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Prasad M.V.N.K.,IDRBT
Advances in Intelligent Systems and Computing | Year: 2014

his paper proposes two algorithms for embedding and extraction of the watermark into the cover image based on magic square and ridgelet transform techniques. Spread-spectrum communication systems use the spread sequences that have good correlation properties. Magic square technique is used as a spreadspectrum technique to spread the watermark. Ridgelet transform is the nextgeneration wavelets as it is effective through line singularities characteristic. Ridgelet transform generates sparse image representation where the most significant coefficient represents the most energetic direction of an image with straight edges. The experiments indicated that these algorithms enabled the cover images to have the good invisibility and made them robust to the general image compression attacks such as JPEG, GIF. © Springer India 2014.

Prasad M.V.N.K.,IDRBT
Smart Innovation, Systems and Technologies | Year: 2015

This paper proposes four different methods for embedding and extraction of the watermark into the cover image based on Curvelet Transform Technique. Magic Square Technique was used in the algorithms for spreading the watermark and embedding into the curvatures of original image. The Curvelet transform is a type of the Wavelet transform technique designed to represent images in sparse mode consisting of all objects having curvature information taken in higher resolution even for lower resolution content. The experiments indicated that these algorithms embedded the watermark efficiently such that the images have possessed robust watermark on extraction after the image compression like JPEG, GIF, scaling, rotation and noise attacks. © Springer India 2015.

Jabbar M.A.,Jawaharlal Nehru Technological University | Deekshatulu B.L.,IDRBT | Chandra P.,Advanced System Laboratory
Communications in Computer and Information Science | Year: 2012

This Paper focuses a new approach for applying association rules in the Medical Domain to discover Heart Disease Prediction. The health care industry collects huge amount of health care data which,unfortunately are not mined to discover hidden information for effective decision making.Discovery of hidden patterns and relationships often goes unexploited. Data mining techniques can help remedy this situation.Data mining have found numerous applications in Business and Scientific domains.Association rules,classification,clustering are major areas of interest in data mining. Among these,association rules have been a very active research area.In our work Genetic algorithm is used to predict more accurately the presence of Heart Disease for Andhra Pradesh Population.The main motivation for using Genetic algorithm in the discovery of high level Prediction rules is that they perform a global search and cope better with attribute interaction than the greedy rule induction algorithms often used in Data Mining. © 2012 Springer-Verlag.

Thurimella A.-K.,Harman International | Padmaja M.T.,IDRBT
Recent Patents on Computer Science | Year: 2010

Software product line engineering (SPLE) is an emerging paradigm for the development of a family of products based on customization and reuse of artifacts. Several advantages such as reduction of time-to-market, improved product quality and reuse could be achieved by employing software product line engineering. Therefore, this is useful for the industrial sector developing product lines and is a fertile area for patents. Variability management, which enables customization and reuse, is the central part of software product line engineering. This paper provides a review of existing patents in the field of variability management. Particular patents include, feature-oriented approaches for variability management, variability at the level of components and source code, approaches for the identification and analysis of variability and rationale-based variability. The review is based on criteria qualifying the identification, instantiation and evolution of variability. Based on this review, a vision is provided on future patents/approaches in the area of software product line engineering. © 2010 Bentham Science Publishers Ltd.

Kalluri H.K.,Vignan University | Prasad M.V.N.K.,IDRBT | Agarwal A.,University of Hyderabad
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

Region of Interest (ROI) extraction is an important task for palmprint identification. Earlier reported works used fixed size ROI for the recognition of palmprints. When the fixed size ROI is used the palm area taken up for recognition is less compared to dynamic ROI extraction. The proposed algorithm focuses on extraction of maximum possible ROI compared to existing fixed and dynamic ROI extraction techniques [7, 19]. The experimental results demonstrate that the proposed approach extracts better ROI on three databases, 1. The PolyU Palmprint Database, 2. CASIA Palmprint Image Database and 3. IIT Delhi Palmprint Database, when compared to the existing fixed size and dynamic size ROI extraction techniques. © 2012 Springer-Verlag.

