S. Djodilatchoumy ¹, N. Rama ², S. Gunasekaran ³

Affiliations
1 Dept. of Computer Sci. & Applications; 3 Spectrophysics Res. Lab., Pachaiyappa’s College, Chennai-600030, IN
2 Dept. of Computer Science, Presidency College, Chennai-600005, IN
3 Spectrophysics Res. Lab., Pachaiyappa’s College, Chennai-600030, IN

Abstract

Anaemia is a common problem in Chronic Kidney Disease (CKD) leading to substantial morbidity and mortality if untreated. The particular cells of the failing kidney are unable to secrete sufficient erythropoietin, the harmone that stimulates erythropoiesis. Treatment of anaemia with erythropoiesisstimulating agents (ESA) has led to increased quality of life and a reduced cardiovascular risk. Therefore anaemic blood has to be identified and proper treatment has to be given. Though investigations on characterisation of anaemic blood and therapeutic effect of erythropoietin in CKD have been done by many, not much work is done on automation of this investigations. The goal of this study is to train the System (Neural Network [NN]) to identify whether the given blood sample is anaemic blood or not and also to examine prospectively the effect of erythropoietin in anaemic patients using the System which is already trained to identify the anaemic blood.

Keywords

Anaemia, Erythropoietin, Chronic Kidney Disease, Neural Network

Full Text

   

A. J. Rajeswari Joe ¹, N. Rama ²

Affiliations
1 Bharathiyar University, Coimbatore - 641 046, Tamil Nadu, IN
2 Presidency College, Chennai - 600005, IN

Abstract

Background/Objectives: The Main aim of this Hybrid Image compression method is that it should provide good picture quality, better compression ratio and also it can remove block artifacts in the reconstructed image. Methods/Statistical analysis: To compress an image using the proposed algorithm, images are first digitized. With the digital Information of an image different types of transformations are applied. In this method wavelet transformations (haar, daubechies wavelet transformations) are used. The output of transformation coefficients are quantized into nearest integer values. Vector Quantization takes an important role in quantizing the transformation coefficients. After quantization they are encoded by using any one of the compression encoding techniques. Huffmann encoding is used for compressing Tablet images and Tablet strip images. It is derived from exact frequencies of text. The variable length code table is an output of Huffmann’s algorithm. The source symbol is encoded and stored in the above table which is further transferred through the channel for decoding. Findings: Since Unsupervised Neural Network learning algorithms are added in this algorithm increase the picture quality is improved and it removes the problems of block artifacts. Conclusion/Application: Since cloud computing provides elastic services, high performance and scalable large data storage, to facilitate long term storage and efficient transmission Image files are compressed and stored using this hybrid compression algorithm to enhance the performance of recent compression algorithms. The compressed and reconstructed images are evaluated using the error measures like CR (Compression Ratio), PSNR (Peak Signal Ratio). It shows that the above explained algorithm provides better results than the traditional results.

Keywords

Cloud Architecture, Self Organizing Feature Map (SOFM), Vector Quantization, Wavelet Transformation

Full Text

   

Anusha ¹, N. Rama ²

Affiliations
1 University of Madras, Chennai - 600005, Tamil Nadu, IN
2 Department of Computer Science, Presidency College, Chennai - 600005, Tamil Nadu, IN

Abstract

Objectives: The purpose of this research paper is aimed to determine the effect of subjective measures website quality and usability on its objective measures. A checklist of high level quality factors and sub factors, factors driving the customer to shop online is proposed and a new website quality and usability evaluation framework is developed to evaluate the quality and usability of online shopping websites from the user’s perspective. Methods: Structural equation modeling was applied to determine the subjective measures with the highest and lowest contributing factors for website quality and usability. A survey with 400 respondents was conducted to validate the proposed model. Findings: The Study has identified the important quality factors of the case study website Flipkart which makes it usable and successful. The results of the study can be used for designing websites with improved quality and usability. The study has significance impact on the economic and societal perspectives as the results will help the online shopping sites to improve their websites to improve the degree of user satisfaction and attract more customers to shop online.

Keywords

Quality Factors, Usability, Website quality.

Full Text

   

C . Periasamy ¹, N. Rama ²

Affiliations
1 Department of Electronics and Communication Engineering, Vedavyasa Institute of Technology, Karadparamba - 673632, Malappuram District, Kerala, IN
2 Department of Computer Science, Presidency College, Chennai - 600005, Tamil Nadu, IN

Abstract

Background: The main objective of this research work is to develop an E-Governance model based on bi-analysis results in standard database environment with aid of SWOT analysis along with cluster analysis. Methods: A model based questionnaire survey was conducted in all BSNL centers of Tamil Nadu, to know the infrastructure facilities like computer, software, hardware, network structure and automation components etc. A separate questionnaire was framed to know the present status of existing problems persisting throughout the BSNL center of Tamil Nadu, India. This research covers the entire research process including: formulating research questions, performing bi-analysis through SWOT analysis and Cluster analysis and modeling best recommendation model for BSNL public sector. Findings: SWOT analysis based Hierarchal clustering model is drawn as one among the best of other comparable models. Also produce 96% as success rate and 4% as weaken rate will promote this as opportunities for others to avail the ICT in BSNL organization. Applications: This model will provide a smooth flow of information, commands, requests and reporting between the government (BSNL administration) and the citizens (BSNL staff and Public), so as to enhance the speed and quality of internal functioning, as well as to provide a user-friendly access to outsiders. Also recommends the users to avail the ICT facility in public sector with maximum of utilization.

Keywords

Cluster Model, Data Mining, E-Governance, ICT (information And Communication Technology), Recommendation System, Swot Analysis

Full Text

   

A. Princy Christy ¹, N. Rama ¹

Affiliations
1 Post Graduate and Research Department of Computer Science, Presidency College, Chennai, IN

Abstract

Objectives: This paper aims at comparing and contrasting relevant researches on predicting the performance of students in Data Science perspective that encompasses machine learning and data mining. Analysis: An architectural framework has been devised for educational data mining. The ultimate motive is to extensively investigate the techniques like classification, regression and recommender systems in predicting the student performance and to explore the prediction accuracy of these techniques as well. For this purpose very many researches that have successfully implemented these techniques were carefully studied and their contribution to predicting the performance were analysed. Findings and Novelty: It came to light that ensembles created by combining classifiers performed well and their accuracy in predicting the performance was commendable compared to the individual performance of the classification, regression and recommender techniques. The nuance of this study is the incorporation of recommender systems along with conventional techniques since these are not commonly used in performance prediction. Tensor factorization in particular has desirable effect in prediction since it takes the time factor into consideration. It is a fact that performance of students increases over time.

Keywords

Ensemble Classifiers, Neural Networks, Performance Prediction, Recommender Systems, Regression.

Full Text