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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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Vitamin B12 Deficiency: Symptoms, and Foods for Vegetarians

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 Vitamin B12 - Health Care

 Vitamin B12 Deficiency Symptoms

  1.  Pale Skin
  2.  Weakness
  3.  Dizziness
  4.  Depression
  5.  Vision Loss / Blurred Vision
  6.  Memory Loss
  7.  Depression
  8.  Mental Issues
  9.  Confusion
  10.  Gas
  11.  Short of breath
  12.  Constipation
  13.  Diarrhea
  14.  Loss of Appetite
  15.  Behavior Change

Vitamin b12 foods For Vegetarians

Vitamin B12 Rich Fruits

  • Apple
  • Mango
  • Orange
  • Banana
  • Blueberries
  • Beetroots

Vitamin B12 Rich Dry Fruits

  • Nuts
  • Almonds
  • Peanuts
  • Dates
  • Walnuts
  • Hazelnuts
  • Pistachios
  • Macadamia Nuts
  • Cashews
  • Apricots
  • Popcorn

Vitamin B12 Rich Vegitables 

  • Potato
  • Fermented Soybeans
  • Alfa Alfa
  • Mushrooms

Vitamin B12 Rich Dairy Products

  • Milk
  • Butter
  • Cheese
  • Yogurt
-----------------------------------HEALTH IS WEALTH------------------------------------------------------------








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PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE

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Cuckoo Search Algorithm for Optimization Problems

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 Particle swarm optimization (PSO) What is meant by PSO? 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. How PSO will optimize? By Improving a Candidate Solution. How PSO Solve Problems? PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). The population of Candidate Solutions (i.e., Particles). What is Search Space in PSO? It is the range in which the algorithm computes th...
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Particle Swarm Optimization (PSO)

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Particle Swarm Optimization (PSO) is a p opulation-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. PSO is a computational method that Optimizes a problem. PSO searches for Optima by updating generations. It is popular is an intelligent metaheuristic algorithm.  In 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. After a number of iterations agents / particles will find out optimal solution in the search space. Q. What is PSO? A. PSO is a computational method that Optimizes a problem. Q. How PSO will optimize? A. By Improving a Candidate Solution. Q. How PSO Solve Problems? A. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to ...
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PSO Python Code || Particle Swarm Optimization in Python || ~xRay Pixy

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Particle Swarm Optimization Implementation in Python Video Chapters: 00:00 Introduction 02:01 Code 05:55 Position Initialization 08:06 PSO Main Loop 08:42 Velocity Calculation 10:02 Position Update 10:36 Fitness Evaluation 13:21 Objective Function 17:44 Result 19:00 Conclusion .....................................................SOURCE CODE......................................................................... import random import numpy as np from tkinter import messagebox #Define Class Particles class Particle: def __init__ (self,position): self.position=position self.velocity=np.zeros_like(position) self.best_position=position self.best_fitness=float('inf') def PSO(ObjF,Pop_Size,D,MaxT): swarm_best_position=None swarm_best_fitness=float('inf') particles=[] #Posotion Initialization position=np.random.uniform(-0.5,0.5,D) particle=Particle(position) particles.append(particle) #Fit...
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how is the LBP |Local Binary Pattern| values calculated? Step-by-Step with Example

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Whale Optimization Algorithm Code Implementation || WOA CODE || ~xRay Pixy

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Grey Wolf Optimization Algorithm

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GWO Python Code || Grey Wolf Optimizer in Python || ~xRay Pixy

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