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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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Soft Computing - Fuzzy Logic | Fuzzy Relations | DOM | FIS || Unit 1 || ...

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Learn Soft Computing Basics step-by-step using Example. Video Chapter: 00:00 Introduction 00:09 Topics Covered 02:13 What is Fuzzy Logic? 07:06 What is Crisp Set? 08:03 What is the Degree of Membership? 09:05 Fuzzy Logic Components 10:46 Fuzzy Logic Operators 11:47 Fuzzy Relations 15:03 Fuzzy Relation Composition 17:28 Fuzzy Inference System 17:52 Defuzzification What is a Crisp Set? A "crisp set" or "crisp logic" refers to the traditional, classical set theory and logic where elements either belong to a set or do not, with no in-between or degrees of membership. In crisp logic, membership is binary—something is either a member of a set (true) or not (false). What is a Fuzzy Logic? Fuzzy logic is a mathematical framework that deals with reasoning and decision-making in the presence of uncertainty and imprecision. Unlike classical (Boolean) logic, which is based on binary values (true or false, 0 or 1), fuzzy logic allows for the representation of parti...
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Solving Transportation Problem ||Operations Research and Optimization|| ...

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Unit 1  - INTRODUCTION TO OPTIMIZATION ALGORITHMS Video Chapters: Optimization Techniques 00:00 Introduction 02:39 Optimization Statement 05:27 Constrained Optimization Problems 06:06 Constrained Optimization Methods 07:38 Transportation Problem 14:40 Unconstrained Optimization Problems 20:15 Conclusion 1. What is Optimization?      Optimization is the process of finding the best solution or set of solutions from a range of possible options, with the aim of maximizing or minimizing a particular objective function. The objective function is a mathematical expression that represents the quantity to be optimized, such as maximizing profit, minimizing cost, or achieving the highest performance. There are two main types of optimization problems: Maximization Problems: These involve finding the maximum value of an objective function. Examples : maximizing profit, revenue, efficiency, or any other desirable outcome. Minimization Problems: These involve ...
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