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This paper presents a relaying algorithm based on Artificial Neural Network (ANN) technique for the protection of transmission line. A feed forward ANN with six inputs and eleven outputs has been developed for the detection and classification of faults. Data was generated by simulating a 400 kV, 50Hz, 100 km transmission line in PSCAD/EMTDC at a sampling frequency of 2 kHz. Three ANN configurations...
This paper presents a classification method for multi-class classification of electromyography (EMG) signals from eight hand movements. The data were collected from 15 subjects. The EMG signals were extracted using 16 time-domain feature extraction methods. The 16 features are reduced using principal component analysis (PCA) to enhance the classification accuracy. The features results from PCA are...
Flood is defined as an overflow of large amount of water beyond its normal limits. Therefore, it has become threat to people's life and can cause damages to properties. However, in Malaysia, the only existing flood warning system are the alarming system which only notify residents nearby flood location to evacuate only when flood occur. Thus, flood water level prediction is very much needed in order...
Roadside vegetation classification has recently attracted increasing attention, due to its significance in applications such as vegetation growth management and fire hazard identification. Existing studies primarily focus on learning visible feature based classifiers or invisible feature based thresholds, which often suffer from a generalization problem to new data. This paper proposes an approach...
Application of neural networks for direct prediction of lateral-directional force and moments coefficients from the measured flight data of the research aircraft is proposed in this paper. Proposed model of neural networks appears to be a suitable practical approach to develop relationship between flight variables. This relationship eliminates the need of aerodynamic model as well as thrust model...
According to skin specialist, skin texture has close relation to an individual's health, hormones, hydration, and allergic symptoms. So by procuring one's image texture sample and exposing it to the imaging device we can identify the skin health. Texture analysis is an important tool to analyze the skin texture. The existing means of skin analysis is applicable only for isotropic images. Isotropic...
This paper presents the results of a study developing artificial neural network system (ANN) for classification of Alzheimer's disease (AD) and healthy patients. The classification is done using biomarkers, from cerebrospinal fluid: albumin ratio (CSF/Serum and/or Plasma), Aβ40 (CSF), Aβ42 (CSF), tau-total (CSF) and tau-phospho (CSF). Neural network input parameters are datasets from Alzbiomarkers...
The development of non-destructive methods like VIS-NIR reflection spectroscopy and artificial neural networks to analyse the rape seeds content of fat and protein was the subject of this work. The research material contained the seeds of 46 winter rapeseed lines obtained from interspecies crossing male sterile lines of MS-8 and 6 control forms. The seeds were pre-cleaned and crude fat and crude protein...
Electrocardiogram (ECG) is used as one of the important diagnostic tool for the detection of the health of a heart. Growing number of heart patients has necessitated development of automatic detection techniques for detecting various abnormalities or arrhythmias of the heart to reduce pressure on physicians and share their load. The present work will help in developing a computer based system that...
In this work realization of automatic scientific articles classification according to Universal Decimal Classifier is presented. Efficiency of neural networks technologies application for current task is researched, and optimal neural network structure and parameters are offered.
Cardiotocography (CTG) is a monitoring technique that is used routinely during pregnancy and labor to assess fetal well-being. CTG consists of two signals which are fetal heart rate (FHR) and uterine contraction (UC). Twenty-one features representing the characteristic of FHR have been used in this work. The features are obtained from a large dataset consisting of 2126 records in UCI Machine Learning...
Water demand forecast has emerged as an imperative component of intelligent Internet and Communication Technologies based methodologies of water management. The need of increased time resolution of forecast in order to implement such methodologies is driving stakeholders to long for new more specialized forecast approaches that will take into account the special drivers of water demand in each case...
The deteriorating quality of natural water resources like lakes, streams and estuaries, is one of the direst and most worrisome issues faced by humanity. The effects of un-clean water are far-reaching, impacting every aspect of life. Therefore, management of water resources is very crucial in order to optimize the quality of water. The effects of water contamination can be tackled efficiently if data...
This study proposes on the prediction and classification of Diabetes Mellitus using Artificial Neural Network (ANN) and hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS). The network was trained by using the data of 100 individuals with mean age of 42 years with an equal proportion of male and female. The performance of each approach is further discussed on the basis of accuracy and validates accurate...
The main objective of this research work is to develop an expert system for the diagnosis and detection of Hepatitis and liver disorders based on various Artificial Neural Networks models. In this research work Artificial Neural Networks models like Back Propagation Algorithm, Probabilistic Neural Networks, Competitive learning Networks, Learning vector quantization and Elman Networks have been used...
A neural network family is commonly used for improving financial forecasting accuracy. This paper proposes a feedback functional link artificial neural network (FFLANN) for the prediction of net asset value (NAV) of Indian Mutual funds which incorporates fewer computational load and fast forecasting capability. It is clear from the root mean square error (RMSE) and mean absolute percentage error (MAPE)...
Electricity demand forecasting is a nonlinear and complex problem. It consists of three levels, including long-term forecast for new power plant planning, medium-term forecast for maintenance scheduling and inventory of fuel, and short-term forecast for daily operations. There are many statistical forecasting techniques applied to short term load forecasting, such as Stochastic Time series, Regression...
In this paper, we investigate neural network ensemble (NNE) classifier and its application to multi-spectral image classification. The effectiveness of the NNE classifier is demonstrated on SPOT multi-spectral image data. Compared with standard classifiers, such as Bayes maximum-likelihood classifier, k-NN classifier, it has shown that the NNE classifier can have better performance on multi-spectral...
In stock market, successful investors can earn maximum profits depended on a stock selection and a suitable time on trading. Generally, investors use two statistical techniques for making a decision, which are the fundamental analysis and the technical analysis. Recently, machine learning models which are a part of artificial intelligence, has been applied to enhance investors for investment. A number...
In this paper various types of classifiers for quantitatively identify teletraffic service devices are proposed. The classification method “K — Nearest Neighbors With Defined Cityblock Metric Distance At Three Nearest Neighbors” is selected. A classifier structure is synthesized based on Adaptive Neuro-Fuzzy Interface Systems (ANFIS) in hybrid learning algorithm and Gaussian type membership function...
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