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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...

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 ...

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...

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 (1969–1972). 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 ...

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...

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 doesn’t immediately settle into its final, stable state. Instead, it ...

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...

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

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 How ACO Works Ant Colony Optimization is inspired by how real ants find the shortest path to food. Ants deposit pheromones on paths they travel, and other ants tend to follow paths with stronger pheromone trails. Over time, this leads to finding the most efficient route.  To explain the Ant Colony Optimization (ACO) applied to the Traveling Salesman Problem (TSP) in a simple and concise way within 2 minutes, here's an approach: Introduction to TSP The Traveling Salesman Problem asks: "Given a set of cities, find the shortest route that visits every city exactly once and returns to the starting point." How ACO Works Ant Colony Optimization is ins...

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