The utilization of ultrasound imaging for early visualization has been imperative in disease
detection, especially in the first responder setting. Over the past decade, rapid …

In this paper, we proposed the multiclass support vector machine (SVM) with the error-
correcting output codes for the multiclass electroencephalogram (EEG) signals classification …

Abstract The diagnosis of Coronary Artery Disease (CAD), Myocardial Infarction (MI) and
carotid atherosclerosis is of paramount importance, as these cardiovascular diseases may …

This paper presents the experimental pilot study to investigate the effects of pulsed
electromagnetic field (PEMF) at extremely low frequency (ELF) in response to …

Smart health is one of the most popular and important components of smart cities. It is a
relatively new context-aware healthcare paradigm influenced by several fields of expertise …

This paper intends to an integrated view of implementing automated diagnostic systems for
breast cancer detection. The major objective of the paper is to be a guide for the readers …

A new approach based on the implementation of multiclass support vector machine (SVM)
with the error correcting output codes (ECOC) is presented for classification of …

In this paper, Probabilistic Neural Network with image and data processing techniques was
employed to implement an automated brain tumor classification. The conventional method …

The purpose of this study is to evaluate the accuracy of the recurrent neural networks
(RNNs) trained with Levenberg–Marquardt algorithm on the electrocardiogram (ECG) beats …

This paper presents a methodology to monitor the liquid petroleum products which pass
through transmission pipes. A simulation setup consisting of an X-ray tube, a detector, and a …