Informatics Publishing Limited

K. Latha ¹, Jaya Mohan Raj ²

Affiliations
1 Department of Obstetrics and Gynecology Nursing, SRM College of Nursing, SRM University, Chennai, IN
2 Department of Community Health Nursing, SRM College of Nursing, SRM University, Chennai, IN

Abstract

Preeclampsia is a condition in which hypertension and proteinuria is present with or without edema in a woman after 20 weeks of gestation, who has been previously normotensive. In spite of known risk factors & screening tests being proposed over years, there is only a small part of association explained on the effects of established risk factors for preeclampsia in most of the studies, recommends for further research on risk factors. To determine Maternal SocioEconomic status & Nulliparity together as risk factor for Preeclampsia among mothers. Total 109 Healthy antenatal mothers in 2 groups, 53 mothers in Group A (Nulliparous Mothers in low socioeconomic status, 56 mothers in Group B (Nulliparous Mothers in Upper Middle Socio-Economic Status) with absence of Clinical Features of Preeclampsia were recruited at 15 16 weeks of Gestation and were followed till 36 weeks of Gestation for the features of Preeclampsia. There was no Significant Difference between both the groups with all the baseline characteristics. At the end of 36 weeks, the overall prevalence of Preeclampsia in Group A was Higher about 73.58 %(39out of 53 mothers), compared to 48.2% (27 out of 56 Mothers) in groupB at t(107) = 2.598, P = 0.011,MD =2.161 ( 95% CI,0.512 3.810) respectively. Screening for risk factors has been relative lack of consideration among health care professionals towards prevention of preeclampsia among the high-risk population .Maternal socioeconomic status and Nulliparity together is considered as a significant double fold risk factor for preeclampsia & hence they should be targeted for intensive Preeclampsia surveillance. Hence the investigator recommends strengthening antenatal care, to establish a strong supportive framework for the guide to health care providers for better understanding of the impact and epidemiology of Preeclampsia in India.

Keywords

Maternal Socioeconomic Status, Nulliparity, Risk Factor, Preeclampsia.

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K. Veeramanikandan ¹, P. Manjamadevi ², K. Latha ¹

Affiliations
1 Department of Computer Science, V.R.S. College of Engineering and Technology, Arasur, Villupuram, IN
2 Department of Computer Science, V.R.S. College of Engineering and Technology, Arasur, Villupuram, IN

Abstract

Keeping in mind the end goal to enhance the security of steganography, this paper concentrated on picture steganography joined with pre-handling of DES encryption. At the point when transmitting the mystery data, firstly, scramble the data expected to cover up by DES encryption is encoded, and after that is composed in the picture through the LSB steganography. Encryption calculation enhances the least coordinating execution between the picture and the qualities of the mystery data to upgrade the opposition to recognition of the picture steganography. The exploratory results demonstrated that the opposition to identification vigor of picture steganography joined with pre-preparing of DES encryption is discovered vastly improved than the way utilizing LSB steganography calculations specifically.

Keywords

Information Hiding, Steganography, DES Encryption, High Security.

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N. Gobinathan ¹, K. Veeramanikandan ¹, M. Sabareesan ¹, K. Latha ²

Affiliations
1 Department of Computer Science, V.R.S. College of Engineering and Technology, Arasur, Villupuram, IN
2 Department of Computer Science, V.R.S. College of Engineering and Technology, Arasur, Villupuram, IN

Abstract

Processing of image arrangements is an exceptionally genuine pattern now. This is affirmed with an immense measure of examines here. The likelihood of a picture arrangement handling and example acknowledgment got to be accessible in light of expanded PC abilities and better photograph and camcorders. The element extraction is one of the fundamental strides amid picture handling and example acknowledgment. This paper introduces a novel grouping of elements of picture arrangements. The proposed order has three gatherings: 1) Components of a solitary picture, 2) Elements of a picture grouping, 3) Semantic elements of a watched scene. The primary gathering incorporates elements extricated from a solitary picture. The second gathering comprises of elements of any sorts of picture successions. The third gathering contains semantic components. Converse element elucidation technique is the iterative system when on every cycle we utilize more elevated amount elements to concentrate lower level elements all the more exactly. The proposed characterization of elements of picture arrangements takes care of an issue of disintegration of the source highlight space into a few gatherings. Converse element illumination system permits to expand the nature of picture preparing amid iterative procedure.

Keywords

Image Sequences, Feature Extraction, Feature Classification, Semantic Features, Feature Clarification.

