19Jul 2014 by chintan

A Keyless Approach to Image Encryption

Image Processing, Security and Encryption, Web | Desktop Application
A Keyless Approach to Image Encryption Maintaining the secrecy and confidentiality of images is a vibrant area of research, with two different approaches being followed, the first being encrypting the images through encryption algorithms using keys, the other approach involves dividing the image into random shares to maintain the images secrecy. Unfortunately heavy computation cost and key management limit the employment of the first approach and the poor quality of the recovered image from the random shares limit the applications of the second approach. In this paper we propose a novel approach without the use of encryption keys. The approach employs Sieving, Division and Shuffling to generate random shares such that with minimal computation, the original secret image can be recovered from the random shares without any loss of image…
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19Jul 2014 by chintan No Comments

Efficient Privacy-Preserving Range Queries over Encrypted Data in Cloud Computing

Cloud Computing, Web | Desktop Application
Efficient Privacy-Preserving Range Queries over Encrypted Data in Cloud Computing With the growing popularity of data and service outsourcing, where the data resides on remote servers in encrypted form, there remain open questions about what kind of query operations can be performed on the encrypted data. In this paper, we focus on one such important query operation, namely range query. One of the basic security primitive that can be used to evaluate range queries is secure comparison of encrypted integers. However, the existing secure comparison protocols strongly rely on the encrypted bit-wise representations rather than on pure encrypted integers. Therefore, in this paper, we first propose an efficient method for converting an encrypted integer z into encryptions of the individual bits of z. We then utilize the proposed security primitive…
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19Jul 2014 by chintan

N-Square Approach For Lossless Image Compression And Decompression

Image Processing, Web | Desktop Application
N-Square Approach For Lossless Image Compression And Decompression There are several lossy and lossless coding techniques developed all through the last two decades. Although very high compression can be achieved with lossy compression techniques, they are deficient in obtaining the original image. While lossless compression technique recovers the image exactly. In applications related to medical imaging lossless techniques are required, as the loss of information is deplorable. The objective of image compression is to symbolize an image with a handful number of bits as possible while preserving the quality required for the given application. In this paper we are introducing a new lossless encoding and decoding technique which even better reduces the entropy there by reducing the average number of bits with the utility of Non Binary Huffman coding through…
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19Jul 2014 by chintan

An Efficient Real Time Moving Object Detection Method for Video Surveillance System

Image Processing, Web | Desktop Application
An Efficient Real Time Moving Object Detection Method for Video Surveillance System Moving object detection has been widely used in diverse discipline such as intelligent transportation systems, airport security systems, video monitoring systems, and so on. In this paper, we propose an efficient moving object detection method using enhanced edge localization mechanism and gradient directional masking for video surveillance system. In our proposed method, gradient map images are initially generated from the input and background images using a gradient operator. The gradient difference map is then calculated from gradient map images. The moving object is then detected by using appropriate directional masking and thresholding. Simulation results indicate that the proposed method consistently performs well under different illumination conditions including indoor, outdoor, sunny, and foggy cases. Moreover, it outperforms well known…
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19Jul 2014 by chintan

Image Authentication and Restoration using BlockWise Fragile Watermarking based on k-Medoids Clustering Approach

Image Processing, Web | Desktop Application
Image Authentication and Restoration using BlockWise Fragile Watermarking based on k-Medoids Clustering Approach This paper proposes a block-wise fragile watermarking scheme based on k-medoids clustering approach. Proposed scheme is effective enough to reconstruct the tampered extensive content of an image. According to the suggested algorithm, first of all image is divided into the blocks and forty eight bits are calculated for each block, which consist of forty five Recovery bits and three Authentication bits namely Union bit, Affiliation bit and Spectrum bit. Authentication bits for a particular block are calculated by extracting five MSBs from each pixel of that block and apply them, on some predefined hash functions. Forty five Recovery bits are calculated using the means of derived clusters and its corresponding mapping bits. These Forty eight bits for…
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19Jul 2014 by chintan

