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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 Algorithm Step-by-Step with Numerical Example [PART - 2]

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Firefly Algorithm


Firefly algorithm is a swarm-based metaheuristic algorithm that was introduced by Yang. Firefly algorithm is used for solving optimization problems. In this video, you will learn the Firefly algorithm with an example.
Firefly Algorithm is inspired by the FLASHING Behavior of Fireflies.
For simplicity certain Assumptions used in Firefly Optimization Algorithm: -
1.) Fireflies are attracted to each other. 2) Attractiveness is proportional to BRIGHTNESS. 3.) Less Brighter Firefly is attracted to the Brighter Firefly. 4.) Attractiveness decrease as the distance between 2 fireflies increase. 5.) If the brightness for both is the same, fireflies move randomly. 6.) New Solutions are generated by Random walks & the Attraction of fireflies.

Firefly Optimization Algorithm Steps:
Initialize Parameters
Initialize Population randomly in the search space.
Compute Fitness values and select the best solution.
Check Stopping Criteria.
While Current Iteration = 1:Maximum Iteration for i=1:Population Size for j=1:Population Size If ( Light Intensity(j) < Light Intensity(i) Move Firefly i towards j. Else Move i Randomly. End If Update New Position and Light Intensity. end end Check new solutions are within bound or not. Merge Population Sort Population Rank Fireflies according to their light intensity Find Best Solution Ever Found Damp Mutation Ratio end

#Metaheuristic #Algorithms
Meta-heuristic Algorithms
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