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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...

Whale Optimization Algorithm for Association Rule Mining.

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 Whale Optimization Algorithm for association rule mining. Input: Number of Maximum Iteration and Population Size, Minsupport, minconfedence.  Step 01: Initialize the population size for n search agents.[Xi(i=1,2,3,...n)] Step 02: Initialize i, A, C, L, and p. Step 03: Compute the fitness value of each search agent/whale. Step 04: X* = the best rule Step 05: While (CurrentIteration <= MaximumIteration ) Step 06: Update a, A, C, L and p. Step 07: For all whale poplation check          if (p<0.5) if(|A|<1)    For each Item in the solution Xi.    Update Items. Else if(|A|=1)     Select a random Item in Xi. Update Items. End if        For each item in the solution Xi. If the Item is odd, it belongs to the antecedent, Otherwise, it belongs to the consequence. End for Step 08: Calculate the fitness of each search agent. Step 09: Update X* if there is a better solution. Ste...
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