├── BEEFCN.m
├── Bees CNN.jpg
├── BeesCNNAlgorithm.m
├── CNNDat.rar
├── FuzzyCost.m
├── FuzzyParameters.m
├── GenerateFuzzy.m
├── GettingFuzzyParameters.m
├── JustLoad.m
├── LICENSE
├── PerformBeeDance.m
├── README.md
└── ReadMe.txt


/BEEFCN.m:
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  1 | function bestfis=BEEFCN(fis,data)
  2 | % Variables
  3 | p0=GettingFuzzyParameters(fis);
  4 | Problem.CostFunction=@(x) FuzzyCost(x,fis,data);
  5 | Problem.nVar=numel(p0);
  6 | alpha=1;
  7 | Problem.VarMin=-(10^alpha);
  8 | Problem.VarMax=10^alpha;
  9 | % Bees Algorithm Parameters
 10 | Params.MaxIt=15;
 11 | Params.nScoutBee = 10;                                  % Number of Scout Bees
 12 | Params.nSelectedSite = round(0.5*Params.nScoutBee);     % Number of Selected Sites
 13 | Params.nEliteSite = round(0.4*Params.nSelectedSite);    % Number of Selected Elite Sites
 14 | Params.nSelectedSiteBee = round(0.5*Params.nScoutBee);  % Number of Recruited Bees for Selected Sites
 15 | Params.nEliteSiteBee = 2*Params.nSelectedSiteBee;       % Number of Recruited Bees for Elite Sites
 16 | Params.r = 0.1*(Problem.VarMax-Problem.VarMin);         % Neighborhood Radius
 17 | Params.rdamp = 0.95;                                    % Neighborhood Radius Damp Rate
 18 | % Starting Bees Algorithm
 19 | results=Runbee(Problem,Params);
 20 | % Getting the Results
 21 | p=results.BestSol.Position.*p0;
 22 | bestfis=FuzzyParameters(fis,p);
 23 | end
 24 | %% Bees
 25 | function results=Runbee(Problem,Params)
 26 | disp('Starting Bees Algorithm Training :)');
 27 | % Cost Function
 28 | CostFunction=Problem.CostFunction;
 29 | % Number of Decision Variables
 30 | nVar=Problem.nVar;
 31 | % Size of Decision Variables Matrixv
 32 | VarSize=[1 nVar];
 33 | % Lower Bound of Variables
 34 | VarMin=Problem.VarMin;
 35 | % Upper Bound of Variables
 36 | VarMax=Problem.VarMax;
 37 | % Some Change
 38 | if isscalar(VarMin) && isscalar(VarMax)
 39 | dmax = (VarMax-VarMin)*sqrt(nVar);
 40 | else
 41 | dmax = norm(VarMax-VarMin);
 42 | end
 43 | %% Bees Algorithm Parameters
 44 | MaxIt=Params.MaxIt;
 45 | nScoutBee = Params.nScoutBee;                % Number of Scout Bees
 46 | nSelectedSite = Params.nSelectedSite;        % Number of Selected Sites
 47 | nEliteSite = Params.nEliteSite;              % Number of Selected Elite Sites
 48 | nSelectedSiteBee = Params.nSelectedSiteBee;  % Number of Recruited Bees for Selected Sites
 49 | nEliteSiteBee = Params.nEliteSiteBee;        % Number of Recruited Bees for Elite Sites
 50 | r = Params.r;                                % Neighborhood Radius
 51 | rdamp = Params.rdamp;                        % Neighborhood Radius Damp Rate
 52 | %% Second Stage
