Informatics Publishing Limited

R. Priya ¹, P. Aruna ¹

Affiliations
1 Department of Computer science and Engineering, Annamalai University, IN
2 Department of Computer Science and Engineering, Annamalai University, IN

Abstract

This paper proposes an automated system for the detection of important anatomical structures such as the Blood Vessels, Optic Disc (OD), Macula and also Exudates in digital fundus retinal images. Blood vessel Detection (BVD) was done using canny edge detection. Optic disc localization was done using pyramidal decomposition and Hausdorff-based template matching. Macula was located using pyramidal decomposition. Exudates are found using machine learning algorithm which uses k-nearest neighbor classifier and a linear discriminant classifier. The procedure has been tested on a database of about 100 color fundus images acquired from a digital non-mydriatic fundus camera and the experimental results show the accuracy of the system.

Keywords

Blood Vessels, Exudates, Macula, Optic Disc.

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M. Karthikeyan ¹, P. Aruna ¹

Affiliations
1 Department of Computer Science and Engineering, Annamalai University, Annamalai Nagar, Chidambaram, Tamil Nadu, IN

Abstract

In the past few years, there has been an exponential increase in the amount of information available on World Wide Web. Web pages have been the potential source of information retrieval and data mining technology, but most HTML documents on Internet are cluttered with large amount of less informative and typically unrelated materials such as large amount of banner ads, navigation bars and copyright notices etc. Such irrelevant information is not part of the main content of the pages, they will seriously harm Web mining and searching. In this paper we develop an automatic HTML generator to generate optimized web pages using Web content mining from the already existing web pages. The input for the HTML generator is any HTML webpage or web pages. The web pages are downloaded manually by the user or by using the download manager developed in the automatic HTML generator. These downloaded pages are mined and useful information's are extracted including keywords and stored in the specific location. By using the keywords Web pages are clustered by Dbscan clustering algorithm to identify website category. With the help of these mined resources a new optimized webpage is created. This web page will be user friendly and noise free in nature and it may contains text, images, audio, video, structured list and hyperlink structures. Although only sample web pages of five different categories are considered, the proposed method can be applied to any web pages that can be mined for knowledge extraction.

Keywords

Web Content Mining, Text Mining, Web Structure Mining, Link Mining, HTML Generator.

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R. Priya ¹, P. Aruna ¹, B. Ramkumar ¹

Affiliations
1 Computer Science and Engineering Department, Annamalai University, Chidambaram, Tamil Nadu, IN

Abstract

Glaucoma is one of the major reasons for blindness. Untreated glaucoma can lead to permanent damage of the optic nerve and resultant visual field loss, which over time can progress to blindness. For this reason, early detection of glaucoma is essential for affected patients. This paper is focused on classifying glaucoma with image-based features from fundus photographs. So in this work, we have detected the glaucoma disease in the retinal optical images. The extent of the disease spread can be identified by extracting the features of the retina. Detection of the disease is done using Probabilistic Neural Network (PNN) classifier. The accuracy of the proposed system is 94.54 %.

Keywords

Retina, Probabilistic Neural Network, Accuracy, Sensitivity, Specificity.

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P. Aruna , R. Ashwini , R. Hemavathi , K. Lalitha

Abstract

The Idea of Designing and Implementation of Security Based ATM theft project is born with the observation in our real life incidents happening around us.  This project deals with prevention of ATM theft from robberies and to overcome the drawback found in existing technology in our society. This system uses controller based embedded system to process real time data collected using the vibration sensor. Whenever the thief attempts to break ATM, vibration sensor used here senses the vibration produced from ATM machine and a beep sound will occur from the buzzer also when the thief threatens the person in ATM, the person should enter the pin in reverse order so that DC Motor is used for closing the door of ATM. Servo motor is used to leak the gas inside the ATM to bring the thief into unconscious stage. The status of the ATM will be sent to the bank management and the nearby police station through GSM. This will prevent the robbery and the person involving in robbery can be easily caught. Here, CCS compiler is used to compile the coding and ISP tools are used to implement the idea and results are obtained.

Keywords

Vibration Sensor, DC Motor, Servo Motor, GSM Technique, CCS Compiler.

