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15.606 Articles

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Prediction Model of the Power System Frequency Using a Cross-Entropy Ensemble Algorithm

Yi Tang, Han Cui and Qi Wang

Frequency prediction after a disturbance has received increasing research attention given its substantial value in providing a decision-making foundation in power system emergency control. With the advancing development of machine learning, analysis power... see more

frequency prediction | machine learning | cross-entropy | ensemble algorithm

Entropy , 2017

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Pneumonia Detection on X-Ray Imaging using Softmax Output in Multilevel Meta Ensemble Algorithm of Deep Convolutional Neural Network Transfer Learning Models

(1) Simeon Yuda Prasetyo (Bina Nusantara University, Indonesia) (2) Ghinaa Zain Nabiilah (Bina Nusantara University, Indonesia) (3) Zahra Nabila Izdihar (Bina Nusantara University, Indonesia) (4) Sani Muhamad Isa (Bina Nusantara University, Indonesia)

Pneumonia is the leading cause of death from a single infection worldwide in children. A proven clinical method for diagnosing pneumonia is through a chest X-ray. However, the resulting X-ray images often need clarification, resulting in subjective judgme... see more

Pneumonia classification | Transfer learning | Deep convolutional neural network | Softmax | Multilevel meta ensemble

International Journal of Advances in Intelligent Informatics , 2023

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Pneumonia Detection on X-Ray Imaging using Softmax Output in Multilevel Meta Ensemble Algorithm of Deep Convolutional Neural Network Transfer Learning Models

Pneumonia classification | Transfer learning | Deep convolutional neural network | Softmax | Multilevel meta ensemble

IJAIN (International Journal of Advances in Intelligent Informatics) , 2023

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TOLL FRAUD DETECTION OF VOIP SERVICES VIA AN ENSELBME OF NOVELTY DETECTION ALGORITHMS

Pilsung Kang,Kyungil Kim,Nam-Wook Cho

Communications fraud has been dramatically increasing with the development of communication technologies and the increasing use of global communications, resulting in substantial losses to telecommunication industry. Due to the widespread deployment of vo... see more

International Journal of Industrial Engineering: Theory; Applications and Practice , 2015

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An Ensemble Model of Machine Learning Algorithms for the Severity of Sickle Cell Disease (Scd) Among Paediatrics Patients

Balogun Jeremiah Ademola, Aderounmu Temilade, Egejuru Ngozi Chidozie, Idowu Peter Adebayo

This study was motivated at developing an ensemble of 3 supervised machine learning algorithms for the assessment of the severity of sickle cell disease (SCD) among paediatric patients. The study collected data from a tertiary hospital in south-western Ni... see more

Computer Reviews Journal , 2018

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Artificial Neural Network Ensembles in Time Series Forecasting: an Application of Rainfall Forecasting in Sri Lanka

Harshani R.K. Nagahamulla,Uditha R Ratnayake,Asanga Ratnaweera

Weather forecasting is a widely researched area in time series forecasting due to the necessity of accurate weather forecasts in various human activities. Out of numerous weather forecasting techniques Artificial Neural Networks (ANN) methodology is one o... see more

The International Journal on Advances in ICT for Emerging Regions , 2013

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Fault Diagnosis Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Power-Based Intrinsic Mode Function Selection Algorithm

Hyungseob Han, Sangjin Cho, Sundeok Kwon and Sang-Bock Cho

In the fault diagnosis system using empirical mode decomposition (EMD), it is important to select the intrinsic mode functions (IMFs) which contain as much fault information as possible and to alleviate the problems of mode mixing and spurious modes. An e... see more

fault diagnosis | improved complete ensemble empirical mode decomposition | intrinsic mode function | neural network

Electronics , 2018

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A Novel Multiscale Ensemble Carbon Price Prediction Model Integrating Empirical Mode Decomposition, Genetic Algorithm and Artificial Neural Network

Bangzhu Zhu

Due to the movement and complexity of the carbon market, traditional monoscale forecasting approaches often fail to capture its nonstationary and nonlinear properties and accurately describe its moving tendencies. In this study, a multiscale ensemble fore... see more

Energies , 2012

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Applying a Multi-Model Ensemble Method for Long-Term Runoff Prediction under Climate Change Scenarios for the Yellow River Basin, China

Linus Zhang and Xiaoliu Yang

Given the substantial impacts that are expected due to climate change, it is crucial that accurate rainfall–runoff results are provided for various decision-making purposes. However, these modeling results often generate uncertainty or bias due to the imp... see more

Water , 2018

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