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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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Firefly Optimization Algorithm

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Firefly algorithm is a swarm-based metaheuristic algorithm that was introduced by Yang. Firefly Algorithm is inspired by the FLASHING Behavior of Fireflies. 

Assumptions

  • Fireflies are attracted to each other.
  • Attractiveness is proportional to BRIGHTNESS. 
  • Less Brighter Firefly is attracted to the Brighter Firefly.
  • Attractiveness decrease as the distance between 2 fireflies increase.
  • If brightness for both is the same, fireflies move randomly.
  • New Solutions are generated by Random walks & the Attraction of fireflies.

Firefly Optimization Algorithm Steps
  1. Initialize Parameters.
  2. Generate Population of n Fireflies.
  3. Calculate Fitness Value for Each Firefly.
  4. Check stopping criteria if (CurrentIteration := 1 to MaximumIteration ).
  5.  Update Position and Light Intensity for Each Firefly.
  6. Report the Best Solution.
Initialize Parameters, Population of Fire Fly Swarm.
Population Size (n) = 20;
Maximum Iteration (Maxt) = 50;
Dimension (d) = 10;
Upper Bound [UB] = 100;
Lower Bound [LB] = -100;

Calculate Fitness Value [Light Intensity] for Each FireFly.
The light intensity of Firefly (i.e., 𝐼_𝑖) at 𝑥_𝑖 is computed by the Value of the Objective Function.

Firefly Position Updated as:
For i = 1 to n -1;
For j = i + 1 to n;
  IF ( 𝑰_𝒋 > 𝑰_𝒊 )
      Update Position. [move Firefly i towards Firefly j ];
    End IF
  End For
 End For

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