A robust skew detection method based on maximum gradient difference and R-signature

Image Processing, Web | Desktop Application
A robust skew detection method based on maximum gradient difference and R-signature In this paper we study the detection of skewed text lines in scanned document images. The aim of our work is to develop a new automatic approach able to estimate precisely the skew angle of text in document images. Our new method is based on Maximum Gradient Difference (MGD) and R-signature. It detects zones that have high variations of gray values in different directions using the MGD transform. We consider these zones as being text regions. R-signature which is a shape descriptor based on Radon transform is then applied in order to approximate the skew angle. The accuracy of the proposed algorithm is evaluated on an open dataset by comparing error rates.
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19Jul 2014 by chintan

Image-based object detection under varying illumination in environments with specular surfaces

Image Processing, Web | Desktop Application
Image-based object detection under varying illumination in environments with specular surfaces Image-based environment representations capture the appearance of the surroundings of a mobile robot and are useful for the detection of novelty. However, image-based novelty detection can be impaired by illumination effects. In this paper we present an approach for the image-based detection of novel objects in a scene under varying lighting conditions and in the presence of objects with specular surfaces. The computation of an illumination-invariant image-based environment representation allows for the extraction of the shading of the environment from camera images. Using statistical models infered from the luminance and the saturation component of the shading images, secularities and shadows are detected and suppressed in the process of novelty detection. Experimental results show that the proposed method outperforms two…
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19Jul 2014 by chintan

A New Block Compressive Sensing to Control the Number of Measurements

Image Processing, Web | Desktop Application
A New Block Compressive Sensing to Control the Number of Measurements Compressive Sensing (CS) aims to recover a sparse signal from a small number of projections onto random vectors. Be- cause of its great practical possibility, both academia and in- dustries have made efforts to develop the CS’s reconstruction performance, but most of existing works remain at the theo- retical study. In this paper, we propose a new Block Compres- sive Sensing (nBCS), which has several benefits compared to the general CS methods. In particular, the nBCS can be dynamically adaptive to varying channel capacity because it conveys the good inheritance of the wavelet transform.
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19Jul 2014 by chintan No Comments

Secure Authentication using Image Processing and Visual Cryptography for Banking Applications

Image Processing, Web | Desktop Application
Secure Authentication using Image Processing and Visual Cryptography for Banking Applications Core banking is a set of services provided by a group of networked bank branches. Bank customers may access their funds and perform other simple transactions from any of the member branch ofces. The major issue in core banking is the authenticity of the customer. Due to unavoidable hacking of the databases on the internet, it is always quite difcult to trust the information on the internet. To solve this problem of authentication, we are proposing an algorithm based on image processing and visual cryptography. This paper proposes a technique of processing the signature of a customer and then dividing it into shares. Total number of shares to be created is depending on the scheme chosen by the bank.…
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19Jul 2014 by chintan No Comments

An Algorithm to Automatically Generate Schedule for School Lectures Using a Heuristic Approach

Web | Desktop Application
An Algorithm to Automatically Generate Schedule for School Lectures Using a Heuristic Approach This paper proposes a general solution for the School timetabling problem. Most heuristic proposed earlier approaches the problem from the students’ point of view. This solution, however, works from the teachers’ point of view i.e. teacher availability for a given time slot. While all the hard constraints (e.g. the availability of teachers, etc.) are resolved rigorously, the scheduling solution presented in this paper is an adaptive one, with a primary aim to solve the issue of clashes of lectures and subjects, pertaining to teachers.
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19Jul 2014 by chintan No Comments