 53 | % Empty Bee Structure
 54 | empty_bee.Position = [];
 55 | empty_bee.Cost = [];
 56 | % Initialize Bees Array
 57 | bee = repmat(empty_bee, nScoutBee, 1);
 58 | % Create New Solutions
 59 | for i = 1:nScoutBee
 60 | bee(i).Position = unifrnd(VarMin, VarMax, VarSize);
 61 | bee(i).Cost = CostFunction(bee(i).Position);
 62 | end
 63 | % Sort
 64 | [~, SortOrder] = sort([bee.Cost]);
 65 | bee = bee(SortOrder);
 66 | % Update Best Solution Ever Found
 67 | BestSol = bee(1);
 68 | % Array to Hold Best Cost Values
 69 | BestCost = zeros(MaxIt, 1);
 70 | %% Bees Algorithm Main Body
 71 | for it = 1:MaxIt
 72 | % Elite Sites
 73 | for i = 1:nEliteSite
 74 | bestnewbee.Cost = inf;
 75 | for j = 1:nEliteSiteBee
 76 | newbee.Position = PerformBeeDance(bee(i).Position, r);
 77 | newbee.Cost = CostFunction(newbee.Position);
 78 | if newbee.Cost<bestnewbee.Cost
 79 | bestnewbee = newbee;
 80 | end
 81 | end
 82 | if bestnewbee.Cost<bee(i).Cost
 83 | bee(i) = bestnewbee;
 84 | end
 85 | end
 86 | % Selected Non-Elite Sites
 87 | for i = nEliteSite+1:nSelectedSite
 88 | bestnewbee.Cost = inf;
 89 | for j = 1:nSelectedSiteBee
 90 | newbee.Position = PerformBeeDance(bee(i).Position, r);
 91 | newbee.Cost = CostFunction(newbee.Position);
 92 | if newbee.Cost<bestnewbee.Cost
 93 | bestnewbee = newbee;
 94 | end
 95 | end
 96 | if bestnewbee.Cost<bee(i).Cost
 97 | bee(i) = bestnewbee;
 98 | end
 99 | end
100 | % Non-Selected Sites
101 | for i = nSelectedSite+1:nScoutBee
102 | bee(i).Position = unifrnd(VarMin, VarMax, VarSize);
103 | bee(i).Cost = CostFunction(bee(i).Position);
104 | end
105 | % Sort
106 | [~, SortOrder] = sort([bee.Cost]);
107 | bee = bee(SortOrder);
108 | % Update Best Solution Ever Found
109 | BestSol = bee(1);
110 | % Store Best Cost Ever Found
111 | BestCost(it) = BestSol.Cost;
112 | % Display Iteration Information
113 | disp(['In Iteration Number ' num2str(it) '  Bees Algorithm Fittest Value Is = ' num2str(BestCost(it))]);
114 | % Damp Neighborhood Radius
115 | r = r*rdamp;
116 | end
117 | disp('Bees Algorithm Came To End :)');
118 | % Store Res
119 | results.BestSol=BestSol;
120 | results.BestCost=BestCost;
121 | % Plot Bees Algorithm Training Stages
122 | set(gcf, 'Position',  [600, 300, 500, 400])
123 | plot(BestCost,':',...
124 | 'LineWidth',2,...
125 | 'MarkerSize',8,...
126 | 'MarkerEdgeColor','g',...
127 | 'Color',[0.1,0.9,0.1]);
128 | title('Bees Algorithm Training')
129 | xlabel('Bees Algorithm Iteration Number','FontSize',10,...
130 | 'FontWeight','bold','Color','m');
131 | ylabel('Bees Algorithm Fittest Value','FontSize',10,...
132 | 'FontWeight','bold','Color','m');
133 | legend({'Bees Algorithm Train'});
134 | end
135 | 
136 | 
137 | 
138 | 
139 | 


