependent SE scheme. It could enable patients to delegate partial access rights to others to operate search functions over their records in a limited time period. The length of the time period for the delegatee to search and decrypt the delegator’s encrypted documents can be controlled. Moreover, the delegatee could be automatically deprived of the access and search authority after a specified period of effective time. It can also support the conjunctive keywords search and resist the keyword guessing attacks. By the solution, only the designated tester is able to test the existence of certain keywords. We formulate a system model and a security model for the proposed Re-dtPECK scheme to show that it is an efficient scheme proved secure in the standard model. The comparison and extensive simulations demonstrate that it has a low computation and storage overhead.
Views: 964 JPINFOTECH PROJECTS
Enabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement
 
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Enabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement
IEEE BIGDATA TOPICS 2016  --FINAL YEAR IEEE COMPUTER SCIENCE PROJECTS
 
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TSYS Center for Research and Development (TCRD) is a premier center for academic and industrial research needs. We at TRCD provide complete support for final year Post graduate Student (M.E / M.Tech / M. Sc/ MCA/ M-phil) who are doing course in computer science and Information technology to do their final year project and journal work. FINAL YEAR IEEE COMPUTER SCIENCE PROJECTS For Latest IEEE BIG DATA Projects Contact: TSYS Center for Research and Development (TSYS Academic Projects) Ph.No: 9841103123 / 044-42607879, Visit us: http://www.tsys.co.in/ Email: [email protected] IEEE TRANSACTION ON BIGDATA 2016 TOPICS 1. K Nearest Neighbour Joins for Big Data on MapReduce: a Theoretical and Experimental Analysis (2016) 2. Frame Interpolation for Cloud-Based Mobile Video Streaming (2016) 3. USTF: A Unified System of Team Formation (2016) 4. Online Censoring for Large-Scale Regressions with Application to Streaming Big Data (2016) 5. Personalized Travel Sequence Recommendation on Multi-Source Big Social Media (2016) 6. An MM-Based Algorithm for -Regularized Least-Squares Estimation With an Application to Ground Penetrating Radar Image Reconstruction (2016) 7. A Hierarchical Fused Fuzzy Deep Neural Network for Data Classification (2016) 8. A Knowledge-based Framework for Power Flow and Optimal Power Flow Analyses (2016) 9. A Dynamical and Load-Balanced Flow Scheduling Approach for Big Data Centers in Clouds (2016) 10. A Note on the Unification of Adaptive Online Learning (2016) 11. Automatic Road Crack Detection Using Random Structured Forests (2016) 12. An Affine Arithmetic-based Framework for Uncertain Power Flow and Optimal Power Flow Studies (2016) 13. Reproducibility in Computational Neuroscience Models and Simulations (2016) 14. Service Rating Prediction by Exploring Social Mobile Users’ Geographic Locations (2016) 15. Adaptive Replication Management in HDFS Based on Supervised Learning (2016) 16. NGD: Filtering Graphs for Visual Analysis (2016) 17. Kvasir: Scalable Provision of Semantically Relevant Web Content on Big Data Framework (2016) 18. A Big Bang-Big Crunch Type-2 Fuzzy Logic System for Machine Vision-Based Event Detection and Summarization in Real-world Ambient Assisted Living (2016) 19. Feature Selection with Annealing for Computer Vision and Big Data Learning (2016) 20. K Nearest Neighbour Joins for Big Data on MapReduce: a Theoretical and Experimental Analysis(2016) 21. Visual Analysis of Cloud Computing Performance Using Behavioral Lines (2016) 22. Universal Nonlinear Regression on High Dimensional Data Using Adaptive Hierarchical Tree (2016) 23. Active CTDaaS: A Data Service Framework Based on Transparent IoD in City Traffic (2016) 24. Measurement of Distinguishing Features of Stable Cognitive and Physical Health Older Drivers (2016) 25. Making Trillion Correlations Feasible in Feature Grouping and Selection (2016) 26. Feedback Autonomic Provisioning for Guaranteeing Performance in MapReduce Systems (2016) 27. A Scalable Data Chunk Similarity based Compression Approach for Efficient Big Sensing Data Processing on Cloud (2016) 28. Computing Affine Equivalence Classes of Boolean Functions by Group Isomorphism (2016) 29. Multimodal Personality Recognition in Collaborative Goal-Oriented Tasks (2016) 30. Interactive Visualization of Large Data Sets (2016) 31. Energy Big Data Analytics and Security: Challenges and Opportunities (2016) 32. A Mobility Analytical Framework for Big Mobile Data in Densely Populated Area (2016) 33. Learning from Weak and Noisy Labels for Semantic Segmentation (2016) 34. Scalable Energy-Efficient Distributed Data Analytics for Crowdsensing Applications in Mobile Environments (2016) 35. A recommendation system based on hierarchical clustering of an article-level citation network (2016) 36. Compressed Sensing-Based Clone Identification in Sensor Networks (2016) 37. Spin-Triplet Superconducting Current in Metal–Oxide Heterostructures With Composite Ferromagnetic Interlayer (2016) 38. Incremental and Decremental Max-flow for Online Semi-supervised Learning (2016) 39. Nonsmooth Penalized Clustering via ℓₚ Regularized Sparse Regression (2016)
Views: 1087 Tsys Globalsolutions
Conjunctive Keyword Search with Designated Tester and Timing Enabled Proxy Re encryption Function
 
