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\def\RunningAuthor{Sarmad M. Hadi et al.}
\firstPage{1944}
\articleType{Original Article}
\receivedDate{06.09.2019}
\acceptedDate{24.12.2019}
\revisedDate{17.12.2019}
\journalVolume{2020, 11}
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\title{Using Artificial Intelligence System for Pattern Identification Based on Features Extracted from Gel Electrophoresis Images}
\author{Sarmad~M.~Hadi\textsuperscript{*}\textsuperscript{1},  
            Al-Faiz~M~Z\textsuperscript{2}, 
            Ali~A.~Ibrahim\textsuperscript{1}~\\[5pt]{\textsuperscript{1}Al-Nahrain university-college of information engineering-information and communication engineering department- Iraq-Baghdad}~\\{\textsuperscript{2}Al-Farahidy University-Iraq-Baghdad}}

\begin{abstract}
Artificial intelligence has many branches of image processing-based applications in terms of classification and identification, error back-propagation neural network is a great match for such applications as long as linear vector quantization (LVQ) and pattern recognition is another great match for recognition of digital images based on their features. The dataset used in this paper are gel electrophoresis images where 6 features had been extracted from the images and used as input to a neural network for learning and then checked for recognition purposed and the system managed to recognize all the 6 images. Six features had been used: average, standard deviation, smoothness, skewness, uniformity, and entropy. A tiny error rate where allowed in the recognition program to cover the variation of the dataset and the test data (gel-electrophoresis images). The proposed system had successfully managed to identify all of the learned data in both LVQ and error-back-propagation. Error-back-propagation proved itself as a great tool in terms of learning time compared with LVQ, which was very slow in terms of learning time and recognition.
\end{abstract}\def\keywordstitle{Keywords}

\begin{keywords}neural networks,\newline linear vector quantization,\newline DNA,\newline gel electrophoresis,\newline Back-Propagation
\end{keywords}
\twocolumn[ \maketitle {\printKwdAbsBox}]
\makeatletter{\raggedright\textsuperscript{*}Corresponding Author\par Name:\ Sarmad~M.~Hadi~\\ Phone:\ +9647506896479~\\  Email:\ sarmad@coie-nahrain.edu.iq
        \par}\vspace*{-11pt}\hrulefill\par{\fontsize{12}{14}\selectfont ISSN: 0975-7538}\par% 
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\section{Introduction}
Learning Vector Quantization (LVQ) is a family of algorithms for statistical pattern classification, which aims at learning prototypes (codebook vectors) representing class regions \unskip~\citep{664249:15652083,664249:15652082}

Backpropagation is another tool in artificial neural networks where its main purpose to sum up errors and learn the network about the dataset and accumulate errors and then update a map of weights in single or multiple layers between input and output in a supervised environment.

The nature of operation in this paper is input-output mapping, where each input must be mapped to a unique and independent output.

The dataset used in this paper was real gel electrophoresis \unskip~\citep{664249:15652081} images and 6 features where extracted according to \unskip~\citep{664249:15652080}.

\textbf{Features extraction}

The extraction of features was done according to \unskip~\citep{664249:15652080}, where 6 features were extracted from the gel electrophoresis images, as shown in Table~\ref{tw-e7605cb2b92a},


