DEALING WITH CONCEPT DRIFTS IN PROCESS MINING USING SECURITY PRIMITIVES
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Shaik. Rasheed , Mr.K.Bhaska
ABSTRACT: This paper is an attempt to enhance the existing Drift Detection with Change process discovery in complex Datasets. After detecting the change points and the regions of change, it is necessary to put them together in perspective. In addition, there are other applications such as deriving a configurable model for the process variants. A configurable process model describes a family of similar process models. The process variants discovered using concept drift can be merged to derive a configurable process model. A recent study revealed that different diversity levels in an ensemble of learning machines are required in order to maintain high generalization on both old and new concepts. Inspired by this study and based on a further study of diversity with different strategies to deal with drifts, we propose a new online ensemble learning approach called Diversity for Dealing with Drifts (DDD) and also explore how to make secure environments with less synchrony and show how it can be used to solve asynchronous Secure Multiparty Computation (SMC). Within the redesign we investigate the problem of solving consensus in a General omission failure model augmented with failure detectors.
Shaik. Rasheed , Mr.K.Bhaska
ABSTRACT: This paper is an attempt to enhance the existing Drift Detection with Change process discovery in complex Datasets. After detecting the change points and the regions of change, it is necessary to put them together in perspective. In addition, there are other applications such as deriving a configurable model for the process variants. A configurable process model describes a family of similar process models. The process variants discovered using concept drift can be merged to derive a configurable process model. A recent study revealed that different diversity levels in an ensemble of learning machines are required in order to maintain high generalization on both old and new concepts. Inspired by this study and based on a further study of diversity with different strategies to deal with drifts, we propose a new online ensemble learning approach called Diversity for Dealing with Drifts (DDD) and also explore how to make secure environments with less synchrony and show how it can be used to solve asynchronous Secure Multiparty Computation (SMC). Within the redesign we investigate the problem of solving consensus in a General omission failure model augmented with failure detectors.
Superior AMBIGUITY Determination Setback Within ATTRIBUTE Based Support Re-Ranking System
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Bathineni.Swethambari and A.Latha
Abstract- A text-based image search is also an important thought in the field of Information retrieval. Reranking is one of the techniques employed to retrieve the images easily. In this paper, we make an attempt to study about the attribute based reranking system, to solve the issue of ambiguity. The target of the approach is to easily retrieve the images easily with an efficient classification models. Some visual and contextual features of the learning space model are derived. It can be applied to the multimedia applications. Then a novel algorithm is designed to extract the discriminant features of the training records for multimedia annotation. Experimental result shows the effectiveness of the system.
Bathineni.Swethambari and A.Latha
Abstract- A text-based image search is also an important thought in the field of Information retrieval. Reranking is one of the techniques employed to retrieve the images easily. In this paper, we make an attempt to study about the attribute based reranking system, to solve the issue of ambiguity. The target of the approach is to easily retrieve the images easily with an efficient classification models. Some visual and contextual features of the learning space model are derived. It can be applied to the multimedia applications. Then a novel algorithm is designed to extract the discriminant features of the training records for multimedia annotation. Experimental result shows the effectiveness of the system.
Chunk -Stage File Replication Scheme:Detection OF Replica Data In Scattered-Distributed method
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Guntur.Susmitha and Subhani.Shaik
Abstract- Nowadays, a large of amount of data is being generated. The dataset contain a large amount of data where there might be possibility of storing duplicate records. The formation of duplicate records is possible only if the data are arranged in heterogeneity mannerism. The discovery of duplicate records from dataset is higher time consuming process. It is an important process in data cleaning and data integration techniques. In some scenario, a web page may display according to the query with its relevant advertisements. The data schema comprised on the association between any real entities. To overcome from this issue, we propose a novel block level deduplication system, which disposes the redundant records from database. It efficiently handles the search queries. The main contribution is the removal of redundant records from the level of parent. By doing so, the effectiveness of the system is enhanced.
