CS AND IT PROJECTS LIST

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1. On the Effect of Location Uncertainty in Spatial Querying Java

2. RiMOM: A Dynamic Multistrategy Ontology Alignment Framework –java /dotnet

3. Similarity-Profiled Temporal Association Mining –java/dotnet

4. Ranking and Suggesting Popular Items -java /

5. Olex: Effective Rule Learning for Text Categorization – java /dotnet

6. Multirelational k-Anonymity --- dotnet/java

7. E-card

8. Electronic Billing

9. Online E- banking

10. Digital Image Forensics via Intrinsic Fingerprints-java

11. A Fast Search Algorithm for a Large Fuzzy Database –java/dotnet

12. Unseen Visible Watermarking: A Novel Methodology for Auxiliary Information Delivery via Visual Contents –java /dotnet

13. A Game Theoretical Framework on Intrusion Detection in Heterogeneous Networks –java

14. Spread-Spectrum Watermarking Security –java./dotnet

15. A Hypothesis Testing Approach to Semifragile Watermark-Based -Authentication –java

16. Robust Blind Watermarking of Point-Sampled Geometry

17. Spatial PrObabilistic Temporal (SPOT) databases

18. Role Engineering via Prioritized -java

19. Discovery of Structural and Functional Features in RNA Pseudoknots-java/dotnet

20. Predicting Missing Items in Shopping Carts –j2ee/dotnet

Effective Collaboration with Information Sharing in Virtual Universities

Abstract
A global education system, as a key area in future IT, has fostered developers to provide various learning systems with low cost. While a variety of e-learning advantages has been recognized for a long time and many advances in e-learning systems have been implemented, the needs for effective information sharing in a secure manner have to date been largely ignored, especially for virtual university collaborative environments. Information sharing of virtual universities usually occurs in broad, highly dynamic network-based environments, and formally accessing the resources in a secure manner poses a difficult and vital challenge. This paper aims to build a new rule-based framework to identify and address issues of sharing in virtual university environments through role-based access control (RBAC) management. The framework includes a role-based group delegation granting model, group delegation revocation model, authorization granting, and authorization revocation. We analyze various revocations and the impact of revocations on role hierarchies. The implementation with XML-based tools demonstrates the feasibility of the framework and authorization methods. Finally, the current proposal is compared with other related work.

A Communication Perspective on Automatic Text Categorization –java/dotnet

Abstract
The basic concern of a Communication System is to transfer information from its source to a destination some distance away. Textual documents also deal with the transmission of information. Particularly, from a text categorization system point of view, the information encoded by a document is the topic or category it belongs to. Following this initial intuition, a theoretical framework is developed where Automatic Text Categorization(ATC) is studied under a Communication System perspective. Under this approach, the problematic indexing feature space dimensionality reduction has been tackled by a two-level supervised scheme, implemented by a noisy terms filtering and a subsequent redundant terms compression. Gaussian probabilistic categorizers have been revisited and adapted to the concomitance of sparsity in ATC. Experimental results pertaining to 20 Newsgroups and Reuters-21578 collections validate the theoretical approaches. The noise filter and redundancy compressor allows an aggressive term vocabulary reduction (reduction factor greater than 0.99) with a minimum loss (lower than 3 percent) and, in some cases, gain (greater than 4 percent) of final classification accuracy. The adapted Gaussian Naive Bayes classifier reaches classification results similar to those obtained by state-of-the-art Multinomial Naive Bayes (MNB) and Support Vector Machines (SVMs).

A Divide-and-Conquer Approach for Minimum Spanning Tree-Based Clustering –java

Abstract
Due to their ability to detect clusters with irregular boundaries, minimum spanning tree-based clustering algorithms have been widely used in practice. However, in such clustering algorithms, the search for nearest neighbor in the construction of minimum spanning trees is the main source of computation and the standard solutions take O(N2) time. In this paper, we present a fast minimum spanning tree-inspired clustering algorithm, which, by using an efficient implementation of the cut and the cycle property of the minimum spanning trees, can have much better performance than O(N2).

Ranking and Suggesting Popular Items java Project

Abstract
We consider the problem of ranking the popularity of items and suggesting popular items based on user feedback. User feedback is obtained by iteratively presenting a set of suggested items, and users selecting items based on their own preferences either from this suggestion set or from the set of all possible items. The goal is to quickly learn the true popularity ranking of items (unbiased by the made suggestions), and suggest true popular items. The difficulty is that making suggestions to users can reinforce popularity of some items and distort the resulting item ranking. The described problem of ranking and suggesting items arises in diverse applications including search query suggestions and tag suggestions for social tagging systems. We propose and study several algorithms for ranking and suggesting popular items, provide analytical results on their performance, and present numerical results obtained using the inferred popularity of tags from a month-long crawl of a popular social bookmarking service. Our results suggest that lightweight, randomized update rules that require no special configuration parameters provide good performance.

Multirelational k-Anonymity --- dotnet/java project abstract

Abstract
k-Anonymity protects privacy by ensuring that data cannot be linked to a single individual. In a k-anonymous data set, any identifying information occurs in at least k tuples. Much research has been done to modify a single-table data set to satisfy anonymity constraints. This paper extends the definitions of k-anonymity to multiple relations and shows that previously proposed methodologies either fail to protect privacy or overly reduce the utility of the data in a multiple relation setting. We also propose two new clustering algorithms to achieve multi relational anonymity. Experiments show the effectiveness of the approach in terms of utility and efficiency