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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 

PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE

 PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE

PSO is a computational method that Optimizes a problem. It is a Population-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. n Particle Swarm Optimization the solution of the problem is represented using Particles. [Flocking birds are replaced with particles for algorithm simplicity]. Objective Function is used for the performance evaluation for each particle / agent in the current population. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to update particle position in each iteration.

Particle Swarm Optimization (PSO) Algorithm step-by-step explanation with Numerical Example and source code implementation. - PART 2 [Example 2]

1.) Initialize Population [Current Iteration (t) = 0]
Population Size = 4;
𝑥𝑖 : (i = 1,2,3,4) and (t = 0)
𝑥1 =1.3;
𝑥2=4.3;
𝑥3=0.4;
𝑥4=−1.2

2.) Fitness Function used:

Compute Fitness Values for Each Particle using fitness function.
𝑓1=1.69;
𝑓2=18.49;
𝑓3=0.16;
𝑓4=1.44;

3.) Initialize Velocity for each particle in the current Population.
𝑣1=0;
𝑣2=0;
𝑣3=0;
𝑣4=0;

4.) Find Personal Best & Global Best (𝐺_𝐵𝑒𝑠𝑡=0.4;) for each Particle.
𝐺_𝐵𝑒𝑠𝑡=0.4;

5.) Calculate Velocity for each Particle.
Calculate Velocity by:

𝑣_1^(0+1)=1∗0 +1∗0.233(1.3 −1.3)+1∗0.801(0.4 −1.3) ;
𝑣_1^1=0.7209;
𝑣_2^1=−3.1229;
𝑣_3^1=0;
𝑣_4^1=1.2816;

6.) Calculate Position for each Particle.
Calculate Particles Position by : 

𝑥_1^(0+1)=1.3 +0.7209=2.0209 ;
𝑥_2^(0+1)=4.3 −3.1229=1.1771;
𝑥_3^(0+1)=0.4+0=0.4;
𝑥_4^(0+1)=−1.2+1.2816=0.0819 ;

7.) Calculate Fitness Values for each Particle (t = 1).
𝑓_1^1=4.084;
𝑓_2^1=1.3855;
𝑓_3^1=0.16;
𝑓_4^1=0.0067;

8.) Repeat Until Stopping Criteria is met.

(Output after 100 iterations )
For More details watch this video: 

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