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S. Padma ¹, K. Latha ²

Affiliations
1 Department of Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering, Chennai – 600119, Tamil Nadu, IN
2 Department of Instrumentation Engineering, Anna University, MIT Campus, Chennai – 600 044, Tamil Nadu, IN

Abstract

Background/Objectives:The major objective of this work is to propose PI controller tuning for Two Input Two Output (TITO) process models using the Teaching Learning Based Optimization (TLBO) algorithm. Methods/Statistical analysis:In this work, the dimension of the heuristic search is assigned as four (Kp1,Ki1,Kp2,Ki2). A weighted sum of objective function is considered in order to achieve the optimal controller values. The proposed method is tested using the Wardle and Wood model and Vinante and Luybenmodel. The efficiency of the proposed method has been validated through a comparative study with the PSO,BFO and FA existing in the literature. Findings:The result confirms that, TLBO tuned controller offers better result compared with other heuristic methods considered in this study for both the reference tracking and disturbance rejection operations. Applications/Improvements:In future, this method can be implemented for 2x2 and 3x3 MIMO systems existing in literature.

Keywords

Distillation Process, PI controller,TITO model, TLBO algorithm, Validation

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K. Latha ¹, R. Rajaram ²

Affiliations
1 Department of Computer Science and Engineering, Anna University of Technology, Tiruchirappalli, IN
2 Department of Information Technology, Thiagarajar College of Engineering, Tamil Nadu, IN

Abstract

One popular Community question answering (CQA) site, Yahoo! Answers, had attracted 120 million users worldwide, and had 400 million answers to questions available. A typical characteristic of such sites is that they allow anyone to post or answer any questions on any subject. Question Answering Community has emerged as popular, and often effective, means of information seeking on the web. By posting questions, for other participants to answer, information seekers can obtain specific answers to their questions. However, CQA is not always effective: in some cases, a user may obtain a perfect answer within minutes, and in others it may require hours and sometimes days until a satisfactory answer is contributed. We investigate the problem of predicting information seeker satisfaction in yahoo collaborative question answering communities, where we attempt to predict whether a question author will be satisfied with the answers submitted by the community participants. Our experimental results, obtained from a large scale evaluation over thousands of real questions and user ratings, demonstrate the feasibility of modeling and predicting asker satisfaction. We complement our results with a thorough investigation of the interactions and information seeking patterns in question answering communities that correlate with information seeker satisfaction. We also explore automatic ranking, creating abstract from retrieved answers, and history updation, which aims to provide users with what they want or need without explicitly ask them for user satisfaction. Our system could be useful for a variety of applications, such as answer selection, user feedback analysis, and ranking.

Keywords

Social Media, Community Question Answering, Information Seeker Satisfaction, Ranking, History Updation.

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K. Latha ¹, P. Ramya ¹, V. Sita ¹, R. Rajaram ²

Affiliations
1 Department of Computer Science and Engineering, Anna University, Tiruchirappalli, IN
2 Department of Information Technology, Thiagarajar College of Engineering, Madurai, IN

Abstract

Web browsers are provided with complex information space where the volume of information available to them is huge. There comes the Recommender system which effectively recommends web pages that are related to the current webpage, to provide the user with further customized reading material. To enhance the performance of the recommender systems, we include an elegant proposed web based recommendation system; Truth Discovery based Content and Collaborative RECommender (TDCCREC) which is capable of addressing scalability. Existing approaches such as Learning automata deals with usage and navigational patterns of users. On the other hand, Weighted Association Rule is applied for recommending web pages by assigning weights to each page in all the transactions. Both of them have their own disadvantages. The websites recommended by the search engines have no guarantee for information correctness and often delivers conflicting information. To solve them, content based filtering and collaborative filtering techniques are introduced for recommending web pages to the active user along with the trustworthiness of the website and confidence of facts which outperforms the existing methods. Our results show how the proposed recommender system performs better in predicting the next request of web users.

Keywords

Recommendation, Content, Collaborative Filtering, Learning Automata, Navigation.

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K. Latha ¹, B. Bhargavi ¹, C. Dharani ¹, R. Rajaram ²

Affiliations
1 Department of Computer Science and Engineering, Anna University of Technology, Tiruchirappalli, Tamil Nadu, IN
2 Department of Information Technology, Thiagarajar College of Engineering, Madurai, Tamil Nadu, IN

Abstract

Ranking search results is essential for information retrieval and Web search. Search engines need to not only return highly relevant results, but also be fast to satisfy users. As a result, not all available features can be used for ranking, and in fact only a small percentage of these features can be used. Thus, it is crucial to have a feature selection mechanism that can find a subset of features that both meets latency requirements and achieves high relevance. In this paper we describe a 0/1 knapsack procedure for automatically selecting features to use within Generalization model for Document Ranking. We propose an approach for Relevance Feedback using Expectation Maximization method and evaluate the algorithm on the TREC Collection for describing classes of feedback textual information retrieval features. Experimental results, evaluated on standard TREC-9 part of the OHSUMED collections, show that our feature selection algorithm produces models that are either significantly more effective than, or equally effective as, models such as Markov Random Field model, Correlation Co-efficient and Count Difference method.

Keywords

Feature Selection, Expectation Maximization, Markov Random Field, Generalization, Document Ranking.

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