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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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GWO In Hindi || Step-By-Step|| ~xRay Pixy

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Learn Grey Wolf Optimizer Step-by-Step using examples in Hindi. Video Chapters: Grey Wolf Optimizer 00:00 Introduction 00:47 Topics Covered 01:28 Grey Wolf Real-life Behavior 04:35 GWO Simulation 09:36 GWO Step-By-Step 16:50  GWO Applications 17:07 GWO Advantages 17:22 GWO Disadvantages 17:29 Conclusion Grey wolves, in the wild, have a natural ability to locate prey and encircle it during a hunt. This process is led by the alpha wolf , with occasional help from the beta and delta wolves . The remaining wolves (omegas) follow the leaders' guidance. In optimization problems, however, the location of the optimal solution (the "prey") is unknown. To mimic this behavior in the Grey Wolf Optimizer (GWO), we make some assumptions: Alpha, beta, and delta are considered the top three best solutions found so far. These three "leader wolves" guide the movement of all other solutions (search agents or omegas). Grey Wolf Optimizer (GWO) is directly inspired by the social ...
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Markov Chains || Step-By-Step || ~xRay Pixy

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Learn Markov Chains step-by-step using real-life examples. Click Here   Video Link Video Chapters: Markov Chains 00:00 Introduction 00:19 Topics Covered 01:49 Markov Chains Applications 02:04 Markov Property 03:18 Example 1 03:54 States, State Space, Transition Probabilities 06:17 Transition Matrix 08:17 Example 02 09:17 Example 03 10:26 Example 04 12:25 Example 05 14:16 Example 06 16:49 Example 07 18:11 Example 08 24:56 Conclusion In computer science, Markov problems are typically associated with Markov processes or Markov models . These are related to topics involving stochastic processes and probabilistic systems where future states depend only on the current state, not on the sequence of states that preceded it. Artificial Intelligence (AI): Markov Decision Processes (MDP): Used in decision-making problems, especially in reinforcement learning. Hidden Markov Models (HMM): Widely used in speech recognition, handwriting recognition, and natural language processing. Machine Le...
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Algorithms Behind Space Missions ~xRay Pixy

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Learn different algorithms used in Space Missions. Video Link Video Chapters: Algorithms Behind Space Missions 00:00 Introduction 00:52 Space Missions 04:26 Space Missions Challenges 07:04 Algorithms Used in Space Missions 10:36 Optimization Techniques 11:44 Conclusion  NASA conducts space missions to explore the universe for various scientific, technological, and practical reasons: Understanding Our Place in the Universe Search for Life Beyond Earth Studying Earth from Space Advancing Technology Supporting Human Exploration Resource Utilization Inspiring Humanity Examples of NASA Space Missions Apollo Program: Sent humans to the Moon (1969ā€“1972). Mars Rovers (Spirit, Opportunity, Perseverance): Explored Mars' surface and geology. Voyager Missions: Studied the outer planets and interstellar space. Hubble Space Telescope: Captured breathtaking images of the universe. International Space Station (ISS): Supports research in microgravity and international collaboration. Different ...
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Genetic Algorithm In Hindi ~xRay Pixy

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Transient Search Optimization Algorithm || Step-By-Step || ~xRay Pixy https://youtu.be/T2lVQ8mYFoM Video Chapters: TSO Algorithm 00:00 Introduction 00:44 Topics Covered 01:14 Transient Behavior 02:57 Transient Search Optimization Algorithm 06:10 TSOA Mathematical Models 10:30 TSOA Step-By-Step 15:32 TSOA Applications 15:58 TSOA Advantages 16:22 TSOA Disadvantages 16:28 Conclusion Evolutionary algorithms (EAs) are optimization methods inspired by the process of evolution in nature. They aim to find the best solutions to problems by mimicking natural selection and genetics. Key Steps in Evolutionary Algorithms : Start with a Population: Think of a population as a group of random guesses or potential solutions to your problem. Each "individual" in the population represents one solution. Evaluate Fitness: Just like in nature, some individuals are better suited to survive in their environment. In EAs, the "fitness" of a solution tells us how good it is at solving the pr...
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Transient Search Optimization Algorithm || Step-By-Step || ~xRay Pixy

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Transient Search Optimization Algorithm || Step-By-Step || ~xRay Pixy Learn Transient Search Optimization Algorithm (TSOA) Step-by-step using examples. Video Chapters: TSO Algorithm 00:00 Introduction 00:44 Topics Covered 01:14 Transient Behavior 02:57 Transient Search Optimization Algorithm 06:10 TSOA Mathematical Models 10:30 TSOA Step-By-Step 15:32 TSOA Applications 15:58 TSOA Advantages 16:22 TSOA Disadvantages 16:28 Conclusion Transient Search Optimization Algorithm (TSOA) is a metaheuristic optimization technique inspired by the concept of transient behavior in physical or natural systems. In simple terms, it mimics how certain systems, like electrical circuits or natural phenomena, go through temporary changes (called transients) before settling into a stable state. What Happens in Electrical Circuits with Inductance and Capacitance? When you switch on or off a circuit containing inductors and capacitors, it doesnā€™t immediately settle into its final, stable state. Instead, it ...
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Vehicle Routing Problem (VRP) ~xRay Pixy

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V ehicle R outing P roblem Learn Vehicle Routing Problems Step-By-Step using Examples. Video Chapters: Vehicle Routing Problem (VRP) 00:00 Introduction 00:18 VRP Example 00:52 VRP Variants 02:43 VRP Objective 03:20 VRP Component 06:06 VRP Graph Representation 05:13 VRP Challenge 05:51 TSP vs VRP 07:17 VRP Real-Life Situations 07:38 VRP Solving Rules 08:57 VRP Method 09:16 Metaheuristics for VRP 10:16 VRP Application Areas 10:24 Metaheuristics to solve VRP 11:39 Conclusion VEHICLE ROUTING PROBLEM  is a fundamental combinatorial optimization and integer programming problem in the fields of transportation, logistics, and operations research. It involves determining the most efficient routes for a fleet of vehicles to traverse in order to deliver goods or services to a set of customers, subject to various constraints. Objective: Minimize the Total Route Cost. While satisfying all Co Capacity Constraint Time Window Constraints VRP Basic Components Depot: The starting and ending point f...
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Black Widow Optimization (BWO) Algorithm || Step-By-Step || ~xRay Pixy

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Learn Black Widow Optimization Algorithm Step-By-Step using Examples. Video Chapters: BWO Algorithm 00:00 Introduction 00:14 Topics Covered 00:31 BWO Algorithm INSPIRATION 01:01 About Black Widow Spider 02:53 Black Widow Spider BEHAVIOR 03:21 BWO Algorithm STEPS 13:22 Conclusion
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