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/Bees CNN.jpg:
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https://raw.githubusercontent.com/SeyedMuhammadHosseinMousavi/Bees-CNN-Algorithm/e77166f89642579aed2f26d3f69c60a15366e212/Bees CNN.jpg


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/BeesCNNAlgorithm.m:
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  1 | %% Bees CNN Algorithm (A Fuzzy Evolutionary Deep Leaning) - Created in 20 Jan 2022 by Seyed Muhammad Hossein Mousavi
  2 | % It is possible to fit deep learning weights and bias using evolutionary
  3 | % algorithm, right after training stage. Here, CNN is used to classify 8
  4 | % face classes. After CNN train, initial fuzzy model is created to aid the
  5 | % learning process. Finally, CNN network weights (from Fully Connected Layer)
  6 | % trains using Bees algorithm
  7 | % to be fitted in a nature inspired manner (here behavior of Bees). You can
  8 | % used your data with any number of samples and classes. Remember, code's
  9 | % parameters are adjusted for this data and if you want to replace your
 10 | % data you may have to change the parameters. Image data is in 64*64 size and
 11 | % in 2 dimensions and stored in 'CNNDat' folder. So, important parameters 
 12 | % are as below:
 13 | % 1.
 14 | % 'numTrainFiles' = you have to change this based on number of your samples
 15 | % in each class. for example if each class has 120 sample, 90 is good
 16 | % enough as 90 samples considered for train and others for test.
 17 | % 2.
 18 | % 'imageInputLayer' = it is size of your image data like [64 64 1]
 19 | % 3.
 20 | % 'fullyConnectedLayer' = it is number of your classes like (8)
 21 | % 4.
 22 | % 'MaxEpochs' = the more the better and more computation run time like 40
 23 | % 5.
 24 | % 'ClusNum' = Fuzzy C Means (FCM) Cluster Number like 3 or 4 is nice
 25 | % 6.
 26 | % These two are from "BEEFCN.m" function :
 27 | % 'Params.MaxIt' = it is iteration number in Bees algorithm. 20 is good
 28 | % 'Params.nScoutBee' = it is population number in Bees algorithm. Like 10.
 29 | % ------------------------------------------------ 
 30 | % Feel free to contact me if you find any problem using the code: 
 31 | % Author: SeyedMuhammadHosseinMousavi
 32 | % My Email: mosavi.a.i.buali@gmail.com 
 33 | % My Google Scholar: https://scholar.google.com/citations?user=PtvQvAQAAAAJ&hl=en 
 34 | % My GitHub: https://github.com/SeyedMuhammadHosseinMousavi?tab=repositories 
 35 | % My ORCID: https://orcid.org/0000-0001-6906-2152 
 36 | % My Scopus: https://www.scopus.com/authid/detail.uri?authorId=57193122985 
 37 | % My MathWorks: https://www.mathworks.com/matlabcentral/profile/authors/9763916#
 38 | % my RG: https://www.researchgate.net/profile/Seyed-Mousavi-17
 39 | % ------------------------------------------------ 
 40 | % Hope it help you, enjoy the code and wish me luck :)
 41 | 
 42 | %% Cleaning
 43 |  clear;
 44 |  clc;
 45 |  warning('off');
 46 |  
 47 | %% CNN Deep Neural Network
 48 | % Load the deep sample data as an image datastore. 
 49 | deepDatasetPath = fullfile('CNNDat');
 50 | imds = imageDatastore(deepDatasetPath, ...
 51 |     'IncludeSubfolders',true, ...
 52 |     'LabelSource','foldernames');
 53 | % Divide the data into training and validation data sets
 54 | numTrainFiles = 90;
 55 | [imdsTrain,imdsValidation] = splitEachLabel(imds,numTrainFiles,'randomize');
 56 | % Define the convolutional neural network architecture.
 57 | layers = [
 58 | % Image Input Layer An imageInputLayer 
 59 |     imageInputLayer([64 64 1])
 60 | % Convolutional Layer 
 61 | convolution2dLayer(3,8,'Padding','same')
 62 | % Batch Normalization 
 63 |     batchNormalizationLayer
 64 | % ReLU Layer The batch
 65 |     reluLayer
 66 | % Max Pooling Layer  
