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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 
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GWO Python Code || Grey Wolf Optimizer in Python || ~xRay Pixy

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SOURCE CODE

import numpy as np
import tkinter as tk
import matplotlib.pyplot as plt
from tkinter import messagebox

def initialization (PopSize,D,LB,UB):
    SS_Boundary = len(LB) if isinstance(UB,(list,np.ndarray)) else 1
    if SS_Boundary ==1:
        Positions = np.random.rand(PopSize,D)*(UB-LB)+LB
    else:
        Positions = np.zeros((PopSize,D))
        for i in range(D):
            Positions[:,i]=np.random.rand(PopSize)*(UB[i]-LB[i])+LB[i]
    return Positions

def GWO(PopSize,MaxT,LB,UB,D,Fobj):
    Alpha_Pos = np.zeros(D)
    Alpha_Fit = np.inf
    Beta_Pos = np.zeros(D)
    Beta_Fit = np.inf
    Delta_Pos = np.zeros(D)
    Delta_Fit = np.inf

    Positions = initialization(PopSize,D,UB,LB)
    Convergence_curve = np.zeros(MaxT)

    l = 0
    while l<MaxT:
        for i in range (Positions.shape[0]):
            BB_UB = Positions[i,:]>UB 
            BB_LB = Positions[i,:]<LB
            Positions[i,:] = (Positions[i,:]*(~(BB_UB+BB_LB)))+UB*BB_UB+LB*BB_LB
            Fitness = Fobj(Positions[i,:])

            if Fitness<Alpha_Fit:
                Alpha_Fit=Fitness
                Alpha_Pos=Positions[i,:]

            if Fitness>Alpha_Fit and Fitness<Beta_Fit:
                Beta_Fit=Fitness
                Beta_Pos=Positions[i,:]
            
            if Fitness>Alpha_Fit and Fitness>Beta_Fit and Fitness<Delta_Fit:
                Delta_Fit=Fitness
                Delta_Pos=Positions[i,:]
        
        a = 2-1*(2/MaxT)
        for i in range (Positions.shape[0]):
            for j in range (Positions.shape[1]):
                r1=np.random.random()
                r2=np.random.random()

                A1 = 2*a*r1-a
                C1 = 2 * r2

                D_Alpha = abs(C1*Alpha_Pos[j]-Positions[i,j])
                X1 = Alpha_Pos[j]-A1*D_Alpha
                
                r1=np.random.random()
                r2=np.random.random()

                A2 = 2*a*r1-a
                C2=2*r2

                D_Beta = abs(C2*Beta_Pos[j]-Positions[i,j])
                X2 = Beta_Pos[j]-A2*D_Beta

                r1 = np.random.random()
                r2 = np.random.random()

                A3 = 2*a*r1-a
                C3 = 2*r2

                D_Delta = abs(C3 * Delta_Pos[j] - Positions[i,j])
                X3 = Delta_Pos[j] - A3 * D_Delta

                Positions[i,j] = (X1 + X2 + X3) / 3
        l += 1
        Convergence_curve[l - 1] = Alpha_Fit
    return Alpha_Fit, Alpha_Pos, Convergence_curve

if __name__ == "__main__":
    def F1(x):
        return np.sum(x ** 2)

    Fun_name = F1
    LB = -100
    UB = 100
    D = 30
    PopSize= 100
    MaxT = 100

    bestfit, bestsol, convergence_curve = GWO(PopSize,MaxT,LB,UB,D,Fun_name)
    print("Best Fitness =", bestfit)
    print("Best Solution = ",bestsol)

# Show the final result in a message box
    root = tk.Tk()
    root.withdraw()
    messagebox.showinfo("GWO Result", f"Best Fitness: {bestfit}\nBest Solution: {bestsol}")




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