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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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Cat and Mouse Optimization Algorithm

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 Cat and Mouse Optimization Algorithm (CMOA)

Cat and Mouse Optimization Algorithm is a population based metaheuristic optimization algorithm. Cat and Mouse Optimization Algorithm mimic the natural behavior of Cat attack on the mouse and Mouse escape from the Cat. In this algorithm population is divided into 2 groups: Group of Cats and Group of Mice. Cat and Mice scan the whole search space in this algorithm with their random movements. Each member in the population is a solution to the given problem. Initial population is evaluated using objective function and based on their fitness values population is sorted. Best values in the population as calculated using objective function are considered as Population for Mice and worst values in the population are considered as Population for Cats.

Position Update Procedure in Cat and Mouse Optimization Algorithm (CMOA):

Position Update in CMOA is divided into 2 phases as given below:

  1. First, Move Cats Towards Mice.
  2. Second, Move Mice away from the Cats to save life (i.e., Escape Mice from the Cat).
Cat and Mouse Optimization Algorithm (CMOA) Pseudocode:
  1. Parameter Initialization Phase: Population Size, Maximum Iterations, Design Variables, Fitness Function and Problem Information.
  2. Initialize Population Randomly in the search space.
  3. Evaluate initial population using fitness function.
  4. Rank Population based on fitness values.
  5. Select Population for Mice.
  6. Select population of Cats.
  7. Update Cats Position.
  8. Update Mice Position.
  9. Display best solution. 

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