Arunkumar S

Bio: Arunkumar S is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

A Mobile Application Based Smart System for Supporting Yoga Activities and Health Monitoring

Abstract: ‘Health is wealth’ which is now changed to ‘Losing health for gaining wealth’ in the modern society. People are having busy schedules and they are not concerned about their health. Studies shows that a lion share of people all over the world undergoes mental stress due to the circumstances and pressures from both family and office. This mental stress factors can physically and mentally deteriorate the creativity and productivity of a person. Yoga can be considered as one of the finest solutions in this case though it refreshes both physically and mentally. Yoga transforms a person balanced with mental, physical and spiritual elements in the right composition. In this technology era, it will be good to integrate yoga with the trends of technology. This work proposes a scheme of integrating and monitoring yoga activities using the concept of Internet of Things (IoT) and a smart application. The body sensors (Pressure, temperature, humidity) attached with the person doing yoga senses relevant data and is processed using a central processor (Smart phone or smart devices) to provide necessary suggestions or feed backs to the user. This work will provide a platform to the yoga practice person to monitor and review their yoga activities by themselves. Our research results claims that the various parameters like blood pressure and heart beat rate is improvised a lot after practicing yoga and our system is much helpful for the yoga persons to practice yoga in an effective manner.

Prediction of Chronic and Infectious Diseases using Machine Learning Classifiers- A Systematic Approach

Abstract: Infectious and chronic diseases devastate millions of people across the world each year. Nonetheless, each type of disease substantiates differently. According to the National Centre for Health Statistics, USA, Infectious diseases or communicable diseases are the ones based on the cause, which spreads from person to person or animal to person caused by microorganisms such as bacteria or parasite and can be cured. Chronic diseases are based on the effect, which may have the origin of infectious disease, prolonged to three or more months, doesn’t spread from one person to another and cannot be cured. Some chronic diseases such as cervical cancer and liver cancer have originated from infectious diseases such as human papillomavirus (HPV) and hepatitis B, C virus. This paper focuses on various machine learning classification techniques in predicting chronic diseases such as Cardio Vascular Disease (CVD), Chronic Kidney Disease (CKD), lung cancer, and infectious diseases such as hepatitis and dengue serotypes. In the analysis, ABC4.5 classifier outperformed with accuracy of 92.76 % than the other classifiers in predicting Chronic Kidney Disease (CKD), Random Forest classifier achieved an accuracy of 90.32% which is higher than Logistic regression of accuracy 83.87% in predicting hepatitis. Hoeffding classifier achieves an accuracy of 88.56% which is higher than the other classifier in predicting Cardio Vascular Disease. Multi swarm optimized Multilayer perceptron achieved an accuracy of 85.18% which is higher than the particle swarmed optimized multilayer perceptron in predicting dengue serotypes. Artificial Neural Network (ANN) classifier outperformed all the classifiers under analysis with an accuracy of 93.00 % in predicting lung cancer.