\begin{table*}[!htbp]
\caption{\boldmath {Features extracted from gel images} }
\label{tw-e7605cb2b92a}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.2229\linewidth-2\tabcolsep}p{\dimexpr.3728000000000001\linewidth-2\tabcolsep}p{\dimexpr.4042999999999999\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}Feature name & Expression  & Description \\
\tblmidrule 
Avg &
  $f1=\mu $ \mbox{}\protect\newline  &
  The average intensity in a region of an object\\
Std &
  $f2=\sqrt{{\textstyle\sum_X}(x-f1)^\wedge2\;H(X)} $ &
  The standard deviation of intensity in a region of an object\\
Smoothness &
  $f3=1-\frac1{(1+f1^{2})} $ \mbox{}\protect\newline  &
  The relative smoothness of intensity in a region\\
Skewness &
  $f4={\textstyle\sum_X}(x-f1)^\wedge3\;H(x) $ &
  Deviation from the symmetry of mean intensity\\
Uniformity &
  $f5={\textstyle\sum_X}\;H^\wedge2(X) $ &
  Sum of a squared element in a histogram\\
Entropy &
  $f6=-\log_2\;H(X)\;{\textstyle\sum_{x\;}}H(X) $ &
  A statistical measure of uncertainty\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {comparison between LVQ and error-back-propagation} }
\label{tw-c61fe81c925a}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.1474\linewidth-2\tabcolsep}p{\dimexpr.25259999999999998\linewidth-2\tabcolsep}p{\dimexpr.20\linewidth-2\tabcolsep}p{\dimexpr.20\linewidth-2\tabcolsep}p{\dimexpr.20\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}Method & Time of learning  & Time of recognition  & Iteration ruired to learn  & Architecture \\
\tblmidrule 
LVQ &
  313 Seconds &
  0.174 Seconds &
  308 &
  384-769-6-6\\
BP &
  7.383 Seconds &
  0.116 Seconds &
  46 &
  384-769-1\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\bgroup
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\begin{figure*}[!htbp]
\centering \makeatletter\IfFileExists{images/041e5cee-6875-475c-a96a-3efa099c7224-upicture1.png}{\includegraphics{images/041e5cee-6875-475c-a96a-3efa099c7224-upicture1.png}}{}
\makeatother 
\caption{\boldmath {Gel electrophoreses image}}
\label{f-c25fbaec644f}
\end{figure*}
\egroup

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\begin{figure*}[!htbp]
\centering \makeatletter\IfFileExists{images/78d7fe9a-06d1-48a3-a9ee-08cb556e98bd-upicture2.png}{\includegraphics{images/78d7fe9a-06d1-48a3-a9ee-08cb556e98bd-upicture2.png}}{}
\makeatother 
\caption{\boldmath {The LVQ network structure}}
\label{f-f0a2a791f43e}
\end{figure*}
\egroup

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\begin{figure*}[!htbp]
\centering \makeatletter\IfFileExists{images/f30b99a7-cea1-401e-8d89-70ebffab9867-upicture3.png}{\includegraphics{images/f30b99a7-cea1-401e-8d89-70ebffab9867-upicture3.png}}{}
\makeatother 
\caption{\boldmath {NN architecture}}
\label{f-bf93c5dfbb9c}
\end{figure*}
\egroup

\bgroup
\fixFloatSize{images/abf79070-6060-4d7f-b9de-9e36957f6b5b-upicture4.png}
\begin{figure*}[!htbp]
\centering \makeatletter\IfFileExists{images/abf79070-6060-4d7f-b9de-9e36957f6b5b-upicture4.png}{\includegraphics{images/abf79070-6060-4d7f-b9de-9e36957f6b5b-upicture4.png}}{}
\makeatother 
\caption{\boldmath {Learning process}}
\label{f-09fb55b5c18b}
\end{figure*}
\egroup

\bgroup
\fixFloatSize{images/eed15832-d6eb-48ed-9827-d35ca6cd79f9-upicture45.png}
\begin{figure*}[!htbp]
\centering \makeatletter\IfFileExists{images/eed15832-d6eb-48ed-9827-d35ca6cd79f9-upicture45.png}{\includegraphics{images/eed15832-d6eb-48ed-9827-d35ca6cd79f9-upicture45.png}}{}
\makeatother 
\caption{\boldmath {Learning process}}
\label{f-38dc6da37ae6}
\end{figure*}
\egroup

