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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 

Dragonfly Optimization Algorithm Step-by-Step with example

Dragonfly Optimization Algorithm (DOA)

Dragonfly Algorithm is developed by Mirjalili in 2016. Dragonfly Algorithm is a metaheuristic algorithm inspired by the behavior of dragonflies in nature. There are about 5000 known species of dragonflies. Dragonfly is a symbol of Strength, Courage, and Happiness in Japan. 

Dragonfly Algorithm Step-by-Step: -
Step 01: Initialize Dragonfly Population Randomly (𝑋_𝑖, Where i = 1,2,3,4,…n). 
Step 02: Initialize Step vector / Size for dragonfly (〖∆𝑋〗_𝑖).
Step 03: While(CurrentIteration < MaximumIteration)
Step 04: Computer Fitness Values for each dragonfly.
Step 05: Update Food sources and enemy. 
Step 06: Update parameters w, s, a, c, f, and e.
Step 07: Calculate S, A, C, and F.
Step 08: Update neighboring radius. 
Step 09: If the dragonfly has at least one neighboring dragonfly. { 
   Update Velocity and Position;
else { Update Position; }
Elseif { Check and correct new position based on boundaries of variable; }

Note: To Improve randomness, we can update the dragonfly position using random walk (i.e., Levy’s Flight).




Dragonfly Optimization Algorithm on Different Engineering Design Problems

Engineering Design Problem
Engineering design problems include different complicated Cost Function (aka Fitness Function / Objective Functions). Engineering Optimization Techniques Aim is to “Find out Optimum solution from all feasible solutions”.

How Metaheuristic Algorithms Solve Engineering Design Problems?
Metaheuristic algorithms used randomization process. Metaheuristic algorithms are suitable for global optimization. For difficult engineering problems, develop and utilize metaheuristic algorithms (which may obtain good results).

Engineering Design Problems Example
A dragonfly optimization algorithm is applied to different engineering design problems: 
Welded Beam Design Optimization Problem
Speed reducer design optimization problem
Compression spring design optimization problem

Dragonfly Optimization Algorithm on Different Engineering Design Problems ~xRay Pixy

#Metaheuristic #Algorithms
Meta-heuristic Algorithms
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