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Hidden Markov Model (HMM)

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Hidden Markov Model (HMM)  VIDEO LINK:  https://youtu.be/YIGCWNG8BIA A Hidden Markov Model (HMM) is a statistical model in which the system has hidden states that cannot be directly observed, but produce observable outputs. It is based on the Markov property, meaning the next state depends only on the current state. Video Chapters: HMM in Artificial Intelligence 00:00 Introduction 00:31 Statistical Model 00:54 HMM Examples 02:30 HMM 03:10 HMM Components 05:23 Viterbi Algorithm 06:23 HMM Applications 06:38 HMM Problems 07:28 HMM in Handwriting Recognition 11:20 Conclusion  HMM COMPONENTS A Hidden Markov Model (HMM) is a statistical model in which the system has hidden states that cannot be directly observed, but produce observable outputs. It is based on the Markov property, meaning the next state depends only on the current state. An HMM consists of states, observations, transition probabilities, emission probabilities, and initial probabilities. It is commonly used in a...
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Donkey and Smuggler Optimization Algorithm || STEP-BY-STEP || ~xRay Pixy

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Donkey and Smuggler Optimization Algorithm


Learn Donkey and Smuggler Optimization Algorithm Step-By-Step with Examples
Video Chapters:
Introduction: 00:00
Donkey's Behavior: 02:02
Donkey Mode: 04:20
Donkey and Smuggler Optimization Algorithm: 06:49
Smuggler Mode: 08:02
Donkey and Smuggler Optimization Algorithm STEPS: 11:54
Donkey and Smuggler Optimization Algorithm FLOWCHART: 15:39
Conclusion: 16:24
About Donkey and Smuggler Optimization Algorithm: - Introduced in 2019 by Ahmed S Shamsaldin et. al. - Nature Inspired Population-Based Metaheuristic Optimization Algorithm. - Used to Solve Complex Optimization Problems. - Implemented to solve Real Life Optimization Problems such as Travelling Salesman Problem (TSP) Routing Problems
    Ambulance Routing

Donkey and Smuggler Optimization Algorithm MODES:
  • Smuggler Mode [Non-Adaptive]
  • Donkey Mode [Adapative]
Donkey and Smuggler Optimization Algorithm STEPS:
  1. Initialize algorithm parameters i.e., Population Size, Dimensions, and Maximum Iterations.
  2. Initialize the agent's position in the search space.
  3. Calculate Fitness values for agents.
  4. Choose the best solution among all and send the donkey.
  5. Reevaluate the vest solution's fitness value. Check if it is a better solution or not.
  6. If the Current Solution is NOT GOOD:
RUN: Re-evaluate the Population Fitness and update the best solution.
FACE and Suicide: Choose the second-best solution as the best solution.
FACE and Support: Use the second-best solution to support the best solution.


Video Chapters: Introduction: 00:00 Donkey's Behavior: 02:02 Donkey Mode: 04:20 Donkey and Smuggler Optimization Algorithm: 06:49 Smuggler Mode: 08:02 Donkey and Smuggler Optimization Algorithm STEPS: 11:54 Donkey and Smuggler Optimization Algorithm FLOWCHART: 15:39 Conclusion: 16:24

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