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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 

Grasshopper Optimization Algorithm (G.O.A.) Step-by-Step with Numerical ...

Grasshopper Optimization Algorithm (G.O.A.) 

Grasshoppers are also known as pests. They destroy fields and crop production. Grasshopper lifecycle contains Eggs, Nymph Phases, and Adult Grasshopper. Grasshopper Optimization Algorithm is a Nature-inspired swarm-based optimization algorithm. Grasshopper Optimization Algorithm (GOA) is inspired by the foraging and swarming behavior of grasshoppers in nature. The grasshopper optimization algorithm is basically inspired by the behavior of adult grasshoppers in nature. Adult grasshoppers can make sudden jumps and cover long-range as compare to nymphs.

This is the mathematical model used to represent grasshopper behavior in this algorithm :
𝑥_𝑖 = 𝑆_𝑖 + 𝐺_𝑖 + 𝐴_i
GrasshopperCurrentPosition = Social Interaction in the group + Force of gravity + Wind Direction.
Normally distributed random values are used in the grasshopper optimization algorithm for grasshopper random behavior in nature.

Grasshopper Optimization Algorithm Steps.
1.) Parameter Initialization. 2.) Population Initialization Phase. 3.) Compute Fitness Value for each grasshopper. 4.) Select the Best Solution Among All. 5.) Check While (CurrentIteration (t) < MaximumIteration (MaxT)). 6.) Normalize distance between grasshoppers in the range [1, 4]. 7.) Update the position of the current grasshopper. 8.) Bring the Current grasshopper back if it goes outside boundaries. 9.) Update Current Best Solution if there any new Best solution. 10.) CurrentIteration = CurrentIteration + 1; // End While Loop 11.) Return Best Solution.

Grasshopper Optimization Algorithm Advantages.
  • Obtain better solution as compare to other metaheuristic algorithms
  • High accuracy
Grasshopper Optimization Algorithm Disadvantages.
  • Easy to fall into local optimam
Grasshopper Optimization Algorithm Numerical Example:

Grasshopper Optimization Algorithm (G.O.A.)  Numerical Example
Topics Covered in this Video Introduction. Grasshopper Optimization Algorithm Inspiration. Grasshopper Optimization Algorithm Mathematical Models. Grasshopper Optimization Algorithm Steps. Grasshopper Optimization Algorithm Numerical Example. Grasshopper Optimization Algorithm Advantages. Grasshopper Optimization Algorithm Disadvantages.




Grasshopper Social Interaction Mathematical Models: Grasshopper social interaction is defied by Attraction and Repulsion.
  • Grasshopper Distance Range = [0,15].
  • Grasshopper Repulsion Range = [0, 2.079].
  • Grasshopper attraction increases in the interval of [2.079, 4] and then decreases.
  • There is neither attraction nor repulsion between grasshoppers when the distance between 2 grasshoppers is 2.079.
  • Attraction Intensity ( f ) = 0.5.
  • Attraction Length (l)  = 1.5.



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Meta-heuristic Algorithms
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