diagnosis. Moreover various studies can be found in medical journals dedicated to Artificial Neural Networks (ANN). In the presented study, a method was developed to learn and detect benign and malignant tumor types in contrast-enhanced breast magnetic resonance images (MRI). The backpropagation algorithm was taken as the ANN learning algorithm. The algorithm (NEUBREA) was developed in C# programming language by using Fast Artificial Neural Network Library (FANN). Having been diagnosed by radiologists, 7 cases of malignant tumor, 8 cases of benign tumor, and 3 normal cases were used as a training set. The results were tested on 34 cases that had been diagnosed by radiologists. After the comparison of the results, the overall accuracy of algorithm was defined as 92%.
In this paper, a novel model is presented for machines and automated guided vehicles' simultaneous scheduling, which addresses an extension of the blocking job shop scheduling problem. An artificial neural network approach is used to estimate machine's breakdown indexes. Since the model is strictly NP-hard and because objectives contradict each other, two developed meta-heuristic algorithms called fuzzy multi-objective invasive weeds optimization algorithm" and fuzzy multi-objective cuckoo search algorithm" with a new chromosome structure which guarantees the feasibility of solutions are developed to solve the proposed problem. Since there is no benchmark available on literature, three other metaheuristic algorithms are developed with a similar solution structure to validate performance of the proposed algorithms. Computational results showed that developed fuzzy multi-objective invasive weeds optimization algorithm had the best performance in terms of solving problems compared to four other algorithms.
The power quality in electrical energy systems is very important and harmonic is the vital criterion. Traditionally Fast Fourier Transform (FFT) and Discrete Fourier Transform (DFT) have been used for the harmonic distortion analysis and in the literature harmonic estimations have been made using different methods. As an alternative method, this paper suggested using Support Vector Machine (SVM) for harmonic estimation. The real power energy distribution system has been examined and the estimation results have been compared with measured real data. The proposed solution approach was comparatively evaluated with the ANN and LR estimation methods. Comparison results show that THD estimation values that were obtained by the SVM method are close to the THD estimation values obtained from ANN (Artificial Neural Network) and LR (Linear regression) methods. The numerical results clearly showed that the SVM method is valid for THD estimation in the power system.
Development of any area often leads to more intensive land use and increase in the generation of pollutants. Modeling these changes is critical to evaluate emerging changes in land use and their effect on stream water quality. The objective of this study was to assess the impact of spatial patterns in land use and population density on the water quality of streams, in case of data scarcity, in the Chugoku district of Japan. The study employed artificial neural network (ANN) technique to assess the relationship between the total phosphorous (TP) in river water and the land use in 21 river basins in the district, and the model was able to reasonably estimate the TP in the stream water. Uncertainty analysis of ANN estimates was performed using the Monte Carlo framework, and the results indicated that the ANN model predictions are statistically similar to the characteristics of the measured TP values. It was observed that any reduction in forested area or increase in agricultural land in the watersheds may cause the increase of TP concentration in the stream. Therefore, appropriate watershed management practices should be followed before making any land use change in the Chugoku district. and Rozvoj územia často súvisí so zintenzívnením využívania krajiny a produkciou znečistenia. Dôležité je modelovanie týchto zmien a ich vplyvu na kvalitu vody v tokoch. Cieľom štúdie je určiť vplyv priestorových zmien pri využívaní krajiny a zmeny hustoty osídlenia na kvalitu vody v tokoch v čase nedostatku vody v oblasti Chugoku, Japonsko. Pri riešení sa využívajú umelé neurónové siete (artificial neural network -ANN), prostredníctvom ktorých sa určuje vzťah medzi celkovým obsahom fosforu (TP) v toku a využívaním kajiny v 21 povodiach oblasti; tento model je schopný vypočítať TP v tokoch. Analýza neurčitosti výsledkov dosiahnutých pomocou ANN bola vykonaná metódou Monte Carlo; výsledky analýzy naznačujú, že predpovede pomocou metódy ANN sú štatisticky podobné meraným hodnotám TP. Bolo zistené, že redukcia lesnatosti a zvýšenie plochy poľnohospodársky využívanej pôdy v povodí môže viesť k zvýšeniu koncentrrácie TP v toku. Je preto potrebné pred zmenou vo využívaní krajiny prijať zodpovedajúce opatrenia v manažmente krajiny, ktoré budú minimalizovať negatívne dôsledky zmien využívania krajiny.
The purpose of this study is to predict the mass loss of newly developed aluminium based alloy. Two different alloys are prepared by cladding process and the sliding friction and wear properties of this alloy against high carbon high chromium steel are investigated at different normal loads (50 N, 60 N and 70 N) under different sliding distances. Tests are carried at a constant speed of 1 m/sec under oil lubricated conditions by preheating the circulating engine oil 20w40 at a temperature of 800C. The mass losses are measured and recorded for every interval. An artificial neural network (ANN) model is developed to predict the mass loss of newly developed aluminium-based alloy. It is observed that the predicted values have shown good agreement with experimental values with a correlation coefficient of 0.999973. This model can also be used to predict the mass loss of any material.