 67 |     % More values means less weights
 68 |     maxPooling2dLayer(4,'Stride',4)
 69 |     %------------------------------
 70 |     convolution2dLayer(3,8,'Padding','same')
 71 |     batchNormalizationLayer
 72 |     reluLayer
 73 |     maxPooling2dLayer(5,'Stride',5)
 74 |     convolution2dLayer(3,8,'Padding','same')
 75 |     batchNormalizationLayer
 76 |     reluLayer
 77 | % Fully Connected Layer (Number of Classes) 
 78 |     fullyConnectedLayer(8)
 79 | % Softmax Layer 
 80 |     softmaxLayer
 81 | % Classification Layer The final layer 
 82 |     classificationLayer];
 83 | % Specify the training options
 84 | options = trainingOptions('sgdm', ...
 85 |     'InitialLearnRate',0.001, ...
 86 |     'MaxEpochs',20, ...
 87 |     'Shuffle','every-epoch', ...
 88 |     'ValidationData',imdsValidation, ...
 89 |     'ValidationFrequency',8, ...
 90 |     'Verbose',false, ...
 91 |     'Plots','training-progress');
 92 | % Train the network 
 93 | [net,info]= trainNetwork(imdsTrain,layers,options);
 94 | 
 95 | %% Bees Algorithm Weight Fitting
 96 | % Converting Serial Network to an Object
 97 | netobj = net.saveobj;
 98 | % Extracting Fully Connected Layer's Weights To Evolve
 99 | FullConn=netobj.Layers(13, 1).Weights;
100 | netbias=netobj.Layers(13, 1).Bias;
101 | 
102 | %% Data for Each Weight
103 | sizefinal=size(FullConn);
104 | sizefinal=sizefinal(1,1);
105 | for i=1:sizefinal
106 | Inputs=FullConn(i,:);
107 | Targets=Inputs;
108 | data.Inputs=Inputs;
109 | data.Targets=Targets;
110 | datam{i}=JustLoad(data);
111 | end;
112 | 
113 | %% Making Basic Fuzzy Model for Each Class Weight
114 | % Fuzzy C Means (FCM) Cluster Number
115 | ClusNum=3; 
116 | % Creating Initial Fuzzy Model to Employ for Each Class Weight
117 | for i=1:sizefinal
118 | fism{i}=GenerateFuzzy(datam{i},ClusNum);
119 | end
120 | 
121 | %% Tarining Bees Algorithm
122 | % Fitting Fully Connected Layer's Weights with Bees Algorithm
123 | for i=1:sizefinal
124 | disp(['Bees Are Working on Weights of Class # (' num2str(i) ')']);
125 | BeesFISm{i}=BEEFCN(fism{i},datam{i}); 
126 | end;
127 | 
128 | %% Train Output Extraction
129 | for i=1:sizefinal
130 | TrTar{i}=datam{i}.TrainTargets;
131 | TrInp{i}=datam{i}.TrainInputs;
132 | TrainOutputs{i}=evalfis(TrInp{i},BeesFISm{i});
133 | end;
134 | % Train Errors
135 | for i=1:sizefinal
136 | tmp=datam{i};
137 | tt=tmp.TrainTargets;
138 | tp=TrainOutputs{i};
139 | Errors{i}=tt-tp;
140 | MSE{i}=mean(Errors{i}.^2);
141 | RMSE{i}=sqrt(MSE{i});  
142 | error_mean{i}=mean(Errors{i});
143 | error_std{i}=std(Errors{i});
144 | end;
145 | % Convereting Output Cell to Matrix
146 | for i=1:sizefinal
147 | EvolvedFullConn(i,:)=TrainOutputs{i}';
148 | end;
149 | 
150 | %% Replacing Evolved Weights
151 | netobj.Layers(13, 1).Weights=EvolvedFullConn;
152 | % New Network
153 | Newnet=netobj.Layers;
154 | % Converting Network to Serial Network
155 | BeesNet = assembleNetwork(Newnet);
156 | 
157 | %% Predict The Labels 
158 | % Common CNN Accuracy
159 | YPred = classify(net,imdsValidation);
160 | YValidation = imdsValidation.Labels;
161 | CNNaccuracy = sum(YPred == YValidation)/numel(YValidation);
162 | % Bees CNN Accuracy
163 | YPredbee = classify(BeesNet,imdsValidation);
164 | YValidationbee = imdsValidation.Labels;
165 | Beesaccuracy = sum(YPredbee == YValidationbee)/numel(YValidationbee);
166 | 
167 | %% Confusion Matrix
168 | figure;
169 | plotconfusion(YPred,YValidation);
170 | title(['CNN Accuracy  =  ' num2str(CNNaccuracy)]);
171 | figure;
172 | plotconfusion(YPredbee,YValidationbee);
173 | title(['Bees-CNN Accuracy  =  ' num2str(Beesaccuracy)]);
174 | 
175 | %% Statistics
176 | fprintf('The CNN Accuracy Is =  %0.4f.\n',CNNaccuracy*100)
177 | fprintf('The Bees CNN Accuracy Is =  %0.4f.\n',Beesaccuracy*100)
178 | 
179 | 


