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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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Artificial Ecosystem Based Optimization Algorithm Step-by-Step ~xRay Pixy

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Artificial Ecosystem Based Optimization Algorithm


Learn Artificial Ecosystem-based Optimization (AEO) Algorithm Step-by-Step with Example. - Algorithm Type: Nature Inspired Population-Based Metaheuristic Optimization - Used to solve Optimization Problems, Real life Engineering Design Optimization Problems - Provide best results when tested on different benchmark functions. - Outperforms other metaheuristics performance.

Video Chapters: Introduction: 00:00 Algorithm Introduction: 01:06 The Ecosystem on Earth: 02:01 Food Chain: 06:28 Artificial Ecosystem-based Optimization Algorithm: 08:27 Artificial Ecosystem-based Optimization Algorithm Steps: 10:41 Mathematical Models: 12:12 Decomposition Process: 19:15 Conclusion: 22:29

An ecosystem is also known as Ecological System. Ecosystem components are Abiotic and Biotic.
  • Abiotic components are non-living parts of the Ecosystem like Rock, water, air, etc.
  • Biotic components are living parts of an ecosystem like Animals, Plants, and microorganisms.
Living Things Grow, Change with time, Develop and Dies. They can reproduce and Divide into many. In the biotic components, we have Producers, Consumers, and decomposers. The artificial Ecosystem-based Optimization (AEO) Algorithm mimics the Producers, consumers, and decomposers' behavior.

Producers are also known as Autotropes for example Plants. Plants can make their own food through photosynthesis. In the photosynthesis process, Green plants use sunlight, water, and carbon dioxide to generate glucose.
Consumers have also known as Heterotropes. Consumers can't manufacture their own food they depend on others for Food/Energy. Consumers are categorized as Primary Consumers: Herbivores, Secondary Consumers: carnivores, and Tertiary Consumers: omnivores.
  • Herbivores depend on producers for their food and energy. They can eat Green plants, vegetables, and Fruits. Herbivore needs a lot of energy you can see sheep, and cows eat all they long.
  • Carnivores demand other consumers for their food.
  • Omnivores depands on herbivores and carnivores for food and energy. Feed on both plants as well as Meat.

The artificial Ecosystem-based Optimization (AEO) Algorithm is inspired by the energy flow in the ecosystem. It mimics the Producers, consumers, and decomposers' behavior.

Artificial Ecosystem-based Optimization (AEO) Algorithm Steps

  1. Initialize algorithm parameters.
  2. Initialize the population for N individuals randomly in the search space.
  3. Calculate fitness values for each individual and select the best among all.
  4. Check if (Current Iteration <= Maximum Iteration) // Stopping Criteria
  5. Update Producers Position using Production operator.
  6. Update Consumer's Position according to Herbivore, Carnivore, Omnivore behavior.
  7. Calculate Fitness values for each and find out the best among all.
  8. Update individual's position using Decomposition Operator.
  9. Calculate Fitness values for each and find out the best among all.
  10. Check algorithm stopping criteria (if met stop else GOTO step 5 and repeat).
  11. Display Best Solution found.







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