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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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Jellyfish Search Optimizer Step-by-Step Leaning with Example ~xRay Pixy

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Jellyfish Search Optimizer (2020)


Video Chapters:
Introduction: 00:00
Jellyfish Search Optimizer: 00:26
About Jellyfish: 01:11
Jellyfish Search Optimize Steps: 06:37
Time Control Calculation: 10:50
Passive Motion: 11:59
Action Motion: 12:52
Ocean Current: 15:27
Conclusion: 16:31

Jellyfish: Sea Animals Without Backbone.
Jellyfish Size: 1-16 inch.
Jellyfish Lifespan: 1 Month / 1 Year depend on species.
Jellyfish Diet: Nutrients Plants, Planktons, Small Fishes, Fish eggs.

Jellyfish Search Optimizer is also known as artificial Jellyfish Search Optimizer. Jellyfish Search Optimizer is inspired by jellyfish food searching behavior in the ocean. We can use Jellyfish Search Optimizer to solve Global Optimization problems, Complex real world optimization problems and other.


Jellyfish movements are result due to:
  1. Ocean Current (Horizontal Movement and Vertical Movement).
  2. Jellyfish Motion inside Swarm (Passive Motion and Active Motion).
Time Control mechanism is used to switch jellyfish motion between Active Motion and Passive Motion.

Jellyfish Search Optimizer Steps:
  1. Initialize important parameters randomly.
  2. Initialize population for N individuals randomly.
  3. Check If (current Iteration<=Maximum iterations)
  4. For all Individuals. (i=1:N)
  5. Calculate Time control mechanisms. 
  6. Update position for N individuals. 
  7. Check updated solution boundary.
  8. Evaluate new solutions.
  9. Increment counter and check stopping criteria. 
  10. Display best solution found. 
STEP 05: Calculate Time control mechanisms c(t). 
MaxT = Maximum Number of Iterations
rand = random value (0,1)

STEP 06: Update position for N individuals. 
Jellyfish Motion updated using:
  1. Ocean Current
  2. Active Motion
  3. Passive Motion
How to Calculate Ocean Current?



How to Calculate Passive Motion?


How to Calculate Active Motion?










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