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/CNNDat.rar:
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https://raw.githubusercontent.com/SeyedMuhammadHosseinMousavi/Bees-CNN-Algorithm/e77166f89642579aed2f26d3f69c60a15366e212/CNNDat.rar


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/FuzzyCost.m:
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 1 | function [z, out]=FuzzyCost(x,fis,data)
 2 |     MinAbs=1e-5;
 3 |     if any(abs(x)<MinAbs)
 4 |         S=(abs(x)<MinAbs);
 5 |         x(S)=MinAbs.*sign(x(S));
 6 |     end
 7 |     p0=GettingFuzzyParameters(fis);
 8 |     p=x.*p0;  
 9 |     fis=FuzzyParameters(fis,p);   
10 |     x=data.TrainInputs;
11 |     t=data.TrainTargets;
12 |     y=evalfis(x,fis);    
13 |     e=t-y;    
14 |     MSE=mean(e(:).^2);
15 |     RMSE=sqrt(MSE);    
16 |     z=RMSE;    
17 |     out.fis=fis;
18 |     out.MSE=MSE;
19 |     out.RMSE=RMSE;
20 |     
21 | end


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/FuzzyParameters.m:
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 1 | function fis=FuzzyParameters(fis,p)
 2 |     nInput=numel(fis.input);
 3 |     for i=1:nInput
 4 |         nMF=numel(fis.input(i).mf);
 5 |         for j=1:nMF
 6 |             k=numel(fis.input(i).mf(j).params);
 7 |             fis.input(i).mf(j).params=p(1:k);
 8 |             p(1:k)=[];
 9 |         end
10 |     end
11 |     nOutput=numel(fis.output);
12 |     for i=1:nOutput
13 |         nMF=numel(fis.output(i).mf);
14 |         for j=1:nMF
15 |             k=numel(fis.output(i).mf(j).params);
16 |             fis.output(i).mf(j).params=p(1:k);
17 |             p(1:k)=[];
18 |         end
19 |     end
20 | end


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/GenerateFuzzy.m:
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 1 | 
 2 | function fis=GenerateFuzzy(data,nCluster)
 3 |     if ~exist('nCluster','var')
 4 |         nCluster='auto';
 5 |     end
 6 |     x=data.TrainInputs;
 7 |     t=data.TrainTargets;
 8 |     % Important Params
 9 |     fcm_U=2;
10 |     fcm_MaxIter=100;
11 |     fcm_MinImp=1e-5;
12 |     %
13 |     fcm_Display=false;
14 |     fcm_options=[fcm_U fcm_MaxIter fcm_MinImp fcm_Display];
15 |     fis=genfis3(x,t,'sugeno',nCluster,fcm_options);
16 | end


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/GettingFuzzyParameters.m:
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 1 | function p=GettingFuzzyParameters(fis)
 2 |     p=[];
 3 |     nInput=numel(fis.input);
 4 |     for i=1:nInput
 5 |         nMF=numel(fis.input(i).mf);
 6 |         for j=1:nMF
 7 |             p=[p fis.input(i).mf(j).params];
 8 |         end
 9 |     end
10 |     nOutput=numel(fis.output);
11 |     for i=1:nOutput
12 |         nMF=numel(fis.output(i).mf);
13 |         for j=1:nMF
14 |             p=[p fis.output(i).mf(j).params];
15 |         end
16 |     end  
17 | end


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/JustLoad.m:
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 1 | function data=JustLoad(data)
 2 | Inputs=data.Inputs';
 3 | Targets=data.Targets';
 4 | Targets=Targets(:,1);
 5 | nSample=size(Inputs,1);
 6 | % Train Data
 7 | pTrain=1.0;
 8 | nTrain=round(pTrain*nSample);
 9 | TrainInputs=Inputs(1:nTrain,:);
10 | TrainTargets=Targets(1:nTrain,:);
11 | % Test Data
12 | TestInputs=Inputs(nTrain+1:end,:);
13 | TestTargets=Targets(nTrain+1:end,:);
14 | % Export
15 | data.TrainInputs=TrainInputs;
16 | data.TrainTargets=TrainTargets;
17 | data.TestInputs=TestInputs;
18 | data.TestTargets=TestTargets;
19 | end


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/PerformBeeDance.m:
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1 | function y = PerformBeeDance(x, r)
2 |     nVar = numel(x);
3 |     k = randi([1 nVar]); 
4 |     y = x;
5 |     y(k) = x(k)+unifrnd(-r, r);
6 | end


