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Poplar Optimization Algorithm || Step-By-Step || ~xRay Pixy

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The Poplar Optimization Algorithm (POA) is a nature-inspired optimization method based on how poplar trees reproduce. It uses sexual propagation (seed dispersal by wind) for exploration and asexual reproduction (cutting and regrowth) for exploitation. Mutation and chaos factors help maintain diversity and prevent premature convergence, making POA efficient for solving complex optimization problems. Learn the Poplar Optimization Algorithm Step-By-Step using Examples. Video Chapters: Poplar Optimization Algorithm (POA) 00:00 Introduction 02:12 POA Applications 03:32 POA Steps 05:50 Execute Algorithm 1 13:45 Execute Algorithm 2 16:38 Execute Algorithm 3 18:15 Conclusion Main Points of the Poplar Optimization Algorithm (POA) Nature-Inspired Algorithm ā€“ Based on the reproductive mechanisms of poplar trees. Two Key Processes : Sexual Propagation (Seed Dispersal) ā€“ Uses wind to spread seeds, allowing broad exploration. Asexual Reproduction (Cuttings) ā€“ Strong branches grow ...
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Poplar Optimization Algorithm || Step-By-Step || ~xRay Pixy

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The Poplar Optimization Algorithm (POA) is a nature-inspired optimization method based on how poplar trees reproduce. It uses sexual propagation (seed dispersal by wind) for exploration and asexual reproduction (cutting and regrowth) for exploitation. Mutation and chaos factors help maintain diversity and prevent premature convergence, making POA efficient for solving complex optimization problems. Learn the Poplar Optimization Algorithm Step-By-Step using Examples. Video Chapters: Poplar Optimization Algorithm (POA) 00:00 Introduction 02:12 POA Applications 03:32 POA Steps 05:50 Execute Algorithm 1 13:45 Execute Algorithm 2 16:38 Execute Algorithm 3 18:15 Conclusion Main Points of the Poplar Optimization Algorithm (POA) Nature-Inspired Algorithm ā€“ Based on the reproductive mechanisms of poplar trees. Two Key Processes : Sexual Propagation (Seed Dispersal) ā€“ Uses wind to spread seeds, allowing broad exploration. Asexual Reproduction (Cuttings) ā€“ Strong branches grow ...
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Benchmarking Optimization Algorithms | Mean and Standard Deviation Calculation

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 Benchmarking Optimization Algorithms Watch Now:  https://youtu.be/uBlACmRLv14 Learn about Benchmark Functions & Role of Mean & Standard Deviation in Metaheuristics Video Chapters: Mean & SD Analysis in Optimization Algorithms 00:00 Introduction 00:33 Why Benchmarking is used in Metaheuristic Algorithms? 03:26 Benchmark Function Testing 07:53 Calculate Mean and SD from Benchmark Functions 12:12 Calculation using Python 12:30 Algorithms Comparison 13:40 Conclusion Benchmarking is essential in metaheuristic algorithms to evaluate and compare their performance using standardized test functions. It helps measure accuracy, stability, and efficiency before applying these algorithms to real-world problems. Key concepts include: Mean (Ī¼): Indicates the average performance of an algorithm. Standard Deviation (Ļƒ): Measures result in variability across multiple runs, reflecting stability. Benchmark Functions: Artificial test functions (e.g., Sphere, Rastrigin, Ackl...
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Brain Storm Optimization Algorithm || Step-By-Step || ~xRay Pixy

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Learn Brainstorm optimization (BSO) algorithm step-by-step using examples. Video Chapters: BSO Algorithm 00:00 Introduction 02:33 Brainstorming Process 03:10 Example 01: Brainstorming Process in Real-Life 05:25 Brain Strom Optimization 08:39 Example 02 14:09 BSO Steps 17:51 Conclusion The Brain Storm Optimization (BSO) algorithm is a swarm intelligence method inspired by human brainstorming. It aims to find optimal solutions by combining clustering, exploration, and exploitation techniques. Why is BSO Useful? It balances global search (exploration) and local search (exploitation) efficiently. It avoids getting stuck in local optima by introducing diversity in solution generation. It is adaptable and can be integrated with machine learning techniques like clustering. Key Concepts: Solution Clustering: Solutions are grouped into clusters to refine the search space. Exploration (Divergent Thinking): New solutions are generated far from existing clusters to discove...
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AI and Deep Learning for Ear Infection Detection ~xRay Pixy