A Mixed Reality Virtual Clothes Try-On System

Web | Desktop Application
A Mixed Reality Virtual Clothes Try-On System Virtual try-on of clothes has received much attention recently due to its commercial potential. It can be used for online shopping or intelligent recommendation to narrow down the selections to a few designs and sizes. In this paper, we present a mixed reality system for 3D virtual clothes try-on that enables a user to see herself wearing virtual clothes while looking at a mirror display, without taking off her actual clothes. The user can select various virtual clothes for trying-on. The system physically simulates the selected virtual clothes on the user's body in real-time and the user can see virtual clothes fitting on the her mirror image from various angles as she moves. The major contribution of this paper is that we automatically…
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19Jul 2014 by chintan

Lossless image compression based on data folding

Image Processing, Web | Desktop Application
Lossless image compression based on data folding The paper presents an approach for lossless image compression in spatial domain for continuous-tone images using a novel concept of image folding. The proposed method uses the property of adjacent neighbor redundancy for prediction. In this method, column folding followed by row folding is applied iteratively on the image till the image size reduces to a smaller pre-defined value. For column folding, elements of even columns are subtracted from elements of odd columns. Thereafter, row folding is applied on odd columns in a similar fashion. In row folding, even rows are subtracted from odd rows and the resultant odd rows are used for next iteration. The difference data, thus obtained, is stored in a tile format; which along with the reduced image is…
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19Jul 2014 by chintan

A chaotic system based fragile watermarking scheme for image tamper detection

Image Processing, Web | Desktop Application
A chaotic system based fragile watermarking scheme for image tamper detection In the past few years, various fragile watermarking techniques have been proposed for image authen- tication and tamper detection. In this paper, a novel chaos based watermarking scheme for image authentication and tamper detection is proposed. Tamper localization and detection accuracy are two important aspects of the authentication watermarking schemes. Our scheme can detect any modifica- tion made to the image and can also indicate the specific locations that have been modified. To improve the security of the proposed scheme two chaotic maps are employed. Since chaotic maps are sensitive to initial values, the corresponding position relation between pixels in the watermarked image and the watermark get disturbed, which helps the watermarking scheme to withstand counterfeiting attacks. Initial values…
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19Jul 2014 by chintan

Linear distance coding for image classification

Image Processing, Web | Desktop Application
Linear distance coding for image classification The traditional SPM approach based on bag-of-features (BoF) requires nonlinear classifiers to achieve good image classification performance. This paper presents a simple but effective coding scheme called Locality-constrained Linear Coding (LLC) in place of the VQ coding in traditional SPM. LLC utilizes the locality constraints to project each descriptor into its local-coordinate system, and the projected coordinates are integrated by max pooling to generate the final representation. With linear classifier, the proposed approach performs remarkably better than the traditional nonlinear SPM, achieving state-of-the-art performance on several benchmarks. Compared with the sparse coding strategy [22], the objective function used by LLC has an analytical solution. In addition, the paper proposes a fast approximated LLC method by first performing a K-nearest-neighbor search and then solving a…
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19Jul 2014 by chintan

Robust Watermarking of Compressed and Encrypted JPEG2000 Images

Image Processing, Web | Desktop Application
Robust Watermarking of Compressed and Encrypted JPEG2000 Images Digital asset management systems (DAMS) generally handle media data in a compressed and encrypted form. It is sometimes necessary to watermark these compressed encrypted media items in the compressed-encrypted domain itself for tamper detection or ownership declaration or copyright management purposes. It is a challenge to watermark these compressed encrypted streams as the compression process would have packed the information of raw media into a low number of bits and encryption would have randomized the compressed bit stream. Attempting to watermark such a randomized bit stream can cause a dramatic degradation of the media quality. Thus it is necessary to choose an encryption scheme that is both secure and will allow watermarking in a predictable manner in the compressed encrypted domain. In…
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19Jul 2014 by chintan

Image compression and decompression using adaptive interpolation

Image Processing, Web | Desktop Application
Image compression and decompression using adaptive interpolation A simple and fast lossy compression and decompression algorithm for digital images is proposed. The method offers varying compression ratios (depending on dimensions of the image) and the acquired decompressed image is close to the original one. A selectable tradeoff between storage size and image quality is allowed,making it possible to adjust degree of compression. Compared to JPEG, it provides us better compression ratio. The suggested method does not restrict itself to any particular type of image.
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19Jul 2014 by chintan

Efficient and Discovery of Patterns in Sequence Data Sets.

