Informatics Publishing Limited

Mohamed Haseen Basha ¹, Prashanth ², Sudhakar Hegade ³, C. R. Vasudeva Murthy ⁴, P. S. Suresh Babu ⁵

Affiliations
1 Department of Pediatrics, S.S. Institute of Medical Sciences & Research Center, Davangere, IN
2 Department of Pediatrics, Ramaiah Medical College, Bangalore, IN
3 Department of Pediatrics, VIMS, Bellary, IN
4 Department of Pathology, International Medical University, Kuala Lumpur, IN
5 Department of pediatrics, J J M Medical College, Davangere, IN

Abstract

Background: PEFR is the easiest and most cost effective method to evaluate respiratory functions. PEFR can be measured by a simple instrument Peak expiratory flow meter. Measuring PEFR has been suggested as an important tool in asthma management by all International guidelines. The aim of this study was to determine the PEFR values in healthy school children of central Karnataka, India.

Method: PEFR was measured in Two thousand and two hundred school children using mini Wright's Peak flow meter. All PEFR measurements were obtained in standing position and the best out of three trials were recorded. Height and weight were recorded. Regression analysis was used to calculate the predicted normal values of PEFR and also to assess its relation to weight and height.

Results: Positive correlation was seen between age, height, weight and PEFR. The boys had higher values of PEFR than girls at all heights. The prediction equation based on height was PEFR = 5.63 (Ht) - 474.43, r = + 0.902 for boys and PEFR = 5.30 (Ht) - 451, r = + 0.876 for girls.

Conclusion: PEFR values in this study were comparatively low to those of North Indian and western school children, High to those of other children of south India. The prediction equation obtained from the study can be used to detect PEFR values in central part of Karnataka.

Keywords

PEFR, Children, Peak Flow Meter, Regression Equation, Height

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Sejal Kumarpal Munoyath ¹, Prathibha Gopalraju ¹, Neha Pal ¹, Kapil Ahuja ¹, Prashanth ¹

Affiliations
1 Department of Oral and Maxillofacial Surgery, M.S.Ramaiah University of Applied Sciences, Bangalore, IN

Abstract

Nocardia is gram positive partially acid fast bacilli and widely present in soil, fresh water, marine water, organic matter habitats and so saprophytic in existence Nocardiasis is a rare bacterial infection and thus bacteria can be localized or systemic suppurative disease in humans. They may be rarely present on the skin and in the upper respiratory tract. Here we present a rare case of buccal space infection where in the culture reported nocardial infection. In our case the nocardiosis seems to be opportunistic and could have been inoculated following fall in the mud in this alcoholic state. Patient was treated with Amoxicillin-clavulanic acid and Amikacin and he responded well.

Keywords

Nocardia, Nocardia Asteroid, Nocardiasis, Buccal Space Infection, Immunocompromised.

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G. M. Sangeetha ¹, Prashanth ²

Affiliations
1 Computer Science and Engineering Department, VTU University, Nitte Meenakshi Institute of Technology Bangalore Karnataka, IN
2 Computer Science and Engineering Department, VTU University, Veerappa Nisty Engineering College Shorapurk Karnataka, IN

Abstract

with increasing amounts of data being generated by businesses and researchers there is a need for fast, accurate and robust algorithms for data analysis. Improvements in databases technology, computing performance and artificial intelligence have contributed to the development of intelligent data analysis. The primary aim of data mining is to discover patterns in the data that lead to better understanding of the data generating process and to useful predictions. Examples of applications of data mining include detecting fraudulent credit card transactions, character recognition in automated zip code reading, and predicting compound activity in drug discovery. Real-world data sets are often characterized by having large numbers of examples, e.g. billions of credit card transactions and potential 'drug-like' compounds; being highly unbalanced, e.g. most transactions are not fraudulent, most compounds are not active against a given biological target; and, being corrupted by noise. The relationship between predictive variables, e.g. physical descriptors, and the target concept, e.g. compound activity, is often highly non-linear. One recent technique that has been developed to address these issues is the support vector machine. The support vector machine has been developed as robust tool for classification and regression in noisy, complex domains. The two key features of support vector machines are generalization theory, which leads to a principled way to choose an hypothesis; and, kernel functions, which introduce non-linearity in the hypothesis space without explicitly requiring a non-linear algorithm. In this paper we introduce support vector machines cascade svm and randomized sampling technique highlight the advantages thereof over existing data analysis techniques, also are noted some important points for the data mining practitioner who wishes to use support vector machines.

Keywords

Support Vector Machine, SVM, Machine Learning, Multiprocessing, Scalability and Accurate Performance, Randomized Algorithm.

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