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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

Glowworm Swarm Optimization (GSO) Algorithm ~xRay Pixy

Glowworm Swarm Optimization (GSO)


Video Timestamps: Introduction : 00:00 Glowworm Swarm Optimization Algorithm (GSO): 01:04 Glowworm Swarm Optimization Mathematical Model: 03:33 Conclusion: 08:40

Glowworms release excess energy as glow. Glowworms emit blue / green glow in the larval form. Glowworm eat other insects and many species also feed on fungus. Glowworms glow for different reasons: Trying to attract mates, Warning off predators' or Attracting other insects for food. Glowworm Swarm Optimization (GSO) Algorithm for solving Optimization Problems. Glowworm Swarm Optimization is introduced by Krishnanand and Ghoose in 2005. Glowworm Swarm Optimization is Nature inspired metaheuristic optimization algorithm. Glowworm Swarm Optimization mimic the lightening behavior of Glowworms in the nature. Glowworm Swarm Optimization is used in various areas such as Engineering, Robotics, Mathematics, Networking, and to solve various problems such as Scheduling problems, Vehicle routing problems and other. Glowworm Swarm Optimization (GSO) Algorithm is successfully applied in various field for example: Vehicle Routing Problems, Wireless Sensor Network Problems and Scheduling Problems.

Glowworm Swarm Optimization Working: Glowworm Swarm Optimization start with population initialization. Randomly distribute the glowworms in the solution space. Each glowworm carry luciferin along with them. Each glowworm represent solution of objective function and certain quantity of luciferin. Among all glowworms brighter solution is considered as agent having better solution. Update luciferin value for each glowworm.

Glowworm Swarm Optimization (GSO) algorithm main phases:

1.) Luciferin Update Phase: Each agent carry luciferin along with it. Fitness values are used to represent Luciferin for each individuals.
2.) Glowworm Movement Update Phase.
3.) Glowworm Probabilistic Mechanisms.
4.) Update Neighborhood Range for each glowworm.

Glowworm Swarm Optimization (GSO) Algorithm Advantages: Glowworm Swarm Optimization (GSO) Algorithm can avoid missing the optimal solution because of intelligent changes of the decision radius.


Swarm Intelligence based Population-based Metaheuristics Watch Now!

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