Informatics Publishing Limited

S. Vasuki ¹, K. Subramanian ²

Affiliations
1 Department of Computer Applications, J.J. College of Arts and Science, Pudukkottai, Tamil Nadu, IN
2 Department of Information Technology, J.J. College of Arts and Science, Pudukkottai, Tamil Nadu, IN

Abstract

The process of detecting outliers is a surveillance that comes into view to move away patently from other surveillances in the model. This arrangement is planned to demonstrate the fixations happened among the explorations occurred between client, and server. In the practical scenario all the individuals obviously are familiar with the procedure of how to transfer a request for the meticulous requirements, and how to get a comeback for that demand. On the other hand no one knows about the inside process of searching information from a huge database. Clustering is one of the best known techniques to maintain the information efficiently into the database. Clustering employs grouping of similar objects (similarity in terms of data content or there may be any other factors also). Outlier detection is one of the main divisions of data mining and deserves further research attention from data mining community. The brilliant technique for text classification process is called Feature Selection. These processes merge with k-means and produce more effective result. Words in the feature vector are grouped and forming a header to that group based on the similarity test. Each cluster is formed based on the behavior of the text with other text and the average mean value. Same words into the cluster are grouped together and produce better data maintenance as well as through this process the data searching by the user is also categorized and fledged in a probabilistic analytical manner. This paper primarily focuses on comparing various outlier detection methods based on clustering and association rule applications and also prove that this present approach is efficient enough to find the outliers and represent the outlier as the cluster head.

Keywords

Clustering, Data Mining, Outlier Detection.

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S. Vasuki ¹, P. Karthikeyan ¹, N. Sambath ¹

Affiliations
1 Electronics and Communication Engineering Department, Velammal College of Engineering and Technology, Madurai, Tamil Nadu, IN

Abstract

Removing noise from the original signal is still a challenging problem for researchers. There have been several algorithms and each approach has its assumptions, advantages, and limitations. This paper proposes an effective image denoising of color images in multiresolution transform domain using modified adaptive shrinkage. Most traditional noise reduction method tends to over-suppress high-frequency details. For overcoming this problem the input image is first decomposed into flat and edge regions. Noise is removed using the alpha map computed from wavelet transform coefficients of LH, HL, and HH bands. Noise is removed in the flat regions by Inner Product method. After removing noise in the flat regions, further noise removal is done in the edge regions using different types of wavelet shrinkage functions. Experimental results show that the NeighShrink can effectively reduce noise without losing sharp details in the noisy images and is suitable for commercial low-cost imaging systems.

Keywords

Image Denoising, Noise Reduction, Shrinkage Functions, Wavelet Transform.

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S. Vasuki ¹, L. Ganesan ²

Affiliations
1 Electronics and Communication Engineering Department, Velammal College of Engineering and Technology, Madurai, TamilNadu, IN
2 Computer Science & Engineering, A. C. College of Engineering and Technology, Karaikudi, TamilNadu, IN

Abstract

This paper proposes an improved texture segmentation algorithm based on the features derived from Dual Tree Complex Wavelet Transform (DTCWT) which is proved to be efficient for texture description. The dual tree introduces limited redundancy, approximate shift invariance and directional selectivity while preserving perfect reconstruction and computational efficiency.DTCWT is applied on the three components of the input color image.Co occurrence features are computed for the resultant sub images.Then, the sub image which has the maximum energy is selected for which local homogeneity is calculated. Various histogram thresholding techniques are applied separately on the resultant homogeneity histogram. The experiments of segmentation provide more encouraging results for textured color images using peak finding algorithm than those based on Mean shift and Otsu multi thresholding algorithms. The results obtained using a set of real world colored textures demonstrated the usefulness of wavelet features in color texture image segmentation.

Keywords

Color Texture Segmentation, Dual Tree Complex Wavelet Transform, Histogram Thresholding, Homogeneity Histogram.

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S. Sindhu Priya ¹, R. Divyalakshmi ¹, S. Vasuki ¹, R.  Rakesh ¹

Affiliations
1 Department of ECE, Periyar Maniammai University, Thanjavur-613403, Tamilnadu, IN

Abstract

Agriculture is the backbone of economic growth. Agriculture is now becoming very complex field because of the traditional method and form of agriculture is beyond the understanding of today’s youth, now-a-days there is a marked shortage in labors and there is no time to monitor and maintain the crops directly. So we people measure the temperature, pressure, humidity and light intensity of each crop variety and maintain it with help of different respectable sensors which are operated by using PLC. We can achieve the expected output by using our sequential logic which is programmed into the PLC. By using PLC we can able to progress the automated continuous process system. In this paper we people discussed about parameters requirement of sensors, selection of suitable sensors for the environmental measurement, sequential logic for each process, inputs and output with respect to input and how we develop the system and how to control the whole system. The purpose of this project is to proper design, selection, control, updating and upgrading of glass house horticulture by using PLC.

Keywords

PLC, Agriculture, Horticulture, Polyhouse, Android App Development, Automatic Greenhouse Farming, Sensors.

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M. Deepan Babu ¹, S. Vasuki ¹, K. Sangeetha ¹, D. Joaniepriya ²

Affiliations
1 Rajiv Gandhi Salai, Kelambakkam, Kancheepuram, District Tamil Nadu, IN
2 Chettinad College of Nursing, Rajiv Gandhi Salai, Kelambakkam, Kancheepuram, District Tamil Nadu, IN

Abstract

A descriptive study to assess the knowledge on pre-menopausal symptoms among middle aged women in a selected village at Kanchipuram district, Tamil Nadu, India. The objectives are to assess the knowledge of premenopausal symptoms among middle aged women in a selected village at Kanchipuram district, Tamil Nadu, India. To find out the association between level of knowledge with demographic variables. The convenience sampling technique was used to select 109 samples. Validity and reliability data collection tools were established. The data were collected by self-administered questionnaires. The collected data were tabulated and analyzed. Descriptive and inferential statistics were used. The study shows that 0% of the women had adequate knowledge, 15% of the women had moderate knowledge, and 94% of the women had inadequate knowledge regarding pre-menopausal symptoms.

Keywords

Knowledge, Pre-Menopausal Symptoms, Middle Aged Women.

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