Code for Computer Science

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