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Kumarakovil, India

Noorul Islam University, formerly Noorul Islam College of Engineering, is a private co-educational university located in Kumarakovil, Thuckalay, Kanyakumari District Tamil Nadu, India. The university was founded in 1989 by Dr.A.P.Majeed khan, who is now its chancellor with a motto to provide quality education to the students. Wikipedia.

Samuel R.K.,Noorul Islam University | Venkumar P.,Kalasalingam University
Materials Today: Proceedings | Year: 2015

Simulated annealing algorithm is one of the well-known tools to achieve the global optima in an optimization problem. It has been widely used and its superiority has been validated to many of the other optimization techniques. Thou the major advantage of SA is to avoid the local optima by accepting weaker solutions, concerns have been raised about the cooling schedule followed by the SA, which plays a very vital role in the quality of result obtained. In this paper we address the cooling scheme of the SA, and propose a new cooling technique called Probabilistic Cooling Scheme (PCS), the SA with PCS is used to optimize the well-known flow shop problems of Carlier and Reeves. The results have been compared to other algorithms found in the literature. SA with PCS was found to achieve superior results in most of the cases. © 2015 Elsevier Ltd. Source

Joselin Herbert G.M.,Noorul Islam University | Iniyan S.,Anna University | Amutha D.,Bethlahem Institute of Engineering
Renewable and Sustainable Energy Reviews | Year: 2014

Energy is the prime mover of economic growth and is vital to sustain a modern economic and social development. Renewable energy applications have brought about significant changes in the Indian energy scenario. The identification and efficient use of various renewable energy resources are the thrust areas in energy development. Wind energy is one of the most environment friendly, clean and safe energy resources. The wind energy will continue to be the biggest renewable energy sector in any country in terms of both installed capacity and total potential. This paper reviews some important factors and techniques to be considered for wind turbine installations such as the wind energy resource assessment techniques, environmental factors, grid integration factors, control strategies, impact of offshore wind turbines and hybrid energy technologies, hydrogen production techniques, feed-in tariff mechanism, modeling of wind turbine components including generators, performance improvement techniques. The cost and economic feasibility of the wind energy conversion system as well as the control strategies of wind turbine generators have also been discussed. © 2014 Elsevier Ltd. Source

Dheeba J.,Noorul Islam University | Selvi S.T.,National Engineering College
Journal of Medical Systems | Year: 2012

Early detection of microcalcification clusters in breast tissue will significantly increase the survival rate of the patients. Radiologists use mammography for breast cancer diagnosis at early stage. It is a very challenging and difficult task for radiologists to correctly classify the abnormal regions in the breast tissue, because mammograms are noisy images. To improve the accuracy rate of detection of breast cancer, a novel intelligent computer aided classifier is used, which detects the presence of microcalcification clusters. In this paper, an innovative approach for detection of microcalcification in digital mammograms using Swarm Optimization Neural Network (SONN) is used. Prior to classification Laws texture features are extracted from the image to capture descriptive texture information. These features are used to extract texture energy measures from the Region of Interest (ROI) containing microcalcification (MC). A feedforward neural network is used for detection of abnormal regions in breast tissue is optimally designed using Particle Swarm Optimization algorithm. The proposed intelligent classifier is evaluated based on the MIAS database where 51 malignant, 63 benign and 208 normal images are utilized. The approach has also been tested on 216 real time clinical images having abnormalities which showed that the results are statistically significant. With the proposed methodology, the area under the ROC curve (A z ) reached 0.9761 for MIAS database and 0.9138 for real clinical images. The classification results prove that the proposed swarm optimally tuned neural network highly contribute to computer-aided diagnosis of breast cancer. © 2011 Springer Science+Business Media, LLC. Source

Dheeba J.,Noorul Islam University | Tamil Selvi S.,National Engineering College
Journal of Medical Systems | Year: 2012

In this paper, a computerized scheme for automatic detection of cancerous lesion in mammograms is examined. Breast lesions in mammograms are an area with an abnormality or alteration in the breast tissues. Diagnosis of these lesions at the early stage is a very difficult task as the cancerous lesions are embedded in normal breast tissue structures. This paper proposes a supervised machine learning algorithm - Differential Evolution Optimized Wavelet Neural Network (DEOWNN) for detection of tumor masses in mammograms. Differential Evolution (DE) is a population based optimization algorithm based on the principle of natural evolution, which optimizes real parameters and real valued functions. By utilizing the DE algorithm, the parameters of the Wavelet Neural Network (WNN) are optimized. To increase the detection accuracy a feature extraction methodology is used to extract the texture features of the abnormal breast tissues and normal breast tissues prior to classification. Then DEOWNN classifier is applied at the end to determine whether the given input data is normal or abnormal. The performance of the computerized decision support system is evaluated using a mini database from Mammographic Image Analysis Society (MIAS). The detection performance is evaluated using Receiver Operating Characteristic (ROC) curves. The result shows that the proposed algorithm has a sensitivity of 96.9% and specificity of 92.9%. © 2011 Springer Science+Business Media, LLC. Source

Mary Amala Bai V.,Noorul Islam University | Manimegalai D.,National Engineering College
International Review on Computers and Software | Year: 2013

The term weighting scheme in text categorization is a vital step in automatic text categorization. Previous studies showed that term weighting techniques contribute more to the accuracy of classification than that of the classifier's contribution for the same. So this work is concentrated on term weighting schemes for text categorization. A new supervised term weighting scheme for text categorization is proposed. The frequency of each term in a document is expressed as probability of the terms in a document. This gives the proportion of each term in a document. This information provides with a very good knowledge on the category of the document. The probability of a term in all the documents of a class when summed up leads to a very important variable which can be used for term weighting in classification. This is basically a document level variable because it is related to the probability of a term in a document. The related new measure is named as td (terms in a document). Its performance when evaluated with reuters-21578 and 20Newsgroup dataset showed interesting increase in performance compared to tf, idf and rf. Compared to rf, this measure works well for both svm (binary classifier) and centroid-based classifiers(multiclass classifier). © 2013 Praise Worthy Prize S.r.l. - All rights reserved. Source

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