Code for Computer Science

Learn Swarm Intelligence - Searching for Optimal Solution Step-By-Step Video Chapters 00:00 Introduction 00:30 Swarm Intelligence 01:04 Swarm Intelligence Features 02:49 Optimal Solution 03:24 Generic Form 03:36 Optimization Problem Key Components 05:00 Optimization Algorithms and Methods 06:26 Learn Swarm Intelligence By Example 10:42 Metaheuristics 11:55 Solve Real Life Optimization Problem using Metaheuristics 12:23 Apply ACO to Solve TSP 15:57 Conclusion

Learn the Yellow Saddle Goatfish Algorithm (YSGAyellow saddle goatfish algorithm ppt) Step-By-Step with Example. YSGA Video Chapters: 00:00 Introduction 03:44 Goatfish Hunting Behavior 05:39 Yellow Saddle Goatfish Algorithm Steps 13:06 Chaser Fish Position Update 14:17 Blocker Fish Position Update 17:51 Conclusion

Learn the Novel Binary Particle Swarm Optimization (Novel BPSO) Algorithm Step-By-Step using Numerical Examples. Novel BPSO Video Chapters: 00:00 Introduction 00:25 Topics Covered 01:38 PSO Key Points 04:46 Binary Particle Swarm Optimization (BPSO) 07:23 BPSO Numerical Example 15:53 Novel Binary Particle Swarm Optimization (NBPSO) 19:00 Conclusion

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<MaxT:         for i in range (Positions.shape[0]):             BB_UB = Positions[i,:]>UB              BB_LB = Positions[i,:]<LB             Positions[i,:] = (Positions[i,:]*(~(BB_UB+BB_LB)))+UB*BB_UB+LB*BB_LB            

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{                         position:absolute;                         background:red;                         border-radius:15px;                  

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"