Pradeep G.,IDRBT |
Ravi V.,Banking Technology
Handbook of Research on Intelligent Techniques and Modeling Applications in Marketing Analytics | Year: 2016
In this chapter, we model association rule mining as a Fuzzy multi-objective global optimization problem by considering several measures of strength such as support, confidence, coverage, comprehensibility, leverage, interestingness, lift and conviction by utilizing various fuzzy aggregator operators. In this, pdel, each measure has its own level of significance. Three fuzzy multi-objective association rule mining techniques viz., Fuzzy Multi-objective Binary Particle Swarm Optimization based association rule miner (FMO-BPSO), a hybridized Fuzzy Multi-objective Binary Firefly Optimization and Threshold Accepting based association rule miner (FMO-BFFOTA), hybridized Fuzzy Multi-objective Binary Particle Swarm Optimization and Threshold Accepting based association rule miner (FMO-BPSOTA) have been proposed. These three algorithms have been tested on various datasets such as book, food, bank, grocery, click stream and bakery datasets along with three fuzzy aggregate operators. From these experiments, we can conclude that Fuzzy-And outperforms all the other operators. © 2017 by IGI Global. All rights reserved.
Ahmad S.,University of Hyderabad |
2016 IEEE Annual India Conference, INDICON 2016 | Year: 2016
IT industry is booming. So, the requirement for resources is also on the increase. Industry requires more processing power and storage capability to meet their goal. Here, Cloud Computing comes in the picture, it provides IT industry the much-needed resources on a large scale at low cost and makes their task easy. Organizations can easily outsource their huge amount of data to cloud storage. However, the privacy of data is a big concern. The data privacy can be achieved by encryption techniques, but it increases the difficulty of securely searching data on the cloud because searching in encrypted data is itself a challenging task. Recently many schemes have been proposed but these schemes do not consider the semantic of the query. We proposed a novel method by combining LSI and hierarchical cluster to get the semantic relation between the result and to reduce the search space respectively. Further, to verify the search result authenticity, we use MAC tree along with a cryptographic signature. Through security and performance analysis we prove that our method is better than previous encrypted searchable schemes. © 2016 IEEE.
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.
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.
Thurimella A.-K.,Harman International |
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.
Channapragada R.S.R.,CMR Institute of Technology |
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  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 |
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 |
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.