ANN ARTIFICIAL NEURAL NETWORK-TECHNOLOGY-RESEARCH PAPER
Artificial neural networks are computational models which work similar to the functioning of a human nervous system. There are several kinds of artificial neural networks. These type of networks are implemented based on the mathematical operations and a set of parameters interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron
Artificial neural network
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ABSTRACT The long course of evolution has given the human brain many desirable characteristics not present in Von Neumann or modern parallel computers. These include massive parallelism, distributed representation and computation, learning ability,
Artificial neural network
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ABSTRACT A neural network is a powerful data modeling tool that is able to capture and represent complex input/output relationships. Imagine the power of the machine which has the abilities of both computers and humans. It would be the most remarkable thing ever. A
Artificial neural network
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INTRODUCTION Anartificial neural networkis an information-processing paradigm that is inspired by the way
Artificial neural networkclassification using a minimal training set- Comparison to conventional supervised classification
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ABSTRACT: Recent research has shown anartificial neural network(ANN) to be capable of pattern recognition and the classification of image data. This paper examines the potential for the application of neural network computing to satellite image processing. A second
Implementation of a fastartificial neural networklibrary (fann)
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Abstract This report describes the implementation of a fastartificial neural networklibrary in ANSI C called fann. The library implements multilayer feedforward networks with support for both fully connected and sparse connected networks. Fann offers support for execution in
Use ofartificial neural networkin pattern recognition
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Abstract Among the various traditional approaches of pattern recognition the statistical approach has been most intensively studied and used in practice. More recently, the addition ofartificial neural networktechniques theory have been receiving significant
Artificial Neural network
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ABSTRACT Combining human and machine capabilities can lead to powerful systems. In this regard we are inspired by a quote by Albert Einstein who said: Computers are incredibly fast, accurate and stupid; humans are incredibly slow, inaccurate and brilliant;
Artificial Neural Network
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Information processing paradigm that is inspired by the way biological nervous systems, such
Classification of Remotely Sensed Data by anArtificial Neural Network : Issues Related to Training Data
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Abstract Arlificial neurul networks have considerable potential for the classification of remotely sensed data. In this paper a feedforwardartificial neural networkusing a variant of ihe backpropagation learning algorithm vas used to classify agricultural crops from synthetic
Relating the land-cover composition of mixed pixels toartificial neural networkclassification output
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Abstract ses, the classification procedures generally used to produce neural networks are attractive for use in the classi- land-cover map are hard techniques which force allocation fication of land cover from remotely sensed data. In common to One class.
Artificial neural networkapproach for short term load forecasting for Illam region
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AbstractIn this paper, the application of neural networks to study the design of short-term load forecasting (STLF) Systems for Illam state located in west of Iran was explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model
Artificial neural network
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Abstract: The main purpose was to evaluate the application of Principal Component Analysis (PCA) as a preprocessing technique of the input data of a Multilayer Perceptron Model (MLP). The objective of the model was the prediction of organic matter removal from pulp To determine some mechanical parameters such as modulus of elasticity (E) and unconfined compressive strength (UCS) of gypsum is difficult, expensive, time consuming and involves destructive tests. The use of indirect estimation methods due to some index
Predicting students academic performance usingartificial neural network : A case study of an engineering course
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ABSTRACT The observed poor quality of graduates of some Nigerian Universities in recent times has been partly traced to inadequacies of the National University Admission Examination System. In this study anArtificial Neural Network(ANN) model, for predicting
Comparative analysis of regression andartificial neural networkmodels for wind turbine power curve estimation
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This paper examines and compares regression andartificial neural networkmodels used for the estimation of wind turbine power curves. First, characteristics of wind turbine power generation are investigated. Then, models for turbine power curve estimation using both
Introduction toartificial neural network(ANN) methods: what they are and how to use them
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Basic concepts of ANNs together with three most widely used ANN learning strategies (error back-propagation, Kohonen, and counter propagation) are explained and discussed. In order to show how the explained methods can be applied to chemical problems, one simple
Predicting students academic performance: comparingartificial neural network decision tree and linear regression
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Predicting students academic performance is critical for educational institutions because strategic programs can be planned in improving or maintaining students performance during their period of studies in the institutions. The performance of the academic
Predictive modelling of coniferous forest age using statistical andartificial neural networkapproaches applied to remote sensor data