Data mining, Web | Desktop Application
Efficient and Discovery of Patterns in Sequence Data Sets. Existing sequence mining algorithms mostly focus on mining for subsequences. However, a large class of applications, such as biological DNA and protein motif mining, require efficient mining of “approximate” patterns that are contiguous. The few existing algorithms that can be applied to find such contiguous approximate pattern mining have drawbacks like poor scalability, lack of guarantees in finding the pattern, and difficulty in adapting to other applications. In this paper, we present a new algorithm called Flexible and Accurate Motif DEtector (FLAME). FLAME is a flexible suffix-tree-based algorithm that can be used to find frequent patterns with a variety of definitions of motif (pattern) models. It is also accurate, as it always finds the pattern if it exists. Using both real…
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16Jul 2014 by chintan

Mining Web Graphs for Recommendations.

Data mining, Web | Desktop Application
Mining Web Graphs for Recommendations. As the exponential explosion of various contents generated on the Web, Recommendation techniques have become increasingly indispensable. Innumerable different kinds of recommendations are made on the Web every day, including music, images, books recommendations, query suggestions, etc. No matter what types of data sources are used for the recommendations, essentially these data sources can be modeled in the form of graphs. In this paper, aiming at providing a general framework on mining Web graphs for recommendations, (1) we first propose a novel diffusion method which propagates similarities between different recommendations; (2) then we illustrate how to generalize different recommendation problems into our graph diffusion framework. The proposed framework can be utilized in many recommendation tasks on the World Wide Web, including query suggestions, image recommendations,…
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16Jul 2014 by chintan

Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques

Data mining, Web | Desktop Application
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques Recommender systems are becoming increasingly important to individual users and businesses for providing personalized recommendations. However, while the majority of algorithms proposed in recommender systems literature have focused on improving recommendation accuracy, other important aspects of recommendation quality, such as the diversity of recommendations, have often been overlooked. In this paper, we introduce and explore a number of item ranking techniques that can generate recommendations that have substantially higher aggregate diversity across all users while maintaining comparable levels of recommendation accuracy. Comprehensive empirical evaluation consistently shows the diversity gains of the proposed techniques using several real-world rating datasets and different rating prediction algorithms.
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16Jul 2014 by chintan

Predicting missing items in shopping cart using fast algorithm

Data mining, Web | Desktop Application
Predicting missing items in shopping cart using fast algorithm Prediction in shopping cart uses partial information about the contents of a shopping cart for the prediction of what else the customer is likely to buy. In order to reduce the rule mining cost, a fast algorithm generating frequent itemsets without generating candidate itemsets is proposed. The algorithm uses Boolean vector with relational AND operation to discover frequent itemsets and generate the association rule. Association rules are used to identify relationships among a set of items in database. Initially Boolean Matrix is generated by transforming the database into Boolean values. The frequent itemsets are generated from the Boolean matrix. Then association rules are to generated from the already generated frequent itemsets. The association rules generated form the basis for prediction. The…
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16Jul 2014 by chintan

A Threshold-based Similarity Measure for Duplicate Detection

Data mining, Web | Desktop Application
A Threshold-based Similarity Measure for Duplicate Detection In order to extract beneficial information and recognize a particular pattern from huge data stored in different databases with different formats, data integration is essential. However the problem that arises here is that data integration may lead to duplication. In other words, due to the availability of data in different formats, there might be some records which refer to the same entity. Duplicate detection or record linkage is a technique which is used to detect and match duplicate records which are generated in data integration process. Most approaches concentrated on string similarity measures for comparing records. However, they fail to identify records which share the semantic information. So, in this study, a thresholdbased method which takes into account both string and semantic similarity…
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16Jul 2014 by chintan

