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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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Hybrid Grasshopper Optimization Algorithm with Genetic Algorithm

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Hybrid Grasshopper Optimization Algorithm and Genetic Algorithm


GOA-GA Hybrid Metaheuristics Video Chapters: Introduction: 00:00 Hybrid Metaheuristics: 01:11 Metaheuristic Hybridization Types: 02:47 Hybrid Grasshopper Optimization Algorithm - Genetic Algorithm: 04:06 Hybrid Grasshopper and Genetic Algorithm Steps: 08:54 Conclusion: 13:00

Hybrid methods are powerful as compared to others. Suppose, we have 2 Algorithms: Algorithm A and Algorithm B. Now suppose we merge the merits of both algorithms and formed Hybrid A-B Algorithm. New Algorithm i.e., Hybrid A-B Algorithm is better as compared to Algorithm A or Algorithm B.

Metaheuristics Hybridization Types:
  • Metaheuristic with Metaheuristic.
  • Metaheuristic with Exact Methods.
  • Metaheuristic with Constraint Programming, Artificial Intelligence.
  • Metaheuristic with Data Mining and Machine Learning Techniques.
Hybrid Grasshopper Optimization Algorithm and Genetic Algorithm |Hybrid G.O.A - G.A.|
  1. Hybrid GOA-GA is a combination of 2 Meta-heuristics.
  2. Author Combined the merits of the Grasshopper Optimization Algorithm and Genetic Algorithm and created Hybrid Grasshopper Optimization Algorithm with Genetic Algorithm to solve the system of non-linear equations.
  3. Metaheuristic Algorithm Challenges: Large Search Space, Variables, Parameters, Algorithm trapped into local optima, High Computation Cost. Hybrid Meta-heuristics are used to overcome such problems.
  4. In Hybrid GOA-GA , Genetic algorithm is used to prevent the algorithm to trapped in local optima. And Grasshopper algorithm is used for the Global search.
  5. Grasshopper algorithm is used for Exploration Phase (i.e., Global Search) and the Genetic Algorithm is used for the Exploitation phase (i.e., Local Search).



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