Informatics Publishing Limited

S. Vijayarani ¹, A. Tamilarasi ²

Affiliations
1 School of Computer Science and Engineering, Bharathiar University, Coimbatore, IN
2 Department of MCA, Kongu Engineering College, Perundurai, Erode, IN

Abstract

With the speedy developments in the hardware technology and the rapid escalation of internet increases the capability to accumulate enormous amount of personal data about consumers and individuals. In various circumstances, these data may be mishandled for a variety of purposes. This huge collection of data can be used for data mining also. The concept of data mining is to extract hidden knowledge from the large database. Applying data mining algorithms to get hidden knowledge which is a sensitive one, then it must be safeguarded from others. To perform data mining tasks in a secured way, privacy becomes very vital. Randomization, Statistical Disclosure Control, Cryptography, K-Anonymity and etc. are some of the privacy techniques to perform the data mining tasks in a privacy preserving way. In this paper, we discuss k-anonymity techniques. The inspiring feature at the back of k-anonymity is that many attributes in the data can often be considered as pseudo-identifiers which can be used in conjunction with public records in order to uniquely identify the records. Here, we have experimented the two k-anonymity techniques such as k-anonymity using clustering and de-clustering. Based on the experimental results, we compare the performance of these techniques using ID3 classifier. The result shows that the de-clustering approach provides stronger privacy protection than clustering approach in many circumstances.

Keywords

Privacy, Anonymity, Clustering, De-Clustering, Decision Tree.

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V. S. Abbiramy ¹, A. Tamilarasi ²

Affiliations
1 Velammal Engineering College, Affiliated to Anna University, Chennai, IN
2 Kongu Engineering College, Affiliated to Anna University of Technology, Coimbatore, IN

Abstract

Spermatozoa morphology is one of the main characteristics used for evaluating semen fertilizing capacity. This paper aims at classifying the morphological assessment of each spermatozoon images obtained from WHO laboratory manual either as normal or abnormal. Images were cropped and resized to 86 x 100 pixels. The resized images are segmented using a threshold based method. The texture of the segmented image segment is evaluated based on the Gray level Co-occurrence matrix (GLCM) and first order statistics (FOS) features are extracted. GLCM for the segmented gray scale image were calculated in 4 angles (0, 45, 90 and 135) at an offset of. Totally 15 GLCM features and 4 FOS features are extracted. The extracted features are then used to train and test the artificial neural network constructed using Feed Forward Neural Network, Radial Basis Neural Network and Elman Back Propagation Neural Network. Experimental results are presented on a dataset of 91 images consisting of 71 abnormal images and 20 normal images. The classification accuracy of 75% is achieved when feed forward neural network is trained with GLCM features, 58% when Recurrent network is trained with FOS features and 75% when Radial basis neural network is trained with the combined features (GLCM+FOS).

Keywords

Segmentation, Gray Level Co-Occurrence Matrix, First Order Statistics, Artificial Neural Network, Image Processing, Classification.

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R. Ramkumar ¹, A. Tamilarasi ²

Affiliations
1 Department of Computer Applications, Maharaja Engineering College, Avinashi, Coimbatore, IN
2 Department of Computer Applications, Kongu Engineering College, Perundurai, Erode, IN

Abstract

The recent advancement in the modern industrial manufacturing system invariably facing lot of problems in many aspects such as machining time, raw material, man power, electricity, and order preference assigned by the customers. Such a complex problem of vagueness and uncertainty can be handled by the theory of fuzzy logic (soft Computing) methodology using a membership function is used to solve a fuzzy mix product selection and we are using the amalgamation of fuzzy job shop scheduling approach to find Profits and higher value of satisfaction over the customer levels have been computed using a fuzzy-Job shop scheduling approach.

Keywords

Fuzzy Logic, Job Shop Scheduling, Production Planning, Multi Criteria Decision Making, Processing Time.

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S. Usha ¹, A. Tamilarasi ², R. Kalaivanan ³

Affiliations
1 University College of Engineering, Panruti, Tamilnadu, IN
2 Kongu Engineering Collge, Erode, Tamilnadu, IN
3 A.R Engineering College, Villupuram, Tamilnadu, IN

Abstract

Cloud security is security principles applied to protect data, applications and infrastructure associated within the cloud computing technology. In this paper a new Multiphase Distributed Network Intrusion Detection system (MDIDS) and prevention framework in a virtual network environment is proposed. It captures and inspects suspicious cloud traffic without interrupting user’s applications and cloud services. The Distributed Denial Of Service (DDOS) attacks caused by the vast flow of requests from various clients to the cloud server at the same time. This DDOS attack is high in Network Intrusion Detection and Counter Measure Selection in Virtual Network Systems (NICE). This is very much reduced in MDIDS and also leads to less CPU Utilization, less virtual machine creation time and less Infrastructure Response Time (IRT).

Keywords

Cloud Computing, Cloud Security, DDOS, Intrusion Detection, Deep Packet Inspection.

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