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Intelligent Traffic Management Using || AI & Metaheuristics || ~xRay Pixy

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Hybrid Artificial Intelligence and Metaheuristics for Smart City TRafci Management Problem Video Chapters: 00:00 Introduction 00:40 Smart Cities 01:14 Traditional Methods for Traffic Management 02:12 Hybrid Approach AI and Metaheuristics 02:47 STEPS for Hybrid  Traffic Management System 08:40 Advantages of Smart Traffic Management System 09:33 Conclusion
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JavaScript Dynamic Barcode Generator || Step-By-Step || ~xRay Pixy

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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>
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JavaScript Analog Clock || Step-By-Step || ~xRay Pixy

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---------------------------- 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;                  
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Hunger Games Search Algorithm || Step-By-Step || ~xRay Pixy

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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
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JavaScript Calculator Step-By-Step ~xRay Pixy

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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"
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Dwarf Mongoose Optimization Algorithm || Step-By-Step || ~xRay Pixy

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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
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Optimization Problems || PART 02 || Design Variables, Constraints, Objec...

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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
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Fireworks Algorithm For Optimization || Step-By-Step || ~xRay Pixy

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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
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Hybrid Metaheuristic || GA-PSO || Step-By-Step ~xRay Pixy

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Hybrid Metaheuristic  Learn Hybrid Metahuristic Step-By-Step (Genetic Algorithm (GA) and Particle Swarm Optimization(PSO)) Hybridization. Hybrid Metahuristic Video Chapters: 00:00 Introduction 00:30 Optimization Problems 02:50 Optimization Process 04:28 Metaheuristic Hybridization 08:56 GA-PSO Hybridization 13:44 Conclusion
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Honey Badger Algorithm (HBA) ||Step-By-Step|| ~xRay Pixy

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Learn Honey Badger Optimization Algorithm Step-By-Step with Examples. Honey Badger Algorithm Video Chapters: 00:00 Introduction 01:00 About Honey Badger 03:33 Honey Badger Algorithm 07:21 Honey Badger during Digging Mode 10:05 Honey Badger during Honey Mode 12:43 Conclusion
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POA - CODE || Pelican Optimization Algorithm Code Implementation ||

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Learn Pelican Optimization Algorithm Code Implementation Step-By-Step POA-CODE Video Chapters: 00:00 Introduction 01:22 Test Function Information Program File 02:37 Pelican Optimization Algorithm Program File 11:23 Main Program File 12:30 Conclusion 1.) Test Function Information File function [LB,UB,D,FitF] = test_fun_info(C) switch C case 'F1' FitF = @F1; LB=-100; UB =100; D =30; case 'F2' FitF = @F2; LB=-10; UB =10; D =30; case 'F3' FitF = @F3; LB=0; UB=1; D=3; end end % F1 function R = F1(x) R=sum(x.^2); end % F2 function R = F2(x) R=sum(abs(x))+prod(abs(x)); end 2.) POA File function[Best_Solution,Best_Location,Sol_con_Curve]=POA(PopSize,MaxT,LB,UB,D,FitF) LB=ones(1,D).*(LB); % Lower limit UB=ones(1,D).*(UB); % Upper limit % POPULATION INITIALIZATION PHASE for i=1:D X(:,i) = LB(i)+rand(PopSize,1).*(UB(i)
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Pelican Optimization Algorithm || Step-By-Step || with Example ~xRay Pixy

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Learn Pelican Optimization Algorithm Step-By-Step with Examples. Video Chapters: Introduction: 00:00 Pelicans Behaviors: 00:34 Pelicans Hunting Behavior: 01:47 Pelican Optimization Algorithm: 03:18 Pelican Optimization Algorithm Steps: 06:36 Conclusion: 12:35
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Whale Optimization Algorithm Code Implementation || WOA CODE || ~xRay Pixy

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Whale Optimization Algorithm Code Implementation Whale Optimization Algorithm Code Files function obj_fun(test_fun) switch test_fun     case 'F1'         x = -100:2:100; y=x;     case 'F2'         x = -10:2:10; y=x; end end function [LB,UB,D,FitFun]=test_fun_info(C) switch C     case 'F1'         FitFun = @F1;         LB = -100;          UB = 100;         D = 30;     case 'F2'         FitFun = @F2;         LB = -10;         UB = 10;         D = 30; end % F1 Test Function     function r = F1(x)         r = sum(x.^2);     end % F2 Test Function     function r = F2(x)         r = sum(abs(x))+prod(abs(x));     end end function Position = initialize(Pop_Size,D,UB,LB) SS_Bounds = size(UB,2); if SS_Bounds == 1     Position = rand(Pop_Size,D).*(UB-LB)+LB; end if SS_Bounds>1     for i = 1:D         UB_i = UB(i);         LB_i = LB(i);         Position(:,i) = rand(Pop_Size,1).*(UB_i-LB_i)+LB_i;      end end end function [Best_Val,Best_Pos,Convergence_Curve]=WOA(
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Improved Grasshopper Optimization Algorithm || STEP-BY-STEP || ~xRay Pixy

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Improved Grasshopper Optimization Algorithm Learn Improved Grasshopper Optimization Algorithm Step-By-Step. Video Chapters: Introduction: 00:00 Improved Grasshopper Optimization Algorithm: 00:53 Grasshopper Optimization Algorithm: 03:18 GOA Mathematical Models: 07:07 IGOA Mathematical Models: 08:25 Conclusion: 09:44
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Feature Selection using Artificial Hummingbird Algorithm || Step-By-Step ||

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Feature Selection using Artificial Hummingbird Algorithm Learn Feature Selection using Artificial Hummingbird Algorithm Step-By-Step with Examples. Video Chapters: Introduction: 00:00 Feature Selection: 01:00 Feature Selection Problem: 02:55 Feature Selection Process: 03:52 Feature Engineering: 05:18 Feature Selection Techniques: 06:42 Feature Extraction: 07:22 Feature Selection Application: 08:15 Feature Selection using Artificial Hummingbird Algorithm: 08:33 Conclusion: 10:34 Artificial Hummingbird Algorithm: 11:00
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Artificial Hummingbird Algorithm || Step-By-Step || ~xRay Pixy

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Learn Artificial Hummingbird Algorithm Introduction: 00:00 Artificial Hummingbird Algorithm Main Component 03:16 Artificial Hummingbird Algorithm Steps: 06:50 Hummingbirds Flisht Skills: 08:55 Hummingbirds Foraging Strategies: 10:18 Artificial Hummingbird Algorithm Flowchart: 13:30 Conclusion: 16:05
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