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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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GWO Python Code || Grey Wolf Optimizer in Python || ~xRay Pixy

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SOURCE CODE import numpy as np import tkinter as tk import matplotlib.pyplot as plt from tkinter import messagebox def initialization (PopSize,D,LB,UB):     SS_Boundary = len(LB) if isinstance(UB,(list,np.ndarray)) else 1     if SS_Boundary ==1:         Positions = np.random.rand(PopSize,D)*(UB-LB)+LB     else:         Positions = np.zeros((PopSize,D))         for i in range(D):             Positions[:,i]=np.random.rand(PopSize)*(UB[i]-LB[i])+LB[i]     return Positions def GWO(PopSize,MaxT,LB,UB,D,Fobj):     Alpha_Pos = np.zeros(D)     Alpha_Fit = np.inf     Beta_Pos = np.zeros(D)     Beta_Fit = np.inf     Delta_Pos = np.zeros(D)     Delta_Fit = np.inf     Positions = initialization(PopSize,D,UB,LB)     Convergence_curve = np.zeros(MaxT)     l = 0     while l...
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