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Journal of Basic and Applied Engineering Research
Print ISSN: 2350-0077; Online ISSN: 2350-0255; Volume 2, Number 11; April-June, 2015 pp. 1001-1005
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Advance Applications of Artificial Neural Network
Sujata Saini1 and Preeti2
1,2
Department of Computer Science and Application, Govt. College for Women, Rohtak
E-mail: [email protected], [email protected]
Abstract—This paper explores the various applications of neural
network. Neural network desire to produce artificial systems capable
of sophisticated computations similar to the human brain. The focus
of this paper is on the importance and the influence of different types
of neural schemas which played a critical role in business and
medical applications.
Neural network is one of the most important components in Artificial
Intelligence. It has been studied for many years in the hope of
achieving human-like performances in many fields like speech
reorganization and computer vision as well as information retrieval.
To make the term 'neural network' used in this paper clear to expand
considerably on its content, it is useful to analyze the general
structure of a neural network and explore the advantages and the
applications of neural network.
Beginning with a primarily definition and typical structure of neural
networks at different stages, Neural networks are studied with respect
to their learning processes and architecture structures like Human
beings in artificial intelligence. A case study on some specific
networks and related algorithms is followed and developed with
regarding neural objects. The applications of neural network models
and related algorithms in information retrieve systems are analyzed
as well as investigated.
widely applicable to risk management and forecasting. Since
the various neural-network systems now in use are
implemented with mathematically sound principles, they hold
out promise for future applications.
2. ARTIFICIAL NEURAL NETWORK
An artificial neural network does not emulate the thought
processes and if/ then logic of the human brain as done by an
expert system. The basic model assumes that information
processing takes place through the interaction of large number
of highly interconnected processing elements called neurons.
Researchers have developed a similar tool with same
mechanism as that of human brain works, and are called as
“Artificial Neural Networks” (ANN) An ANN consists of a
large number of simple processing elements-thatinterconnected.
1. INTRODUCTION
An artificial network is an interconnected group of nodes, akin
to the vast network of neurons in a brain. Each circular node
represents Neural Networks basically aim at mimicking the
structure and functioning of the human brain, to create
intelligent behavior. Researchers have attempted to build a
silicon-based electronic network that is modeled on the
working and form of the human brain. Neural networks can
performed successfully where other methods can not,
predicting system behavior, recognizing and matching
complicated, vague, or incomplete data patterns. Apply
Artificial Neural networks to pattern recognition,
interpretation, prediction, diagnosis, planning, monitoring,
debugging, instruction, repair, and control.
Recently, applications of artificial neural net- works have been
increasing in business and medical fields for last few years.
More and more development tools are emerging on the
market. Many neural-network systems have been shown to
work well in identifying intricate patterns, learning from
experience, reaching some conclusions, and making
predictions. Neural-network systems have already been at
work for over 10 years in the finance world. Now, they are
Fig. 1.1: Artificial neural network (structure of neuron)
3. NEURAL NETWORK ARCHITECTURES
There are three fundamental different classes of network
architectures:
1) Single-layer feed forward Networks :
The single
layered neural network where neurons are organized in
the form of layers(Fig. 1.2). In the simplest form of a
Sujata Saini and Preeti
1002
layered network, an input layer of source nodes that
projects onto an output layer of neurons, but not vice
versa. This network is strictly a feed forward type
network. In single-layered network, there are only one
input and one output layer. Input layer is not counted as a
layer whether no mathematical calculations take place at
this layer.
2) Multilayer feed forward Networks : The second class of
a feed forward neural network establishes itself by the
presence of one or more hidden layers(Fig. 1.3). The
function of hidden neuron is to intervened between the
external input and the network output in some useful
manner. The input signal applied to the neurons in the
second layer. In Multilayer, The output signal of the
second layer is used as inputs to the third layer, and so on
for the rest of the network.
3) Recurrent networks: A recurrent neural network has at
least one feedback loop(Fig. 1.4). A recurrent network
consists of a single layer of neurons with each neuron
feeding its output signal back to the inputs of all the other
neurons. Self-feedback refer to a situation where the
output of a neuron is fed back into its own input. The
presence of feedback loops have a profound impact on
the learning capability of the network and on its
performance.
( Graphical Representation )
Fig. 1.2: Single-layer Feed-forward Network
Fig. 1.4: Recurrent Network
4. APPLICATIONS OF ARTIFICIAL NEURAL
NETWORK
1) Regression analysis
Regression analysis is a statistical process used for estimating
the relationships among variables. It includes many techniques
for modeling and analyzing several variables, Regression
analysis is widely used for prediction and forecasting. where
its use have substantial overlap with the field of machine
learning. Regression analysis is usually used to understand
which among the independent variables are related to the
dependent variable, and to explore the forms of these
relationships.
