Heart rate variability for medical decision support systems: A review
Abstract Heart Rate Variability (HRV) is a good predictor of human health because the heart
rhythm is modulated by a wide range of physiological processes. This statement embodies …
rhythm is modulated by a wide range of physiological processes. This statement embodies …
A systematic review on artificial intelligence-based techniques for diagnosis of cardiovascular arrhythmia diseases: challenges and opportunities
Cardiovascular health-related problem is a rapidly increasing integrated field concerning the
processing and fetching the information from cardiovascular systems for early detection and …
processing and fetching the information from cardiovascular systems for early detection and …
[HTML][HTML] A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction
Predicting electricity demand data is considered an essential task in decisions taking, and
establishing new infrastructure in the power generation network. To deliver a high-quality …
establishing new infrastructure in the power generation network. To deliver a high-quality …
Etemadi reliability-based multi-layer perceptrons for classification and forecasting
Multi-layer perceptrons (MLPs) rank among the most popular and widely employed
intelligent approaches for approximating the relationships between dependent and …
intelligent approaches for approximating the relationships between dependent and …
Efficient predictor of pressurized water reactor safety parameters by topological information embedded convolutional neural network
Accurate forecasts for pressurized water reactor safety parameters are essential to ensure
the safe operation of nuclear reactors. Potential of artificial neural networks on this task is …
the safe operation of nuclear reactors. Potential of artificial neural networks on this task is …
Optimal classification of N-back task EEG data by performing effective feature reduction
R Patel, K Gireesan, R Baskaran, NVC Shekar - Sādhanā, 2022 - Springer
Many studies have been carried out related to the analysis of cognitive workload
assessment using the N-back task. However, fixed analytic functions like time-frequency …
assessment using the N-back task. However, fixed analytic functions like time-frequency …
Detection of bradycardia from electrocardiogram signals using feature extraction and snapshot ensembling
One of the most common diagnostic techniques for detecting certain cardiovascular
diseases is using electrocardiogram (ECG) readings. Doctors around the world mostly rely …
diseases is using electrocardiogram (ECG) readings. Doctors around the world mostly rely …
[HTML][HTML] Enhancing XRF sensor-based sorting of porphyritic copper ore using particle swarm optimization-support vector machine (PSO-SVM) algorithm
Z Liu, J Kou, Z Yan, P Wang, C Liu, C Sun… - International Journal of …, 2024 - Elsevier
X-ray fluorescence (XRF) sensor-based ore sorting enables efficient beneficiation of
heterogeneous ores, while intraparticle heterogeneity can cause significant grade detection …
heterogeneous ores, while intraparticle heterogeneity can cause significant grade detection …
Person identification with arrhythmic ECG signals using deep convolution neural network
A Al-Jibreen, S Al-Ahmadi, S Islam, AM Artoli - Scientific Reports, 2024 - nature.com
Over the past decade, the use of biometrics in security systems and other applications has
grown in popularity. ECG signals in particular are attracting increased attention due to their …
grown in popularity. ECG signals in particular are attracting increased attention due to their …
An end-end arrhythmia diagnosis model based on deep learning neural network with multi-scale feature extraction
L Jiahao, L Shuixian, Y Keshun, Z Bohua - Physical and Engineering …, 2023 - Springer
This study presents an innovative end-to-end deep learning arrhythmia diagnosis model that
aims to address the problems in arrhythmia diagnosis. The model performs pre-processing …
aims to address the problems in arrhythmia diagnosis. The model performs pre-processing …