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Confusion Matrix with Real-Life Examples || Artificial Intelligence || ~...

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Learn about the Confusion Matrix with Real-Life Examples. A confusion matrix is a table that shows how well an AI model makes predictions. It compares the actual results with the predicted ones and tells which are right or wrong. It includes True Positive (TP), False Positive (FP), False Negative (FN), and True Negative (TN). Video Chapters: Confusion Matrix in Artificial Intelligence 00:00 Introduction 00:12 Confusion Matrix 03:48 Metrices Derived from Confusion Matrix 04:26 Confusion Matrix Example 1 05:44 Confusion Matrix Example 2 08:10 Confusion Matrix Real-Life Uses #artificialintelligence #machinelearning #confusionmatrix #algorithm #optimization #research #happylearning #algorithms #meta #optimizationtechniques #swarmintelligence #swarm #artificialintelligence #machinelearning
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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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PSO Python Code || Particle Swarm Optimization in Python || ~xRay Pixy

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Grey Wolf Optimization Algorithm

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Grey Wolf Optimization Algorithm Numerical Example

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