2) Pattern and sequence recognition
Pattern and sequence recognition is a branch of machine
learning that focuses on the recognition of patterns and
regularities in data, although it is in few cases considered to be
nearly synonymous with machine learning. . In pattern
recognition, there may be a higher interest to formalize,
visualize and explain
the pattern. The terms pattern
recognition, machine learning, sequence recognition, data
mining and knowledge discovery in databases. Pattern
recognition has its origins in engineering and the term refer
from computer vision. Pattern recognition is generally
categorized according to type of learning procedure used to
generate the output value.
3) Swarm Intelligence
This is an approach to, as well as application of artificial
intelligence is similar to neural network. Programmers study
represent how intelligence emerges in natural systems like
swarms of bees even though on an individual level, a bee just
follows simple rules. They study relationships in nature like
some prey-predator relationships that give an insight in to
how intelligence emerges in a swarm or collection from
simple rules at an individual level.
4) Machines learning
Fig. 1.3: Multi-layer Feed-forward Network
In Machine learning and cognitive science, artificial neural
networks are a family of statistical learning algorithms
inspired by biological neural networks (the central nervous
Journal of Basic and Applied Engineering Research
Print ISSN: 2350-0077; Online ISSN: 2350-0255; Volume 2, Number 11; April-June, 2015
Advance Applications of Artificial Neural Network
systems of animals as well as humans, in particular the brain)
and are used to estimate or approximate functions in ANN. It
has strong ties to statistical and mathematical optimization,
which deliver methods, theory and application domains to the
field. Machine learning is also described as a scientific
discipline that explores the
construction and study
of algorithms that can learn from data. Such algorithms are
operate by building a model from example inputs and using
that to make predictions or decisions, rather than following
strictly static program instructions. Machine learning is
closely related to overlaps with computational statistics a
discipline that also specializes in prediction-making.
5) Social computing
As we know, Social problems vary form country to country.
So, researchers in the field of social science implement the
approaches in their native problems by their own ways. Huge
amount of data analysis is involved in many problems of that
kind. Whenever, Finding different patterns from this data,
inexact or hierarchical matching of patterns and making future
predictions for intelligent decision making are the real
challenges. By analysis of huge amount of data, application of
computational tools like Artificial Neural Network like
Association rules, Decision trees, Cluster Analysis are widely
used. Social computing use the approaches like Data
processing, including filtering, Clustering, Blind signal
separation and compression.
6) Stock market prediction
Neural networks , as well as artificial intelligence methods,
have become very important in making stock market
predictions. Much research on the applications of NNs for
solving business problems have proven their advantages over
statistical and other methods that do not include Artificial
intelligence, although there is no optimal methodology for a
certain problem. In order to identify the main benefits and
limitations of previous methods in NN applications and to find
connections between methodology and data models, problem
domains and results obtained a comparative analysis of
selected applications is conducted. It can be concluded from
analysis that Neural-nets are most implemented in forecasting
stock prices, returns, and stock modeling, and the most
frequent methodology is the Back-propagation algorithm.
The importance of neural-net integration with other artificial
intelligence methods is emphasized by numerous authors.
Inspite of many benefits, there are some limitations that
should be investigated, such as the relevance of the results,
and the "best" topology for the certain problems.
7) Medical Diagnoses
Evaluations of the key prognostic factors in different forms of
cancer have shown that we must have more precise therapy
guidelines and also more accurate prediction of the patients
outcome. Statistical analysis should be very useful for the
clinician as a tool which provide more clarity to the
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complicated classification systems, risk group categories or
therapeutic options. The TNM system is a key tool in
oncology, describing the anatomic extent of the different
forms of cancer are helpful to the clinician in the process of
therapeutic choice. The system has its own limitations,
although it has specifications for every organ location that
does not comprisemany newer markers or pathological
findings, which are necessary for specific diagnosis and
therapy. This is the main reason, why new prediction
instruments are needed which would adjust to every specific
clinical parameter, giving results of great accuracy. ANNs are
a possible solution, permitting to discover nonlinear
relationships between all the parameters (depend on each other
or independent), being superior to the logistic regression,
which need supplementary modeling in order to have a
comparable flexibility. With the speed and power of the actual
computer hardware and dedicated software, ANNs can easily
correlate different prediction factors. Find the hidden
interactions among variables, predict an outcome for a group
of patients, stratify patients in risk groups, or approximate a
function and complete a known pattern. Other possible
applications of the ANNs in medicine include, but are not
limited to the imaging, diagnosis, pathology and prognosis
evaluation of appendicitis, back pain, dementia, myocardial
infarction, arrhythmias, psychiatric disorders, acute pulmonary
embolism or sexually transmitted diseases.
8)
Heavy Industries and Space
Robotics and cybernetics have taken a leap combined step with
artificially intelligent expert systems. An entire manufacturing
process is totally automated now, controlled and maintained by
a computer system in car manufacture, machine tool
production, computer chip production and almost every hightech process.