Channapragada R.S.R.,CMR Institute of Technology | Prasad M.V.N.K.,IDRBT
Advances in Intelligent Systems and Computing | Year: 2016

This paper presents a watermarking method using fractals. In the method discussed here, the host image is encoded by the proposed fractal coding method. To embed the watermark evenly over the whole host image, specific Range blocks are selected. Then, the scrambled watermark is inserted into the selected Range blocks. Finally, the watermarked image is obtained by the fractal decoding method. Simulation results have proven the imperceptibility of the proposed scheme and shown the robustness against various attacks. © Springer International Publishing Switzerland 2016.

Kumar S.,University of Hyderabad | Syam Kumar P.,IDRBT
Souvenir of the 2015 IEEE International Advance Computing Conference, IACC 2015 | Year: 2015

Storage virtualization is the most applied word in the industry due to its importance. Now a day's data become more import, to hold and to extract needful information. Datacenter become an integral part of any organization, so its management too. For best and efficient result as well as proper storage utilization and management we need storage area network (SAN). In the environment of SAN, there is the compatibility issue with the different vendors and their drivers, so we are going for storage virtualization. Storage virtualization is applied in SAN environment. The classical techniques [1] to achieve storage virtualization is suffering from many problems like improper disk utilization, high latency, power consumption, different attacks and security issues. In this paper we design and implement storage virtualization technique EC2S2 to get better yield in terms of security, high throughput, efficient management and least latency. Through the security and performance analysis we show that our method is secure and efficient. © 2015 IEEE.

Jabbar M.A.,AEC | Deekshatulu B.L.,IDRBT | Chndra P.,ASL
Proceedings of International Conference on Circuits, Communication, Control and Computing, I4C 2014 | Year: 2014

Recent survey shows that heart disease is a leading cause of death in India and in world wide. Significant life savings can be achieved, if a timely and cost effective clinical decision system is developed. Adverse reactions occur if a disease is not diagnosed properly. A clinical decision support system can assist health care professionals for early diagnosis of heart disease from patient's medical data. Machine learning and modern data mining methods are useful for predicting and classifying heart disease. In this paper we wish to develop effective alternating decision tree approach for early diagnosis of heart disease. Alternating decision tree is a new type of classification rule. It is a generalization of decision trees, voted decision stumps and voted decision trees. We have applied our approach on heart disease patient records collected from various hospitals in Hyderabad. Optimization of features improves efficiency of earning algorithm. We used PCA to determine essential features of heart disease data. Experimental results show that our decision support system achieves high accuracy and proving its usefulness in the diagnosis of heart disease. © 2014 IEEE.

Jabbar M.A.,Jawaharlal Nehru Technological University | Deekshatulu B.L.,IDRBT | Chandra P.,Scientist Advanced System Laboratory
Advances in Intelligent Systems and Computing | Year: 2013

Associate classification is a scientific study that is being used by knowledge discovery and decision support system which integrates association rule discovery methods and classification to a model for prediction. An important advantage of these classification systems is that, using association rule mining they are able to examine several features at a time. Associative classifiers are especially fit to applications where the model may assist the domain experts in their decisions. Cardiovascular deceases are the number one cause of death globally. An estimated 17.3 million people died from CVD in 2008, representing 30% of all global deaths. India is at risk of more deaths due to CHD. Cardiovascular disease is becoming an increasingly important cause of death in Andhra Pradesh. Hence a decision support system is proposed for predicting heart disease of a patient. In this paper we propose a new Associate classification algorithm for predicting heart disease for Andhra Pradesh population. Experiments show that the accuracy of the resulting rule set is better when compared to existing systems. This approach is expected to help physicians to make accurate decisions. © 2013 Springer-Verlag.

Jain A.,Indian Institute of Technology Guwahati | Prasad M.V.N.K.,IDRBT
Proceedings - 11th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2015 | Year: 2015

The voluminous biometric databases make it computationallydifficult to match a query against each enrolledimage in the database as is required in identification. To makethe process efficient, various indexing techniques have beendeveloped. We propose a novel fingerprint indexing schemeusing dynamic clustering and m-ary trees in this paper. Weconstruct a triangle spiral for each fingerprint image fromwhich we obtain O(n) triangles unlike many other indexingapproaches. These triangles are used to extract robust translationand rotation invariant features. Experiments with thebenchmark databases confirm the superiority of our approachto the other existing techniques. © 2015 IEEE.

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