Efficient Multi-dimensional Fuzzy Search for Personal Information Management Systems

Data mining, Web | Desktop Application
Efficient Multi-dimensional Fuzzy Search for Personal Information Management Systems With the explosion in the amount of semi-structured data users access and store in personal information management systems, there is a critical need for powerful search tools to retrieve often very heterogeneous data in a simple and efficient way. Existing tools typically support some IR-style ranking on the textual part of the query, but only consider structure (e.g., file directory) and metadata (e.g., date, file type) as filtering conditions. We propose a novel multi-dimensional search approach that allows users to perform fuzzy searches for structure and metadata conditions in addition to keyword conditions. Our techniques individually score each dimension and integrate the three dimension scores into a meaningful unified score. We also design indexes and algorithms to efficiently identify the most…
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16Jul 2014 by chintan

Enabling Multilevel Trust in Privacy Preserving Data Mining

Data mining, Web | Desktop Application
Enabling Multilevel Trust in Privacy Preserving Data Mining Privacy Preserving Data Mining (PPDM) addresses the problem of developing accurate models about aggregated data without access to precise information in individual data record. A widely studied perturbation-based PPDM approach introduces random perturbation to individual values to preserve privacy before data are published. Previous solutions of this approach are limited in their tacit assumption of single-level trust on data miners. In this work, we relax this assumption and expand the scope of perturbation-based PPDM to Multilevel Trust (MLT-PPDM). In our setting, the more trusted a data miner is, the less perturbed copy of the data it can access. Under this setting, a malicious data miner may have access to differently perturbed copies of the same data through various means, and may combine…
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16Jul 2014 by chintan

Slicing A New Approach to Privacy Preserving Data Publishing.

Data mining, Security and Encryption, Web | Desktop Application
Slicing A New Approach to Privacy Preserving Data Publishing. Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that general- ization loses considerable amount of information, especially for high-dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi- identifying attributes and sensitive attributes.
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16Jul 2014 by chintan

Advance Mining of Temporal High Utility Itemset

Data mining, Web | Desktop Application
Advance Mining of Temporal High Utility Itemset The stock market domain is a dynamic and unpredictable environment. Traditional techniques, such as fundamental and technical analysis can provide investors with some tools for managing their stocks and predicting their prices. However, these techniques cannot discover all the possible relations between stocks and thus there is a need for a different approach that will provide a deeper kind of analysis. Data mining can be used extensively in the financial markets and help in stock-price forecasting. Therefore, we propose in this paper a portfolio management solution with business intelligence characteristics. We know that the temporal high utility itemsets are the itemsets with support larger than a pre-specified threshold in current time window of data stream. Discovery of temporal high utility itemsets is an…
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16Jul 2014 by chintan

A Framework for Personal Mobile Commerce Pattern Mining and Prediction

Data mining, Web | Desktop Application
A Framework for Personal Mobile Commerce Pattern Mining and Prediction In many applications, including location based services, queries may not be precise. In this paper, we study the problem of efficiently computing range aggregates in a multidimensional space when the query location is uncertain. Specifically, for a query point Q whose location is uncertain and a set S of points in a multi- dimensional space, we want to calculate the aggregate (e.g., count, average and sum) over the subset S_ of S such that for each p ∈ S_, Q has at least probability θ within the distance γ to p. We propose novel, efficient techniques to solve the problem following the filtering-and-verification paradigm. In particular, two novel filtering techniques are proposed to effectively and efficiently remove data points from…
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16Jul 2014 by chintan

Investigation and Analysis of New Approach of Intelligent Semantic Web Search Engines

Data mining, Web | Desktop Application
Investigation and Analysis of New Approach of Intelligent Semantic Web Search Engines As we know that www is allowing peoples to share the huge information from big database repositories. The amount of information grows billions of databases. Hence to search particular information from these huge databases we need specialized mechanism which helps to retrive that information efficiently. now days various types of search engines are available which makes information retrieving is difficult. but to provide the better solution to this proplem ,semantic web search engines are playing vital role.basically main aim of this kind of search engines is providing the required information is small time with maximum accuracy.
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16Jul 2014 by chintan

