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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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Remora Optimization Algorithm Step-by-Step Learning with Example ~xRay Pixy

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Remora Optimization Algorithm (ROA)


Remora Optimization Algorithm (ROA) is recently proposed Bionics based, Nature Inspired Metaheuristic Optimization Algorithm used to solve Global Optimization Problems. Remora Optimization Algorithm is proposed by Heming Jia, Xiaoxu Peng and Chunbo Lang in 2021. Remora Optimization Algorithm is basically inspired by the Parasitic features of remora and Random Host Replacement of remora. Remora use suction technique for their survival. They attached themselves to the host animals such as Whales, Sea Turtles, Sharks, Swordfish and other. They use their suction disk to easily attach themselves with host.

Remora clean host body from Parasites, Bacteria's, and in return they get their food for survival. They also eat the leftover food from their host. In ROA, Whale Optimization Algorithm and Swordfish Optimization Algorithm is used to update remora position in the search space. In ROA, the fusion framework is used by switching between Remora and two host (Whale, Swordfish). Remora follow 2 host Whale and Swordfish in this algorithm.
Remora Optimization Algorithm Advantages:
  • Solve Global Optimization Problems.
  • Better as compare to heuristic algorithms.
Remora Optimization Algorithm Limitations:
  • Slow Convergence Rate.
  • Poor Solution Accuracy.
  • For some engineering problems stuck in local optima.
Remora Optimization Algorithm Steps:
  • Initialize population for N remora.
  • Using fitness function evaluate performance for each remora.
  • Find out the best and worst remora in the current population.
  • Update algorithm parameters.
  • Update Remora Position
  • Again evaluate performance for updated remoras.
  • Compare solution and display best among all.
  • Find out the best and worst remora in the current population.
  • Update algorithm parameters.
  • Update Remora Position.
  • Again evaluate performance for updated remoras.
  • Compare solution and display best among all.


Meta-heuristic Algorithms   CLICK HERE...

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