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/README.md:
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 1 | # Bees CNN Algorithm
 2 | - [![View Bees CNN Evolutionary Algorithm on File Exchange](https://www.mathworks.com/matlabcentral/images/matlab-file-exchange.svg)](https://www.mathworks.com/matlabcentral/fileexchange/105510-bees-cnn-evolutionary-algorithm)
 3 | 
 4 | - Bees CNN Algorithm (A Fuzzy Evolutionary Deep Learning) - Created in 20 Jan 2022 by Seyed Muhammad Hossein Mousavi
 5 | 
 6 | <div align="justify">
 7 |  
 8 | - It is possible to fit deep learning weights and bias using evolutionary algorithm, right after training stage. Here, CNN is used to classify 8 face classes. After CNN train, initial fuzzy model is created to aid the learning process. Finally, CNN network weights (from Fully Connected Layer) trains using Bees algorithm to be fitted in a nature inspired manner (here behavior of Bees). You can used your data with any number of samples and classes. Remember, code's parameters are adjusted for this data and if you want to replace your data you may have to change the parameters. Image data is in 64*64 size and in 2 dimensions and stored in 'CNNDat' folder. So, important parameters are as below:
 9 | 
10 | </div>
11 | 
12 | ![Bees CNN](https://user-images.githubusercontent.com/11339420/150426815-417019d7-f7af-4de2-890e-582411724840.jpg)
13 |  
14 | - 1.
15 | - 'numTrainFiles' = you have to change this based on number of your samples in each class. for example if each class has 120 sample, 90 is good enough as 90 samples considered for train and others for test.
16 | - 2.
17 | - 'imageInputLayer' = it is size of your image data like [64 64 1]
18 | - 3.
19 | - 'fullyConnectedLayer' = it is number of your classes like (8)
20 | - 4.
21 | - 'MaxEpochs' = the more the better and more computation run time like 40
22 | - 5.
23 | - 'ClusNum' = Fuzzy C Means (FCM) Cluster Number like 3 or 4 is nice
24 | - 6.
25 | - These two are from "BEEFCN.m" function :
26 | - 'Params.MaxIt' = it is iteration number in Bees algorithm. 20 is good 'Params.nScoutBee' = it is population number in Bees algorithm. Like 10.
27 | 
28 | 
29 | - Feel free to contact me if you find any problem using the code: 
30 | - Author: SeyedMuhammadHosseinMousavi
31 | - My Email: mosavi.a.i.buali@gmail.com 
32 | - Hope it help you, enjoy the code and wish me luck :)
33 | 
34 | 
35 | 


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/ReadMe.txt:
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 1 | %% Bees CNN Algorithm (A Fuzzy Evolutionary Deep Leaning) - Created in 20 Jan 2022 by Seyed Muhammad Hossein Mousavi
 2 | % It is possible to fit deep learning weights and bias using evolutionary
 3 | % algorithm, right after training stage. Here, CNN is used to classify 8
 4 | % face classes. After CNN train, initial fuzzy model is created to aid the
 5 | % learning process. Finally, CNN network weights (from Fully Connected Layer)
 6 | % trains using Bees algorithm
 7 | % to be fitted in a nature inspired manner (here behavior of Bees). You can
 8 | % used your data with any number of samples and classes. Remember, code's
 9 | % parameters are adjusted for this data and if you want to replace your
10 | % data you may have to change the parameters. Image data is in 64*64 size and
11 | % in 2 dimensions and stored in 'CNNDat' folder. So, important parameters 
12 | % are as below:
13 | % 1.
14 | % 'numTrainFiles' = you have to change this based on number of your samples
15 | % in each class. for example if each class has 120 sample, 90 is good
16 | % enough as 90 samples considered for train and others for test.
17 | % 2.
18 | % 'imageInputLayer' = it is size of your image data like [64 64 1]
19 | % 3.
20 | % 'fullyConnectedLayer' = it is number of your classes like (8)
21 | % 4.
22 | % 'MaxEpochs' = the more the better and more computation run time like 40
23 | % 5.
24 | % 'ClusNum' = Fuzzy C Means (FCM) Cluster Number like 3 or 4 is nice
25 | % 6.
26 | % These two are from "BEEFCN.m" function :
27 | % 'Params.MaxIt' = it is iteration number in Bees algorithm. 20 is good
28 | % 'Params.nScoutBee' = it is population number in Bees algorithm. Like 10.
29 | % ------------------------------------------------ 
30 | % Feel free to contact me if you find any problem using the code: 
31 | % Author: SeyedMuhammadHosseinMousavi
32 | % My Email: mosavi.a.i.buali@gmail.com 
33 | % My Google Scholar: https://scholar.google.com/citations?user=PtvQvAQAAAAJ&hl=en 
34 | % My GitHub: https://github.com/SeyedMuhammadHosseinMousavi?tab=repositories 
35 | % My ORCID: https://orcid.org/0000-0001-6906-2152 
36 | % My Scopus: https://www.scopus.com/authid/detail.uri?authorId=57193122985 
37 | % My MathWorks: https://www.mathworks.com/matlabcentral/profile/authors/9763916#
38 | % my RG: https://www.researchgate.net/profile/Seyed-Mousavi-17
39 | % ------------------------------------------------ 
40 | % Hope it help you, enjoy the code and wish me luck :)
41 | 


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