SCHOOL CA 90032, USA The paper demonstrating about

 

 

 

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ASSIGNMENT

  LITERATURE REVIEW

Submitted by: 
Namitha sudhakaran

Roll: 178920

Subject: business research methodology

Submitted to: Dr. Ritanjali Majhi

Date of submission: 28th of january, 2018

Running Head: artificial neural networks in business

 

 

 

 

Literature
review

 

Namitha
sudhakaran

 

National  Institute 
of  Technology,  Warangal

 

 

 

 

 

 

 

 

 

 

 

 

 

 

                                               

                                                        
 ABSTRACT

Artificial neural networks are commonly used in
business but the studies and finding regarding that is very few in number. They
are connected nodes or units and each unit passes a  signal through it. Here i am trying to go
through the main functional areas and uses of artificial neural networks by
reviewing the literature papers on the topic. In the past decade it grow up and
performing many activities in wide variety of areas. reviewed more than 6
papers to find out different functional areas and usages of artificial neural
network.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

                                                
INTRODUCTION

Utilization of neural networks are largely increased
in the two decades. Artificial neural networks are computational structures
that are used to emulate the knowledge in the central nervous system. Here I am
trying to analyze the recent papers in artificial neural network to prepare a
literature review on the papers that are already presented. i had gone through
some papers and trying to convey the main ideas of the particular one. They
have high efficiency and easily adaptable to use in different kind of analysis.
Most applications of this can be published in bankruptcy prediction  and stock forecasting. Most common research
area of artificial neural network should be come under finance in future. All the studies revealing the  importance of this artificial intelligence
method and illustrate about  recent
research for both academics and practitioners. This review paper not only
emphasizes the historical progressions in the field of neural networks, it
discusses the prospective development in the neural network research areas.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Paper1: Artificial
Neural Networks: State of the Art in Business Intelligence

Author :Sunil Sapra.Department
of Economics and Statistics, California State University, Los Angeles, CA
90032, USA

The paper demonstrating about how ANN is used for
business and the important of ANN in business forecasting.ANNs are an excellent
tool for forecasting, but their results are difficult to interpret since ANNs
introduce complex interactions. In the absence of appropriate controls, the
ANNs can over-fit the data producing overoptimistic predictions. ANNs work very
well for large complex data sets in comparison with statistical methods. A key
weakness of the ANNs  is that they do not
possess sound statistical theory for inference, diagnostics, and model
selection. ANNs used carefully, can outperform statistical methods for certain
problems. in some areas ANN failed to do up to the expectation. things have
changed over the past few years due to the feasibility of deep networks made
possible by new training techniques, availability of billions of documents,
images and videos available for training purposes with the rise of internet,
and the realization that graphical processing units (GPUs), the specialized
chips used in PCs and videogame consoles to generate graphics, are also well
suited to modeling neural networks. With deeper networks, more training data
and powerful new hardware, deep neural networks have made rapid progress in the
areas of speech recognition, image classification and language translation. now
it cover almost all areas and performing up to the expectation.

PAPER 2: Deep
learning in neural networks: An overview

AUTHOR: jurgen schmidhuber

this paper dealing with historical survey of usage
and popularity of artificial neural network In recent years, deep artificial
neural networks have won numerous contests in pattern recognition and machine
learning. This historical survey compactly summarizes relevant work, Shallow
and Deep Learners are distinguished by the depth of credit assignment paths
which are chains of possibly learnable, normal links between actions and
effects. deep supervised learning , unsupervised learning, reinforcement
learning & evolutionary computation, and indirect search for short programs
encoding deep and large networks. and their usage on different historical
survey areas.

