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Avascular Necrosis (AVN) || Early Detection, Better Outcomes || ~xRay Pixy

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Avascular Necrosis (AVN) is a condition where blood flow to the bone is reduced, causing bone cells to die. This leads to pain, joint damage, and difficulty in movement, especially in the hip. Early diagnosis and proper treatment can prevent permanent bone damage and improve quality of life. Video Chapter: AVN 00:00 Introduction 00:45 What is AVN? 01:55 About Bone Tissue 02:49 AVN Causes 03:38 AVN Symptoms 04:11 AVN Diagnosis 04:56 AVN of femoral head 05:33 How AVN Develops 07:28 Conclusions #optimization #algorithm #metaheuristic #robotics #deeplearning #ArtificialIntelligence #MachineLearning #computervision #research #projects #thesis #Python #optimizationproblem #optimizationalgorithms 

POA - CODE || Pelican Optimization Algorithm Code Implementation ||

Learn Pelican Optimization Algorithm Code Implementation Step-By-Step POA-CODE Video Chapters: 00:00 Introduction 01:22 Test Function Information Program File 02:37 Pelican Optimization Algorithm Program File 11:23 Main Program File 12:30 Conclusion

1.) Test Function Information File
function [LB,UB,D,FitF] = test_fun_info(C) switch C case 'F1' FitF = @F1; LB=-100; UB =100; D =30; case 'F2' FitF = @F2; LB=-10; UB =10; D =30; case 'F3' FitF = @F3; LB=0; UB=1; D=3; end end % F1 function R = F1(x) R=sum(x.^2); end % F2 function R = F2(x) R=sum(abs(x))+prod(abs(x)); end

2.) POA File
function[Best_Solution,Best_Location,Sol_con_Curve]=POA(PopSize,MaxT,LB,UB,D,FitF) LB=ones(1,D).*(LB); % Lower limit UB=ones(1,D).*(UB); % Upper limit % POPULATION INITIALIZATION PHASE for i=1:D X(:,i) = LB(i)+rand(PopSize,1).*(UB(i) - LB(i)); % Initial population end % FITNESS VALUES CALCULATION for i =1:PopSize L=X(i,:); FitnessVal(i)=FitF(L); end %% for t=1:MaxT %% update the best condidate solution [Best_Agent_Val , Best_Agent_Loc]=min(FitnessVal); if t==1 Best_Pos=X(Best_Agent_Loc,:); % Optimal location Best_Val=Best_Agent_Val; % The optimization objective function elseif Best_Agent_Val<Best_Val Best_Val=Best_Agent_Val; Best_Pos=X(Best_Agent_Loc,:); end %% UPDATE location of food Agents_Target=[]; g=randperm(PopSize,1); Agents_Target=X(g,:); Agents_Target=FitnessVal(g); %% for i=1:PopSize %% PHASE 1: Moving towards prey (exploration phase) I=round(1+rand(1,1)); if FitnessVal(i)> Agents_Target New_Pos=X(i,:)+ rand(1,1).*(Agents_Target-I.* X(i,:)); %Eq(4) else New_Pos=X(i,:)+ rand(1,1).*(X(i,:)-1.*Agents_Target); %Eq(4) end New_Pos= max(New_Pos,LB); New_Pos = min(New_Pos,UB); % Updating X_i using (5) New_Fit = FitF(New_Pos); if New_Fit <= FitnessVal(i) X(i,:) = New_Pos; FitnessVal(i)=New_Fit; end %% END PHASE 1: Moving towards prey (exploration phase) %% PHASE 2: Winging on the water surface (exploitation phase) New_Pos=X(i,:)+0.2*(1-t/MaxT).*(2*rand(1,D)-1).*X(i,:);% Eq(6) New_Pos= max(New_Pos,LB); New_Pos = min(New_Pos,UB); % Updating X_i using (7) New_Fit = FitF(New_Pos); if New_Fit <= FitnessVal(i) X(i,:) = New_Pos; FitnessVal(i)=New_Fit; end %% END PHASE 2: Winging on the water surface (exploitation phase) end best_so_far(t)=Best_Val; average(t) = mean (FitnessVal); end Best_Solution=Best_Val; Best_Location=Best_Pos; Sol_con_Curve=best_so_far; end


3.) Main File
clc clear all %Test Function Test_Fun='F3'; % Total Number of Pelicans PopSize=50; % Maximum number of iteration MaxT=500; % Test Function Details [LB,UB,D,FitF]=test_fun_info(Test_Fun); % POA Calculation [BestVal,BestLoc,Sol_con_Curve]=POA(PopSize,MaxT,LB,UB,D,FitF); subplot(1,1,1); semilogy(Sol_con_Curve,'Color','r'); title('Convergence Curve'); xlabel('Iteration'); ylabel('Best Value'); axis tight grid on box on legend ('POA') % Display Solution display(['Best Position' [num2str(Test_Fun)],' = ', num2str(BestLoc)]); display(['Best Solution' [num2str(Test_Fun)],' = ', num2str(BestVal)]);

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