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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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WFLO in Python || Optimal Placement of Wind Turbines using PSO in Python...

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Wind turbine optimal placement using particle swarm optimization Implementation in Python. Video Chapters: 00:00 Introduction 00:30 Key Points 03:17 Implementation 05:50 Flowchart 06:32 Code 23:47 Apply PSO 31:53 Output SOURCE CODE import numpy as np import math import random #Probability Distribution Function def PDF(U,k,c): return (k / c) * (U / c)**(k - 1) * math.exp(-((U / c)**k)) #Calculate Alpha def Cal_alpha(Z,Z_o): return 0.5 / math.log (Z/Z_o) #Calculate Full Wake Effect def Full_WE(u_o,a,alpha,X,R_1): return u_o*(1-(2*a/(1+alpha*(X/R_1)**2))) #Calculate Partial Wake Effect def Partial_WE(u_o,a,alpha,X,R_1,A_Partial,A_Total): return u_o * (1-(2*a/(1+alpha*(X/R_1)**2)))*(A_Partial-A_Total) #Calculate No Wake Effect def No_WE(u_o): return u_o #Calculate Power def Power(u,Ideal_Power): if u<3: return 0 elif 3<=u<=12: return Ideal_Power elif 12<=u<=25: return 518.4 else: return 0 #Calcu...
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How to Initialize Population by Good-Point Set in Python ~xRay pixy

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How to Initialize Population by Good-Point Set in Python ~xRay pixy
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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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Implement TSP in Python ||Travelling Salesman Problem|| ~xRay Pixy

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Travelling salesman problem implementation in Python. Video Chapters: 00:00 Introduction 00:34 TSP Code 06:51 Calculate the Total Distance 11:17 Find Out the Optimal Route and Minimum Distance 15:03 Output 16:00 Conclusion
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