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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 

Harmony Search Algorithm Numerical Example | Step-By-Step |~xRay Pixy

Harmony Search Algorithm


Video Chapters: Introduction: 00:00 Harmony Search Algorithm: 01:00 Harmony Search Algorithm Example 1: 04:00 Harmony Search Algorithm Numerical Example 2: 07:26 Harmony Search Algorithm Numerical Example 3: 11:36 Conclusion: 15:00

How does harmony search algorithm work?

Harmony Search Algorithm (HSA) is introduced by Zong Woo Geem and Joong Hoon Kim in 2001. Harmony Search is a music inspired optimization algorithm. Harmony Search Algorithm is basically inspired by the Music Harmony.

Music Harmony refers to the relationship between sound waves coming either from musical instruments or human voices. It is the process by which individual sounds are joined together simultaneously. It is the combination of sound pitches in the music. Pitch is an aspect of sound that we can hear. Through pitch we can check weather sound is High or Low than other musical sound.

Harmony Search Algorithm Main Rules: Musician has 3 choices
  1. Play Famous piece of Music (i.e., known).
  2. Play something similar to the famous piece of Music.
  3. Compose New Music.
Harmony Search Algorithm Steps:
  1. Initialize algorithm parameters such as Harmony Memory, Maximum Iterations, Pitch Adjustment Rate, Harmony Adjustment Rate and Band Width.
  2. Construct initial harmony randomly.
  3. Select Best and Worst Harmony in the harmony memory.
  4. Check While (current iteration <= Maximum Iteration)
  5. Harmony Memory Generation.
  6. Pitch Adjustment.
  7. Update Harmony [harmony replacement after comparison).
  8. Return best harmony as optimal solution.

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