Guntur.Susmitha and Subhani.Shaik
Abstract- Nowadays, a large of amount of data is being generated. The dataset contain a large amount of data where there might be possibility of storing duplicate records. The formation of duplicate records is possible only if the data are arranged in heterogeneity mannerism. The discovery of duplicate records from dataset is higher time consuming process. It is an important process in data cleaning and data integration techniques. In some scenario, a web page may display according to the query with its relevant advertisements. The data schema comprised on the association between any real entities. To overcome from this issue, we propose a novel block level deduplication system, which disposes the redundant records from database. It efficiently handles the search queries. The main contribution is the removal of redundant records from the level of parent. By doing so, the effectiveness of the system is enhanced.
Glaucoma Detection And Classification Using Adaptive Thresholding
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P.Kondala rao,G.Prathibha
Glaucoma is an optic neuropathy which is one of the main causes of permanent blindness worldwide. This paper presents an automatic image processing based method for detection of glaucoma from the digital fundus images. In this proposed work, the discriminatory parameters of glaucoma infection, such as cup to disc ratio (CDR) and blood vessels in different regions of the optic disc has been used as features and fed as inputs to learning algorithms for glaucoma diagnosis. These features which have discriminatory changes with the occurrence of glaucoma are strategically used for training the classifiers to improve the accuracy of identification. The segmentation of optic disc and cup based on adaptive threshold of the pixel intensities lying in the optic nerve head region. Unlike existing methods the proposed algorithm is based on an adaptive threshold that uses local features from the fundus image for segmentation of optic cup and optic disc making it invariant to the quality of the image and noise content which may find wider acceptability. The experimental results indicate that such features are more significant in comparison to the statistical or textural features as considered in existing works. The proposed work achieves an accuracy of 95.10% A comparison of the proposed work with the existing methods indicates that the proposed approach has improved accuracy of classification glaucoma from a digital fundus which may be considered clinically significant.
P.Kondala rao,G.Prathibha
Glaucoma is an optic neuropathy which is one of the main causes of permanent blindness worldwide. This paper presents an automatic image processing based method for detection of glaucoma from the digital fundus images. In this proposed work, the discriminatory parameters of glaucoma infection, such as cup to disc ratio (CDR) and blood vessels in different regions of the optic disc has been used as features and fed as inputs to learning algorithms for glaucoma diagnosis. These features which have discriminatory changes with the occurrence of glaucoma are strategically used for training the classifiers to improve the accuracy of identification. The segmentation of optic disc and cup based on adaptive threshold of the pixel intensities lying in the optic nerve head region. Unlike existing methods the proposed algorithm is based on an adaptive threshold that uses local features from the fundus image for segmentation of optic cup and optic disc making it invariant to the quality of the image and noise content which may find wider acceptability. The experimental results indicate that such features are more significant in comparison to the statistical or textural features as considered in existing works. The proposed work achieves an accuracy of 95.10% A comparison of the proposed work with the existing methods indicates that the proposed approach has improved accuracy of classification glaucoma from a digital fundus which may be considered clinically significant.
Spline Polynomial Approach For The Design Of Quadrature Mirror Filters
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P.S.S.S.Guptha, M.Usha
ABSTRACT: This paper presents a design method of the optimum quadrature mirror filters (QMF), by using spline polynomial functions. It is known that linear phase FIR filters cannot exactly satisfy the condition that the squared amplitude frequency responses are mirror images of each other about the line which has led to the name quadrature mirror flters. The solution is to approximate the response of each frequency band by using spline polynomial functions that insures continuous superior order derivatives. In the proposed approach, the prototype filter is optimized with the novelty of using spline polynomials in the transition band. Several design examples are included to illustrate the proposed algorithm.
P.S.S.S.Guptha, M.Usha
ABSTRACT: This paper presents a design method of the optimum quadrature mirror filters (QMF), by using spline polynomial functions. It is known that linear phase FIR filters cannot exactly satisfy the condition that the squared amplitude frequency responses are mirror images of each other about the line which has led to the name quadrature mirror flters. The solution is to approximate the response of each frequency band by using spline polynomial functions that insures continuous superior order derivatives. In the proposed approach, the prototype filter is optimized with the novelty of using spline polynomials in the transition band. Several design examples are included to illustrate the proposed algorithm.
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