Code for Computer Science

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

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

Artificial Gorilla Troops Optimizer Learn Artificial Gorilla Troops Optimizer Step-By-Step with Example. Video Chapters: Artificial Gorilla Troops Optimizer Introduction: 00:00 Gorilla Lifestyle and Behavior: 02:32 Artificial Gorilla Troops Optimizer Steps: 05:13 Step 01 - Algorithm Parameters: 06:51 Step 02 - Population Initialization: 07:17 Step 03 - Population Evaluation: 08:31 Step 04 - Position Update - Exploration Phase: 09:13 Step 05 - Updated Population Evaluation: 15:05 Step 06 - Fitness Values Comparision: 15:35 Step 07 - Select Best Solution: 15:45 Step 08 - Position Update - Exploitation Phase: 16:00 Step 09 - Updated Population Evaluation: 19:10 Step 10 - Fitness Values Comparision: 19:20 Step 11 - Select Best Solution: 19:25 Step 12 - Check Stopping Condition: 19:54 Step 13 - Display Best Solution: 20:02 Conclusion: 20:18

Research Paper Writing for Beginners Simple Steps for Writing a Research Paper Video Chapters: Introduction: 00:00 Key Tool in Research Paper: 00:25 What is Research Paper: 02:35 What is Knowledge: 03:21 Research Process: 04:07 Research Areas: 04:26 Research Paper Layout: 05:32 Pieces of writing the research paper: 06:07 Experiments Role in Research: 08:48 Research Paper Keywords: 10:16 Research Paper Title: 10:47 Research Paper Abstract Section: 11:43 Research Paper Introduction Section: 12:32 Research Paper Literature Review: 13:58 Research Paper Method section: 14:35 Research Paper Result and Discussion Section: 15:09 Research Paper Conclusion Section: 16:04 Research Paper Plagiarism: 16:50 Research Paper References: 17:10 citation Methods: 17:30 Figures, Tables, Graphs, Bar Chart: 18:50 Conclusion: 19:00

Squirrel Search Algorithm (SSA) Learn Squirrel Search Optimization Algorithm Step-By-Step with Example. Video Chapters: Introduction: 00:00 Squirrel Search Algorithm: 01:11 Squirrel Search Algorithm MODEL: 03:33 Squirrel Search Algorithm STEPS: 06:18 Squirrel Search Algorithm MATHEMATICAL MODELS: 06:26 Conclusion: 15:37

Wind Turbine Optimal Positioning using Particle Swarm Optimization Algorithm Video Chapters: Introduction: 00:00 Wind Energy Projects Objectives: 01:15 Wind Turbine: 04:16 Wind Farm: 05:20 Jensen Wake Effect Model: 06:55 Wind Farm Layout: 09:05 3 Scenarios for Optimal Wind Turbine Positions: 12:02 Metaheuristics for Wind Energy Optimization: 13:54 Optimal Wind Turbine Placement Using Particle Swarm Optimization: 14:53 Optimization Process Flowchart: 20:08 Conclusion: 21:00

All Members-Based Optimizer (AMBO) Learn All Members-Based Optimizer Step-by-Step with Examples. Algorithm Type: Metaheuristic Optimization Technique Algorithm Main Idea: Make more use of the Population Matrix. Tested on Different Benchmark Test Functions. Algorithm Performance: Provide Better results in comparison with different metaheuristic optimization algorithms. Used for Solving Optimization Problems. ALGORITHM MAIN IDEA Make use of the Population Matrix and All Members can play role in Updating Algorithm Population. ALL MEMBERS-BASED OPTIMIZER STEPS STEP 01: Initialize Algorithm Important Parameters. STEP 02: Initialize Population Randomly in the Search Space. STEP 03: Evaluate Initial Population using the Fitness Function. STEP 04: Check While (Current Iteration < Maximum Iteration) Do STEP 05: Update Members Position and Best Member Position. STEP 06: Update Population Members using STAGE 01. STEP 07: Update Population Members using STAGE 02. STEP 08: Save Best Solut

Elephant Herding Optimization Algorithm Learn Elephant Herding Optimization Algorithm Step-By-Step with Examples. Elephant Herding Optimization Algorithm - Introduced in 2015 - Inspired by Elephant Herding Behavior. - Main Operator used: + Elephant Clan Updating Operator + Elephant Separating Operator - Used to Solve Optimization Problems.

Python Turtle Graphics || Create a Happy Birthday Message to Someone Special || Feirnds || Family Members ||

Donkey and Smuggler Optimization Algorithm Learn Donkey and Smuggler Optimization Algorithm Step-By-Step with Examples Video Chapters: Introduction: 00:00 Donkey's Behavior: 02:02 Donkey Mode: 04:20 Donkey and Smuggler Optimization Algorithm: 06:49 Smuggler Mode: 08:02 Donkey and Smuggler Optimization Algorithm STEPS: 11:54 Donkey and Smuggler Optimization Algorithm FLOWCHART: 15:39 Conclusion: 16:24 About Donkey and Smuggler Optimization Algorithm: - Introduced in 2019 by Ahmed S Shamsaldin et. al. - Nature Inspired Population-Based Metaheuristic Optimization Algorithm. - Used to Solve Complex Optimization Problems. - Implemented to solve Real Life Optimization Problems such as Travelling Salesman Problem (TSP) Routing Problems     Ambulance Routing Donkey and Smuggler Optimization Algorithm MODES: Smuggler Mode [Non-Adaptive] Donkey Mode [Adapative] Donkey and Smuggler Optimization Algorithm STEPS: Initialize algorithm parameters i.e., Population Size, Dimensions, and

TURTLE GRAPHICS USING PYTHON || Turtle Star || 08 ||  STEPS Python Turtle Graphics Design First Import Turtle Here, variable r is considered a Turtle. Set Turtle Speed. Set Line Width. Set Background Color as while.  Select Colors for the Design. Create a List of Colors. For Loop Initialization. Access Colors from the List inside Loop. Set Direction for Turtle. SOURCE CODE import turtle r = turtle.Turtle() r = turtle.Pen() r.speed(1000) r.width(2) turtle.bgcolor('white') c = ['blue','lime','red','black','gold','aqua','purple','silver'] for x in range (600):     r.pencolor(c[x%8])     r.forward(x)     r.right(160) r.done() OUTPUT

SOURCE CODE import turtle r = turtle.Turtle() r = turtle.Pen() r.width(4) r.speed(50) turtle.bgcolor('black') c = ['white','red','blue','orange'] for i in range (700):     r.pencolor(c[i%4])     r.left(i)     r.right(100) r.done()

Python Turtle Graphics || Flower Design 0 6|| SOURCE CODE from turtle import Turtle r = Turtle() r.screen.bgcolor('black') color = ['red','lime','yellow'] r.screen.tracer(0,0) for x in range(150):     r.circle(x)     r.color(color[x%3])     r.left(60) r.screen.exitonclick() r.screen.mainloop() OUTPUT

Python Turtle Graphics || SPIRAL Design 05 ||  SOURCE CODE import turtle r = turtle.Turtle() s = turtle.Screen() s.bgcolor('white') r.width(2) r.speed(20) color = ('lime','aqua','red','indigo') for i in range (300):     r.pencolor(color[i%3])     r.forward(i*4)     r.right(121) OUTPUT

PYTHON TURTLE GRAPHICS || Turtle Star || SOURCE CODE from turtle import * color('blue','yellow') begin_fill() while True:     forward(300)     left(170)     if abs(pos())<1:         break end_fill() OUTPUT