Paper 2: Improved system identification using
artificial neural networks and analysis of individual differences in responses
of an identified neuron

Author: AliciaCo stalago Meruelo
 David M.Simpson Sandor M.Veres  Philip L Newlan

This paper deals with the modeling and process of
artificial neural networks mathematical modelling is used to understand the
coding properties and dynamics of responses of neurons and neural
networks.  analyze the effectiveness of
Artificial Neural Networks (ANNs) as a modeling tool for motor neuron
responses.  ANNs used  to model the synaptic responses of an
identified motor neuron, the fast extensor motor neuron, of the desert locust
in response to displacement of a sensory organ, the femoral chord tonal organ,
which monitors movements of the tibia relative to the femur of the leg. The aim
of the study was three, first to determine the potential value of ANNs as tools
to model and investigate neural networks, second to understand the
generalization properties of ANNs across individuals and to different input
signals and third, to understand individual differences in responses of an
identified neuron. The performance of the models generated by the ANNs was compared
with those generated through previous mathematical models of the same neuron.
The results suggest that ANNs are significantly better than LNL and Wiener
models in predicting specific neural responses to Gaussian White Noise, but not
significantly different when tested with sinusoidal inputs. They are also able
to predict responses of the same neuron in different individuals irrespective
of which animal was used to develop the model, although notable differences
between some individuals were evident. this paper is all about the application
of ANN in the medical industry.

 

Paper3: Failure load prediction of single lap
adhesive joints using artificial neural networks

The objective of this paper was to predict the
failure load in single lap adhesive joints subjected to tensile loading by
using artificial neural networks. Experimental data obtained single lap
adhesive joints with various geometric models under the tensile loading. The
data are arranged in a format such that two input parameters cover the length
and width of bond area in single lap adhesive joints and the corresponding
output is the ultimate failure load. An artificial neural network model was
developed to estimate relationship between failure loads by using geometric
dimensions of bond area as input data. A three-layer feed forward artificial
neural network that utilized a particular algorithm model was used in order to
train network. It was observed that artificial neural network model can
estimate failure load of single lap adhesive joints with acceptable error. The
results showed that the artificial neural network is an efficient alternative
method to predict the failure load of single lap adhesive joints.

 

 

Papre4: Artificial neural networks in
business: Two decades of research

Author : MichalTká?
  Robert vernor

This paper dealings with the research studies and
progress that had already happened in the area of artificial neural network and
what are the areas and upcoming trends that the artificial intelligence are
covered when the business trends are developed more and more, this paper
include the literature review of the papers which are already  done in the field of ANN artificial neural
networks have been extensively used in many business applications. Despite the
growing number of research papers, only few studies have been presented
focusing on the overview of published findings in this important and popular
area. Moreover, the majority of these reviews were introduced more than 15
years ago. The aim of this work is to expand the range of earlier surveys and
provide a systematic overview of neural network applications in business
between 1994 and 2015. We have covered a total of 412 articles and classified
them according to the year of publication, application area, type of neural network,
learning algorithm, benchmark method, citations and journal. Our investigation
revealed that most of the research has aimed at financial distress and
bankruptcy problems, stock price forecasting, and decision support, with
special attention to classification tasks. Besides conventional multilayer feed
forward network with gradient descent back propagation, various hybrid networks
have been developed in order to improve the performance of standard models.
Even though neural networks have been established as well-known method in
business, there is enormous space for additional research in order to improve
their functioning and increase our understanding of this influential area.

 

 

Paper5: Studying the Effect of Activation
Function on Classification Accuracy Using Deep Artificial Neural Networks

Author: Serwa A ,Faculty of
Engineering in El- Mataria

Artificial Neural Networks (ANN) is widely used
in remote sensing applications. Optimizing ANN still an enigmatic field of
research especially in remote sensing. This reaserch work is a trial to
discover the ANN activation function to be used perfectly in classification the
first step is preparing the reference map then assume a selected activation
function and receive the ANN  output. The
last step is comparing the output with the reference to reach the accuracy
assessment. The research result is fixing the activation function that is
perfect to be used in remote sensing classification. A real multi-spectral
Landsat 7 satellite images were used and was classified and the accuracy of the
classification was assessed with different activation functions. The sigmoid
function was found to be the best activation function. and the entire paper is
dealing with the different kind and area of application of ANN in remote sensing.

