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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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Brain Storm Optimization Algorithm || Step-By-Step || ~xRay Pixy

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Learn Brainstorm optimization (BSO) algorithm step-by-step using examples.
Video Chapters: BSO Algorithm
00:00 Introduction
02:33 Brainstorming Process
03:10 Example 01: Brainstorming Process in Real-Life
05:25 Brain Strom Optimization
08:39 Example 02
14:09 BSO Steps
17:51 Conclusion

The Brain Storm Optimization (BSO) algorithm is a swarm intelligence method inspired by human brainstorming. It aims to find optimal solutions by combining clustering, exploration, and exploitation techniques.

Why is BSO Useful?

  • It balances global search (exploration) and local search (exploitation) efficiently.

  • It avoids getting stuck in local optima by introducing diversity in solution generation.

  • It is adaptable and can be integrated with machine learning techniques like clustering.

Key Concepts:

  • Solution Clustering: Solutions are grouped into clusters to refine the search space.

  • Exploration (Divergent Thinking): New solutions are generated far from existing clusters to discover new possibilities.

  • Exploitation (Convergent Thinking): Refining solutions near the best-known solutions to improve precision.

  • Selection: The best solutions are kept for the next iteration, ensuring continuous improvement.

Applications:

  • Optimization Problems (Engineering, Logistics, Scheduling)

  • Machine Learning & Data Mining (Pattern Recognition, Feature Selection)

  • Swarm Intelligence Research (Enhancing AI-driven problem-solving)

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PSO Python Code || Particle Swarm Optimization in Python || ~xRay Pixy

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Grey Wolf Optimization Algorithm

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