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259 results on '"Vairamuthu, S."'

41. Electrocardiographic Abnormalities in Canine Dilated Cardiomyopathy and their Management

Author

Sesha Saikrishna K, Jeyaraja K, Vairamuthu S, Shafiuzama Md, and Selvaraj P

Abstract

Dilated cardiomyopathy (DCM) is one of the commonly acquired myocardial disease of dogs which most often is accompanied by electrocardiographic abnormalities. The present study was aimed to identify the electrocardiographic (ECG) abnormalities that were present in DCM affected dogs as well as their management. A total of 52 dogs with DCM were evaluated electrocardiographically using RMS Vesta 301i as per standard procedure. Pre-therapeutic electrocardiographic evaluation revealed normal sinus rhythm in 15 cases (28.84%), sinus tachycardia in 18 cases (34.61%), atrial fibrillation in 8 cases (15.38%), ST coving in 6 cases (11.53%), reduced ‘R’ amplitude in 5 cases (9.6%), junctional premature complexes in 2 cases (3.84%), ventricular premature complex in 2 (3.84%), atrial flutter in 1 case (1.92%) and isorhythmic atrioventricular disassociation in 1 case (1.92%). All the cases were treated for DCM using standard protocol. Atrial fibrillation and flutter were treated with Diltiazem @ 1-1.5 mg/kg three times a day and Digoxin @ 0.003 mg/kg twice a day orally and ventricular premature complex was treated with Sotalol @ 1-2 mg/kg two times a day orally. Six cases of atrial fibrillation was presented for follow-up and all achieved effective control of heart rate i.e., rate control and two of them reverted to normal sinus rhythm i.e., rhythm control, one to atrial flutter and three persisted with atrial fibrillation. VPC’s in both the dogs were successfully controlled by 15th day of therapy.

Published
2022
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44. Exponential Chimp Optimization Algorithm based Deep Neuro‐Fuzzy Network with MapReduce framework for fake news detection in big data analytics.

Author

Kanchanamala, Pendela, Selva Rani, B., and Vairamuthu, S.

Subjects
OPTIMIZATION algorithms, FAKE news, SOCIAL media, BIG data, CHIMPANZEES, TUMOR markers
Abstract

Summary: In the present epoch of computing, the world has changed from older conventional print media to social platform channels. Fake news articles have the prospects to handle the opinions of the public and so may harm human groupings. Therefore, it is necessary to explore the authenticity and credibility of the news flash being shared on the internet community. Hence, this research paper devises an efficient and robust fake news detection model, named Exponential Chimp Optimization Algorithm (EChOA)‐based Deep Neuro‐Fuzzy Network (DNFN) for detecting fake news. The introduced model utilizes a MapReduce framework that includes the mapper and reducer phases for processing big data for detecting fake news. First phase of processing is the Mapper work, in which every input used in the database is processed and creates an intermediate key‐value pair. In the reducer phase, the fusion of features is performed by arranging the features with the help of computing the optimal parameter and Rand similarity coefficient using a Deep Q Network (DQN). Here, the detection of fake news is obtained by DNFN, and the DNFN is done using implemented EChOA. The EChOA‐based DNFN effectively generates robust and effective fake news detection performance by choosing the optimal feature subsets through feature fusion. The EChOA is designed by integrating the Exponential Weighted Moving Average (EWMA) and Chimp Optimization Algorithm (ChOA). Moreover, the EChOA‐based DNFN method outperformed various former fake news detection approaches and attains the highest performance based on the testing accuracy is 0.909, sensitivity is 0.937, and specificity is 0.891 using the FakeNewsNet dataset. [ABSTRACT FROM AUTHOR]

Published
2023
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50. PREDICTION OF FROZEN SEMEN DOSES PRODUCTION IN DAIRY STUDS USING MACHINE LEARNING ALGORITHM

Author

JEEVAN C, Karthickeyan SMK, Vairamuthu S, Tirumurugan K. G., and Gopinathan A

Abstract

The ability to predict the frozen semen doses produced per ejaculate would be of considerable benefit for the management of skill, human resource, capital and time. The new computing paradigm called machine learning involves in predicting dependent variable by learning complex and non-linear relationship among independent variables. The purpose of this study is to develop prediction model using one of the conventional and machine learning modelling techniques called Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) respectively. A Total of 1,57,532 ejaculates data were used for modelling. The modelling involved prediction of frozen semen doses produced per ejaculate using independent variables namely volume of ejaculate, ejaculate number, sperm concentration, initial motility and post thaw motility. Various combinations of architectural parameters were employed to explore optimum configuration for each model. The ANN (R2=90.66) modelling was observed to be efficient over MLR (R2=73.52). The root mean squared error (RMSE) value was found to be lower in ANN (33.89) when compared to MLR (57.31). Hence, the ANN modelling approach is efficient to predict frozen semen doses that could be produced per ejaculate.

Published
2022
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