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Abstract. Age is a powerful variable that can be of signiFIcant value when modelling the health of forest-dominated ecosystem. Traditional investigations have attempted to extract age information from remotely sensed data by regressing the spectral values within situ
Evaluation ofartificial neural networkapplications in transportation engineering
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The increased interest in artificial neural networks (ANNs) seen in government and private research as well as business and industry has included relatively little activity in transportation engineering. The position that ANNs, as a branch of artificial intelligence, hold
Application ofartificial neural networkin stage-discharge relationship
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ABSTRACT: The prediction of discharge and its variability in a river is an essential component of surface-water planning. For that purpose, a functional relationship between stage and discharge is established with the help of field measurement and the relationship
Application ofArtificial Neural Networkfor stock market predictions: A review of literature
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Abstract-The prevailing Nation in society is that wealth brings comfort and luxury, so it is a challenging and daunting task to find out which is more effective and accurate method for stock rate prediction so that a buy or sell signal can be generated for given stocks. Predicting
Influence of missing values onartificial neural networkperformance
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Abstract The problem of databases containing missing values is a common one in the medical environment. Researchers must find a way to incorporate the incomplete data into the data set to use those cases in their experiments. Artificial neural networks (ANNs) cannot
Functional linkartificial neural networkfor classification task in data mining
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Let us first recall a general model of anartificial neural networkthat consists of s simple computational units or neurons, indexed as V={1,, s}, where s=| V| is called the network size. Some of these units may serve as external inputs or outputs and hence we assume that
Designing anartificial neural networkmodel for the prediction of thrombo-embolic stroke
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ABSTRACT In this study, a functional model of ANN is proposed to aid existing diagnosis methods. This work investigated the use of Artificial Neural Networks (ANN) in predicting the Thrombo-embolic stroke disease. The Backpropogation algorithm was used to train the ANN
Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform andartificial neural network
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ABSTRACT Cardiac Arrhythmias shows a condition of abnormal electrical activity in the heart which is a threat to humans. This paper presents a method to analyze electrocardiogram (ECG) signal, extract the features, for the classification of heart beats
Artificial neural networkaided image analysis system for cell counting
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Background: In histological preparations containing debris and synthetic materials, it is difficult to automate cell counting using standard image analysis tools, ie, systems that rely on boundary contours, histogram thresholding, etc. In an attempt to mimic manual cell
Optical character recognition (OCR) for printed devnagari script usingartificial neural network
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ABSTRACT There are about 300 million people in India who speak Hindi and write Devnagari script. Research in Optical Character Recognition (OCR) is popular for its application potential in banks, post offices, defense organizations and library automation etc.
Monthly inow forecasting using autoregressivearticial neural network
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Abstract: There are many forecasting models, but not all of them are able to monthly inow forecasting. In this study, the abilities of static and dynamicartificial neural networkmodel for Dez reservoir inow forecasting compared. The 47-years monthly discharges used, so that
Sensory evaluation of virgin olive oils byartificial neural networkprocessing of dynamic head-space gas chromatographic data
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Abstract: A different approach to the traditional sensory method was used for the sensory quality evaluation of virgin olive oils. Two hundred and four oil samples differing in their quality, and extracted from olives of various varieties, ripeness, sanitary state and
Application of multiple regression andartificial neural networktechniques to predict shear wave velocity from wireline log data for a carbonate reservoir South
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Abstract Estimation of shear wave velocity (Vs) using log data is an important approach in the seismic exploration and characterization of a hydrocarbon reservoir. So far all the available empirical models for Vs prediction are mathematical models that incorporate only
An analysis of the performance ofartificial neural networktechnique for stock market forecasting
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AbstractIn this paper, we showed a method to forecast the daily stock price using neural networks and the result of the Neural Network forecast is compared with the Statistical forecasting result. Stock price prediction is one of the emerging field in neural network
A new approach for the short-term load forecasting with autoregressive andartificial neural networkmodels
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Abstract: In this paper, a new approach to the short-term load forecasting using autoregressive (AR) andartificial neural network(ANN) models is introduced and applied to the power system of Turkey by using the consumption values of electrical energy for three
Image classification using support vector machine andartificial neural network
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AbstractImage classification is one of classical problems of concern in image processing. There are various approaches for solving this problem. The aim of this paper is bring together two areas in which areArtificial Neural Network(ANN) and Support Vector Machine
-TECHNOLOGY-RESEARCH PAPER-SOFTWARE SALES SERVICE on basic ofartificial neural network
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AbstractAnArtificial Neural Network(ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is
Artificial Neural Networkfor Precipitation and Water Level Predictions of Bedup River.
