Posts

Showing posts from June 21, 2022

New Post

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

Image
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

Aquila Optimization Algorithm Step-by-Step Explanation ~xRay Pixy

Image
  Video Chapters: Introduction: 00:00 Aquila Optimizer: 00:31 Aquila Hunting Methods: 02:09 Aquila Optimizer Steps: 03:33 Aquila Optimizer Mathematical Models: 06:07 Conclusion: 13:00 Aquila Optimization Algorithm is inspired by the Aquila Behavior in the nature. This algorithm is basically inspired by the aquilas hunting methods. How they catch their prey in the real life? Aquila Hunting Methods: Method 01: High Soar with Vertical Stoop. [i.e., Expanded Exploration] Method 02: Contour Flight with Short Glide Attack. [i.e., Narrowed Exploration] Method 03: Low Flight with Slow Decent Attack. [i.e., Expanded Exploitation] Method 04: Walking and Grab the Prey. [i.e., Narrowed Exploitation] Aquila Optimization Algorithm Steps: Step 01: Initialize Algorithm Parameters and Population Randomly. Step 02: Check While (Current Iteration <= Maximum Iteration) Step 03: Evaluate Agents Performance using Fitness Function. Step 04: For all agents update Location mean value. Step 05: Update Le...
More posts