Code for Computer Science

Water heater control system using Neuro Fuzzy Hybrid System. Video Chapters: 00:00 Introduction 01:38 Hybrid Approach 02:37 Neural Network 03:08 Neural Network Components 05:38 Fuzzy Logic 12:09 Neuro-Fuzzy Hybrid System 14:16 Numerical Example 22:22 Conclusion A neuro-fuzzy hybrid system, also known as a neuro-fuzzy system or fuzzy neural network, combines the principles of neural networks and fuzzy logic to create a more robust and versatile system for solving complex problems, particularly in the field of artificial intelligence and control systems. This hybrid approach aims to take advantage of the strengths of both neural networks and fuzzy logic to handle uncertainty, nonlinearities, and complex patterns effectively. The key components and concepts involved in a neuro-fuzzy hybrid system: Neural Networks Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized into layers, i...

Learn the Crystal Structure Algorithm Step-by-step with examples. Video Chapters: 00:00 Introduction 01:05 Key Points 01:41 Lattice 02:27 Lattice Types 03:48 Regular Crystal Shapes 04:05 Crystal Structure Formation 05:18 Crystal Characteristics 06:52 Crystal Structure Algorithm Algorithm Steps 14:11 Conclusion

MySQL Workbench Step-By-Step Video Chapters: 00:00 Server Connection 00:47 CREATE DATABASE 01:05 CREATE TABLE 02:14 INSERT INTO TABLE 03:53 MySQL Table Using PHP Script 08:44 Conclusion

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...

How to Initialize Population by Good-Point Set in Python ~xRay pixy

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...

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

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...

SOURCE CODE <html> <head> <title>Dynamic Barcode Generator</title> <link rel="stylesheet" href="bar.css"/>  <script src="https://cdn.jsdelivr.net/npm/jsbarcode@3.11.3/dist/JsBarcode.all.min.js"></script> </head> <body> <center> <h1>Dynamic Barcode Generator</h1> <div id="box">   <input id="barcode-input" type ="text" placeholder="Enter Text for Barcode"/>   <button onclick="BarCodeGenerate()" >Generate Barcode</button> <br><br>   <svg id ="barcode"></svg> </div>   <script>      function BarCodeGenerate(){          var text = document.getElementById("barcode-input").value;          JsBarcode("#barcode",text);      }             </script> </center> </body> </html>

---------------------------- clock.html --------------------------------------- <html> <head> <title>Javascript Analog Clock</title> <link rel ="stylesheet" href="clock.css"> <script src = "clock.js"></script> </head> <body> <div id="clockBox">      <div id="hour"></div>      <div id="minute"></div>      <div id="second"></div>      <div id="point"></div> </div> </body> </html> ------------------------------ clock.css ----------------------------------------- #clockbox{           height: 650px;           width: 650px;           background: url(clc.png);           background-size:100%;           margin:auto;         } #hour, #minute, #second{      ...

Learn Hunger Games Search (HGS) Optimization Algorithm Step-By-Step Hunger Games Search Algorithm Video Chapters: 00:00 Introduction 00:30 About Hunger Games Search Algorithm 06:00 Algorithm Steps 16:00 Conclusion

HTML + JAVASCRIPT CODE <html> <head> <title>JavaScript Calculator</title> <link rel="stylesheet" href="calc.css"/> </head> <body> <div class="front"> <form name="form"> <input id="clac" type="text" name="result"><br><br> <input type= "button" value ="1" onclick="form.result.value += '1'"> <input type= "button" value ="2" onclick="form.result.value += '2'"> <input type= "button" value ="3" onclick="form.result.value += '3'"> <input type= "button" value ="+" onclick="form.result.value += '+'"> <br><br> <input type="button" value="4" onclick="form.result.value += '4'"> <input type="button" value="5"...

Video Chapters: Dwarf Mongoose Optimization Algorithm 00:00 Introduction 02:10 About Mongoose 03:55 Mongoose Communication 05:00 Mongoose Lifestyle 07:23 Dwarf Mongoose Optimization Algorithm Steps 11:29 Optimization Process Start 16:00 Conclusion New Metaheuristic Optimization Algorithm Dwarf Mongoose Optimization We can use this algorithm to solve different optimization problems as when this algorithm is tested on different continuous, discrete optimization problems it provides efficient results. So, we can use this algorithm to solve complex optimization problems This algorithm is basically inspired by the foraging behavior of mongooses in real life. Dwarf Mongoose Optimizer is introduced in 2022 by Jeffrey and all. It is a swarm intelligence-based optimization algorithm that we can use to solve complex optimization problems. This algorithm provides efficient results in comparison with seven different algorithms as you can see here Particle Swarm Optimizer, Gray wolf O...

Learn how to solve Optimization problems Step-By-Step. Video Chapters: Optimization Problems PART - 02 00:00 Introduction 00:16 Optimization Problems 01:51 Design Variables 02:34 Constraints 03:13 Objective Function 04:57 Conclusion

Fireworks Algorithm For Optimization Video Link Learn Fireworks Algorithm For Optimization || Step-By-Step || Video Chapters: Firework Algorithm For Optimization 00:00 Introduction 00:55 About Fireworks 04:07 Fireworks Algorithm Steps 05:41 Set Off Fireworks 08:11 Calculate the Total Number of Spark 10:02 Sparks Location Calculation 14:00 conclusion Firework Algorithm For Optimization Key Points It is a Swarm intelligence Based Metaheuristic Algorithm. We can use Fireworks Algorithm to solve complex optimization problems in real life. The Fireworks Algorithm is basically inspired by the explosion process of Fireworks in real life. Fireworks Algorithm mimics this Fireworks explosion behavior to find out the optimal solution. The fireworks algorithm simulates a simple process. 1. Initialize the population for (N) fireworks. 2. Evaluate fireworks performance using an objective function. 3. Set Off N fireworks. 4. Calculate the number of sparks each firework yield and the...