A Novel Ant based Clustering of Gene Expression Data using MapReduce Framework

Genes which exhibit similar patterns are often functionally related. Microarray technology provides a unique tool to examine how a cells gene expression pattern chang es under various conditions. Analyzing and interpreting these gene expression data is a challenging task. Clustering is one of the useful and popular methods to extract useful patterns from these gene expression data. In this paper multi colony ant based clustering approach is proposed. The whole processing procedure is divided into two parts: The first is the construction of Minimum spanning tree from the gene expression data using MapReduce version of ant colony optimization techniques. The second part is clustering, which is done by cutting the costlier edges from the minimum spanning tree, followed by one step k - means clustering procedure. Applied to different file sizes of gene expression data over different number of processors, the proposed approach exhibits good scalability and accuracy

Karyotype of a Bagrid catfish, Mystus vittatus, from the freshwater system of Chidambaram, Tamil Nadu, India

Karyological characters of Mystus vittatus (Bagridae) in the freshwater system of Chidambaram were studied by examining metaphase chromosome spreads from the gill tissues. The examination of 149 metaphase spreads prepared from 25 fingerling specimens indicated that the chromosome number of this species was 2n=54 and the arm number was 12 for metacentric, 36 for submetacentric, and 30 for acrocentric type. The prepared karyotypes of this species consisted, of 3 pairs of metacentric (m), 9 pairs of submetacentric (sm) and 15 pairs of acrocentric (a) chromosomes. The chromosome formula can be represented as 2n = 3m + 9sm + 15a. This karyotype is significantly different from same species reported by others. Karyological parameters showed that centrometric index, arm ratio, relative length, and length variation range of chromosome of this fish species are between 14.97-50.00, 1.00-5.68, 3.12-18.48, and 0.60-3.56, respectively. The largest chromosome in this species is a pair of submetacentric chromosomes. Considering the number of chromosomes, it seems likely that M. vittatus, is a diploid origin fish

Real Coded Binary Artificial Bee Colony (RC-BABC) Based Feature Selection and Relieff Based Feature Extraction Techniques for Heart Disease Prediction

Diagnosing heart disease is really a challenging task for which several intelligent diagnostic systems were developed for enhancing the performance of diagnosing heart disease. However, in these systems, low accuracy of predicting heart disease is still a challenging task. To provide better accuracy in predicting heart risks, a novel feature selection approach is proposed which employs Real Coded Binary Artificial Bee Colony (RC-BABC) optimization algorithm with adaptive size for feature elimination. This method has the advantages of reducing algorithmic computational time, improving prediction accuracy, enhanced data quality, and saves resources in successive data collection phases. Once the features are selected, the important feature extraction phase uses ReliefF based feature extraction method to extract the features from the heart disease data set. The scores of features are computed by estimating a comparison of feature values and class values neighbor samples. The proposed Real Coded Binary Artificial Bee Colony (RC-BABC) optimization algorithm is compared with three well known methods namely an artificial neural network (ANN), K-means clustering approach and Classification and Regression Algorithm (C&RT) with measures like accuracy, precision, recall and F1-score. The proposed method achieved 96.77% of accuracy,98.8% of recall, 97.8% of precision and 98.34% of F1-score

Egocentric Activity Recognition Using HOG, HOF and MBH Features

recognizing egocentric actions is a challenging task that has to be addressed in recent years. The recognition of first person activities helps in assisting elderly people, disabled patients and so on. Here, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. In this research work, the recognition is done using the features like Histogram of Oriented Gradients (HOG), Histogram of optical Flow (HOF) and Motion Boundary Histogram (MBH). The extracted features are given as input to the classifiers like Support Vector Machine (SVM) and k Nearest Neighbor (kNN). The performance results showed that SVM gave better results than kNN classifier for both categories

HEPATOPROTECTIVE ACTIVITY OF AQUEOUS EXTRACTS OF CHRYSANTHEMUM INDICUM FLOWERS ON PARACETAMOL INDUCED LIVER INJURY IN ALBINO RATS

Objective: The present investigation was evaluated that protective activity of aqueous extract of flowers of Chrysanthemum indicum studied againstparacetamol-induced hepatotoxicity in animal model.Methods: Bioactive functional groups, such as alcohol, carboxylic acid, and amines, were present in the aqueous extract of flowers of C. indicumidentified by Fourier transform infrared spectroscopy. The animals were grouped into 5 and each group has 6 animals and induced the hepatic failure.Silymarin was used as reference standard. Aqueous extract of flowers of C. indicum treated in a different dose which was compared with control groupof animals.Results: Aqueous extract of flowers of C. indicum reduced the level of aspartate transaminase (AST), alanine transaminase (ALT), serum bilirubin,protein, triglycerides, and cholesterol compared than paracetamol treated Group II animals. Histopathological studies were confirmed that reductionof necrosis and inflammation in the liver cells.Conclusion: Thus, these results revealed that the aqueous extract of flowers of C. indicum shown very significant (p<0.01) hepatoprotection againstparacetamol-induced hepatic failure in animal model by reducing AST, ALT, serum total bilirubin, protein, triglycerides, and cholesterol levels.Keywords: Hepatoprotective activity, Chrysanthemum indicum, Paracetamol

High Gain Interleaved Boost Converter for Fuel Cell Applications

Fuel cell is one of the promising technologies for distributed generation. For designing high efficiency fuel cell power systems, a suitable DC-DC converter is required. Among the various topologies, interleaved converters using switched capacitor are considered as a better solution for fuel cell systems due to high conversion efficiency. The objective of the paper is to design and implement a high gain interleaved converter using switched capacitors for fuel cell systems. In the proposed interleaved converter, the front end inductors are magnetically cross-coupled to improve the electrical performance and reduce the weight and size. Also, switched capacitors are used to improve the voltage gain of the converter. The proposed converter has been performed. Simulation study of interleaved converter using switched capacitors interfaced with fuel cells has been studied using Matlab/Simulink. A prototype has been developed to verify the simulation results

High Gain Interleaved Boost Converter for Fuel Cell Applications

Fuel cell is one of the promising technologies for distributed generation. For designing high efficiency fuel cell power systems, a suitable DC-DC converter is required. Among the various topologies, interleaved converters using switched capacitor are considered as a better solution for fuel cell systems due to high conversion efficiency. The objective of the paper is to design and implement a high gain interleaved converter using switched capacitors for fuel cell systems. In the proposed interleaved converter, the front end inductors are magnetically cross-coupled to improve the electrical performance and reduce the weight and size. Also, switched capacitors are used to improve the voltage gain of the converter. The proposed converter has been performed. Simulation study of interleaved converter using switched capacitors interfaced with fuel cells has been studied using Matlab/Simulink. A prototype has been developed to verify the simulation results

Texture and Color Feature Extraction Form Ceramic Tiles for Various Flaws Detection Classification

Image analysis involves investigation of the image data for a specific application. Normally, the raw data of a set of images is analyzed to gain insight into what is happening with the images and how they can be used to extract desired information. In image processing and pattern recognition, feature extraction is an important step, which is a special form of dimensionality reduction. When the input data is too large to be processed and suspected to be redundant then the data is transformed into a reduced set of feature representations. The process of transforming the input data into a set of features is called feature extraction. Features often contain information relative to color, shape, texture or context. In the proposed method various texture features extraction techniques like GLCM, HARALICK and TAMURA and color feature extraction techniques COLOR HISTOGRAM, COLOR MOMENTS AND COLOR AUTO-CORRELOGRAMare implemented for tiles images used for various defects classifications