Data Classification Using Neural Networks for Bacteria Analysis

SHEFFIELD HALLAM UNIVERSITY
FACULTY OR DEPARTMENT
55-700241 APPLICABLE ARTIFICIAL INTELLIGENCE
FIRST SIT COURSEWORK
DATA CLASSIFICATION USING NEURAL NETWORKS
ACADEMIC SESSION 2017-2018
STUDENT NAME
STUDENT REGISTRATION NUMBER
DATE OF SUBMISSION

ABSTRACT
This paper seeks to perform classification of bacteria data obtained from the lab experiment
carried out. The analysis and research experiment is performed using the classification learner
application of the MATLAB r2017a software. Neural networks are a key branch in artificial
intelligence (Helena, et al., 2003). The system teaches that the data collected can be used to
execute tasks instead of programming computational systems to perform definite tasks. The
training using different inputs improves the performance of the classifier ensuring that the data
set on the bacteria electrochemical reaction is as close as possible to the set targets. The results
and analysis or evaluation section captures the results on the errors encountered plotted in
histogram, performance scales, confusion figures and ROC.
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TABLE OF CONTENTS
ABSTRACT....................................................................................................................................1
INTRODUCTION...........................................................................................................................3
REQUIREMENTS ANALYSIS......................................................................................................7
DESIGN CONSIDERATIONS.......................................................................................................8
IMPLEMENTATION AND TESTING..........................................................................................9
EVALUATION.............................................................................................................................19
CONCLUSION..............................................................................................................................22
BIBLIOGRAPHY..........................................................................................................................23
APPENDIX....................................................................................................................................26
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INTRODUCTION
There are five classes of bacteria under study in this research paper. An artificial neural
network is made up of many artificial neurons which are correlated together in accordance with
explicit network architecture. The objective of the neural network is to convert the inputs into
significant outputs. The teaching mode can be supervised or unsupervised. The Escherichia coli
is a gram-negative rod from the family Enterobacteriaceae. Most of these bacteria are found in
the intestinal tract. The EHEC is a subset of pathogenic E. coli that can cause diarrhea or
hemorrhagic colitis in humans. The Hemorrhagic colitis occasionally progresses to hemolytic
uremic syndrome which is an important cause of acute renal failure in children and the morbidity
and mortality in adults. The pathogenic strains of the organism are distinguished from the normal
flora by their possession of the virulence factors such as exotoxins.
Figure 1 Colorized scanning electron micrograph depicting the Escherichia coli: CDC Public
Health Image Library
The bacteria data was obtained from a medical chart and is represented using
multidimensional datasets. The classification of the dataset as well as its clustering is required
and is significant in the study and analysis of data (Cacoullos).
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Data Classification in Neural Networks
Neural networks are a key branch in artificial intelligence (Helena, et al., 2003). The system
teaches that the data collected can be used to execute tasks instead of programming
computational systems to perform definite tasks. For the data provided on the five classes of
bacteria, using the ANN, one can quickly establish a pattern in the data that may be useful in
decision making or to make a conclusion about a given behavior based on the phenomenon
(R.Furferi, 2011). The models are pragmatic and they are useful in the field of medical diagnosis
with relationships draw from very dissimilar data using the artificial intelligence techniques.
Some of the key applications of the neural networks are in the detection of faults in systems,
product inspection, speech recognition and in financial systems to determine a bankruptcy (Yu-
guo & Hua-peng, 2010).
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There are a number of algorithms that can be employed in training and testing the data
that needs to be classified such as the back propagation neural network and the multilayer
feedforward network (Hajmeer M. B., 2006). A feedforward multiple layer neural network in
this case will classify the data as set in the separate classes from the 37 instances of the data tests.
The technique is limited to a given range of performance, cost-benefit analysis, and
implementation (Saravanan & Sasithra, 2014). The major disadvantage in using ANN is to find
the most appropriate grouping of training, learning and transfer function for classifying the data
sets with growing number of features and classified sets. The most preferred method for ANN
dataset classification is the back-propagation algorithm (Zhang, 2000). It has the best
combination of training, learning and transfer function for the classification of dataset.
Unfortunately, the combination does not support very large data set. The data set in this paper
only covers 37 samples which are tested over different scales in the electrochemical reaction.
The back-propagation algorithm (Priyadarshini, 2010) was developed by Rojalina Priyadarshini.
The BPNN is considered to have a highly predictive ability with stable and well-functioning
constructs that are useful in the classification of the data.
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