9) Aviation
Generally, airlines use expert systems in planes to monitor
atmospheric conditions and system status. The plane can be
put on autopilot once any course is set for the fixed
destination.
10) Weather Forecast
Basically, Neural networks are used for predicting weather
conditions. Previous data is fto be fed for a neural network,
which learns the pattern and uses that knowledge to predict
weather patterns.
11) Gaming and Decision making
Artificial Neural Networks(ANNs) have been in wide use
since at least the 1980s for among other things, complex
modeling algorithm and various recognition, prediction and
filtering tasks. Their ability to learn and evolve has made them
attractive as well as efficient to many different fields of
research and innovation, including gaming.
As an opponent AI has a huge impact on a game's enjoybility,
the ability for such an opponent to learn and get better over
Journal of Basic and Applied Engineering Research
Print ISSN: 2350-0077; Online ISSN: 2350-0255; Volume 2, Number 11; April-June, 2015
Sujata Saini and Preeti
1004
time is intriguing. The actions of non-player-characters
(NPCs) in any games are either completely pre-determined or
rule-based and dependent on different conditions being met .
This can be done in such a manner that the NPC will still
appear very intelligent and life-like to the player, but often
there will be predictable and/or exploitable patterns which will
make the game easier than intended or make remind the player
too much that they are playing against a computer. This
approach can ruin the game for the player looking for an
immersive experience or a good challenge. This is why the
idea of learning and evolving NPC by means of an ANN, is
very interesting.
5. ADVANTAGES OF ARTIFICIAL NEURAL
NETWORK
1.
2.
3.
4.
5.
6.
7.
Adaptive learning:
An ability to learn that how to do tasks based
on the data given for training or initial
experience.
Self-Organization:
An Artificial Neural Network can create its own
organisation or representation of the information
it receives during learning time.
Real Time Operation:
An ANN computations may be carried out in
parallel and special hardware devices are being
designed and manufactured which take
advantage of this capability.
Pattern recognition is a powerful technique used for
harnessing the information in the data and generalizing
about it. Neural nets learn to recognize the different
patterns which existence is in the data set.
Generally, the system is developed through learning
rather than programming.. Neural nets teach themselves
patterns in the data freeing the analyst for more
interesting work.
Neural networks are flexible in a particular changing
environment. Neural networks may take some time to
learn a sudden drastic change where they are excellent at
adapting to constantly changing information.
Neural networks can build informative models whenever
the conventional approaches fail. Because neural
networks can handle very complex interactions so they
can easily model data which is too difficult to model with
traditional approaches such as inferential statistics or
programming.
conventional approaches. Depending on the nature of the
applications and the strength of the internal data patterns you
can generally expect a network to train quite well. This phase
applies to problems where the relationships may be quite
dynamic or non-linear. Artificial Neural Network provide an
analytical alternative to conventional techniques which are
often limited by strict assumptions of normality, linearity,
variable independence etc.
7. ACKNOWLEDGMENT
The research thesis was finished under the instruction of Dr.
Sudesh Lather. The student is grateful to her for her help in
the whole process. Special thanks also to Professor Nisha
Malik and Professor Suman for providing the reading list
and helpful comments. This work was supported in part by a
grant from the National Science Foundation.
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6. CONCLUSION
In this paper, we discussed about the Artificial neural
network, working of (ANN). Also training phases of an ANN,
there are various advantages of Artificial Neural network over
Journal of Basic and Applied Engineering Research
Print ISSN: 2350-0077; Online ISSN: 2350-0255; Volume 2, Number 11; April-June, 2015
Advance Applications of Artificial Neural Network
About the Author
Sujata saini was born in Rohtak, in 1993. Sujata
saini currently studying M.S.C Computer Science From G.C.W,Rohtak . She
received the B.S.C degree in Computer Science from Maharishi Dayanand
University, Rohtak in 2013 and her research experience includes 2 years as
Junior Researchist under Mrs. Anju Narwal (PHD Scholar) From Maharishi
Dayanand. She works in a multi disciplinary environment involving Software
Engineeering, Social Media and Communication, DBMS, AI, Neural
Networks, Genetic Algorithm, Machine Learning and Robotics .
Preeti was born in Delhi, in 1991. Preeti
currently studying M.S.C Computer Science From G.C.W,Rohtak . She
received the B.C.A degree in Computer Application from Maharishi
Dayanand University, Rohtak in 2013 and she works in a multi disciplinary
environment involving DBMS, Artificial Intelligence, Neural Networks and
Software Engineering .
Journal of Basic and Applied Engineering Research
Print ISSN: 2350-0077; Online ISSN: 2350-0255; Volume 2, Number 11; April-June, 2015
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