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We look at the neural network as a non-linear probability density function (pdf) transformer by stochastic learning cumulative (SLC) technique. We formulate a potential function that drives a neural network to non-linearly transform the input pdf to the desired pdf. We show the working of the algorithm using synthetic data drawn from three different pdfs and estimate the parameters of the distributions...
Monitoring crop areas is a key issue in remote sensing studies. A Crop Proportion Phenology Index (CPPI) model has previously been developed for estimation of winter wheat areas. Here we test the CPPI model in different areas using remote sensing data for varied kernel functions, including linear regression (LR), Artificial Neural Network (ANN), and Support Vector Regression (SVR). The differences...
In this study, we deemed further to evaluate the performance of Neural Network (NN) and Support Vector Machine (SVM) in classifying the gait patterns between autism and normal children. Firstly, temporal spatial, kinetic and kinematic gait parameters of forty four subjects namely thirty two normal subjects and twelve autism children are acquired. Next, these three category gait parameters acted as...
In this paper, the effect of different hyperspectral images feature extraction techniques in ANN and SVM classifiers is investigated. While a high accuracy and efficiency for HLFE method was shown in ANN classifier in the literature, in this study it is shown that using the RBF kernel in SVM provides increased accuracy for PCA and KPCA meanwhile poor classification accuracy is achieved for HLFE. Therefore...
In this paper a survey on fault diagnosing techniques of electronic circuits are presented which are related mainly to industrial applications. Diagnozing the faults in circuit boards is very essential for achieving better reliability and easy maintainance of electronic systems. The circuit fault finding diagnosis is treated as the pattern recognition case and uses machine learning methodology. Increasing...
In recent years, the detection of drowsiness based on Electroencephalogram (EEG) signal has been paid great attentions. Most of the popular algorithms used for Brain Computer Interface (BCI) applications are, the Support Vector Machine (SVM) and the Artificial Neuronal Network (ANN)). The challenge is to developed a drowsiness detection system that is at once adapt to an embedded implementation and...
Poses recognition is an important research topic because some situations require silent communication (sign language, surgeon poses to the nurse for assistance etc.). Traditionally, poses recognition requires high quality expensive cameras and complicated computer vision algorithms. This is not the case thanks to the Microsoft Kinect sensor which provides an inexpensive and easy way for real time...
In this paper, artificial neural network (ANN) based fuzzy filter is proposed for removal of impulse noise from gray images. ANN is used for classification of noisy and non-noisy pixels from the image corrupted by impulse noise. Based on the classification, fuzzy filtering is done adjusting the corrupted and non-corrupted pixels. In this method, feature set comprises of predicted error, absolute difference...
Convolutional neural networks (CNN) accelerators have been proposed as an efficient hardware solution for deep learning based applications, which are known to be both compute-and-memory intensive. Although the most advanced CNN accelerators can deliver high computational throughput, the performance is highly unstable. Once changed to accommodate a new network with different parameters like layers...
This study presents spatial analysis of Dengue Fever (DF) outbreak using Geographic Information System (GIS) in the state of Selangor, Malaysia. DF is an Aedes mosquito-borne disease. The aim of the study is to map the spread of DF outbreak in Selangor by producing a risk map while the objective is to identify high risk areas of DF by producing a risk map using GIS tools. The data used was DF dengue...
Publications of financial news articles impact the decisions made by investors and, therefore, change the market state. It makes them an important source of data for financial predictions. Forecasting models based on information derived from news have been recently developed and researched. However, the advantages of combining different categories of news articles have not been investigated. This...
Support vector machines (SVMs) have been recognized as a potential tool for supervised classification analyses in different domains of research. In essence, SVM is a binary classifier. Therefore, in case of a multiclass problem, the problem is divided into a series of binary problems which are solved by binary classifiers, and finally the classification results are combined following either the one-against-one...
The fault on Overhead Contact Wire (OCW) causes traffic disruptions in railway transportation. The inspection and maintenance on OCW is inconvenient as it locates highly above the ground. Condition monitoring system has been installed to monitor the status of the OCW automatically. Huge amount of data have been collected by these systems. However, a general problem of these systems is the analysis...
In latest years, learning algorithm based Kernel function has been playing crucial role in the research area. Support Vector Machines are getting a large success due to their good performances in classification and regression. Regularization Networks and Support Vector Regression are kernel methods solving difficult learning tasks as estimating a nonlinear system from distributed data. In this work,...
A concept of Four Properties (SiQi) of Chinese herbs is the important part of traditional Chinese medicine theory. The Chinese clinical medicine is a process of dialectical theory of governance of Chinese medicine prescriptions based these four properties. The Chinese medicine prescription uses a "Cold" and "Hot" model to judge the properties of Chinese herbs, and also judge the...
The Ultra-Wide Band (UWB) signals recently have attracted increasing attention in the area of material identification due to their potential of providing very high data rates at relatively short ranges and their capability of being obtained nondestructively and contactless. The Support Vector Machines (SVM) offers one of the most robust and accurate classification capability among the well-known such...
Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. To enhance the selection of channel with less noise among the white spaces (idle channels), the a priory knowledge of Radio Frequency (RF) power is very important. Computational Intelligence (CI) techniques cans be applied to these scenarios...
Digital fundus photographs are often used to provide clinical diagnostic information about several pathologies such as diabetes, glaucoma, macular degeneration and vascular and neurologic disorders. To allow a precise analysis, digital fundus image quality should be assessed to evaluate if minimum requirements are present. Focus is one of the causes of low image quality. This paper describes a method...
Being able to identify machining processes that produce specific machined surfaces is crucial in modern manufacturing production. Image processing and computer vision technologies have become indispensable tools for automated identification with benefits such as reduction in inspection time and avoidance of human errors due to inconsistency and fatigue. In this paper, the Support Vector Machine (SVM)...
We present a novel approach to automated estimation of agreement intensity levels from facial images. To this end, we employ the MAHNOB Mimicry database of subjects recorded during dyadic interactions, where the facial images are annotated in terms of agreement intensity levels using the Likert scale (strong disagreement, disagreement, neutral, agreement and strong agreement). Dynamic modelling of...
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