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Markov Chains || Step-By-Step || ~xRay Pixy

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Learn Markov Chains step-by-step using real-life examples. Video Chapters: Markov Chains 00:00 Introduction 00:19 Topics Covered 01:49 Markov Chains Applications 02:04 Markov Property 03:18 Example 1 03:54 States, State Space, Transition Probabilities 06:17 Transition Matrix 08:17 Example 02 09:17 Example 03 10:26 Example 04 12:25 Example 05 14:16 Example 06 16:49 Example 07 18:11 Example 08 24:56 Conclusion
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How do Face Recognition Algorithms Detect Human Faces

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First, learn about FACE DETECTION. Then face recognition would be an easy task for you. Because most of the computer algorithms work first on face detection after that they can recognize. First learn, how to detect humans or anything you want to detect (objects/ birds/other). When your software will be able to detect it. Then train it for recognition. I worked on Human face recognition. First, I created software for human face detection. Once your software can detect it. You can easily train it for recognition/identification.
Learn Image Processing in MATLAB
https://www.youtube.com/playlist?list=PLVLAu9B7VtkZAknbwBKJAI9Ny7c0M3nTT
Face Detection Systems are used for the identification of a person from digital images. The face detection system can extract the different facial features from a digital image. A set of facial features extracted from a digital image includes eyes, nose, mouth, and face region. Face recognition is a difficult task due to the different facial expressions. With the help of a face detection system presence of a human face can be detected easily. For the detection of different facial features, the Viola-Jones algorithm is used in the proposed work. To make the task more manageable, Viola-Jones requires full view frontal upright faces.
video is helpful for those who are working on Face Detection or Face Recognition Projects. - In this video, Different Facial Features like Eyes, Nose, and Mouth are Detected from the Digital Images. - The algorithm used for Facial Features detection is: Viola-Jones Algorithm. - Viola-Jones Algorithm is used for the Face Detection Projects for Face Detection. - Viola-Jones Algorithm works on different parameters and MATLAB Functions. - In this video, you can learn how to use the Viola-Jones Algorithm for the Detection of Different Facial Features from the Digital Images. Tools used: 1.) MATLAB SOFTWARE 2.) Digital Images for testing In this video, we detected different facial features such as Eyes, Nose, and Mouth. The Algorithm used for face detection is the Viola-Jones algorithm. Different models are available for face recognition. These models can detect the face, eyes, and nose. But the detection process is very slow. To solve this problem, the viola-jones algorithm is applied to detect the different facial features. Viola-jones algorithm is to face and provides accurate results from the input digital image. Software Required - MATLAB and Digital Images for Testing purposes. Facial FEATURES: In any recognition system, the selection of feature extraction plays a vital role. There are a lot of feature extraction techniques proposed efficiency of the proposed system is not acceptable due to the lack of appropriate feature selection. Working: A framework for the purposed face detection system is designed. First, upload the test image from the database for feature detection. Than the viola-jones algorithm is applied to the input image for the detection of different facial features.

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PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE

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Cuckoo Search Algorithm for Optimization Problems

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Particle Swarm Optimization (PSO)

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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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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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 Grey Wolf Optimization Algorithm Numerical Example Grey Wolf Optimization Algorithm Steps 1.) Initialize Grey Wolf Population. 2.) Initialize a, A, and C. 3.) Calculate the fitness of each search agent. 4.) 𝑿_𝜶 = best search agent 5.) 𝑿_𝜷 = second-best search agent 6.) 𝑿_𝜹 = third best search agent. 7.) while (t<Max number of iteration) 8.) For each search agent       update the position of the current search agent by the above equations end for 9.) update a, A, and C 10.) Calculate the fitness of all search agents. 11.) update 𝑿_𝜶, 𝑿_𝜷, 𝑿_𝜹 12.) t = t+1 end while 13.) return 𝑿_𝜶 Grey Wolf Optimization Algorithm Numerical Example STEP 1.  Initialize the Grey wolf Population [Initial Position for each Search Agent] 𝒙_(𝒊  )  (i = 1,2,3,…n)     n = 6 // Number of Search Agents  [ -100, 100] // Range Initial Wolf Position      3.2228     4.1553    -3.8197     4.2330  ...
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Whale Optimization Algorithm Code Implementation || WOA CODE || ~xRay Pixy

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