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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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Brain Storm Optimization Algorithm || Step-By-Step || ~xRay Pixy

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Learn Brainstorm optimization (BSO) algorithm step-by-step using examples.
Video Chapters: BSO Algorithm
00:00 Introduction
02:33 Brainstorming Process
03:10 Example 01: Brainstorming Process in Real-Life
05:25 Brain Strom Optimization
08:39 Example 02
14:09 BSO Steps
17:51 Conclusion

The Brain Storm Optimization (BSO) algorithm is a swarm intelligence method inspired by human brainstorming. It aims to find optimal solutions by combining clustering, exploration, and exploitation techniques.

Why is BSO Useful?

  • It balances global search (exploration) and local search (exploitation) efficiently.

  • It avoids getting stuck in local optima by introducing diversity in solution generation.

  • It is adaptable and can be integrated with machine learning techniques like clustering.

Key Concepts:

  • Solution Clustering: Solutions are grouped into clusters to refine the search space.

  • Exploration (Divergent Thinking): New solutions are generated far from existing clusters to discover new possibilities.

  • Exploitation (Convergent Thinking): Refining solutions near the best-known solutions to improve precision.

  • Selection: The best solutions are kept for the next iteration, ensuring continuous improvement.

Applications:

  • Optimization Problems (Engineering, Logistics, Scheduling)

  • Machine Learning & Data Mining (Pattern Recognition, Feature Selection)

  • Swarm Intelligence Research (Enhancing AI-driven problem-solving)

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Cuckoo Search Algorithm for Optimization Problems

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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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Grey Wolf Optimization Algorithm Numerical Example

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

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