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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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Optimization Engineering | Metaheuristic Optimization Algorithm Basic Fundamentals

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 Q. What is Optimization? 

A. Optimization means Optimum Point Where conditions are best and most favorable. Optimization algorithms help to obtain the best solutions for complex problems. Different numerical methods for optimization are used to design better systems. 

Q. Why we do Optimization?

A. To Find the better/best among different possible solutions

Q. Why Objective functions are used?

A. Objective functions are used to Maximize or Minimize values that you are trying to Optimize. Using objective functions you can obtain a minimum or maximum value.

Q. Define Meta-heuristic optimization. 

A. Metaheuristic algorithms plays important role in solving real-life problems. Metaheuristic algorithms are Optimization methods used to solve complex engineering problems. A Metaheuristic is an advanced technique for finding good solutions to a complex problem. 

Q. Define multi-objective optimization problems?    

A. When designers want to optimize two or more two objective functions simultaneously is known as a multi-objective optimization problem (aka multi-objective).


Q. Where metaheuristic algorithms are used? 

A. Metaheuristic algorithms are used

  • Machine Learning
  • Data Mining 
  • System Modeling
  • Simulation
  • Engineering designs
  • Fluid Dynamics
  • Telecommunications
  • Routing Problems
  • Biology
  • Finance 

Q. Metaheuristic algorithms design criteria? 

A. Two important design criteria's for metaheuristic optimization are:

  • Exploration of the search space
  • The exploitation of the best solution found

Q. Role of random values in optimization? 

In this video, you will learn about metaheuristic algorithm basic fundamentals.

Video Link: https://youtu.be/PZ1kjwZ_pl4

Click Here to Watch Now

Q. How Optimization algorithm works? 

A. Optimization is performed randomly. It means the optimization process starts randomly by creating a set of random solutions.

Step 01: Create random solutions (i.e., Create initial population randomly). 

Step 02: Compute fitness value for each solution. 

Step 03: Combine, move or evolve the initial population over a pre-defined number of iteration. 

Step 04: Repeat until the best solution is obtained. 

 In metaheuristic algorithms, the mechanism of combining, moving, or evolving the solution during optimization plays a major role to obtain the best solution among all.

Q. different types of optimization algorithm.

Hunting Search

Altruism Algorithm

Spiral Dynamic Algorithm (SDA)

Strawberry Algorithm

Artificial Algae Algorithm (AAA) 

Bacterial Colony Optimization

Differential Search Algorithm (DS

Flower pollination algorithm (FPA)

Krill Herd

Water Cycle Algorithm 

Black Holes Algorithm

Cuttlefish Algorithm

Plant Propagation Algorithm

Social Spider Optimization (SSO)

Spider Monkey Optimization (SMO) algorithm

Animal Migration Optimization (AMO) Algorithm

Artificial Ecosystem Algorithm (AEA)

Grey Wolf Optimizer

Seed Based Plant Propagation Algorithm

Lion Optimization Algorithm (LOA): A Nature-Inspired

Self-propelled Particles

Differential Evolution (DE)

Bacterial Foraging Optimization

Harmony Search (HS)

MBO: Marriage in Honey Bees Optimization

Artificial Fish School Algorithm

Bacteria Chemotaxis (BC) Algorithm

Social Cognitive Optimization (SCO)

Artificial Bee Colony Algorithm

Bees Algorithm

Glow-worm Swarm Optimization (GSO)

Honey-Bees Mating Optimization (HBMO) Algorithm

Invasive Weed Optimization (IWO)

Shuffled Frog Leaping Algorithm (SFLA)

Central Force Optimization

Intelligent Water Drops algorithm, or the IWD algorithm

River Formation Dynamics

Biogeography-based Optimization (BBO)

Roach Infestation Optimization (RIO)

Bacterial Evolutionary Algorithm (BEA)

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