Sequential Anomaly Detection in the Presence of Noise and Limited Feedback

Data mining, Web | Desktop Application
Sequential Anomaly Detection in the Presence of Noise and Limited Feedback This paper describes a methodology for detecting anomalies from sequentially observed and potentially noisy data. The proposed approach consists of two main elements: (1) filtering, or assigning a belief or likelihood to each successive measurement based upon our ability to predict it from previous noisy observations, and (2) hedging, or flagging potential anomalies by comparing the current belief against a time-varying and data-adaptive threshold. The threshold is adjusted based on the available feedback from an end user. Our algorithms, which combine universal prediction with recent work on online convex programming, do not require computing posterior distributions given all current observations and involve simple primal-dual parameter updates. At the heart of the proposed approach lie exponential-family models which can be…
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16Jul 2014 by chintan

Clustering Methods in Data Mining with its Applications in High Education

Data mining, Web | Desktop Application
Clustering Methods in Data Mining with its Applications in High Education Data mining is a new technology, developing with database and artificial intelligence. It is a processing procedure of extracting credible, novel, effective and understandable patterns from database. Cluster analysis is an important data mining technique used to find data segmentation and pattern information. By clustering the data, people can obtain the data distribution, observe the character of each cluster, and make further study on particular clusters. In addition, cluster analysis usually acts as the preprocessing of other data mining operations. Therefore, cluster analysis has become a very active research topic in data mining. As the development of data mining, a number of clustering methods have been founded, The study of clustering technique from the perspective of statistics, based on…
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16Jul 2014 by chintan

A Novel Algorithm for Automatic Document Clustering

Data mining, Web | Desktop Application
A Novel Algorithm for Automatic Document Clustering Internet has become an indispensible part of today’s life. World Wide Web (WWW) is the largest shared information source. Finding relevant information on the WWW is challenging. To respond to a user query, it is difficult to search through the large number of returned documents with the presence of today’s search engines. There is a need to organize a large set of documents into categories through clustering. The documents can be a user query or simply a collection of documents. Document clustering is the task of combining a set of documents into clusters so that intra cluster documents are similar to each other than inter cluster documents. Partitioning and Hierarchical algorithms are commonly used for document clustering. Existing partitioning algorithms have the limitation…
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16Jul 2014 by chintan

Dynamic Personalized Recommendation on Sparse Data

Data mining, Web | Desktop Application
Dynamic Personalized Recommendation on Sparse Data Recommendation techniques are very important in the fields of E-commerce and other Web-based services. One of the main difficulties is dynamically providing high-quality recommendation on sparse data. In this paper, a novel dynamic personalized recommendation algorithm is proposed, in which information contained in both ratings and profile contents are utilized by exploring latent relations between ratings, a set of dynamic features are designed to describe user preferences in multiple phases, and finally a recommendation is made by adaptively weighting the features. Experimental results on public datasets show that the proposed algorithm has satisfying performance.
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16Jul 2014 by chintan

Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases

Data mining, Web | Desktop Application
Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases Mining high utility itemsets from a transactional database refers to the discovery of itemsets with high utility like profits. Although a number of relevant algorithms have been proposed in recent years, they incur the problem of producing a large number of candidate itemsets for high utility itemsets. Such a large number of candidate itemsets degrades the mining performance in terms of execution time and space requirement. The situation may become worse when the database contains lots of long transactions or long high utility itemsets. In this paper, we propose two algorithms, namely utility pattern growth (UP-Growth) and UP-Growth+, for mining high utility itemsets with a set of effective strategies for pruning candidate itemsets. The information of high utility itemsets is…
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16Jul 2014 by chintan