\bgroup
\fixFloatSize{images/da7c987d-992c-4813-88b3-37b8b33f230c-upicture46.png}
\begin{figure*}[!htbp]
\centering \makeatletter\IfFileExists{images/da7c987d-992c-4813-88b3-37b8b33f230c-upicture46.png}{\includegraphics{images/da7c987d-992c-4813-88b3-37b8b33f230c-upicture46.png}}{}
\makeatother 
\caption{\boldmath {Learning process}}
\label{f-d5f761831ea0}
\end{figure*}
\egroup

\bgroup
\fixFloatSize{images/be4064d3-dfb3-44a5-8bc4-908c601321d1-upicture47.png}
\begin{figure*}[!htbp]
\centering \makeatletter\IfFileExists{images/be4064d3-dfb3-44a5-8bc4-908c601321d1-upicture47.png}{\includegraphics{images/be4064d3-dfb3-44a5-8bc4-908c601321d1-upicture47.png}}{}
\makeatother 
\caption{\boldmath {Learning process}}
\label{f-59a85b3f3307}
\end{figure*}
\egroup

\bgroup
\fixFloatSize{images/e221aa01-0bfb-4416-b25a-cf6bb6596e1b-upicture5.png}
\begin{figure*}[!htbp]
\centering \makeatletter\IfFileExists{images/e221aa01-0bfb-4416-b25a-cf6bb6596e1b-upicture5.png}{\includegraphics{images/e221aa01-0bfb-4416-b25a-cf6bb6596e1b-upicture5.png}}{}
\makeatother 
\caption{\boldmath {Identification process flowchart}}
\label{f-224ebb924fca}
\end{figure*}
\egroup
\textbf{Data set formulation steps}

The gel electrophoresis images, as shown in Figure~\ref{f-c25fbaec644f}, must be saved in a previously defined location in the computer or the network. A MATLAB file reads the contents of the folder that contains the images and automatically loads each image. The features of each image were extracted and saved into a text file. After reading all images and saving a text file for each image, a single text file was created from all the text files. The features then converted to binary and then standardized using Z-score standardization \unskip~\citep{664249:15652079}, which can be calculated using Equation~(\ref{dfg-307ae5837d12}), as shown below,
\let\saveeqnno\theequation
\let\savefrac\frac
\def\dispfrac{\displaystyle\savefrac}
\begin{eqnarray}
\let\frac\dispfrac
\gdef\theequation{1}
\let\theHequation\theequation
\label{dfg-307ae5837d12}
\begin{array}{@{}l}Z(ij)=(a(ij)-\mu)/\sigma \end{array}
\end{eqnarray}
\global\let\theequation\saveeqnno
\addtocounter{equation}{-1}\ignorespaces 
Where,

Z(ij) is the new value 

a(ij) is the old value

\ensuremath{\mu }=the mean of the column of the input value

\ensuremath{\sigma  }=the standard deviation of the column of the input value.

i=1{\textemdash} n (n=the number of rows or inputs)

j=1 {\textemdash}k (k=the number of bits that represents each input), then a MATLAB m file reads the contents of the folder that contains the images, and the features extracted from the equation, as shown above, where accumulated and saved together into a single text file.

Features were extracted from the gel electrophoresis images, which are,

Average standard deviation smoothness skewness uniformity entropy 

Each feature then converted to a signed binary, which is represented by 64 binary bits, so each sample was represented by 384 bits, then a matrix of 384 columns and x rows was created, where x represents the number of gel electrophoresis images.

The input data had been structures as follows,

Where x inputs had been used, and each row in the above matrix represents a different input, and then each transposed row was fed into the neural network for learning.

Each row represents a single input, where each row is unique (because each row was generated from different gel electrophoresis images).

\textbf{Proposed model overview}

Two systems had been used for training and identification purposes: LVQ and Back-Propagation. 

\textbf{Linear Vector Quantization}

The LVQ was used to train the neural network. The data-set used was the actual features extracted from gel electrophoresis images (the text file).

The LVQ network structure was 384-769-6-6 (384 input neurons - 769 neurons in the first hidden layer - 6 neurons in the second hidden layer - 6 neurons in the output layer), as shown below in Figure~\ref{f-f0a2a791f43e}.

