neural network research papers-22

Identification of 72 phytoplankton species by radial basis function neural network analysis of flow cytometric data
L Boddy, CW Morris, MF Wilkins ,Marine Ecology , 2000 ,

ABSTRACT: Radial basis function artificial neural networks (ANNs) were trained to
discriminate between phytoplankton species based on 7 flow cytometric parameters
measured on axenic cultures. Comparison was made between the performance of 

A higher order Bayesian neural network with spiking units
A Lansner ,International Journal of Neural Systems, 1996 ,

We treat a Bayesian con?dence propagation neural network, primarily in a classi?er context.
The one-layer version of the network implements a naive Bayesian classi?er, which requires
the input attributes to be independent. This limitation is overcome by a higher order 

Altering the synchrony of stimulus trace processes: Tests of a neural-network model
JE Desmond, JW Moore ,Biological cybernetics, 1991 ,Springer

A previously described neural-network model (Desmond 1991; Desmond and Moore 1988;
Moore et al. 1989) predicts that both CS-onset-evoked and CS-offset-evoked stimulus trace
processes acquire associative strength during classical conditioning, and that CR 

A neural network model for estimating option prices
M Malliaris ,Applied Intelligence, 1993 ,Springer

A neural network model that processes financial input data is developed to estimate the
market price of options at closing. The network’s ability to estimate closing prices is
compared to the Black-Scholes model, the most widely used model for the pricing of 

Classification of asteroid spectra using a neural network
ES Howell, E Merenyi , RESEARCH-ALL SERIES-, 1994 ,

Abstract. The 52-color asteroid survey (Bell et al., 1988) together with the 8-color asteroid
survey (Zellner et al., 1985) provide a data set of asteroid spectra spanning 0.3-2.5 pm. An
artificial neural network clusters these asteroid spectra based on their similarity to each 

Neural network weight selection using genetic algorithms
DJ Montana ,Intelligent Hybrid Systems, 1995 ,

Page 1. Neural Network Weight Selection Using Genetic Algorithms David Montana presented
by:  14 Page 15. Weighted Probabilistic Neural Network (WPNN) • WPNN is a pattern classification
algorithm which falls into the broad class of ”nearest-neighbor-like” algorithms. 

Adaptive regularization in neural network modeling
C Svarer, L Andersen  Neural Networks: Tricks of , 1998 ,Springer

In this paper we address the important problem of optimizing regularization parameters in
neural network modeling. The suggested optimization scheme is an extended version of the
recently presented algorithm [25]. The idea is to minimize an empirical estimate-like the 

Neural network models of categorical perception
RI Damper ,Attention, Perception, & Psychophysics, 2000 ,Springer

Abstract Studies of the categorical perception (CP) of sensory continua have a long and rich
history in psychophysics. In 1977, Macmillan, Kaplan, and Creelman introduced the use of
signal detection theory to CP studies. Anderson and colleagues simultaneously proposed 

Real-time neural network processing of gestural and acoustic signals
M Lee, A Freed ,Proceedings of the International , 1991 ,

Abstract We have added a new object to the MAX language to perform neural computations.
By placing the neural processing in the context of a flexible realtime musical programming
environment, we can conduct experiments rapidly on the application of the adaptive 

Prediction of protein structural classes by neural network
YD Cai ,Biochimie, 2004 ,

The results observed by Muskal and Kim [1] suggested that the structural class of a protein
may basically depend on its amino acid composition. Many efforts [2–14] have been made to
predict the structural class of a protein based on its amino acid composition. For a 

Evolving neural network agents in the NERO video game
Proceedings of the , 2005 ,

Abstract-In most modern video games, character behavior is scripted; no matter how many
times the player exploits a weakness, that weakness is never repaired. Yet if game
characters could learn through interacting with the player, behavior could improve during 

Editing training data for kNN classifiers with neural network ensemble
Y Jiang ,Advances in Neural Networks–ISNN 2004, 2004 ,Springer

Since kNN classifiers are sensitive to outliers and noise contained in the training data set,
many approaches have been proposed to edit the training data so that the performance of
the classifiers can be improved. In this paper, through detaching the two schemes adopted 

Reducing fitness evaluations using clustering techniques and neural network ensembles
Genetic and Evolutionary Computation–GECCO 2004, 2004 ,Springer

