Project Dissertation Report

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This enhances the speed and accuracy of the classifiers.

Principal Component Analysis (PCA) is a technique used for feature extraction which helps to retrieve intrinsic information from high dimensional data in eigen spaces to solve the curse of dimensionality problem.

Then remaining points are classified by trained SVM classifier.

Finally, the four clustering label vectors through majority vot- ing ensemble are combined, i.e., each point is assigned a class label that obtains he maximum number of votes among the four clustering solutions.

We Provide computer science, Information Technology and Computer Engineering related projects for ME, MTech and MS students.

You can download your project from our readymade project list specifically collected for Master of Engineering(ME), Master Of Technology(MTech) and Master of Science(MS).This paper is a comparative study for educational purpose.It provides analysis based on practical experiments carried out on a number of security solutions regarding their ability to detect ARP spoofing.Our analysis provides means for security instructors to evaluate and select the appropriate security solutions for their hands-on labs.In addition, we clearly show that ARP spoofing has not been given enough attention by most tested security solutions, even though this attack presents a serious threat, is very harmful and more dangerously it is easy to conduct.The performance of the proposed MOGA-SVM, classification and clustering method has been compared to MOGA-BP, SVM, BP.The performance are measured in terms of Silhoutte Index, ARI Index respectively.The curse of dimensionality means n m, where n is a large number of features and m is a small number of samples (may be too less).Neural Networks (NN) and Support Vector Machine (SVM) are implemented and their performances are measured in terms of predictive accuracy, specificity, and sensitivity.This technique is implemented on microarray cancer data to select training data using multiobjective genetic algorithm with non-dominated sorting (MOGA-NSGA-II).The two objective functions for this multiobjective techniques are optimization of cluster compactness as well as separation simultaneously. the individual chromosome which gives the optimal value of the compactness and separation.


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