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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...

Metaheuristic Optimization Algorithms

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 Optimization Engineering - Metaheuristic Optimization Algorithms Optimization plays a very important role in science and engineering. Optimization aim is to find out the minimum or maximum value using any objective function or cost function. In optimization different Metaheuristic Algorithms are used to solve complex problems in various fields such as Engineering Problems, Medical Problems, Computer Problems, and different real-life problems that can not be solved using classical methods. Metaheuristic optimization algorithms are classified into two main categories as Single-based optimization algorithms and Population-based optimization algorithms.  Single-based Meta-heuristic algorithms are also known as Trajectory Algorithms. Single-based metaheuristic algorithms provide the single solution in every iteration. Single-based Metaheuristic algorithm examples: Tabu Search, Guided Local Search, Iterated Local Search, Stochastic Local Search, Iterated Local Search, Variable ne...
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