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Bedup River with estimations made to absent precipitation data, both usingArtificial Neural Network(ANN). Studies to predict water level in the state of Sarawak, Malaysia have been actively carried out. However, among problem faced was absent precipitation readings,
Feature extraction and automatic recognition of plant leaf usingartificial neural network
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Abstract. Plant recognition is an important and challenging task. Leaf recognition plays an important role in plant recognition and its key issue lies in whether selected features are stable and have good ability to discriminate different kinds of leaves. From the view of plant
Comparison ofartificial neural networktransfer functions abilities to simulate extreme runoff data
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Abstract. Approximately most of rainfall-runoff models have a good performance, especially where rainfall and obtained runoff data are near to average in standard normal distribution. While in the term of hydrology when modelling is the issue, simulation of extreme data will
Prediction of Nigerian crude oil viscosity usingartificial neural network
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Abstract The viscosity parameter is a very important fluid property in reservoir engineering computations. It should be determined in the laboratory but most of the time; the data is not either reliable or unavailable. Hence, empirical correlations were derived to estimate them.
Anartificial neural networkapproach for credit risk management
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ABSTRACT The objective of the research is to analyze the ability of theartificial neural networkmodel developed to forecast the credit risk of a panel of Italian manufacturing companies. In a theoretical point of view, this paper introduces a literature review on the
A comparison study for intrusion database (Kdd99, Nsl-Kdd) based on self organization map (SOM)artificial neural network
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Abstract Detecting anomalous traffic on the internet has remained an issue of concern for the community of security researchers over the years. The advances in the area of computing performance, in terms of processing power and storage, have fostered their ability to host
Anartificial neural networkmodel to forecast exchange rates
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ABSTRACT For the purposes of this research, the optimal MLP neural network topology has been designed and tested by means the specific genetic algorithm multi-objective Pareto- Based. The objective of the research is to predict the trend of the exchange rate Euro/USD
Improved Ferrite Number Prediction in Stainless Steel Arc Welds Using Artificial Neural Networks-Part 1: Neural Network Development
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Although process variables will not be considered in this study, it is assumed that a robust artificial neural networkbased on com- position alone will provide a solid foun- dation for including process variables in the future. Neural Network Theory
Modeling and prediction of rainfall usingartificial neural networkand ARIMA techniques
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ABSTRACT Climate and rainfall are highly non-linear and complicated phenomena, which require sophisticated computer modelling and simulation for accurate prediction. An artificial intelligence technology allows knowledge processing and can be used. as forecasting tool.
