Code for Computer Science

Learn how to perform diversity analysis in metaheuristic algorithms step-by-step. Video Chapters:  Diversity Analysis in Metaheuristic Algorithms 00:00 Introduction 00:10 Methods to Balance Selection 03:08 Enhance Diversification in PSO and Prevent Premature Convergence 06:58 Diversity Maintaining Strategies in Optimization Algorithms 11:04 Diversity-Based Indicators  13:02 Conclusion

Learn how to do Diversity Analysis in Metaheuristic Algorithms. Video Chapters: Diversity Analysis 00:00 Introduction 01:00 What is Diversity? 02:18 Genotypic Diversity and Phenotypic Diversity Example 04:53 Diversity Analysis in Metaheuristic 08:57 How to Measure Diversity in Metaheuristic Algorithms? 09:40 How to Keep Diversity in Metaheuristic Algorithms? 11:11 Wind Turbine Layout Diversity Analysis 14:02 Low Diversity and High Diversity in Metaheuristic Algorithms 15:00 Diversity Analysis Techniques 17:30 Diversity Monitor and Control Techniques 18:40 Conclusion

Video Link Multi-Block Local Binary Pattern || Calculate LBP Corner Pixel Values ||  https://youtu.be/o8qfJWQ_FG0  Local Binary Patterns (LBP) is a simple and efficient technique used in image processing to describe the texture or patterns within an image. LBP is widely used for applications like face recognition and texture classification since it is easy to compute and very effective at capturing the texture in photos.  Step How LBP WORKS:  For each pixel in the image, LBP looks at the pixel’s neighbors, typically the 8 pixels surrounding it in a 3x3 grid. LBP compares each of these neighboring pixels with the center pixel. If the neighboring pixel has a value greater than or equal to the center pixel, it's marked as 1; otherwise, it's marked as 0. This comparison forms a binary number for the pixel.  The binary number is then converted into a decimal value. This value represents the texture pattern at that pixel. By doing this for every pixel in the image, LBP creates a new

Video Chapters: Algorithms Comparison 00:00 Introduction  00:18 Metaheuristic Algorithms 00:46 Why Comparision? 02:05 Algorithms Comparision 08:50 Comparision Table 12:14 Hybrid Algorithms 13:27 Conclusion

Learn how to calculate Pareto optimal solutions. Video Chapters: Pareto Optimality 00:00 Introduction 00:24 Pareto Optimality 02:33 Pareto Optimality Importance 03:29 Pareto Optimality Disadvantages 03:54 Pareto Optimality Applications 04:02 Example 1 Robot in Field 05:39 Steps to Calculate Pareto Optimality 07:41 Example 2 Math Example 11:03 Example 3 Resource Allocation Problem 16:09 Conclusion

Learn Multi-Objective Optimization:  PSO Example for Robots with Different Battery Levels Video Chapters: Robotics and PSO 00:00 Introduction 00:36 Robots Finding the Optimal Path 05:45 Multi-Objective Optimization 06:44 Objectives 08:46 Flowchart 09:08 Results 11:15 Conclusion

Learn Bat Algorithm Implementation in Python. Video Chapters: Bat Algorithm 00:00 Introduction 00:42 Bat Algorithm Key Concepts 01:58 Bat Algorithm Pseudocode 02:35 Objective Function 02:49 Parameters 03:09 Python Code 06:30 BA Main Loop Start 12:30 Result The Bat Algorithm is a nature-inspired optimization algorithm developed by Xin-She Yang in 2010. It is based on the echolocation behavior of bats. Bats use echolocation to detect prey, avoid obstacles, and navigate in the dark. The algorithm simulates this behavior to find optimal solutions in complex optimization problems. Applications: The Bat Algorithm has been used in various fields, including engineering design, image processing, data mining, and robotics, for solving complex optimization problems. PYTHON CODE: import numpy as np # Define the objective function  def objective_function(x):     return np.sum(x**2) # Initialize the bat population def initialize_bats(n_bats, dim, lower_bound, upper_bound, f_min, f_max, A0, r0):  

Learn the role of deep learning in robotics. Video Chapters: Smart Robots 00:00 Introduction 00:42 Why Optimization? 03:47 Deep Learning  04:28 Role of Deep Learning in Robotics 07:03 How Deep Learning Applied in Robotics 09:58 Example Mobile Robot 14:33 Conclusion

Learn the role of Metaheuristics in Advanced Solutions in Marine Robotics. Video Chapters: Marine Robotics 00:00 Introduction 00:25 Marine Robots 02:11 Marine Robotics Applications   04:14 Marine Robotics Technologies 05:46 Marine Robotics Challenges 06:21 Marine Robotics Algorithms 10:30 Metaheuristic Algorithms in Marine Robotic 13:09 Conclusion 

Learn how to create an Objective Function for any real-life optimization problem. 00:00 Introduction 00:25 STEPS 02:41 Numerical Example 06:19 Objective Function Verification 07:48 Graphical Representation  08:31 Conclusion

Learn how to optimize hyperparameters using PSO. Video Chapters: Hyperparameter Optimization 00:00 Introduction 01:30 Problem 02:15 Hyperparameters 03:24 Problem Setup 04:15 Numerical Example Step-By-Step 12:00 Conclusion

Learn how to Optimize Hyperparameters in Deep Learning Using Particle Swarm Optimization (PSO) Video Chapters: Deep Learning + Particle Swarm Optimization 00:00 Introduction 00:59 Deep Learning 03:05 ANN Example 06:55 Deep Learning Applications 07:55 How to use Deep Learning 10:55 Deep Learning Tools 11:15 Hyperparameter Optimization Example 14:04 Hyperparameter 14:22 Conclusion 

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

Learn Artificial Intelligence and Metaheuristic Algorithm Real Life Applications Video Chapters: AI and Metaheuristics  00:00 Introduction 00:55 Metaheuristics 01:49 Artificial Intelligence 03:11 AI and Metaheuristics Collaboration 05:21 Robot Path Planning EXAMPLE 1 09:51 Route Optimization EXAMPLE 2 12:10 Outro

Learn Multi-Objective Particle Swarm Optimization Step-By-Step. Video Chapters: Multi-Objective PSO 00:00 Introduction 00:46 Multi-Objective Optimization 02:55 MOO Applications 04:00 Example 1 Multi-Objective Optimization Problem 07:26 Example 2 Multi-Objective Optimization Problem 08:25 Methods for Multi-Objective Optimization 09:50 Multi-Objective Particle Swarm Optimization 12:00 Example 3 Multi-Objective Optimization Problem 17:58 STEPS for Multi-Objective Particle Swarm Optimization 21:55 Conclusion