ML researcher with a focus on applied ML. Applying ML to real-life biomedical problems.
Currently exploring XAI algorithms for deep neural networks.
The fouling factor is an operating index for measuring an undesirable effect of solids' deposition on the heat transfer ability of heat exchangers. Accurate prediction of the fouling factor helps appropriate scheduling of the cleaning cycles. Since diverse factors affect this operating feature, it is sometimes hard to estimate the fouling factor accurately using simple empirical or traditional intelligent methods. Therefore, this study employs five machine-learning algorithms to estimate the fouling factor as a function of operating and constructing variables.
Respiratory ailments such as asthma, chronic obstructive pulmonary disease (COPD), pneumonia, and lung cancer are life-threatening. Respiration rate (RR) is a vital indicator of the wellness of a patient. Continuous monitoring of RR can provide early indication and thereby save lives. However, a real-time continuous RR monitoring facility is only available at the intensive care unit (ICU) due to the size and cost of the equipment. This paper describes a novel approach for RR estimation using motion artifact correction and machine learning (ML) models with the PPG signal features.
Hypertension is a potentially unsafe health ailment, which can be indicated directly from the blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous, and noninvasive BP measurement system is proposed using the photoplethysmograph (PPG) signal and demographic features using machine learning (ML) algorithms.
Bring out the time machine and travel back to 1912! Go on Titanic's maiden voyage and see if you would survive its titanic crash. This app uses a Decision Tree trained on the Titanic Dataset to predict whether you will survive or not. The machine learning model was trained in Kaggle. The aim of this project is to make a web app using Streamlit and deploy it in Heroku.