Issues in development ofartificial neural networkbased control chart pattern recognition schemes
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Abstract Control chart pattern recognition has become an active area of research since late 1980s. Much progress has been made, in which there are trends to heighten the performance ofartificial neural network(ANN)-based control chart pattern recognition
Artificial neural networkmodel for forecasting foreign exchange rate
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AbstractThe present statistical models used for forecasting cannot effectively handle uncertainty and instability nature of foreign exchange data. In this work, anartificial neural networkforeign exchange rate forecasting model (AFERFM) was designed for foreign
Rainfall-Runoff prediction based onartificial neural network(A case study: Jarahi Watershed)
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Abstract: The present study aims to utilize anArtificial Neural Network(ANN) to modeling the rainfallrunoff relationship in a catchment area located in a semiarid region of Iran. The paper illustrates the applications of the feed forward back propagation for the rainfall forecasting
A new scientific approach of intelligentartificial neural networkengineering for predicting shelf life of milky white dessert jeweled with pistachio
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AbstractThis paper highlights the capability of artificial neural networks for predicting shelf life of milky white dessert jeweled with pistachio. Linear layer (train) and generalized regression models were developed and compared with each other. Neurons in each hidden
Comparison ofartificial neural networkand multiple linear regression in the optimization of formulation parameters of leuprolide acetate loaded liposomes
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ABSTRACT. Purpose: We planned to optimize the effect of formulation variables on the percent drug entrapment (PDE) of the liposomes encapsulating leuprolide acetate by reverse phase evaporation method usingArtificial neural network(ANN) and Multiple linear
Prediction of melting point for drug-like compounds using principal component-genetic algorithm- artificial neural network
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Principal component-genetic algorithm-multiparameter linear regression (PC-GA-MLR) and principal component-genetic algorithm- artificial neural network(PC-GA-ANN) models were applied for prediction of melting point for 323 drug-like compounds. A large number of
One hour ahead load forecasting usingartificial neural networkfor the western area of saudi arabia
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AbstractLoad forecasting has become in recent years one of the major areas of research in electrical engineering. Most traditional forecasting models and artificial intelligence neural network techniques have been tried out in this task. Artificial neural networks (ANN) have
Genetic algorithm based weights optimization ofartificial neural network
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Abstract: To develop an accurate process model usingArtificial Neural Network(ANN), the learning process or training and validation are among the important steps. In the training process, a set of input-output patterns is repeated to the ANN. From that, weights of all the
Short-term load forecasting usingartificial neural network
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Abstract-- Artificial neural network(ANN) has been used for many years in sectors and disciplines like medical science, defence industry, robotics, electronics, economy, forecasts, etc. The learning property of ANN in solving nonlinear and complex problems called for its
Forecasting the Istanbul stock exchange national 100 index using anartificial neural network
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HE stock market has been one of the most popular investments owing to its high returns. On the other hand, there is some risk to investment in the stock market due to its unpredictable behaviors. Thus, an intelligent prediction model for stock market forecasting would be
Artificial neural networkbased prosody models for Finnish text-to-speech synthesis
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ABSTRACT This thesis presents a series of experiments conducted on Finnish prosody for text-to-speech synthesis using artificial neural networks. The study serves the purpose of mapping and extracting out the relevant factors that have an effect on prosody in general be
Comparison of response surface methodology andartificial neural networkin predicting the microwave-assisted extraction procedure to determine zinc in fish
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ABSTRACT In this paper, the estimation capacities of the response surface methodology (RSM) andartificial neural network(ANN), in a microwave-assisted extraction method to determine the amount of zinc in fish samples were investigated. The experiments were
Junction extraction byartificial neural networksystem-JEANS
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ABSTRACT: The paper presents a road junction operator, which was developed for medium resolution black-and-white orthoimages. The operator uses a feed-forward neural network applied for a running window to decide whether it contains a 3-or 4-arm road junction or not.
Anartificial neural networkmodel for neonatal disease diagnosis
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Abstract The significance of disease diagnosis by artificial intelligence is not obscure now a day. The increasing demand ofArtificial Neural Networkapplication for predicting the disease shows better performance in the field of medical decision making. This paper
Brain cancer classification using GLCM based feature extraction inartificial neural network
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AbstractBrain tumor is one of the major reasons of death among people. It is indication that the chances of survival can be greater than before if the tumor is detected correctly at its early stage. This paper classifies the type of tumor usingArtificial Neural Network(ANN) in
An UnsupervisedArtificial Neural NetworkMethod for Satellite Image Segmentation.
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Abstract: Image segmentation is an essential step in image processing. The goal of segmentation is to simplify and/or to change the representation of an image into a form easier to analyze. Many image segmentation methods are available but most of these
Handwritten devanagari character recognition usingartificial neural network
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ABSTRACT The reading skills of computer are still way behind that of human beings. Most character recognition systems cannot read degraded documents and handwritten characters or words. Devanagari, an alphabetic script, is used by over 500 million people all over the -SOFTWARE SALES SERVICE-https://www.engpaper.net--