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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 

Metaheuristics Performance Comparison | PSO vs JAYA Algorithm | ~xRay Pixy

Metaheuristics Performance Comparison


Comparison of the Metaheuristic Algorithms Performances Video Chapters: Introduction: 00:00 Metaheuristic Algorithms Applications: 01:41 Metaheuristics Comparision: 02:43 Testing Parameters: 06:16 Testing Output: 07:24 Optimal Values: 09:00 Computation Time: 12:00 Conclusion: 13:20 Questions Answered in this Video: Which is the best metaheuristic algorithm?

Learn how to Compare Metaheuristic Optimization Algorithms Performance using the Standard Test Functions.
Standard Test Functions used for Testing:
- Unimodel Test Functions
- Multi-Model Test Functions

Metaheuristics Algorithms used for Comparision:
- Particle Swarm Optimization (PSO) Algorithm
- Jaya Optimization Algorithm (JOA)

Testing Output is checked based on:
- Optimal Value
- Computation Time

Using these test functions we will analyze the algorithm's Stability, Convergence Speed, and Accuracy. The comparison between the two metaheuristics is done using the standard test functions. Standard test functions such as Unimodal test functions, Multi-model test functions, and Fixed Dimension test functions. We are using the standard test functions to verify the Effectiveness and Flexibility of these two metaheuristic optimization algorithms.
  • Unimodel Test Function: Unimodel test function reflects the Good convergence property and the exploitation capability of any metaheuristic algorithms. We can check the exploitation capability and the convergence of any metaheuristic using unimodal functions. In the unimodal test functions, the fitness curves represent the algorithm convergence speed.
  • Multi-model Test Functions: Multimodal test functions are used to test the Local search and Global searchability of any metaheuristic algorithm. We can say any algorithm is successful when there is a balance between the Local search and Global search which is the Exploitation phase and the Exploration phase. We can use the multi-model test functions to check the local search and the global search ability of any metaheuristic algorithm and we can also analyze the algorithm Stability, Convergence speed, and convergence accuracy.
  • Fixed Dimension Test Function: Fixed Dimension test functions are used for verification purposes. Fixed Dimension test functions are used for verification. We can verify the algorithm's stability, convergence speed, and accuracy.

We use three standard test functions to compare the Particle Swarm Optimization algorithm and the Jaya optimization algorithm to verify the Effectiveness, flexibility, and performance of metaheuristics. Jaya Optimization Algorithm provides better results as compared to Particle Swarm Optimization Algorithm.













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