Informatics Publishing Limited

K. Perumal ¹, M. Jessie Pauline Jeya Priya ²

Affiliations
1 Department of Computer Applications, Madurai Kamaraj University, Madurai – 625021, Tamil Nadu, IN
2 Department of I.T, C.S.I. College of Arts and Science for Women, Madurai – 625007, Tamil Nadu, IN

Abstract

Recent improvements in wireless technologies allows mobile nodes to become reachable while moving around IPv6 network environment. Since mobility management mechanism is needed for the end users while roaming. In this paper we propose a Fast Handover Mobile IPv6 technique when the mobile node moves from one network to another network. After the successful completion of handover, the proposed system performs Duplicate Address Detection (DAD) for new mobile node. In this paper we propose Trust based Multipoint Relay-Neighbor Discovery Protocol (TMPR-NDP) for performing duplicate address detection. Finally, our simulation result shows that the proposed handover mechanism reduces latency, packet loss caused during handoff process and reduces the processing time caused during DAD.

Keywords

Care of Address (CoA), Elliptic Curve Cryptography (ECC), Fast Handoff, Handover, Mobile IPv6.

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D. Anithadevi ¹, K. Perumal ¹

Affiliations
1 Department of Computer Applications, Madurai Kamaraj University, Madurai - 625002, Tamil Nadu, IN

Abstract

Background/Objectives: In recent years, medical imaging plays an important role to detect diseases. Especially, Magnetic Resonance Imaging (MRI) images are indispensable for tumor detection. In medical image processing, the Region Based Segmentation (RBS) algorithms have attained an essentially significance to detect the tumor and are utilized for optimum results with the segregation of tumor part in the MRI image. The aim of the work is to provide effective algorithm to extract tumor and size, the process of de-noising technique, segmentation and extraction is the best way for this. At first, the enhanced de-noising method helps to enhance the MRI image to extract the tumor alone. Methods/Statistical Analysis: In image processing, automatic image segmentation plays a vital role and for RBS, the selection of seed has to done automatically to achieve this. Even if this technique is well performed with noises, the images are difficult to segment due to pixel similarity and the presence of noise. The noises can be removed by the combinations of median and Stationary Wavelet Transform (SWT) before preceding this, contrast enhancement is needed. In this paper, the combined features of de-noising technique are used for minimizing the effect of noises in the MRI brain images. After the process of denoising, the segmented results will be a better one than non-de-noise (i.e. Original) images. The extracted tumor results are compared by the various quality metrics as MSE, PSNR, NCC, AD, NAE, SE etc. with the ground truth image. This enhanced de-noising technique is used to test 50 images and is performance evaluated based on their MSE and PSNR. Findings: The enhanced de-noising technique gives better result than existing de-noising technique. Thus, the tumor extraction can be done easily. Improvements/Applications: This technique is used mainly for medical imaging applications.

Keywords

Image De-noising, Image Segmentation, Median Filter, MRI, Region Based Segmentation, Stationary Wavelet Transform

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V. Krishna Kondragunta ¹, V. Karuppuraj ¹, K. Perumal ¹

Affiliations
1 Shri. A.M.M. Murugappa Chettiar Research Centre, Taramani, Chennai-600113, Tamil Nadu, IN

Abstract

Ganoderma lucidum, a medicinal fungus called Linzhi in China and Reishi in Japan, is an economically important fungal species and commonly utilized for its nutraceutical properties. G. lucidum mushroom does not have cytotoxicity and has been demonstrated to be safe due to its' long history of oral administration. G. lucidum was subjected to an intensive scientific research since 80s, demonstrating the multiplicity of its medicinal uses and it is a major source for many bioactivities. Among many known and tested bioactive compounds, particularly polysaccharides occupy a precious position as immuno modulators. Anti-oxidant activity of polysaccharides has been identified from G. lucidum. In the present investigation, 4 different indigenous isolates of G. lucidum (GL-01 to GL-04) were tested for its antioxidant and folic acid content. The DPPH radical scavenging activity of G. lucidum was reported and has the maximum antioxidant activity (95.36±0.02%) recorded for GL-01 at 1mg/ml concentration and it is followed by the same strain at 0.8mg/ml concentration (94.92±0.01%). GL-02, GL-03 and GL-04 of methanol extract shown 93.18±0.17, 93.55±0.41 and 92.5±0.34% scavenging activity respectively. The maximum activity with water extract (62.18±1.96%) recorded by GL-03 at 0.8mg/ml concentration. Methanol extract has shown excellent antioxidant potential compare to water extract. Maximum folic acid content (77.7 μg/100g) was recorded in GL-03. The positive results of free radical scavenging activity by fruit bodies of this is another alternative platform to new finding in field of pharmaceutical and nutraceuticals.

Keywords

Anti-Oxidant Activity, Free Radical Scavenging Activity, DPPH, Folic Acid and Ganoderma lucidum.

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P. Mohana Chelvan ¹, K. Perumal ²

Affiliations
1 Dept. of Computer Science, Hindustan College of Arts and Science, Chennai - 603 103, IN
2 Dept. of Computer Applications, Madurai Kamaraj University, Madurai – 625 021, IN

Abstract

Data mining is indispensable for business organizations for extracting useful information from the huge volume of stored data which can be used in managerial decision making to survive in the competition. Due to the day-to-day advancements in information and communication technology, these data collected from e-commerce and e-governance are mostly high dimensional. Data mining prefers small datasets than high dimensional datasets. Feature selection is an important dimensionality reduction technique. The subsets selected in subsequent iterations by feature selection should be same or similar even in case of small perturbations of the dataset and is called as selection stability. It is recently becomes important topic of research community. The selection stability has been measured by various measures. This paper analyses the selection of the suitable search method and stability measure for the feature selection algorithms and also the influence of the characteristics of the dataset as the choice of the best approach is highly problem dependent.

Keywords

Data Mining, Feature Selection, Feature Selection Algorithms, Selection Stability, Stability Measures.

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