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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

Manta Ray Foraging Optimization (MRFO) Algorithm Example

Manta Ray Foraging Optimization (MRFO) Algorithm 

Manta Ray Foraging Optimization (MRFO) Algorithm Example

Step 01: Initialize Population Size

Suppose, Population Size = 4;

Lower Bound = -10;

Upper Bound = 10;

Maximum Iteration = 4;

Suppose Initial Population

 1.1

 2

 0.9

 3

Step 02: Compute Fitness Value for each using fitness function.

Fitness Values

1.21

4

0.81

9

Step 03: Obtain Best Solution

Best solution = Minimum Fitness Value in the current population

Best Solution = 0.81

Step 04: Check Stopping Criteria

While (Current < Maximum Iteration)

 1 < 4   ((True) move to next step ) 

If stopping criteria is then stop and return the best cost.

Step 05: Update Position for each individual.

For i = 1 to PopulationSize

For i = 1:4

If (rand < 0.5) 

THEN Cyclone Foraging

Else

Chain Foraging

End if

Step 06: Compute Fitnee Value for Each individual and Select Best Individual.

Step 07: Perform Somersault Foraging. 

Step 08: Compute Fitness Value for Each.

End For

End While

Step 09: Return Best Solution Found.

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