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Recent Papers

Novel Approach for Heart Disease using Data Mining Techniques

Data mining is the process of analyzing large sets of data and then extracting the meaning of the data. It helps in predicting future trends and patterns, allowing business in decision making. Presently various algorithms are available for clustering the proposed data, in the existing work they used K mean clustering, C4.5 algorithm and MAFIA i.e. Maximal Frequent Item set algorithm for Heart disease prediction system and achieved the accuracy of 89%. As we can see that there is vast scope of improvement in our proposed system, in this paper we will implement various other algorithms for clustering and classifying data and will achieved the accuracy more than the present algorithm. Several Parameters has been proposed for heart disease prediction system but there have been always a need for better parameters or algorithms to improve the performance of heart disease prediction system.

Published by: Era Singh Kajal, Ms. Nishika

Author: Era Singh Kajal

Paper ID: V2I4-1156

Paper Status: published

Published: July 15, 2016

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A Review on ACO based Scheduling Algorithm in Cloud Computing

Task scheduling plays a key role in cloud computing systems. Scheduling of tasks cannot be done on the basis of single criteria but under a lot of rules and regulations that we can term as an agreement between users and providers of cloud. This agreement is nothing but the quality of service that the user wants from the providers. Providing good quality of services to the users according to the agreement is a decisive task for the providers as at the same time there are a large number of tasks running at the provider’s side. In this paper we are performing comparative study of the different algorithms for their suitability, feasibility, adaptability in the context of cloud scenario.

Published by: Meena Patel, Rahul Kadiyan

Author: Meena Patel

Paper ID: V2I4-1155

Paper Status: published

Published: July 15, 2016

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Robust data compression model for linear signal data in the Wireless Sensor Networks

The data compression is one of the popular power efficiency methods for the lifetime improvement of the sensor networks. The wavelet based signal decomposition for data compression, entropy encoding or arithmetic encoding like methods are being used for the purpose of compression in the sensor networks to elongate the lifetime of the wireless sensor networks. The proposed method is based upon the combination of the wavelet signal decomposition of the signal compression with the entropy encoding method of Huffman encoding for the purpose of data compression of the sensed data on the sensor nodes. The compressed data (reduced sized data) consumes the less energy for the small packets in comparison with the non-compressed packets, which directly affects its lifetime. The proposed model has been recorded with more than 70% compression ratio, which is way higher than the existing models. The proposed model has been also evaluated for the signal quality after compression and elapsed time. In both of the latter parameters, the proposed model has been found efficient. Hence, the proposed model effectiveness has been proved from the experimental results.

Published by: Sukhcharn Sandhu

Author: Sukhcharn Sandhu

Paper ID: V2I4-1154

Paper Status: published

Published: July 15, 2016

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Consumer Trend Prediction using Efficient Item-Set Mining of Big Data

Habits or behaviors presently prevalent amid customers of goods or services. Customer trends trail extra than plainly what people buy and how far they spend. Data amassed on trends could additionally contain data such as how customers use a product and how they converse concerning a brand alongside their communal network. Understanding Customer Trends and Drivers of Deeds provides an overview of the marketplace, analyzing marketplace data, demographic consumption outlines inside the group, and the key customer trends steering consumption. The report highlights innovative new product progress that efficiently targets the most pertinent customer demand states, and proposals crucial recommendations to capitalize on evolving customer landscapes.

Published by: Yukti Chawla, Parikshit Singla

Author: Yukti Chawla

Paper ID: V2I4-1153

Paper Status: published

Published: July 14, 2016

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A Novel Approach for Detection of Traffic Congestion in NS2

Traffic congestions are formed by many factors; some are predictable like road construction, rush hour or bottle-necks. Drivers, unaware of congestion ahead eventually join it and increase the severity of it. The more severe the congestion is, the more time it will take to clear. In order to provide drivers with useful information about traffic ahead a system must: Identify the congestion, its location, severity and boundaries and Relay this information to drivers within the congestion and those heading towards it. To form the picture of congestion they need to collaborate their information using vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communication. Once a clear picture of the congestion has formed, this information needs to be relayed to vehicles away from the congestion so that vehicles heading towards it can take evasive actions avoiding further escalation its severity. Initially, a source vehicle initiates a number of queries, which are routed by VANETs along different paths toward its destination. During query forwarding, the real-time road traffic information in each road segment is aggregated from multiple participating vehicles and returned to the source after the query reaches the destination. This information enables the source to calculate the shortest-time path. By allowing data exchange between vehicles about route choices, congestions and traffic alerts, a vehicle makes a decision on the best course of action.

Published by: Arun Sharma, Kapil Kapoor, Bodh Raj, Divya Jyoti

Author: Arun Sharma

Paper ID: V2I4-1152

Paper Status: published

Published: July 13, 2016

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Research Paper

Authentication using Finger Knuckle Print Techniques

In this paper, a new approach is proposed for personal authentication using patterns generated on dorsal of finger. The texture pattern produced by the finger knuckle is highly unique and makes the surface a distinctive biometric identifier. Important part in knuckle matching is variation of number of features which come by in pattern form of texture features. In this thesis, the emphasis has been done on key point and texture features extraction. The key point features are extracted by SIFT features and the texture features are extracted by Gabor and GLCM features. For the SIFT and GLCM features matching process is done by hamming distance and for the Gabor features matching is done by correlation. The database of 40 different subjects has been acquired by touch less imaging by use of digital camera. The authentication system extracts features from the image and stores the template for later authentication. The experiment results are very promising for recognition of second minor finger knuckle pattern.

Published by: Sanjna Singla, Supreet Kaur

Author: Sanjna Singla

Paper ID: V2I4-1151

Paper Status: published

Published: July 13, 2016

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