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A Review on IEEE-754 Standard Floating Point Arithmetic Unit

Floating point operations in digital systems form an integral part in the design of many digital processors. Digital Signal Processor is the most important application of floating point operations. In the recent years many approaches for floating point operations have been proposed and their merits and demerits are compared. For floating point operations the operands are first converted into IEEE 754 format in either single precision or double precision format. The arithmetic operations are performed on the significant part of the IEEE format. In this paper various floating point operation unit architectures are reviewed. Few designers work on high speed architectures for reducing the delay of the overall circuit while others work on the area utilization parameters. Then the conclusion is drawn based on various architectural analyses.

Published by: Monika Maan, Abhay Bindal

Author: Monika Maan

Paper ID: V2I3-1182

Paper Status: published

Published: June 16, 2016

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Hybrid Exemplar-Based Image in Painting Algorithm using Non Local Total Variation Model

Exemplar-based algorithms are a popular technique for image in painting. They mainly have two important phases: deciding the filling-in order and selecting good exemplars. Traditional exemplar-based algorithms are to search suitable patches from source regions to fill in the missing parts, but they have to face a problem: improper selection of exemplars. To improve the problem we introduction modified exemplar based using non local total variation model which include two main step patch priority and patch completion. Experimental results show the superiority of the proposed method compared to the competitive methods. The proposed method may be used for restoration of digital images of defective or damaged artifacts.

Published by: Preeti Gupta, Kuldip Pahwa

Author: Preeti Gupta

Paper ID: V2I3-1181

Paper Status: published

Published: June 16, 2016

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Multi-level Authentication for Internet of Things to Establish Secure Healthcare Network

The Cloud based healthcare monitoring sensor networks (C-HMSN) consist of a number of wireless nodes connected to each other using wireless connections. As these wireless nodes are connected to base stations so they are highly prone areas for hacking attacks. During data analysis there is need to secure cryptographic keys when the HMSN nodes are in working condition, for secure propagation of the sensitive information. An Efficient corporate key management and distribution scheme is required to maintain the data security in HMSNs. Existing cryptographic key management and distribution technique usually consume higher amount of energy and put larger computational overheads on Wireless Sensor Nodes. The cryptographic keys are used on different levels of HMSN communication i.e. neighbor nodes, cluster heads and base stations. In this paper we will present corporate improved key management architecture, called SECURE KEY EXCHANGE adaptable for the HMSNs, to enable comprehensive, trustworthy, user-verifiable, and cost-effective key management. It allows only authorized applications to use the keys and administrator can remotely issue authenticated commands and verify system output. In addition, it also has to be improved to work with HMSN nodes, which means it must use less computational power of the HMSN. The wireless sensor node should be energy efficient, increasing the life of wireless sensor network.

Published by: Shilpa Kansal, Navpreet Kaur

Author: Shilpa Kansal

Paper ID: V2I3-1179

Paper Status: published

Published: June 16, 2016

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Review Paper

Intrusion Detection System by Machine Learning-A Review

Efficient intrusion detection is needed as a defense of the network system to detect the attacks over the network. A feature selection and classification based Intrusion Detection model is presented, by implementing feature selection, the dimensions of NSL-KDD data set is reduced then by applying machine learning approach, we are able to build Intrusion detection model to find attacks on system and improve the intrusion detection using the captured data. With the increasing number of new unseen attacks the purpose of this model is to develop a system for intrusion detection, and the model will be capable of detecting new and previously unseen attacks using the basic signatures and the features of known attack.

Published by: Aanchal Kumar, Er. Jaspreet Kaur, Er. Inderpreet Kaur

Author: Aanchal Kumar

Paper ID: V2I3-1178

Paper Status: published

Published: June 15, 2016

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Research Paper

Software Defect Prediction using Ensemble Learning Survey

Machine learning is a science that explores the building and study of algorithms that can learn from the data. Machine learning process is the union of statistics and artificial intelligence and is closely related to computational statistics. Machine learning takes decisions based on the qualities of the studied data using statistics and adding more advanced artificial intelligence heuristics and algorithms.

Published by: Ramandeep Kaur, Er. Harpreet Kaur, Er. Jaspreet Kaur,

Author: Ramandeep Kaur

Paper ID: V2I3-1177

Paper Status: published

Published: June 15, 2016

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Review Paper

Review of Diabetes Detection by Machine Learning and Data Mining

The most common action in data mining is classification. It recognizes patterns that describe the group to which an item belongs. It does this by examining existing items that already have been classified and inferring a set of rules. Similar to classification is clustering. The major difference being that no groups have been predefined. Prediction is the construction and use of a model to assess the class of an unlabeled object or to assess the value or value ranges of a given object is likely to have. The next application is forecasting.

Published by: Preeti Verma, Inderpreet Kaur, Jaspreet Kaur, ,

Author: Preeti Verma

Paper ID: V2I3-1176

Paper Status: published

Published: June 15, 2016

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