Informatics Publishing Limited

A. K. Wahi , J. Ravi , S. Hemalatha , P. N. Singh

Abstract

Objective : The evaluation of antidiabetic activity of alcoholic and aqueous extracts of whole plant of Daemia extensa R Br. Materials and methods: The antidiabetic activity of both the extracts were evaluated using alloxan (120 mg/kg; i.p.) induced hyperglycemic rats. The potency of alcoholic and aqueous extracts were compared with that of reference drug chlopropamide. The blood glucose level was measured by using glucometer. Results: The alcoholic extract produced a highly significant fall in Blood Glucose Level (BGL) at 1 h after a single dose of the extract and in prolong treatment (i.e. for a week) the antidiabetic activity was maintained at par with the reference drug chlopropamide. Aqueous extract possess antidiabetic activity which was maintained upto 3 hours after a single dose and later the activity decreases but upon prolonged treatment, the peak activity was found on second day. Conclusion: Alcoholic extract of D. extensa is almost equipotent to chlorpropamide. This qualifies it to be used in ethnomedical diabetic management.

Keywords

Daemia extensa, Anti-diabetic Activity, Alloxan

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K. Parish Venkata Kumar ¹, K. Anji Reddy ², J. Ravi ¹

Affiliations
1 Department of Computer Applications, V.R. Siddhartha Engineering College, Vijayawada, IN
2 Department of Computer Applications, V.R. Siddhartha Engineering College, Vijayawada, IN

Abstract

Relational databases are the most fashionable repository for structured data, in a relational database many relations are linked together via E-R links. Multirelational classification is the procedure of building a classifier based on data stored in multiple relations and making predictions with it. Existing approaches of Inductive Logic Programming (recently, also known as Relational Mining) have proven effective with high accuracy in multirelational classification. Unfortunately, the most of them suffer from scalability problems with regard to the number of relations present in databases. In this paper, we propose a new approach, called Tuple ID Predication, which includes a set of novel and powerful methods for multirelational classification, including 1) tuple ID propagation, an efficient and flexible method for virtually joining relations, 2) new definitions for predicates and decision-tree nodes, which involve aggregated information to provide essential statistics for classification, and 3) a selective sampling method for improving scalability with regard to the number of tuples. Based on these techniques, we propose two scalable and accurate methods for multirelational classification: Tuple ID Predication Rule, a rule-based method and Mine-Tree, a decision-tree-based method. Our comprehensive experiments on both real and synthetic data sets demonstrate the high scalability and accuracy. It is very useful in effective decision making.

Keywords

Classification, Tuple ID, Data Mining, Decision Making, Relational Databases, Predication, Relations.

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J. Ravi ¹, K. B. Raja ²

Affiliations
1 Department of ECE, Global Academy of Technology, Bangalore, Karnataka, IN
2 Department of ECE, University Visvesvaraya College of Engineering, Bangalore, Karnataka, IN

Abstract

Automation in every field of daily life is required the need of mechanized human identification and verification for ensuring the security. The study of physiological or the behavioural information is referred to as biometrics. Face recognition is a highly active research area with a wide variety application. In this paper, we propose Hybrid Domain Based Face Recognition System (HDFRS) for different databases. The original face image is resized to uniform dimensions of 2^p x 2^q. The DT-CWT of a signal x (n) is constructed using two critically-sampled DWTs in parallel with same data. The five levels Dual-Tree Complex Wavelet Transform (DT-CWT) is applied on face image to obtain DT-CWT coefficients. The matrix of DT-CWT coefficients is segmented in to 3x3 matrixes. The Local Binary Pattern (LBP) algorithm is applied on each 3x3 matrix to get final features. The Euclidean Distance (ED) is used to compare features of test face image with data base images. It is observed that the values of False Rejection Rate (FRR), False Acceptance Rate (FAR) and Total Success Rate (TSR) are better in the proposed model compare to existing method.

Keywords

DT-CWT, Euclidean Distance, Face Recognition, LBP.

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N. R. Pradeep ¹, J. Ravi ¹

Affiliations
1 Department of Electronics and Communication Engineering, Global Academy of Technology, IN

Abstract

Fingerprint identification relies on computations and classification models based on images to identify individuals at their most basic level. For feature extraction, several image preprocessing approaches are used, and image locality bifurcations of different kinds are used for classification. For feature extraction and classification, artificial neural networks (ANNs) are proposed. ANN machine learning method and Gabor filter are introduced in this paper for feature extraction and classification respectively. Artificial Neural Networks and Gabor filtering features are used to create the feature vector. An algorithm based on the extracted features was developed to create a multiclass classifier. Special Database - NIST SD4 served as the basis for evaluation in this research. The Error matrix led to the discovery that, in terms of accuracy, the approach was superior to many traditional machine learning algorithms like Support Vector Machine, Random Forest, Decision Tree and KNN.

Keywords

Artificial Neural Network (ANN), Gabor Filter, Machine Learning, Feature Extraction, Classifiers.Artificial Neural Network (ANN), Gabor Filter, Machine Learning, Feature Extraction, Classifiers.

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