A self-adaptive migration model genetic algorithm for data mining applications

Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others. © 2007 Elsevier Inc. All rights reserved

An Efficient Storage Mechanism to Distribute Disk Load in a VoD Server

In this paper, a storage mechanism is devised to balance the load and to provide immediate service to the clients with a start-up delay of 2ms to 7 ms. The video storage is based on the probability of the clients requesting for the video. Videos with higher probability of being requested are stored and replicated to ensure guaranteed retrieval. Parity generation scheme is employed to provide reliability to non-popular videos. The system is also capable of handling disk failures transparently and thereby providing a reliable service to the clients

A Secure Image Steganography using LSB, DCT and Compression Techniques on Raw Images

Steganography is an important area of research in recent years involving a number of applications. It is the science of embedding information into the cover image viz., text, video, and image (payload) without causing statistically significant modification to the cover image. The modern secure image steganography presents a challenging task of transferring the embedded information to the destination without being detected. In this paper we present an image based steganography that combines Least Significant Bit(LSB), Discrete Cosine Transform(DCT), and compression techniques on raw images to enhance the security of the payload. Initially, the LSB algorithm is used to embed the payload bits into the cover image to derive the stego-image. The stego-image is transformed from spatial domain to the frequency domain using DCT. Finally quantization and runlength coding algorithms are used for compressing the stego-image to enhance its security. It is observed that secure images with low MSE and BER are transferred without using any password, in comparison with earlier works

Pathway clusters of aging genes using data mining techniques

Exploring and identifying novel aging genes has been the current area of interest in Gerontology. A variety of techniques have been proposed to identify the genes that affect the centenarians and the focus is on the study of genes of interest affecting older population. However the study of aging related pathways using computational methods has not been discussed explicitly so far. In this paper, an attempt is made to cluster the aging genes into different biological pathways using data mining techniques. Text mining is used to identify the most relevant keywords from different pathway databases, which is used as one of the feature describing a gene. K-means clustering is done on the aging pathway dataset. The clusters formed are in good agreement with the background knowledge about the aging genes and their pathways. The quality of the K-means clustering is quite promising as it well separates the different aging genes into their respective pathways

Template based Mole Detection for Face Recognition

Face recognition is used for personal identification. The Template based Mole Detection for Face Recognition (TBMDFR) algorithm is proposed to verify authentication of a person by detection and validation of prominent moles present in the skin region of a face. Normalized Cross Correlation (NCC) matching, complement of Gaussian template and skin segmen tation is used to identify and validate mole by fixing predefined NCC threshold values. It is observed that the NCC values of TBMDFR are much higher compared to the existing algorithms

A Quality Hybrid Service Discovery Protocol

Hybrid protocol combines the advantages of proactive and reactive routing in adhoc network. The routing is initially established with some proactively prospected routes and then serves the demand from additionally activated nodes through reactive flooding. In this paper we propose A Quality Hybrid Service Discovery Protocol (QHSDP) for discovering services. A broadcast mechanism is used to get the service and routing information of the nodes present inside the zone. The routing and service information reduces the packet flooding in the network hence reducing collision and increasing packet delivery efficiency. Reduced control packets in turn reduces the battery power consumption. A query message is bordercasted through the peripheral nodes to the nodes outside the zone. This makes the discovery procedure more sclable, hence increasing the node’s coverage and reducing the latency in the proposed technology compared to the existing technolog

Web Caching and Prefetching with Cyclic Model Analysis of Web Object Sequences

Web caching is the process in which web objects are temporarily stored to reduce bandwidth consumption, server load and latency. Web prefetching is the process of fetching web objects from the server before they are actually requested by the client. Integration of caching and prefetching can be very beneficial as the two techniques can support each other. By implementing this integrated scheme in a client-side proxy, the perceived latency can be reduced for not one but many users. In this paper, we propose a new integrated caching and prefetching policy called the WCP-CMA which makes use of a profit-driven caching policy that takes into account the periodicity and cyclic behaviour of the web access sequences for deriving prefetching rules. Our experimental results have shown a 10%-15% increase in the hit ratios of the cached objects and 5%-10% decrease in delay compared to the existing schem

Interestingness measure on privacy preserved data with horizontal partitioning

Association rule mining is a process of finding the frequent item sets based on the interestingness measure. The major challenge exists when performing the association of the data where privacy preservation is emphasized. The actual transaction data provides the evident to calculate the parameters for defining the association rules. In this paper, a solution is proposed to find one such parameter i.e. support count for item sets on the non transparent data, in other words the transaction data is not disclosed. The privacy preservation is ensured by transferring the x-anonymous records for every transaction record. All the anonymous set of actual transaction record perceives high generalized values. The clients process the anonymous set of every transaction record to arrive at high abstract values and these generalized values are used for support calculation. More the number of anonymous records, more the privacy of data is amplified. In experimental results it is shown that privacy is ensured with more number of formatted transactions

Ir_urfs_vf: Image Recommendation with User Relevance Feedback Session and Visual Features in Vertical Image Search

In recent years, online shopping has grown exponentially and huge number of images are available online. Hence, it is necessary to recommend various product images to aid the user in effortless and efficient access to the desired products. In this paper, we present image recommendation framework with user relevance feedback session and visual features (IR_URFS_VF) to extract relevant images based on user inputs. User feedback is retrieved from image search history with clicked and un-clicked images. Image features are computed off-line and later used to find relevance between images. The relevance between images is determined by cosine similarity and are ranked based on clicked frequency and similarity score between images. Experiments results show that IR_URFS_VF outperforms CBIR method by providing more relevant ranked images to the user input query