The extreme learning machine learning algorithm with tunable activation function

2012 ◽  
Vol 22 (3-4) ◽  
pp. 531-539 ◽  
Author(s):  
Bin Li ◽  
Yibin Li ◽  
Xuewen Rong
Keyword(s):  
Machine Learning ◽  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Activation Function ◽  
Machine Learning Algorithm ◽  
Learning Machine

SMART IDENTIFICATION OF POWER QUALITY EVENTS USING NEW STOCKWELL TRANSFORM AND MACHINE LEARNING ALGORITHM

2019 ◽  
Vol XVI (4) ◽  
pp. 95-113
Author(s):  
Muhammad Tariq ◽  
Tahir Mehmood
Keyword(s):  
Machine Learning ◽  
Power Quality ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Noisy Environments ◽  
Satisfactory Performance ◽  
Feature Values ◽  
Learning Machine ◽  
Stockwell Transform ◽  
Electrical Utilities

Accurate detection, classification and mitigation of power quality (PQ) distortive events are of utmost importance for electrical utilities and corporations. An integrated mechanism is proposed in this paper for the identification of PQ distortive events. The proposed features are extracted from the waveforms of the distortive events using modified form of Stockwell’s transform. The categories of the distortive events were determined based on these feature values by applying extreme learning machine as an intelligent classifier. The proposed methodology was tested under the influence of both the noisy and noiseless environments on a database of seven thousand five hundred simulated waveforms of distortive events which classify fifteen types of PQ events such as impulses, interruptions, sags and swells, notches, oscillatory transients, harmonics, and flickering as single stage events with their possible integrations. The results of the analysis indicated satisfactory performance of the proposed method in terms of accuracy in classifying the events in addition to its reduced sensitivity under various noisy environments.

Study on Deep Structure of Extreme Learning Machine (DS-ELM) for Datasets with Noise

2014 ◽  
Vol 989-994 ◽  
pp. 3679-3682 ◽  
Author(s):  
Meng Meng Ma ◽  
Bo He
Keyword(s):  
Extreme Learning Machine ◽  
Simple Structure ◽  
Learning Algorithm ◽  
Deep Structure ◽  
Noisy Data ◽  
Machine Learning Algorithm ◽  
Feed Forward Neural Networks ◽  
Learning Machine ◽  
Hidden Layer ◽  
Auto Associative Neural Network

Extreme learning machine (ELM), a relatively novel machine learning algorithm for single hidden layer feed-forward neural networks (SLFNs), has been shown competitive performance in simple structure and superior training speed. To improve the effectiveness of ELM for dealing with noisy datasets, a deep structure of ELM, short for DS-ELM, is proposed in this paper. DS-ELM contains three level networks (actually contains three nets ): the first level network is trained by auto-associative neural network (AANN) aim to filter out noise as well as reduce dimension when necessary; the second level network is another AANN net aim to fix the input weights and bias of ELM; and the last level network is ELM. Experiments on four noisy datasets are carried out to examine the new proposed DS-ELM algorithm. And the results show that DS-ELM has higher performance than ELM when dealing with noisy data.

scholarly journals Computer System Integration Technology of Hydraulic Engineering Design Based on Machine Learning Algorithm

2021 ◽  
Vol 2083 (3) ◽  
pp. 032058
Author(s):  
Ting Liu
Keyword(s):  
Machine Learning ◽  
Engineering Design ◽  
Computer System ◽  
Risk Prediction ◽  
System Integration ◽  
Learning Algorithm ◽  
Hydraulic Engineering ◽  
Machine Learning Algorithm ◽  
Water Conservancy ◽  
Learning Machine

Abstract With the development of water conservancy informatization, the research on water information system integration is born, which is the need of water conservancy informatization construction at present and also an urgent problem to be solved. Based on the machine learning algorithm, combined with the actual needs of water conservancy business field, the overall framework of computer system integration for water conservancy engineering design is put forward. The overall framework includes: resource layer, comprehensive integration layer and user layer, which exchange data with configuration monitoring software by means of communication. The analytic hierarchy process in machine learning algorithm is used to construct the risk prediction index system, and the risk prediction index and initial prediction results are taken as the input and output of extreme learning machine algorithm in machine learning algorithm. The simulation results show that the prediction accuracy of this method is 94.88%, which can accurately predict the risks existing in hydraulic engineering design computer system and improve the system security.

scholarly journals Classification of Aggressive Behaviors Based on sEMG Feature Extraction and Machine Learning Algorithm

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 656-656
Author(s):  
Youngjun Kim ◽  
Uchechukuwu David ◽  
Yeonsik Noh
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Learning Machine

Abstract New surface electromyography (sEMG) feature extraction approach combined with Empirical Mode Decomposition (EMD) and Dispersion Entropy (DisEn) is proposed for classifying aggressive and normal behaviors from sEMG data. In this study, we used the sEMG physical action dataset from the UC Irvine Machine Learning repository. The raw sEMG was decomposed with EMD to obtain a set of Intrinsic Mode Functions (IMF). The IMF, which includes the most discriminant feature for each action, was selected based on the analysis by Hibert Transform (HT) in the time-frequency domain. Next, the DisEn of the selected IMF was calculated as a corresponding feature. Finally, the DisEn value was tested using five different classifiers, such as LDA, Quadratic DA, k-NN, SVM, and Extreme Learning Machine (ELM) for the classification task. Among these ML algorithms, we achieved classification accuracy, sensitivity, and specificity with ELM as 98.44%, 100%, and 96.72%, respectively.

scholarly journals Expenditure Predicting using Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 2169-2172
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Stock Market ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Personal Finances ◽  
Learning Machine ◽  
A Company

As we know in today’s world managing expenses is a very challenging thing. By analyzing our previous expenses, we can predict our upcoming expenses. Now digitalization is everywhere so we can get bank transaction history easily, just by getting the data from transaction history we can predict the estimation of upcoming expense. We can do this using machine learning, machine learning is used in many things one of them is prediction. We are using linear regression algorithm, it is a machine learning algorithm used in prediction. The main aim of this project is to build a system that helps in managing personal finances of the user. This project has mainly three modules, first is to collect the data and prepare it to be used in algorithm, next is to build a network between the algorithm and the dataset. The last one is prediction in which system is going to predict the expenses. Particularly we are predicting the expense of next month. We can also use this system in stock market for predicting the next step if stocks of a company will rise or fall do, this can help us in making money from stock market and manage our expense.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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