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2016 IEEE Transaction on Cloud Computing For More Details::Contact::K.Manjunath - 09535866270 http://www.tmksinfotech.com and http://www.bemtechprojects.com
Views: 219 manju nath
Contributory Broadcast Encryption with Efficient Encryption and Short Ciphertexts
 
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Contributory Broadcast Encryption with Efficient Encryption and Short Ciphertexts To get this project in ONLINE or through TRAINING Sessions, Contact:JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83. Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai, Thattanchavady, Puducherry -9. Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690 , Email: [email protected], web: www.jpinfotech.org Blog: www.jpinfotech.blogspot.com ABSTRACT: Traditional broadcast encryption (BE) schemes allow a sender to securely broadcast to any subset of members but require a trusted party to distribute decryption keys. Group key agreement (GKA) protocols enable a group of members to negotiate a common encryption key via open networks so that only the group members can decrypt the ciphertexts encrypted under the shared encryption key, but a sender cannot exclude any particular member from decrypting the ciphertexts. In this paper, we bridge these two notions with a hybrid primitive referred to as contributory broadcast encryption (ConBE). In this new primitive, a group of members negotiate a common public encryption key while each member holds a decryption key. A sender seeing the public group encryption key can limit the decryption to a subset of members of his choice. Following this model, we propose a ConBE scheme with short ciphertexts. The scheme is proven to be fully collusion-resistant under the decision n-Bilinear Diffie-Hellman Exponentiation (BDHE) assumption in the standard model. Of independent interest, we present a new BE scheme that is aggregatable. The aggregatability property is shown to be useful to construct advanced protocols.
Views: 518 jpinfotechprojects
A Hop-by-Hop Routing Mechanism for Green Internet in NS2
 
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A Hop-by-Hop Routing Mechanism for Green Internet in Network Simulator 2 (NS2) To get this project in ONLINE or through TRAINING Sessions, Contact: JP INFOTECH, Old No.31, New No.86, 1st Floor, 1st Avenue, Ashok Pillar, Chennai -83.Landmark: Next to Kotak Mahendra Bank. Pondicherry Office: JP INFOTECH, #45, Kamaraj Salai,Thattanchavady, Puducherry -9.Landmark: Next to VVP Nagar Arch. Mobile: (0) 9952649690, Email: [email protected], web: http://www.jpinfotech.org, Blog: http://www.jpinfotech.blogspot.com In this paper we study energy conservation in the Internet. We observe that different traffic volumes on a link can result in different energy consumption; this is mainly due to such technologies as trunking (IEEE 802.1AX), adaptive link rates, etc. We design a green Internet routing scheme, where the routing can lead traffic in a way that is green. We differ from previous studies where they switch network components, such as line cards and routers, into sleep mode. We do not prune the Internet topology. We first develop a power model, and validate it using real commercial routers. Instead of developing a centralized optimization algorithm, which requires additional protocols such as MPLS to materialize in the Internet, we choose a hop-by-hop approach. It is thus much easier to integrate our scheme into the current Internet. We progressively develop three algorithms, which are loop-free, substantially reduce energy consumption, and jointly consider green and QoS requirements such as path stretch. We further analyze the power saving ratio, the routing dynamics, and the relationship between hop-by-hop green routing and QoS requirements. We comprehensively evaluate our algorithms through simulations on synthetic, measured, and real topologies, with synthetic and real traffic traces. We show that the power saving in the line cards can be as much as 50 percent.
Views: 468 JPINFOTECH PROJECTS
CONNECTING SOCIAL MEDIA TO E-COMMERCE: COLD-START PRODUCT RECOMMENDATION USING MICROBLOGGING
 
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In recent years, the boundaries between e-commerce and social networking have become increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign on the websites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper we propose a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce websites to users at social networking sites in “coldstart” situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users’ social networking features to another feature representation for product recommendation. In specific, we propose learning both users’ and products’ feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users’ social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service SINA WEIBO and the largest Chinese B2C e-commerce website JINGDONG have shown the effectiveness of our proposed framework. To get the source code contact 9003628940 [email protected]
Views: 467 IEEE PROJECTS

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