In the present study, bainite fraction results of continuous cooling of high strength low alloy steels have been modeled by artificial neural networks. The artificial neural network models were constructed by 16 input parameters including chemical compositions (C, Mn, Nb, Mo, Ti, N, Cu, P, S, Si, Al, V), Nb in solution, austenitizing temperature, initial austenite grain size and cooling rate over the temperature range of the occurrence of phase transformations. The value for the output layer was the bainite fraction. According to the input parameters in feed-forward back-propagation algorithm, the constructed networks were trained, validated and tested. To make a decision on the completion of the training processes, two termination states are declared: state 1 (ANN-I model) means that the training of neural network was ended when the maximum epoch of process reached (1000) while state 2 (ANN-II model) means the training ended when minimum error norm of network gained. The entire statistical evaluators of ANN-II model has higher performance than those of ANN-I. However, both of the models exhibit valuable results and the entire statistical values show that the proposed ANN-I and ANN-II models are suitably trained and can predict the bainite fraction values very close to the experimental ones.
Heart disease diagnosis is an important non-invasive technique. Therefore, there exists an effort to increase the accuracy of arrhythmia classification based on ECG signals. In this work, we present a novel approach of heart arrhythmia detection. The model consists of two parts. The first part extracts important features from raw ECG signal using Auto-Encoder Neural Network. Extracted features obtained by Auto-Encoder represent an input for the second part of the model, the Gradient Boosting and Feedforward Neural Network classifiers. For comparison purposes, we evaluated our approach by using MIT-BIH ECG database and also following recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) for ECG class labeling. We divided our experiment into two scenarios. The first scenario represents the classification task for the patient-adapted paradigm and the second one was dedicated to the inter-patient paradigm. We compared the measured results to the state-of-the-art methods and it shows that our method outperforms the state-of-the art methods in the Ventricular Ectopic (VEB) class for both paradigms and Supraventricular Ectopic (SVEB) class in the inter-patient paradigm.
There are two basic types of artificial neural networks: Multi-Layer Perceptron (MLP) and Radial Basis Function network (RBF). The first type (MLP) consists of one type of neuron, which can be decomposed into a linear and sigmoid part. The second type (RBF) consists of two types of neurons: radial and linear ones. The radial basis function is analyzed and then used for decomposition of RBF network. The resulting Perceptron Radial Basis Function Network (PRBF) consists of two types of neurons: linear and extended sigmoid ones. Any RBF network can be directly converted to a four-layer PRBF network while any MLP network with three layers can be approximated by a five-layer PRBF network. The new PRBF network is then a generalization of MLP and RBF network abilities. Learning strategies are also discussed. The new type of PRBF network and its learning via repeated local optimization is demonstrated on a numerical example together with RBF and MLP for comparison. This paper is organized as follows: Basic properties of MLP and RBF neurons are summarized in the first two chapters. The third chapter includes novel relationship between sigmoidal and radial functions, which is useful for RBF decomposition and generalization. Description of new PRBF network, together with its properties, is subject of the fourth chapter. Numerical experiments with a PRBF and their requests are given in the last chapters.
Gas exchange of Carex cinerascens was carried out in Swan Islet Wetland Reserve (29°48' N, 112°33' E). The diurnal photosynthetic course of C. cinerascens in the flooded and the nonflooded conditions were analyzed through the radial basis function (RBF) neural network approach to evaluate the influences of environmental variables on the photosynthetic activity. The inhibition of photosynthesis induced by soil flooding can be attributed to the reduced stomatal conductance (gs), the deficiency of Rubisco regeneration and decreased chlorophyll (Chl) content. As revealed by analysis of artificial neural network (ANN) models, gs was the dominant factor in determining the photosynthesis response. Weighting analysis showed that the effect of water pressure deficit (VPD) > air temperature (T) > CO2 concentration (Ca) > air humidity (RH) > photosynthetical photon flux density (PPFD) for the nonflooded model, whereas for the flooded model, the factors were ranked in the order VPD > C a > RH > PPFD > T. The different photosynthetic response of C. cinerascens found between the nonflooded and flooded conditions would be useful to evaluate the flood tolerance at plant species level. and M. Li ... [et al,.].
Image de-noising is a traditional task related to linear and non-linear 2D filtering methods. The artificial neural network (ANN) can be also used as a kind of a sophisticated non-linear filter on local pixel neighborhood (3x3). The disadvantage of linear systems and neural networks is their sensitivity to impulse (isolated) noise. That is why the median and the other rank based filters are better in this case. The opposite situation is in the case of Gaussian noise when the mean filtering has higher value of signal / noise ratio (SNR) than the median filter. The first aim of our paper is to define k-robustness of local de-noising. Then it is easy to build up a new class of k-robust de-noising systems consisting of input frame, robust preprocessing, ANN and robust postprocessing. Implementation details related to signal processing and learning are also included. The third aim of our paper is to learn 1-robust and 2-robust systems to have maximum possible SNR for Gaussian noise on real MR image of human brain.