\textbf{Error-Back-Propagation }

\unskip~\citep{664249:15652085,664249:15652084}. The structure of the Back-Propagation network used in this paper is as follows,

(384-769-1), 384 binary inputs (384 neurons)- 769 neurons in the hidden layer and one decimal output (one neuron) as shown in Figure~\ref{f-bf93c5dfbb9c}.

\textbf{Learning process}

below is a flowchart of the learning process Figures~\ref{f-09fb55b5c18b}, \ref{f-38dc6da37ae6}, \ref{f-d5f761831ea0} and~\ref{f-59a85b3f3307},

The learning process is recursive and it consists of the following steps,


  
  \begin{enumerate}
  \item \relax Each input (six features) must be fed to the system 
  \item \relax The output of the system calculated
  \item \relax The error is calculated according to equation 2, as shown below,
  \item \relax Desired output is a decimal number between (1-6) where each number represents a different input and that number is unique and will be used later for identification purposes. 
  \end{enumerate}
  
\let\saveeqnno\theequation
\let\savefrac\frac
\def\dispfrac{\displaystyle\savefrac}
\begin{eqnarray}
\let\frac\dispfrac
\gdef\theequation{2}
\let\theHequation\theequation
\label{dfg-172a255c7f26}
\begin{array}{@{}l}a.\;error=desired\;output-actual\;output\;\;\;\end{array}
\end{eqnarray}
\global\let\theequation\saveeqnno
\addtocounter{equation}{-1}\ignorespaces 
5. The actual output is the summation of weighted inputs (between the hidden layer and the output neuron)

 6. Then the second input was fed to the system (six feathers of the second image)

 7. Another error was calculated according to Equation~(\ref{dfg-172a255c7f26}). 

 8. The errors were saved in an array

 9. The maximum error was calculated 

10. A max error must be smaller than a threshold (10\ensuremath{^{-4}}), if not, 

 11. The all six images must be fed again to the network  

 12. Another max error is calculated  

 13. Compared again with the max error allowed 

14. During this step above, the weights map must be updated accordingly until the goal is reached 

 15. That is the end of the learning process 

 16. The weights map must be saved in an external file. 

 The identification process is similar to the learning process as below,


  
  \begin{enumerate}
  \item \relax load the gel-electrophoresis image to the system
  \item \relax extract the features from the image
  \item \relax save the features in an external file
  \item \relax load the weight matrix
  \item \relax load the features
  \item \relax standardize it
  \item \relax calculate the output of the network (forward only)
  \item \relax subtract the output from desired outputs (1-6)
  \item \relax when the difference is very low (10-4), the system declares an identification
  \item \relax if not, the system declares that ``no match found''
  \end{enumerate}
  A flowchart for the identification process is as shown below in Figure~\ref{f-224ebb924fca}.
    
\section{Results and Discussion}
After 296 LVQ Epoch, the system learned, then the system was tested by the learned data and it was successful 100\% in identifying the learned data.

The LVQ is mainly used for classification of the input data, but in the proposed system, it was used for identification, but it was very slow in the learning process compared to error-back-propagation, as shown below in Table~\ref{tw-c61fe81c925a}.

So, the error-back-propagation had been used as an alternative, and as shown above, in Table~\ref{tw-e7605cb2b92a}, it shows much better results in all terms.
    
\section{Conclusions}
From the results above, the following points had been concluded,

1-The obtained results show that LVQ can be used in identification in spite of the fact that LVQ's main job is classification.

2-The time of learning heavily depends on the number of inputs loaded into the system.

3-The standardization is a must because of the nature of the input data.

4-The proposed system can be used efficiently in forensics.

5- The proposed system can replace direct database queries efficiently because it does not have to search through all inputs (as in the normal SQL query).

6-Error-back-propagation is a better replacement for LVQ for the identification process.
    

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