scholarly journals Sleep stage classification using extreme learning machine and particle swarm optimization for healthcare big data

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Nico Surantha ◽  
Tri Fennia Lesmana ◽  
Sani Muhamad Isa
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Particle Swarm ◽  
Sleep Stages ◽  
Support Vector ◽  
Swarm Optimization ◽  
Learning Machine ◽  
Portable Sensor

AbstractRecent developments of portable sensor devices, cloud computing, and machine learning algorithms have led to the emergence of big data analytics in healthcare. The condition of the human body, e.g. the ECG signal, can be monitored regularly by means of a portable sensor device. The use of the machine learning algorithm would then provide an overview of a patient’s current health on a regular basis compared to a medical doctor’s diagnosis that can only be made during a hospital visit. This work aimed to develop an accurate model for classifying sleep stages by features of Heart Rate Variability (HRV) extracted from Electrocardiogram (ECG). The sleep stages classification can be utilized to predict the sleep stages proportion. Where sleep stages proportion information can provide an insight of human sleep quality. The integration of Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) was utilized for selecting features and determining the number of hidden nodes. The results were compared to Support Vector Machine (SVM) and ELM methods which are lower than the integration of ELM with PSO. The results of accuracy tests for the combined ELM and PSO were 62.66%, 71.52%, 76.77%, and 82.1% respectively for 6, 4, 3, and 2 classes. To sum up, the classification accuracy can be improved by deploying PSO algorithm for feature selection.

scholarly journals Driving Drowsiness Detection with EEG Using a Modified Hierarchical Extreme Learning Machine Algorithm with Particle Swarm Optimization: A Pilot Study

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 775 ◽  
Author(s):  
Yuliang Ma ◽  
Songjie Zhang ◽  
Donglian Qi ◽  
Zhizeng Luo ◽  
Rihui Li ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Particle Swarm ◽  
Machine Learning Techniques ◽  
Superior Performance ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Swarm Optimization ◽  
Eeg Data ◽  
Learning Machine

Driving fatigue accounts for a large number of traffic accidents in modern life nowadays. It is therefore of great importance to reduce this risky factor by detecting the driver’s drowsiness condition. This study aimed to detect drivers’ drowsiness using an advanced electroencephalography (EEG)-based classification technique. We first collected EEG data from six healthy adults under two different awareness conditions (wakefulness and drowsiness) in a virtual driving experiment. Five different machine learning techniques, including the K-nearest neighbor (KNN), support vector machine (SVM), extreme learning machine (ELM), hierarchical extreme learning machine (H-ELM), and the proposed modified hierarchical extreme learning machine algorithm with particle swarm optimization (PSO-H-ELM), were applied to classify the subject’s drowsiness based on the power spectral density (PSD) feature extracted from the EEG data. The mean accuracies of the five classifiers were 79.31%, 79.31%, 74.08%, 81.67%, and 83.12%, respectively, demonstrating the superior performance of our new PSO-H-ELM algorithm in detecting drivers’ drowsiness, compared to the other techniques.

scholarly journals Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
C. V. Subbulakshmi ◽  
S. N. Deepa
Keyword(s):  
Machine Learning ◽  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Particle Swarm ◽  
Learning Paradigm ◽  
Swarm Optimization ◽  
Generalization Performance ◽  
Learning Machine ◽  
Hidden Layer ◽  
Medical Dataset

Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO) algorithm with the extreme learning machine (ELM) classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN), proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.

scholarly journals Wheel/Rail Adhesion State Identification of Heavy-Haul Locomotive Based on Particle Swarm Optimization and Kernel Extreme Learning Machine

2020 ◽  
Vol 2020 ◽  
pp. 1-6 ◽  
Author(s):  
Jianhua Liu ◽  
Linfan Liu ◽  
Jing He ◽  
Changfan Zhang ◽  
Kaihui Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Particle Swarm ◽  
Support Vector ◽  
Swarm Optimization ◽  
State Identification ◽  
Kernel Parameter ◽  
Heavy Haul ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

The traction performance of heavy-haul locomotive is subject to the wheel/rail adhesion states. However, it is difficult to obtain these states due to complex adhesion mechanism and changeable operation environment. According to the influence of wheel/rail adhesion utilization on locomotive control action, the wheel/rail adhesion states are divided into four types, namely normal adhesion, fault indication, minor fault, and serious fault in this work. A wheel/rail adhesion state identification method based on particle swarm optimization (PSO) and kernel extreme learning machine (KELM) is proposed. To this end, a wheel/rail state identification model is constructed using KELM, and then the regularization coefficient and kernel parameter of KELM are optimized by using PSO to improve its accuracy. Finally, based on the actual data, the proposed method is compared with PSO support vector machines (PSO-SVM) and basic KELM, respectively, and the results are given to verify the effectiveness and feasibility of the proposed method.

scholarly journals Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Xue-cun Yang ◽  
Xiao-ru Yan ◽  
Chun-feng Song
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Extreme Learning Machine ◽  
Kernel Function ◽  
Particle Swarm ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Coal Slurry ◽  
Swarm Optimization ◽  
Learning Machine

For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM) is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM) and kernel function extreme learning machine prediction model (KELM). The results prove that mean square error (MSE) for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.

Sign in / Sign up

Export Citation Format

Share Document