 

 

 

 

 

 

 

 

 

 

Paper6 : Artificial
Neural Networks Controller for Crude Oil Distillation Column of Baiji Refinery

Authors :Duraid Fadhil
Ahmed and Ali Hussein Khalaf

This paper is dealing with a specific application of
ANN in the area of crude oil distillation column of a particular refinery and
how the process are going on there with the help of ANN.A neural networks
controller is developed and used to regulate the temperatures in a crude oil
distillation unit. Two types of neural networks are used; neural networks
predictive and nonlinear autoregressive moving average (NARMA-L2) controllers.
The neural networks controller that is implemented in the neural network
toolbox software uses a neural network model of a nonlinear plant to predict
future plant performance. Artificial neural network in MATLAB simulator is used
to model Baiji crude oil distillation unit based on data generated from
aspen-HYSYS simulator. A comparison has been made between two methods to test
the effectiveness and performance of the responses. The results show that a
good improvement is achieved when the NARMA-L2 controller is used. Also shown
priority of neural networks NARMA-L2 controller which gives less offset value
and the temperature response reach the steady state value in less time with
lower over-shoot compared with neural networks predictive controller.

 

 

Paper 7: applications of artificial neural
networks for medical diagnostics and prognostics

Jaouher Ben Ali  
University of Sousse, Tunisia

 

Application of ANN in the medical field is already
discussed previously since it is one or other way related with business aspects
again we have to go to in detail on the another application In the medical
field, diagnostic and prognostic remain the most important step to identify
disease type and thereby define the adequate treatment before reaching
catastrophic and fatal states. However, clinical symptoms and syndromes are not
sufficient to detect some diseases. Consequently, the definition of new
advanced techniques for medical diagnostics and prognostics are becoming of
great interest to assist specialists in clinical researches and hence to ensure
safety for millions of people. Artificial neural networks (ANNs) are inspired
by the way that the brain performs computations: they are classified as one of
the best and most used soft computing techniques. In this context, two
innovative methods for early-stage Alzheimer’s disease diagnosis and blood
glucose level prediction of Type 1 diabetes prediction and other cancer image
analysis will be presented, as well as the result interpretation and some case
studies. The aim of this work is to show the great assistance provided by these
advanced techniques to the medical staff where the big data are processed
through a trained ANNs leading accurate statistics leading suitable diagnostic
decision making

 

 

 

 

                                             
CONCLUSION

Artificial neural networks have been taken an
enormous attention in last two decades. Much of the research has focused on
various business disciplines, however, only a small number of surveys have been
published in this area. Presented paper has examined 412 neural network
applications in different areas of business published between 1994 and 2015 in
well-known influential journals.

Proper integration of met heuristic methods into the
neural network methodology might be a key for achieving the optimal
performance. In general, neural networks have been successfully applied in wide
range of business tasks and were able to detect complex and nonlinear
relationships without requiring any specific assumptions about the distribution
or characteristics of the data.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

REFERANCE

·       Artificial
Neural Networks: State of the Art in Business Intelligence Sunil Sapra.Department of Economics
and Statistics, California State University, Los Angeles, CA 90032, USA

·       of
artificial neural networks for medical diagnostics and prognostics

·       Artificial
neural networks in business: Two decades of research  Author : MichalTká?   Robert vernor

·       Deep
learning in neural networks: An overview 
AUTHOR: jurgen schmidhuber

·       Improved
system identification using artificial neural networks and analysis of individual
differences in responses of an identified neuron   Author: Alicia Co stalago Meruelo  David M.Simpson Sandor M.Veres  Philip L Newlan

 

·       Artificial
neural networks in business: Two decades of research Michal Tká?c1, Robert
Verner? University of Economics in Bratislava, Department of Quantitative
Methods, Tajovského 13, 04013 Ko?sice, Slovakia