Informatics Publishing Limited

R. Nagendra ¹, R. Kannan ¹, R. Bhavani ¹, P. Shanmuga Johari ², A. Nallapa Reddy ³, B. C. Jaiprakash ⁴

Affiliations
1 Center for Geoscience and Engineering, Anna University, Chennai - 600 025, IN
2 Department of Earth Sciences, University of Windsor, Windsor, Ontario, CA
3 Regional Geology Laboratory, Oil and Natural Gas Corporation Ltd , Chennai - 600 034, IN
4 Forward basin, ONGC, Tnpura Project, Badarghat Complex, Agarthala - 799 014, IN

Abstract

Twenty-four Pleistocene-Holocene deepwater benthic foramimferal species were identified based upon taxonomic criteria from a 26 m core sample collected during Academik Aleksandr Sidorenko cruise. The quantitative data of benthic foraminifera) taxa is treated statistically using multi vanate (both factor and cluster) techniques to understand the relationship between species assemblages and environmental parameters. The temporal distribution of samples in cluster II closely coincides with the distribution of higher loading values of factor 1 Similar associations were identified for cluster III with factor 2 and cluster IV with factor 3. However the faunal relative abundances along with %. Total Organic Content (TOC) values broadly divides the core into two environmentally significant zones viz, Zone 1 (between 1322 6m and 1321 6 m) which is characterised by high abundance of Cassiduhna cannata followed by Pullenia bulloides and Hoeglundina elegans having positive relationship with low TOC values ranging from 0 12 to 1 14 and Zone 2 (between 1321 6m and 1320 1 m), which is characterised by high percentages of TOC values ranging from 1 32 to 2 52, whereas Buhmina aculeata shows maximum abundance followed by Bohvina robusta, Cibiadoides kullenbergi and Osangulana culter, suggesting that these species prefer high nutrient environment.

Keywords

Benthic Foraminifera, Pleistocene, Holocene, Tuticonn, Bay of Bengal.

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R. Nagendra ¹, R. Raja ¹, A. Nallapa Reddy ², B. C. JaiPrakash ³, R. Bhavani ¹

Affiliations
1 Centre for Geoscience and Engineering, Anna University, Chennai - 600 025, IN
2 Regional Geology Laboratory, Oil & Natural Gas Corporation Ltd., Chennai - 600 034, IN
3 Geology Division, ONGC, Tripura Project, Badarghat complex, Agartala - 799 014, IN

Abstract

No Abstract.

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S. Pasupathy ¹, R. Bhavani ¹

Affiliations
1 Dept. of CSE, FEAT, Annamalai University, Tamil Nadu, IN

Abstract

This paper presents the results evaluated from our study on metrics used in object oriented software design strategies. This delivers tool-dependent metrics results and has even implications on the results of analyses based on these metrics results. The process provides a practical, systematic, start-to-finish method of selecting, designing and implementing software metrics. These metrics were evaluated using object oriented metrics tools for the purpose of analyzing quality of the product, encapsulation, inheritance, message passing, polymorphism, reusability and complexity measurement. It defines a ranking of the classes that are most vital note down and maintainability. The results can be of great assistance to quality engineers in selecting the proper set metrics for their software projects and to calculate the metrics, which was developed using a chronological object oriented life cycle process.

Keywords

Object Oriented Paradigm, Object Oriented Metrics, Data Collection, Software Quality Estimation.

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N. Kumaran ¹, R. Bhavani ¹

Affiliations
1 Department of Computer Science and Engineering, Annamalai University, Annamalainagar, Tamilnadu, IN

Abstract

The main objective of content based medical image retrieval (CBMIR) is to efficiently retrieve medical images that are visually similar to a query image. Medical images are usually retrieved on the basis of low level and high level features. This work addresses the concept of texture based spine MR image retrieval in the wavelet compressed domain. We proposed two statistical methods such as Haralick features and texturespectrum for spine MR image features extraction and project them to a set of signatures. The created texture features are classifying, according to various types of spine MR images using k-mean clustering algorithm. Then the research is carried out by calculating the distance between the signatures in the database images and the query image. These methods are applied around 300 spine MR images and improvements of retrieval efficiency were found with usual precision and recall analysis.

Keywords

Haralick Features, K-Mean Cluster, Texture Spectrum, Wavelet Compressed Domain.

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L. R. Sudha ¹, R. Bhavani ¹, Sudheer Reddy Bandi ¹

Affiliations
1 Department of C.S.E, Annamalai University, Chidambaram, IN

Abstract

Automatic gender identification plays an important role in identification of a person. We have presented a study and analysis of gender classification based on gait. In this work gait of a person is represented by simple binary moment features of seven regions such as head/shoulder region, front of torso, back of torso, front thigh, back thigh, front calf/foot and back calf/foot. These features are computed from parameter values of ellipses that fit body parts enclosed by different regions. Then the extracted features are used for training and testing different pattern classifiers like kNN (k-Nearest Neighbor) and SVM (Support Vector Machine) to classify the gender. Apart from accuracy, other measures more appropriate for imbalanced problems are also considered in this paper. We analyzed the performance of SVM with various kernel types and kNN with various distance measures and k (number of neighbors)value. Experimental results show that SVM classifier with linear kernel gives better results when compared to kNN classifier. The classification results are more reliable than those reported in previous papers. The proposed system is evaluated using side view videos of CASIA dataset B.

Keywords

Appearance Based Features, Binary Moments, Ellipse Features, Gait Analysis, Gender Classification, and Human Silhouette.

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P. K. Kathal ¹, R. Nagendra ², R. Raja ², A. Nallapa Reddy ³, B. C. Jayaprakash ⁴, R. Bhavani ²

Affiliations
1 Centre of Advanced Study in Geology, Dr. H.S.G. University, Sagar - 470 003, IN
2 Centre for Geoscience and Engineering, Anna University, Chennai - 600 025, IN
3 Regional Geology Laboratory, Oil and Natural Gas Corporation Ltd., Chennai - 600 034, IN
4 Geology Divsion, ONGC, Tripura Project, Badarghat Complex, Agartala - 799 014, IN

Abstract

No Abstract.

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R. Bhavani ¹, Ajoe Thambi ¹

Affiliations
1 Department of Computer Science and Engineering, Government College of Technology, Coimbatore, IN

Abstract

Optimized utilization of water for agriculture is a big challenge in today’s world. Internet of Things (IoT) based solutions along with machine learning techniques help in achieving effective utilization of waters in farming landspace. This paper presents sensor-based acquisition of soil moisture, temperature and humidity from the farm. Data are then stored in the server and clustered into two groups. Next machine learning based classification models like Naïve Bayes (NB), K-Nearest Neighbor (K-NN) and Support Vector Machines (SVM) are applied to decide irrigation need. The performance measures of the classification models show that K-NN classifier performs better than the other two classification models considered in this study

Keywords

Classification Algorithms, Decision Support Systems, Internet of Things, Machine learning Algorithms, Smart Irrigation

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