Sensitive Label Privacy Protection on Social Network Data

Data mining, Web | Desktop Application
Sensitive Label Privacy Protection on Social Network Data This paper is motivated by the recognition of the need for a ner grain and more personalized privacy in data publication of social networks. We propose a privacy protection scheme that not only prevents the disclosure of identity of users but also the disclosure of selected features in users' pro les. An individual user can select which features of her pro le she wishes to conceal. The social networks are modeled as graphs in which users are nodes and features are labels. Labels are denoted either as sensitive or as non-sensitive. We treat node labels both as background knowledge an adversary may possess, and as sensitive information that has to be protected. We present privacy protection algorithms that allow for graph data to be…
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16Jul 2014 by chintan

Privacy against Aggregate Knowledge Attacks

Data mining, Web | Desktop Application
Privacy against Aggregate Knowledge Attacks This paper focuses on protecting the privacy of individuals in publication scenarios where the attacker is ex- pected to have only abstract or aggregate knowledge about each record. Whereas, data privacy research usually focuses on defining stricter privacy guarantees that assume increasingly more sophisticated attack scenarios, it is also important to have anonymization methods and guarantees that will address any attack scenario. Enforcing a stricter guarantee than required increases unnecessarily the information loss. Consider for example the publication of tax records, where attackers might only know the total income, and not its con- stituent parts. Traditional anonymization methods would pro- tect user privacy by creating equivalence classes of identical records. Alternatively, in this work we propose an anonymization technique that generalizes attributes, only as much…
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16Jul 2014 by chintan

Adapting a Ranking Model for Domain-Specific Search

Data mining, Web | Desktop Application
Adapting a Ranking Model for Domain-Specific Search An adaptation process is described to adapt a ranking model constructed for a broad-based search engine for use with a domain-specific ranking model. It’s difficult to applying the broad-based ranking model directly to different domains due to domain differences, to build a unique ranking model for each domain it time-consuming for training models. In this paper,we address these difficulties by proposing algorithm called ranking adaptation SVM (RA-SVM), Our algorithm only requires the prediction from the existing ranking models, rather than their internal representations or the data from auxiliary domains The ranking model is adapted for use in a search environment focusing on a specific segment of online content, for example, a specific topic, media type, or genre of content. a domain-specific ranking model…
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16Jul 2014 by chintan

Efficient Similarity Search over Encrypted Data

Data mining, Web | Desktop Application
Efficient Similarity Search over Encrypted Data amount of data have been stored in the cloud. Although cloud based services offer many advantages, privacy and security of the sensitive data is a big concern. To mitigate the concerns, it is desirable to outsource sensitive data in encrypted form. Encrypted storage protects the data against illegal access, but it complicates some basic, yet important functionality such as the search on the data. To achieve search over encrypted data without compromising the privacy, considerable amount of searchable encryption schemes have been proposed in the literature. However, almost all of them handle exact query matching but not similarity matching; a crucial requirement for real world applications. Although some sophisticated secure multi-party computation based cryptographic techniques are available for similarity tests, they are computationally intensive…
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16Jul 2014 by chintan

A Bayesian Approach to Filtering Junk E-Mail

Data mining, Web | Desktop Application
A Bayesian Approach to Filtering Junk E-Mail Abstract In addressing the growing problem of junk E-mail on the Internet, we examine methods for the automated construction of lters to eliminate such unwanted mes- sages from a user's mail stream. By casting this prob- lem in a decision theoretic framework, we are able to make use of probabilistic learning methods in conjunc- tion with a notion of di erential misclassi cation cost to produce lters which are especially appropriate for the nuances of this task. While this may appear, at rst, to be a straight-forward text classi cation prob- lem, we show that by considering domain-speci c fea- tures of this problem in addition to the raw text of E-mail messages, we can produce much more accurate lters. Finally, we show the ecacy of such…
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16Jul 2014 by chintan

