Code for Computer Science

ACO in Hindi Learn Ant Colony Optimization Algorithm (ACO) in Hindi. Video Chapters: ACO Algorithm 00:00 Introduction 00:27 Topics Covered 00:53 About Ant Colony Optimization 01:07 ACO Applications 01:57 ACO Inspiration  07:06 Real Ants Simulation as Artificial Ants 10:00 ACO Mathematical Models 10:26 ACO Steps 19:09 Conclusion #optimization #algorithm #metaheuristic #robotics #deeplearning #ArtificialIntelligence #MachineLearning #computervision #research #projects #thesis #Python

Learn how to implement an obstacle-avoiding path planning for a robot using the Grey Wolf Optimization (GWO) in a static environment. #optimization #algorithm #metaheuristic #robotics #deeplearning #ArtificialIntelligence #MachineLearning #computervision #research #projects #thesis #Python

Learn Path Planning in Robotics: Techniques, Algorithms, and Applications Video Chapters: Robot Path Planning using PSO 00:00 Introduction 00:14 Topics Covered  01:30 Robot Path Planning 03:07 Robot Classification Based on Configuration 04:28 Robot Classification Based on Performance 05:53 Importance of Robot Path Planning 08:19 Map Representation 09:29 Robot Path Planning Applications 10:21 Challenges in Robot Path Planning 11:51 Comparison Metaheuristics vs Traditional Methods for Path Planning 14:30 PSO for Robot Path Planning 19:00 Conclusion

Learn Particle Swarm Optimization (PSO) In Hindi  Video Chapters: PSO in Hindi 00:00 Introduction 00:58 Topics Covered 01:46 What is Optimization? 07:08 What is the PSO Algorithm? 07:48 PSO Inspiration 09:44 Particle Swarm Optimization Simulation  12:58 PSO Step-By-Step 23:06 PSO Challenges and Solutions 24:31 PSO Applications 24:51 Conclusion

Learn the Rat Swarm Optimization Algorithm Step-By-Step using the Example Video Chapters: Rat Swarm Optimizer 00:00 Introduction 00:16 Topics Covered 00:54 Algorithm Performance Analysis 01:18 Rat Swarm Optimizer 01:50 Rat's Behavior in Nature 03:19 Chasing Behavior 04:23 Fighting Behavior  05:29 RSO Mathematical Models 12:22 RSO Step-By-Step 18:04 RSO Advantages, Disadvantages, and Applications  20:18 Conclusion  #optimization #algorithm #metaheuristic #robotics #deeplearning #ArtificialIntelligence #MachineLearning #computervision #research #projects #thesis #Python

Learn the Cuckoo Search Algorithm in Hindi using Examples. Video Chapters: Cuckoo Search Algorithm (CSA) 00:00 Introduction 00:48 Topics Covered  01:16 Metaheuristic Optimization Algorithm Introduction 03:42 What is the Cuckoo Search Algorithm 05:38 CSA Rules 07:15 CSA Key Concepts 11:42 CSA Step-By-Step  24:24 Conclusion

Learn the Salp Swarm Algorithm step-by-step with examples. Video Chapters: Salp Swarm Algorithm (SSA)  00:00 Introduction 00:14 Topics Covered in this Video 00:53  Introduction to Salp Swarm Algorithm 03:56 SSA Working 05:17 SSA Mathematical Models 10:27 SSA Advantages  10:51 SSA Disadvantages 11:08 SSA Structure 11:38 SSA Applications 12:20 Real-Life Application using SSA 12:55 Optimizing Routing in Sensor Networks Using Salp Swarm Algorithm  18:15 Conclusion

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):