In many real-world applications of evolutionary computation, it is essential to reduce the
number of fitness evaluations. To this end, computationally efficient models can be
constructed for fitness evaluations to assist the evolutionary algorithms. When 

Associative memory on a small-world neural network
LG Morelli, G Abramson ,The European Physical Journal , 2004 ,Springer

We study a model of associative memory based on a neural network with small-world
structure. The efficacy of the network to retrieve one of the stored patterns exhibits a phase
transition at a finite value of the disorder. The more ordered networks are unable to 

A neural network based system for intrusion detection and classification of attacks
M Moradi ,Queen University, Canada, 2004 ,

Abstract With the rapid expansion of computer networks during the past decade, security
has become a crucial issue for computer systems. Different soft-computing based methods
have been proposed in recent years for the development of intrusion detection systems. 

Breathing pulses in an excitatory neural network
SE Folias ,SIAM J. Appl. Dyn. Syst, 2004 ,

Abstract. In this paper we show how a local inhomogeneous input can stabilize a stationary-
pulse solution in an excitatory neural network. A subsequent reduction of the input amplitude
can then induce a Hopf instability of the stationary solution resulting in the formation of a 

Multilayer feedforward neural network based on multi-valued neurons (MLMVN) and a backpropagation learning algorithm
Soft Computing-A Fusion of Foundations, , 2007 ,Springer

Abstract A multilayer neural network based on multi-valued neurons (MLMVN) is considered
in the paper. A multi-valued neuron (MVN) is based on the principles of multiple-valued
threshold logic over the field of the complex numbers. The most important properties of 

A neural-network-based methodology for the prediction of surface roughness in a turning process
A Kohli ,The International Journal of Advanced Manufacturing , 2005 ,Springer

A neural-network-based methodology is proposed for predicting the surface roughness in a
turning process by taking the acceleration of the radial vibration of the tool holder as
feedback. Upper, most likely and lower estimates of the surface roughness are predicted 

Electronic nose and neural network use for the classification of honey
S Benedetti, S Mannino, AG Sabatini ,Apidologie, 2004 ,

Abstract–Seventy samples of honey of different geographical and botanical origin were
analysed with an electronic nose. The instrument, equipped with 10 Metal Oxide
Semiconductor Field Effect Transistors (MOSFET) and 12 Metal Oxide Semiconductor (

A neural network model of chemotaxis predicts functions of synaptic connections in the nematode Caenorhabditis elegans
NA Dunn, SR Lockery, JT Pierce-Shimomura ,Journal of , 2004 ,Springer

The anatomical connectivity of the nervous system of the nematode Caenorhabditis elegans
has been almost completely described, but determination of the neurophysiological basis of
behavior in this system is just beginning. Here we used an optimization algorithm to 

Single hidden layer artificial neural network models versus multiple linear regression model in forecasting the time series of total ozone
G Bandyopadhyay ,Int. J. Environ. Sci. Tech, 2007 ,

ABSTRACT: Present paper endeavors to develop predictive artificial neural network model
for forecasting the mean monthly total ozone concentration over Arosa, Switzerland. Single
hidden layer neural network models with variable number of nodes have been developed 

Discriminative training of a neural network statistical parser
ACL’04, 2004 ,

Abstract Discriminative methods have shown significant improvements over traditional
generative methods in many

Machine learning application to the Korean freshwater ecosystems
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This paper considers the advantage of Machine Learning (ML) implemented to freshwater ecosystem research. Currently, many studies have been carried out to find the patterns of environmental impact on dynamics of communities in aquatic ecosystems. Ecological

Designing a machine learning Based framework for enhancing performance of livestock mobile application system
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a real intelligent one. Figure 10. Typical learning. 4.3.3. Steps in Developing Machine Learning Application Below are steps that will be used in developing machine learning application . 1) Collect data. Data will be collected

On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning
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Page 1. On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning

Machine Learning Application for Stock Market Prices Prediction.
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The development of a vibrant application for analyzing and predicting stock market prices is a basic tool aimed at increasing the rate of investors interest in stock markets. This paper explains the development and implementation of a stock price prediction application using

Extremal Entropy: Information Geometry, Numerical Entropy Mapping, and Machine Learning Application of Associated Conditional Independences
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Entropy and conditional mutual information are the key quantities information theory provides to measure uncertainty of and independence relations between random variables. While these measures are key to diverse areas such as physics, communication, signal

