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The purpose of this paper is to propose a modular integrating algorithm. This algorithm can let the program detect multiple arrhythmias and is very easy to add more diseases detection algorithm. Also, it can save the repeated calculations in multiple algorithms. By a real test of the program, the result is that the computing time of the integrating algorithm is 46.86% less than the sum of the computing...
Motor Imagery (MI) is a highly supervised method nowadays for the disabled patients to give them hope. This paper proposes a differentiation method between imagery left and right hands movement using Daubechies wavelet of Discrete Wavelet Transform (DWT) and Levenberg-Marquardt back propagation training algorithm of Neural Network (NN). DWT decomposes the raw EEG data to extract significant features...
DNA microarrays are normally used to measure the expression values of thousands of several genes simultaneously in the form of large matrices. This raw gene expression data may contain some missing cells. These missing values may affect the analysis performed subsequently on these gene expression data. Several imputation methods, like K-Nearest Neighbor Imputation (KNNImpute), Singular Value Decomposition...
This paper discusses the development and application of a decomposition neural network rule extraction algorithm for nonlinear regression problems, the algorithm is called the piece-wise linear artificial neural network or PWL-ANN algorithm. Rules in the form of linear equations are generated by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network (ANN)...
Creating a neural network based classification model is commonly accomplished using the trial and error technique. However, this technique has several difficulties in terms of time wasted and the availability of experts. In this article, an algorithm that simplifies structuring neural network classification models is proposed. The algorithm aims at creating a large enough structure to learn models...
Machine learning (ML) based applications that require data stream processing have become quite common over the past few years. To deal with continuous and massive streams of data, low computational and memory costs are required from the ML techniques employed; these requirements can be partially fulfilled by using constructive neural networks (CoNN) algorithms. The automatic definition of the Neural...
Gaussian mixture models (GMM) remain popular in pattern classification applications due to their well understood Bayesian framework and the availability of good training algorithms such as the expectation maximization (EM) algorithm. EM is a non-discriminative training algorithm. The performance of a GMM trained with the EM algorithm can often fall short of other discriminative pattern classification...
Cataclysmic variable (CV) stars are binary stars that consist of two components: a white dwarf primary, and a mass transferring secondary. Due to the relative faint of cataclysmic variable and a large number of irregular changes, it is not easy to get valuable data and important research results on observation. But they have significant meaning on the subsequent research of these spectra. In general,...
Classification of cancer patients into treatment groups is essential for appropriate diagnosis to increase survival. Previously, a series of papers, largely published in the breast cancer domain have leveraged Computational Intelligence (CI) developments and tools, resulting in ground breaking advances such as the classification of cancer into newly identified classes — leading to improved treatment...
Kidney plays an important role in human bodies. It maintains homeostasis and removes some harmful substance by making and ejecting urine. Renal cell carcinoma, especially clear cell renal cell carcinoma (ccRCC), is the most common type of kidney disease that accounts for 2∼3% of human malignancies. Early diagnosis and accurate classification of ccRCC is an important factor to decrease the motility...
This paper presents, genetic algorithm based hybrid selective harmonic elimination scheme for recently introduced Nested Neutral Point Clamped (NNPC) Converter. The Selective harmonic elimination (SHE) modulation scheme reduces device stress, switching losses and filter size in high power and high voltage applications. SHE technique aims to solve non-linear and transcendental equations while keeping...
For industrial applications, Bidirectional DC-DC converters (BDCs) are used in recent years. And also their efficiency results are improved to apply different control methods. ANN algorithms is one of the new control topic in literature. This paper attempts to improve the dynamic performance of bidirectional dc-dc converter. And it deals with a novel control scheme related with an adaptive input voltage...
One of the most critical problems of autonomous energy systems (so called Off-Grid systems) is the keeping of the power quality parameters (PQP) in the requested limits. This paper focuses on development of a simple binary classification as a tool for the forecasting of a PQP. This tool will help as an advisor for a shifting of the load in the Off-Grid system, which keeps the PQP in the requested...
Along with the development of social network, more and more people know the world by reading news. The problem about what kind of emotion is inspired when people read news is very worthy of discussion. This paper will mix Deep Belief Networks (DBN) model and Support Vector Machine (SVM) to a hybrid neural network model by using the Contrast Divergence (CD) algorithm to estimate the weights when training...
Feature selection is an increasingly important part of machine learning. The purpose of feature selection is dimension reduction in a large multi-dimensional data set and it can be the key step of successful knowledge discovery in those problems where the number of features is large. This research area has huge practical significance because it accelerates decisions and improves performance. The requirements...
The relevance of this study is stipulated by the necessity of designing algorithms allowing to improve the efficiency of characters recognition on images with complex backgrounds subjected to noise, affine and projective transformations. Purpose: Development of algorithms and software system allowing to improve the efficiency of characters recognition on images with complex backgrounds subjected to...
This paper presents a novel approach to estimate delay differences of each stage in a standard MUX-based physical unclonable function (PUF). Test data collected from PUFs fabricated using 32 nm process are used to train a linear model. The delay differences of the stages directly correspond to the model parameters. These parameters are trained by using a least mean square (LMS) adaptive algorithm...
An Artificial Neural Network (ANN) is a statistical data modeling tool inspired by the functionality and the structure of the biological nervous system. An ANN consists of processing elements known as neurons that are interconnected to each other and work in unison to answer a particular problem. Neural networks can be used in places where detecting trends and extracting patterns are too complex to...
According to the World Health Organization, cardiovascular diseases (CVD) are the main cause of death worldwide. An estimated 17.5 million people died from CVD in 2012, representing 31% of all global deaths. The electrocardiogram (ECG) is a central tool for the pre-diagnosis of heart diseases. Many advances on ECG arrhythmia classification have been developed in the last century; however, there is...
Intensive Care Unit (ICU) admission is a major factor that affects the healthcare budget. ICU cost is extremely high because its resources are consumed through highly advanced equipment providing quality healthcare service for patients. Thus, the need for a predictive model for the decision to transfer stroke in-patients to the ICU is very important. Also, this predictive model will help to lower...
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