Orchestrating the development lifecycle of machine learning-based IoT applications: A taxonomy and survey
Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML
techniques unlock the potential of IoT with intelligence, and IoT applications increasingly …
techniques unlock the potential of IoT with intelligence, and IoT applications increasingly …
{CSI}{NN}: Reverse engineering of neural network architectures through electromagnetic side channel
Machine learning has become mainstream across industries. Numerous examples prove the
validity of it for security applications. In this work, we investigate how to reverse engineer a …
validity of it for security applications. In this work, we investigate how to reverse engineer a …
Diabetes disease prediction using machine learning on big data of healthcare
A Mir, SN Dhage - 2018 fourth international conference on …, 2018 - ieeexplore.ieee.org
Healthcare domain is a very prominent research field with rapid technological advancement
and increasing data day by day. In order to deal with large volume of healthcare data we …
and increasing data day by day. In order to deal with large volume of healthcare data we …
Counterfactual and factual reasoning over hypergraphs for interpretable clinical predictions on ehr
Abstract Electronic Health Record modeling is crucial for digital medicine. However, existing
models ignore higher-order interactions among medical codes and their causal relations …
models ignore higher-order interactions among medical codes and their causal relations …
Detection of atrial fibrillation using a machine learning approach
The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical
practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke …
practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke …
Development and validation of a deep learning model for predicting treatment response in patients with newly diagnosed epilepsy
Importance Selection of antiseizure medications (ASMs) for epilepsy remains largely a trial-
and-error approach. Under this approach, many patients have to endure sequential trials of …
and-error approach. Under this approach, many patients have to endure sequential trials of …
CSI neural network: Using side-channels to recover your artificial neural network information
Machine learning has become mainstream across industries. Numerous examples proved
the validity of it for security applications. In this work, we investigate how to reverse engineer …
the validity of it for security applications. In this work, we investigate how to reverse engineer …
[HTML][HTML] Hypergraph transformers for ehr-based clinical predictions
Electronic health records (EHR) data contain rich information about patients' health
conditions including diagnosis, procedures, medications and etc., which have been widely …
conditions including diagnosis, procedures, medications and etc., which have been widely …
Fault prediction of transformer using machine learning and DGA
D Saravanan, A Hasan, A Singh… - … on computing, power …, 2020 - ieeexplore.ieee.org
The Power Transformer are the most Crucial part of power System and its failure may result
in not only interrupted power supply but also great economic loss. So, it is important to …
in not only interrupted power supply but also great economic loss. So, it is important to …
Comparison of different machine learning models for diabetes detection
R Katarya, S Jain - 2020 IEEE International Conference on …, 2020 - ieeexplore.ieee.org
Diabetes metilus which is commonly known as diabetes is a major metabolic disorder which
has a severe effect on a human being. Diabetes results in high blood sugar. In a human …
has a severe effect on a human being. Diabetes results in high blood sugar. In a human …