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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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BARC Previous Year Question Papers Solved

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 BARC Computer Science and Information Technology Previous Year Question  Solved 

1.) The worst-case time complexity of Quick Sort is:

  • O(nlogn)
  • O(n)
  • O(n^2)
  • None
Answer: O(n^2)
Explanation: Quicksort is based on the divide and conquers paradigm. Quicksort expected average running time is O(nlogn)

2.) In C programming operator '&' is used to represent:
  • Logical AND
  • Bitwise AND
  • Logical OR
  • Bitwise OR
Answer: Bitwise AND
Explanation: logical AND (&&), bitwise AND (&), Logical OR (||), Bitwise OR(|)

3.) When a static variable is initialized:
Answer: First time when a loop is executed.
Explanation: Static variables only execute once.

4.) What is the maximum height of the AVL tree with 7 nodes? Assume that height of the tree with a single node is 0.
Answer: 3.

5.) Data structure used in the recursive algorithm is:
Answer: Stack

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PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE

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Cuckoo Search Algorithm for Optimization Problems

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 Cuckoo Search Algorithm - Metaheuristic Optimization Algorithm What is Cuckoo Search Algorithm? Cuckoo Search Algorithm is a Meta-Heuristic Algorithm. Cuckoo Search Algorithm is inspired by some Cuckoo species laying their eggs in the nest of other species of birds. In this algorithm, we have 2 bird Species.  1.) Cuckoo birds   2.) Host Birds (Other Species) What if Host Bird discovered cuckoo eggs? Cuckoo eggs can be found by Host Bird.  Host bird discovers cuckoos egg with Probability of discovery of alien eggs.  If Host Bird Discovered Cuckoo Bird Eggs. The host bird can throw the egg away. Abandon the nest and build a completely new nest. Mathematically, Each egg represent a solution and it is stored in the host bird nest. In this algorithm Artificial Cuckoo Birds are used. Artificial Cuckoo can lay one egg at a time. We will replace New and better solutions with less fit solutions. It means eggs that are more similar to host bird has opportunity to de...
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PSO (Particle Swarm Optimization) Example Step-by-Step

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 Particle swarm optimization (PSO) What is meant by PSO? PSO is a computational method that Optimizes a problem. It is a Population-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. n Particle Swarm Optimization the solution of the problem is represented using Particles. [Flocking birds are replaced with particles for algorithm simplicity]. Objective Function is used for the performance evaluation for each particle / agent in the current population. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to update particle position in each iteration. How PSO will optimize? By Improving a Candidate Solution. How PSO Solve Problems? PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). The population of Candidate Solutions (i.e., Particles). What is Search Space in PSO? It is the range in which the algorithm computes th...
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Particle Swarm Optimization (PSO)

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Particle Swarm Optimization (PSO) is a p opulation-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. PSO is a computational method that Optimizes a problem. PSO searches for Optima by updating generations. It is popular is an intelligent metaheuristic algorithm.  In Particle Swarm Optimization the solution of the problem is represented using Particles. [Flocking birds are replaced with particles for algorithm simplicity]. Objective Function is used for the performance evaluation for each particle / agent in the current population. After a number of iterations agents / particles will find out optimal solution in the search space. Q. What is PSO? A. PSO is a computational method that Optimizes a problem. Q. How PSO will optimize? A. By Improving a Candidate Solution. Q. How PSO Solve Problems? A. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to ...
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how is the LBP |Local Binary Pattern| values calculated? Step-by-Step with Example

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

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Particle Swarm Optimization Implementation in Python Video Chapters: 00:00 Introduction 02:01 Code 05:55 Position Initialization 08:06 PSO Main Loop 08:42 Velocity Calculation 10:02 Position Update 10:36 Fitness Evaluation 13:21 Objective Function 17:44 Result 19:00 Conclusion .....................................................SOURCE CODE......................................................................... import random import numpy as np from tkinter import messagebox #Define Class Particles class Particle: def __init__ (self,position): self.position=position self.velocity=np.zeros_like(position) self.best_position=position self.best_fitness=float('inf') def PSO(ObjF,Pop_Size,D,MaxT): swarm_best_position=None swarm_best_fitness=float('inf') particles=[] #Posotion Initialization position=np.random.uniform(-0.5,0.5,D) particle=Particle(position) particles.append(particle) #Fit...
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Grey Wolf Optimization Algorithm

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 Grey Wolf Optimization Algorithm  (GWO) Grey Wolf Optimization Grey Wolf Optimization Algorithm is a metaheuristic proposed by Mirjaliali Mohammad and Lewis, 2014. Grey Wolf Optimizer is inspired by the social hierarchy and the hunting technique of Grey Wolves. What is Metaheuristic? Metaheuristic means a High-level problem-independent algorithmic framework (develop optimization algorithms). Metaheuristic algorithms find the best solution out of all possible solutions of optimization. Who are the Grey Wolves? Wolf (Animal): Wolf Lived in a highly organized pack. Also known as Gray wolf or Grey Wolf, is a large canine. Wolf Speed is 50-60 km/h. Their Lifespan is 6-8 years (in the wild). Scientific Name: Canis Lupus. Family: Canidae (Biological family of dog-like carnivorans). Grey Wolves lived in a highly organized pack. The average pack size ranges from 5-12.  4 different ranks of wolves in a pack: Alpha Wolf, Beta Wolf, Delta Wolf, and Omega Wolf. How Grey Wolf Optimiza...
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Bat algorithm Explanation Step by Step with example

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There are about 1000 species of Bats. Bat Algorithm is based on the echolocation behavior of Micro Bats with varying pulse rates of emission and loudness. All bats use echolocation to sense distance and background barriers.  Microbats are small to medium-sized flying mammals. Micro Bats used a Sonar that is known as Echolocation to detect their prey. Bats fly randomly with the velocity at the position with a fixed frequency and loudness for prey.  Q. Whats is Frequency? A. Frequency is the number of waves that pass a fixed point in unit time. Wavelength is the minimum distance between two nearest particles which are in the same phase. Here, Sound waves are used by microbats to detect prey.  Q. What is Position? A. A place where something or someone is located.  Q. What is Velocity? A. Speed of something in a given direction.  Q. What is loudness. A. Loudness refers to how soft or loud sound seems to listeners.  Q. What is pulse rate? ...
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Grey Wolf Optimization Algorithm Numerical Example

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Whale Optimization Algorithm Code Implementation || WOA CODE || ~xRay Pixy

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