Machine Learning Application to Improve COCOMO Model using Neural Networks
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Millions of companies expend billions of dollars on trillions of software for the development and maintenance. Still many projects result in failure causing heavy financial loss. Major reason is the inefficient effort estimation techniques which are not so suitable for the current

Machine Learning Methods Application to Search for Regularities in Chemical Data
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The success of approach that was put forward in IMET has given an impetus to many investigations which were connected with machine learning application to inorganic chemistry and materials science and carried-out in various countries

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Machine Learning is increasingly prevalent in Stock Market trading. The goal of this paper is to investigate whether the machine learning technique is able to retrieve information from past prices and predict price movement and future trends. We explore using trend trading

Machine learning application in optimization of flexible circuit configuration
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One solution is to place semi-rigid transistors on a flexible polymer substrate. Next, we can optimize the transistor placement on the substrate using particle swarm optimization to minimize some objective (area, factor of safety, etc.). This has been successfully performed

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In Todays world, most of world population has access to banking services. Consumers has increased many fold in last few years. For the banks, risks related to bank loans has increased especially after The Great Recession (2007 2012) and job threats due to

Machine Learning Application On Detecting Nudity In Images
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We built a mobile app that help people get opinions and recommendations from their social network. The app enables people to create picture poll and have friends to vote on it. Since it contains user-generated content, it is essential to censor the content to ensure that there is

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In recent years, mobile devices have developed significantly in terms of technical capabilities, computing power, storage capacity and ability of sensing different activities via intelligent built-in sensors. In this perspective, capabilities of ultimate mobile phone

A Machine Learning Application for Latency Prediction in Operational 4G Networks
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Measuring performance on Internet is always challenging. When it comes to the mobile networks, the variety of technology characteristics coupled with the opaque network configuration make the performance evaluation even a more difficult task. Latency is one of

Selection of indicators by machine learning : Application to estimate permanent grassland plant richness
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Indicator based on key species or other data from field observation are very useful to manage permanent grasslands or to control result-oriented agri-environment schemes. These indicators must generally fulfil the following features: purpose relevance (ie optimize

ILP for Cosmetic Product Selection-Use of Smart Phone for Real-World Machine Learning Application
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In this study, we design a real-world machine learning system using a smart phone. This system can acquire images taken with the camera of a smart phone using learners (ILP and SVM) and automatically diagnose the new image. To develop this system, we implement an

Analysis of Machine Learning Research and Application
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similarity category. This algorithm is simple and computational speed, particularly the faster speed of classification, when compared with other algorithms. C. Machine Learning application in Data Mining Machine learning methods

Building real time object detection iOS application using machine learning
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(NPL) with a limited number of APIs and is only available for iOS 11 and above . Figure 6. General machine learning application structure. Copied from Apple documentation forward usage. In order to start integrating machine learning application , one just needs

Data visualisation and machine learning web application with potential use in sports data analytics
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data 3. Can a machine learning application , such as the one implemented in this project, be used in sports science to make predictions about athletic performance using biomarker data 2 Page 11. 1.3.2 Research Objectives

Increasing Portable Machine Learning Performance by Application of Rewrite Rules on Google Tensorflow Data Flow Graphs
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3 1.1 Contributions The goal of this project is to be able to execute as much of an existing Google Ten- sorflow machine learning application within the data parallel programming language Lift. This approach allows the Lift programming language to find a method in order to
s, but there has been difficulty in
extending them to natural language parsing. One problem is that much of the work on 

Application of a radial basis function neural network for diagnosis of diabetes mellitus
P Venkatesan ,CURRENT SCIENCE- , 2006 ,

In this article an attempt is made to study the applicability of a general purpose, supervised
feed forward neural network with one hidden layer, namely. Radial Basis Function (RBF)
neural network. It uses relatively smaller number of locally tuned units and is adaptive in 

Modulation identification using neural network for cognitive radios
D Maldonado ,Software Defined Radio , 2005 ,

ABSTRACT This paper presents a signal modulation classifier design using artificial neural
networks. We analyze system-level issues including carrier synchronization, bandwidth
estimation, and modulation classification. This is an extension of previous work with the