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Found 7 papers, 1 papers with code
ABO3 Perovskites' Formability Prediction and Crystal Structure Classification using Machine Learning
Renewable energy sources are of great interest to combat global warming, yet promising sources like photovoltaic (PV) cells are not efficient and cheap enough to act as an alternative to traditional energy sources.
A Prospective Approach for Human-to-Human Interaction Recognition from Wi-Fi Channel Data using Attention Bidirectional Gated Recurrent Neural Network with GUI Application Implementation
Our study, on the other hand, utilizes a Multiple Input Multiple Output radio link between a WiFi router and an Intel 5300 NIC, with the time-series Wi-Fi channel state information based on 2. 4 GHz channel frequency for mutual human-to-human concurrent interaction recognition.
Prediction Model for Mortality Analysis of Pregnant Women Affected With COVID-19
Due to the accuracy of the model, pregnant mother can expect immediate medical treatment based on their possibility of death due to the virus.
Telehealthcare and Telepathology in Pandemic: A Noninvasive, Low-Cost Micro-Invasive and Multimodal Real-Time Online Application for Early Diagnosis of COVID-19 Infection
In this research, a unique cloud-based application for the early identification of individuals who may have COVID-19 infection is proposed.
Survival Prediction of Heart Failure Patients using Stacked Ensemble Machine Learning Algorithm
Cardiovascular disease, especially heart failure is one of the major health hazard issues of our time and is a leading cause of death worldwide.
Identification of the Resting Position Based on EGG, ECG, Respiration Rate and SpO2 Using Stacked Ensemble Learning
Rest is essential for a high-level physiological and psychological performance.
Development of a Risk-Free COVID-19 Screening Algorithm from Routine Blood Tests Using Ensemble Machine Learning
Intrigued by the parametric deviations in immunological and hematological profile of a COVID patient, this research work leveraged the concept of COVID-19 detection by proposing a risk-free and highly accurate Stacked Ensemble Machine Learning model to identify a COVID patient from communally available-widespread-cheap routine blood tests which gives a promising accuracy, precision, recall and F1-score of 100%.
