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The objective of the present paper is to demonstrate the potential of Computational Intelligence in applications pertaining to the automatic identification - categorisation of Cardiotocograms using Machine Learning Algorithms and Artificial Neural Networks whose purpose is to distinguish between healthy or pathological cases leading to mortality during birth or fetal cerebral palsy. Interest is also...
Obtaining an accurate model of a real-world system using linear systems theory can prove to be a complex task due to the nonlinear characteristics that systems exhibit. Neural networks have the ability to reproduce the complex nonlinear relations which makes them a useful tool in system identification and modeling. The purpose of this paper is to obtain the model of a thermal power plant feedwater...
A Smart Grid approach to electric distribution system management needs to front uncertainties in generation and demand thus making forecasting an up-to-date area of research in electric energy systems. This works aims to propose a day-ahead load forecasting procedure for a medium voltage customer. The load forecasting is performed through the implementation of an artificial neural network (ANN). The...
This research proposes the use of Artificial Neural Networks to diagnose industrial networks communication via Profibus DP Protocol. These diagnostics are based on information provided by the Physical Layer from the Profibus DP Protocol. In order to analyze the physical layer, an Artificial Neural Network first analyzes signal samples transmitted through the industrial network. In case these signals...
This paper is towards the controller design using extreme learning machine (ELM) for the longitudinal dynamics of a generic hypersonic flight vehicle (HFV). The basic idea is to train the data learning from previous controller and then obtain the optimal weight. In the first step, the existed the back-stepping controller with high order neural networks (HONNs) is borrowed to collect the required data...
Hepatitis B is a potentially life-threatening liver infection caused by the hepatitis B virus. The virus interferes with the function of the liver while replicating in hepatocytes. It is a major global health problem and the most serious type of viral hepatitis. Chronic liver disease is caused by viral hepatitis and putting people at high risk of death from cirrhosis of the liver and liver cancer...
In this paper, the least trimmed sum of absolute deviations (LTA) estimator, frequently used in robust linear parametric regression problems, will be generalized to nonparametric least trimmed sum of absolute deviations-artificial neural network (LTA-ANN) for nonlinear regression problems. In linear parametric regression problems, the LTA estimator usually have good robustness against outliers and...
MicroRNAs (miRNAs) are special non-coding RNAs that perform important roles through their target genes. Biologists' conventional miRNA knowledge discovery is time-consuming, labor-intensive, and error-prone. Semantic technologies, which are created upon domain ontologies, can greatly enhance miRNA knowledge discovery. Unfortunately, yet no specific miRNA domain ontologies currently exist. It thus...
In this paper, according to the analysis of present situation of information technology(IT) industry and talent demand, and the reference of occupation classification at home and abroad, the employment direction of IT talents is divied on the basis of the post characteristics and post classification. At the same time, the ability and quality model of the compound IT talents is proposed, the predicting...
In this paper, we developed an improved inverse compensator to ameliorate the dynamic performance of the two-dimensional sensor. The main feature of the improved compensator is the use of a special state observer, which is designed on the state-space model the two-dimensional sensor. As the state-space model can fully describe the complex sensor, the obtained compensator will be more effective in...
The theory of three-way decisions provides an additional option to the conventional two-way decisions that use only two options, namely, accepting or rejecting. The third option is called a non-commitment decision that usually means a decision in deferment or requiring further observations. Recent studies provide an evaluation-based framework of three-way decisions, in which one can make a decision...
In this paper we present a Dynamic Sampling Framework for use with multi-class imbalanced data containing any number of classes. The framework makes use of existing sampling techniques such as RUS, ROS, and SMOTE and ties the classification algorithm into the sampling process in a wrapper like manner. In doing so the framework is able to search for a desirably sampled training set, thus eliminating...
Electronic Learning (e-Learning) is used to educate people in these days. Using e-Learning, a number of world ranking universities are starting different courses for high school level to degree level and even at post graduate level through distance learning. This paper describes the best-known different machine learning techniques to boost up the e-Learning education standard and model. Comprehensively...
Automatic control of fuel cell stacks (FCS) using non-adaptive and adaptive radial basis function (RBF) neural network methods are investigated in this paper. The neural network RBF inverse model is used to estimate the compressor voltage for fuel cell stack control at different current demands, reduction in the compressor gain (30% and 20%) and manifold leak (15%) in order to prevent the oxygen starvation...
To predict the continuous value of target variable using the values of explanation variables, we often use multiple linear regression methods, and many applications have been successfully reported. However, in some data cases, multiple linear regression methods may not work because of strong local dependency of target variable to explanation variables. In such cases, the use of the k nearest-neighbor...
The BP Neural Network's application in financial pre-warning is studied in this paper. Use the dynamic cluster method to classify enterprise's standardized data and get enterprise's pre-warning model through training the BP neural network using classified data. Discuss its implementation on computer in J2EE platform. Through taking test on the panel data, this method can provide accurate forecast...
The goal of an ensemble construction with several classifiers is to achieve better generalization than that of a single classifier. And proper diversity among classifiers is considered as the condition for an ensemble construction. This paper investigates synthetic pattern for diversity among classifiers. It alters input feature values of some patterns with the values of other patterns to get synthetic...
Sunspot area is an important feature to measure the solar activities. Prediction of sunspot area can provide useful information for solar activities and space weather studies etc. In this paper, we propose a smoothed monthly mean sunspot area prediction method using artificial neural network. The prediction model is built by training the area data before the eighteenth solar cycle, and then forecast...
with the advent of multi Gbits/s speed communication systems, it has become a high priority to develop and implement high speed and efficient error control codes to make the system reliable. Today's error correction codes are mostly based on single algorithm codes which work for a specific conditions and have their own merits and demerits. In this paper two concatenated error control codes are implemented...
Colleges employment forecasting based on least squares support vector machine is proposed in the paper. Least squares support vector machine is an improved support vector machine, which can use equality constraints for the error instead of inequality constraints. Colleges employment rate of Xinjiang agricultural university from 1997 to 2006 is used to show the effectiveness of least squares support...
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