Posts

Showing posts from January 7, 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

Black Hole Optimization Algorithm Step-by-Step with Example ~xRay Pixy

Image
 Black Hole Optimization Algorithm || BHO Algorithm The black hole algorithm is a Natural Heuristic Algorithm by simulating the ‘‘Black Hole’’ phenomenon in the universe.  Black Hole Optimization Algorithm is a new bio-inspired metaheuristic approach based on the observable fact of black hole phenomena. Black Hole : Region in the space where Gravity is so Strong. No object can escape from its powerful gravitational pull. Black holes are formed in the  space when star of massive size collapses. Anything falls into black hole is forever gone from our universe. Black Hole Optimization Algorithm Steps: Step 01: Randomly Initialize population for N candidate solutions in the search space. Consider Stars as initial population and each start is candidate solution. And Best  Candidate among all (i.e., Best Solution) is considered as Black Hole. Step 02: Calculate Fitness Values for each agent in the current population. Calculate fitness values for each candidate and best am...
More posts