Opinion Mining for web search

Data mining, Web | Desktop Application
Opinion Mining for web search Generally, search engine retrieves the information using Page Rank, Distance vector algorithm, crawling, etc. on the basis of the user’s query. But it may happen that the links retrieved by search engine are may or may not be exactly related to the user’s query and user has to check all the links to know whether the needed information is present in the document or not, it becomes a tedious and time consuming job for the user. Our focus is to cluster different documents based on subjective similarities and dissimilarities. Our proposed tool ‘Web Search Miner’  which is based on the concept of  user opinions mining, which uses k-means search algorithm and distance measure based on Term frequency & web document frequency for mining the search…
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16Jul 2014 by chintan

web usage mining using apriori

Data mining, Web | Desktop Application
web usage mining using apriori The enormous content of information on the World Wide Web makes it obvious candidate for data mining research. Application of data mining techniques to the World Wide Web referred as Web mining where this term has been used in three distinct ways; Web Content Mining, Web Structure Mining and Web Usage Mining. E Learning is one of the Web based application where it will facing with large amount of data. In order to produce the E-Learning  portal usage patterns and user behaviors, this paper implements the high level process of Web Usage Mining using advance Association Rules algorithm  call D-Apriori Algorithm. Web Usage Mining consists of three main phases, namely Data Preprocessing, Pattern Discovering and Pattern Analysis. Server log files become a set of raw…
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16Jul 2014 by chintan

Sales & Inventory Prediction using Data Mining

Data mining, Web | Desktop Application
Sales & Inventory Prediction using Data Mining Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations.
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16Jul 2014 by chintan

Hiding Sensitive Association Rule for Privacy Preservation

Data mining, Web | Desktop Application
Hiding Sensitive Association Rule for Privacy Preservation Data mining techniques have been widely used in various applications. However, the misuse of these techniques may lead to the disclosure of sensitive information. Researchers have recently made efforts at hiding sensitive association rules. Nevertheless, undesired side effects, e.g., non sensitive rules falsely hidden and spurious rules falsely generated, may be produced in the rule hiding process. In this paper, we present a novel approach that strategically modifies a few transactions in the transaction database to decrease the supports or confidences of sensitive rules without producing the side effects. Since the correlation among rules can make it impossible to achieve this goal, in this paper, we propose heuristic methods for increasing the number of hidden sensitive rules and reducing the number of modified…
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16Jul 2014 by chintan

Effective Pattern Discovery for Text Mining

Data mining, Web | Desktop Application
Effective Pattern Discovery for Text Mining Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase)-based approaches should perform better than the term-based ones, but many experiments do not support this hypothesis. This paper presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information.
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16Jul 2014 by chintan

Data leakage Detection

Security and Encryption, Web | Desktop Application
Data leakage Detection While doing business, sometimes sensitive data must be handed over to supposedly trusted third parties. For example, a hospital may give patient records to researchers who will devise new treatments. Similarly, a company may have partnerships with other companies that require sharing customer data. Another enterprise may outsource its data processing, so data must be given to various other companies. We call the owner of the data the distributor and the supposedly trusted third parties the agents. Our goal is to detect when the distributor’s sensitive data has been leaked by agents, and if possible to identify the agent that leaked the data. We consider applications where the original sensitive data cannot be perturbed. Perturbation is a very useful technique where the data is modified and made…
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16Jul 2014 by chintan

Medical Disease diagnosis using Data Mining

Data mining, Web | Desktop Application
Medical Disease diagnosis using Data Mining The healthcare industry collects a huge amount of data which is not properly mined and not put to the optimum use. Discovery of these hidden patterns and relationships often goes unexploited. Our research focuses on this aspect of Medical diagnosis by learning pattern through the collected data of diabetes, hepatitis and heart diseases and to develop intelligent medical decision support systems to help the physicians. In this paper, we propose the use of decision trees C4.5 algorithm, ID3 algorithm and CART algorithm to classify these diseases and compare the effectiveness, correction rate among them.
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