Informatics Publishing Limited

Laxmi Arun ¹, V. L. Divyalakshmi ¹, P. Sharmila ¹, S. Rakshitha ¹, Amith Jacob ¹

Affiliations
1 Vidya Vardhaka College of Engineering, Mysore, Karnataka, IN

Abstract

With the advent of web and the concerned technologies the end users can generate or share any number of documents. A service such as collaborative edition of online documents is commonly provided through Cloud Computing as Software as a Service. However, the Cloud paradigm requires the end user’s to trust their service providers. Nowadays companies provide infrastructure facilities such as storage space, processing capacity, applications, etc., for user’s and companies. When confidential information of user’s or that of a company is stored on such remote places there are chances of security breaches. This fact discourages companies and users to adopt to cloud services. To counter this drawback, in this paper we propose a solution for this problem by providing an additional user layer for Microsoft Azure Documents. In our paper, we have used a supplement layer to the existing user layer in the browser. This layer encrypts the data before storing it on the server. Accessing the information is impossible without the right password.

Keywords

Microsoft Azure Cloud Service for documentation Virtualization Encryption

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Ananthi Sheshasayee ¹, P. Sharmila ²

Affiliations
1 Department of Computer Science and Applications, Quaid-e-Milleth government college for Women, Chennai, Tamil Nadu, IN
2 Department of Computer Applications, Vivekanandha Institute of Information and Management Studies, Tiruchengode, Tamil Nadu, IN

Abstract

The Requirement Engineering is the most important phase of the software development life cycle which is used to translate the imprecise, incomplete needs and wishes of the potential users of software into complete, precise and formal specifications. These specifications can be decomposed on application of a data mining techniques, clustering. The process of clustering the requirements allows reducing the cost of software development and maintenance. In this research two most frequently used algorithms in clustering namely k means and fuzzy c means are used. The output generated is then analyzed for evaluating the performance of the two clustering algorithms. The requirements specified by the different stakeholders of the library are used as the input. The data mining tool WEKA was used for clustering. The clustering algorithms were then analyzed for accuracy and performance. On analysis the fuzzy c means algorithm was found to be more suitable for clustering of library requirements. The results proved to be satisfactory.

Keywords

Clustering, Data Mining, Fuzzy C Means, K Means, Library, Requirements

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P. Sharmila , A. Ponmalar , R. Skanda Gurunathan

Abstract

The rapidly aging population causes long waiting times for taking bus pass. Diagnosing record of real-time data of each who are all using bus passes, Bus pass automation would be beneficial for government to implement proper and better rates for passes and also it would be useful for people who forget to renew their bus passes. In this paper, we propose a facility to take bus pass using android mobile application. This system provides a facility to remind when the bus pass is about to expire. It is also used to take print out of the pass, so that it can be shown to the conductors who are unfamiliar with android phones.

Keywords

GPS, GUI, BPAS, QR, SPOOFING

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G. R. Jothilakshmi ¹, P. Sharmila ², Arun Raaza ³

Affiliations
1 Vels University, Pallavaram, Chennai - 43, Tamil Nadu, IN
2 Comm System, Vels University, Pallavaram, Chennai - 43, Tamil Nadu, IN
3 Centre for Advanced Research and Development, Vels University, Pallavaram, Chennai - 43, Tamil Nadu, IN

Abstract

Objectives: Now a days, the major health risk in woman is breast cancer. In order to reduce the death rate, early detection of cancerous region in mammogram images is needed. But finding the lesion part and its spread from the mammogram image is very difficult. To identify the type of cancer, segmentation of lesion region is important. To perform further breast cancer classification, this paper proposes an improved segmentation algorithm using digital mammogram. Method/Analysis: An automated method is used to segment the affected mammogram in a effective manner using split and merging technique based on region based segmentation method by identifying a seed point .The proposed algorithm uses morphological operation to remove the noise digitally and region split and merge technique to remove the background and separate the affected region in the image. Findings: The efficiency of this proposed algorithm is calculated by measuring five different parameters Mean, Variance, standard deviation, entropy, correlation and the output is compared with existing technique and it is observed that proposed method shows better results than previous threshold decomposition method. Improvement: From the segmented output the features are extracted for finding the type of cancer from the mass and Micro calcification region

Keywords

Breast Cancer, Microcalcification, Mammogram Images, Region Based Segmentation, Segmentation, Seed Selection.

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P. Sharmila ¹, J. Ilakkiya ¹

Affiliations
1 Department of Information Technology, Sri Sairam Institute of Technology, Chennai, Tamilnadu, IN

Abstract

The probability that a customer is not benefited by investing in a particular share, while there is still a chance for calculating accurate result that changes in milliseconds, has a huge impact on the profit for the customer as well as the organization they is associated with. Considering this criteria, a new clustering algorithm called the K-Means clustering method (KMC) is proposed. There are speculative results that witness K-means has stronger clustering semantic strength than other clustering methods in data mining. Can also get suggestions to avoid the risk of investing in an unprofitable share.

Keywords

Churn, K-Means Clustering Method, Map-Reduce, Subtractive Clustering Method, Fuzzy C-Means, Semantic-Driven Subtractive Clustering Method, Axiomatic Fuzzy Sets.

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