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Learn how AI and deep learning revolutionize ear infection detection, enabling accurate, fast, and automated diagnosis using advanced image processing and machine learning techniques. Video Chapters: Ear Infection Detection using AI and DL 00:00 Introduction 00:14 My Experience with Ear Infections 01:15 Topics Covered 02:24 Ear Infections 02:48 Ear Infection Signs 03:55 Ear Infection Preventions 04:29 Ear Infection Types 05:19 Ear Infection Causes 06:14 How Bacteria and Fungus Grow in Ear 07:26 My Mistakes 08:49 Doctors Advise after Ear Infection 09:45 Ear Infection Common Symptoms 10:37 Automated Ear Infection Detection with Deep Learning AI 15:09 Smartphone Otoscopes 16:04 Conclusion Ear fungus, also known as otomycosis , is a fungal infection of the outer ear canal. If an ear infection is not treated on time, it can lead to serious complications.  Hearing Loss ā€“ Persistent infections can damage the eardrum and middle ear structures, leading to partial or permanent hearing loss....
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Sperm Swarm Optimizer || Step-By-Step || ~xRay Pixy

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Sperm Swarm Optimizer (SSO) || Step-By-Step || ~xRay Pixy Video Link Click Here:   SSO Learn Sperm Swarm Optimizer Step-By-Step Using Examples. Video Chapters: Sperm Swarm Optimizer 00:00 Introduction 01:20 Topic Covered 01:41 Fertilization Process 07:02 Sperm Swarm Optimizer Simulation 10:14 Sperm Swarm Optimizer Steps 15:27 Conclusion The Sperm Swarm Optimization (SSO) algorithm is a method inspired by how sperm move toward an egg during fertilization.  Swarm Movement : Each sperm (candidate solution) is represented as a point in a multidimensional search space. The sperms work together to find the best solution (the egg). Each sperm's position (location) is adjusted based on its current velocity, its personal best, and the global best.The algorithm keeps updating positions and velocities until an optimal solution is found or a stopping condition is met. Exploration and Exploitation : Exploration: Randomized factors (pH and temperature) allow wide search. Exploitat...
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Nash Equilibrium In Game Theory ~xRay Pixy

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 Video Link  CLICK HERE... Learn Nash Equilibrium In Game Theory Step-By-Step Using Examples. Video Chapters: Nash Equilibrium  00:00 Introduction 00:19 Topics Covered 00:33 Nash Equilibrium  01:55 Example 1  02:30 Example 2 04:46 Game Core Elements 06:41 Types of Game Strategies 06:55  Prisonerā€™s Dilemma  07:17  Prisonerā€™s Dilemma Example 3 09:16 Dominated Strategy  10:56 Applications 11:34 Conclusion The Nash Equilibrium is a concept in game theory that describes a situation where no player can benefit by changing their strategy while the other players keep their strategies unchanged.  No player can increase their payoff by changing their choice alone while others keep theirs the same. Example : If Chrysler, Ford, and GM each choose their production levels so that no company can make more money by changing their choice, itā€™s a Nash Equilibrium Prisonerā€™s Dilemma : Two criminals are arrested and interrogated separately. Each has two ...
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Firefly Algorithm In Hindi ~xRay Pixy

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Learn Firefly Algorithm Step-By-Step using Numerical Examples. Video Chapters: Firefly Algorithm 00:00 Introduction 00:40 Topics Covered 01:01 Firefly Algorithm 01:42 Firefly Algorithm Applications 01:57 Firefly Algorithm Working 03:50 Firefly Algorithm Mathematical Models 05:52 Firefly Algorithm Step-By-Step 13:58 Firefly Algorithm Advantages & Limitations 14:29 Conclusion Firefly Algorithm In Hindi ~xRay Pixy Click here  Video Link The Firefly Algorithm (FA) is a nature-inspired optimization algorithm developed by Xin-She Yang in 2008. It mimics the behavior of fireflies, specifically their flashing patterns, which are used for attracting mates or prey. Firefly Algorithm Core Concept Attraction : The attractiveness of a firefly is proportional to its brightness. A brighter firefly attracts less bright fireflies. Brightness : The brightness is associated with the fitness of the solution at a firefly's position. Movement : A less bright firefly moves toward a brighter firefly....
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Chernobyl Disaster Optimizer || Step-By-Step || ~xRay Pixy

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Chernobyl Disaster Optimizer || Step-By-Step || ~xRay Pixy VIDEO LINK... Learn the Chernobyl disaster optimizer (CDO) Step-By-Step using Examples. Video Chapters: Chernobyl Disaster Optimizer (CDO) 00:00 Introduction 02:05 Chernobyl Disaster Optimizer  02:31 Topics Covered 02:05 Chernobyl Disaster || How Chernobyl Disaster Happened?  05:40 3 Radiation Released after Chernobyl Disaster  07:35 How CDO Simulates Chornobyl Nuclear Disaster 09:44 How particle attack models Mathematically 10:44 CDO Step-By-Step  14:52 Conclusion On April 26, 1986, Reactor 4 at the Chernobyl Nuclear Power Plant in Ukraine had a test while running at low power. Things went wrong, causing an explosion and a fire that destroyed the reactor. This accident released a huge amount of radiation into the air, affecting many people and the environment. Key Isotopes Released in the Chernobyl Disaster: Iodine-131 : Short-lived but highly radioactive; primarily affects the thyroid gland. Cesium-137 : Lo...
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