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C. Bhuvaneswari ¹, P. Aruna ¹, D. Loganathan ²

Affiliations
1 Department of Computer Science and Engineering, Annamalai University, IN
2 Department of Computer Science and Engineering, Pondicherry Engineering College, IN

Abstract

Lung diseases are one of the most common diseases that affect the human community worldwide. When the diseases are not diagnosed they may lead to serious problems and may even lead to transience. As an outcome to assist the medical community this study helps in detecting some of the lung diseases specifically bronchitis, pneumonia and normal lung images. In this paper, to detect the lung diseases feature extraction is done by the proposed shape based methods, feature selection through the genetics algorithm and the images are classified by the classifier such as MLP-NN, KNN, Bayes Net classifiers and their performances are listed and compared. The shape features are extracted and selected from the input CT images using the image processing techniques and fed to the classifier for categorization. A total of 300 lung CT images were used, out of which 240 are used for training and 60 images were used for testing. Experimental results show that MLP-NN has an accuracy of 86.75 % KNN Classifier has an accuracy of 85.2 % and Bayes net has an accuracy of 83.4% of classification accuracy. The sensitivity, specificity, F-measures, PPV values for the various classifiers are also computed. This concludes that the MLP-NN outperforms all other classifiers.

Keywords

Feature Extraction, Multilayer Perceptron, Neural Networks, Bayes Net, Sensitivity, Specificity, F-Measure.

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R. Priya ¹, P. Aruna ¹

Affiliations
1 Department of Computer Science and Engineering, Annamalai University, IN

Abstract

Diabetic retinopathy (DR) is an eye disease caused by the complication of diabetes and we should detect it early for effective treatment. As diabetes progresses, the vision of a patient may start to deteriorate and lead to diabetic retinopathy. As a result, two groups were identified, namely non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). In this paper, to diagnose diabetic retinopathy, three models like Probabilistic Neural network (PNN), Bayesian Classification and Support vector machine (SVM) are described and their performances are compared. The amount of the disease spread in the retina can be identified by extracting the features of the retina. The features like blood vessels, haemmoraghes of NPDR image and exudates of PDR image are extracted from the raw images using the image processing techniques and fed to the classifier for classification. A total of 350 fundus images were used, out of which 100 were used for training and 250 images were used for testing. Experimental results show that PNN has an accuracy of 89.6 % Bayes Classifier has an accuracy of 94.4% and SVM has an accuracy of 97.6%. This infers that the SVM model outperforms all other models. Also our system is also run on 130 images available from "DIARETDB0: Evaluation Database and Methodology for Diabetic Retinopathy" and the results show that PNN has an accuracy of 87.69% Bayes Classifier has an accuracy of 90.76% and SVM has an accuracy of 95.38%.

Keywords

Probabilistic Neural Network, Bayesian Classification, Support Vector Machine, Sensitivity, Specificity, Accuracy.

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M. Pauline ¹, P. Aruna ¹, B. Shadaksharappa ²

Affiliations
1 Department of Computer Science and Engineering, Annamalai University, IN
2 Department of Computer Science and Engineering, Shirdi Sai Engineering College, IN

Abstract

The Authors have proposed a model that first captures the fundamentals of software metrics in the phase 1 consisting of three primitive primary software engineering metrics; they are person-months (PM), function-points (FP), and lines of code (LOC). The phase 2 consists of the proposed function point which is obtained by grouping the adjustment factors to simplify the process of adjustment and to ensure more consistency in the adjustments. In the proposed method fuzzy logic is used for quantifying the quality of requirements and is added as one of the adjustment factor, thus a fuzzy based approach for the Enhanced General System Characteristics to Estimate Effort of the Software Projects using productivity has been obtained. The phase 3 takes the calculated function point from our work and is given as input to the static single variable model (i.e. to the Intermediate COCOMO and COCOMO II) for cost estimation. The Authors have tailored the cost factors in intermediate COCOMO and both; cost and scale factors are tailored in COCOMO II to suite to the individual development environment, which is very important for the accuracy of the cost estimates. The software performance indicators are project duration, schedule predictability, requirements completion ratio and post-release defect density, are also measured for the software projects in my work. A comparative study for effort, performance measurement and cost estimation of the software project is done between the existing model and the authors proposed work. Thus our work analyzes the interactional process through which the estimation tasks were collectively accomplished.

Keywords

Effort Adjustment Factor (EAF), Kilo Lines of Code (KLOC), Total Effort Multiplier (TEM), Scale Factors, Cost Drivers, Value Added Factors.

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