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Life Skills for Everyday Success ~xRay Pixy

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Life skills are the basic abilities we need to handle daily challenges and live a healthy, balanced life. They help us think clearly, manage our feelings, make good decisions, solve problems, and build good relationships with others. The World Health Organization (WHO) highlights 10 important life skills: 1.) Thinking skills: decision-making, problem-solving, creative thinking, critical thinking 2.) Social skills: communication, empathy, interpersonal skills 3.) Emotional skills: self-awareness, coping with emotions, coping with stress Life skills are the tools that make us stronger, wiser, and calmer in real life — at home, in school, at work, and in the community :) Life Skills for Everyday Success ~xRay Pixy https://youtu.be/AMsUfKRl4kw Video Chapters: Life Skills 00:00 Introduction 01:07 Life Skills 09:42 Real Life Challenge 13:44 Task For You #LifeSkills #SuccessTools #StressFreeLiving #algorithm #optimization #research #happylearning #algorithms #meta #optimizationtechniques #swa...
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Soft Computing - Neural Networks || Module 2 || ~xRay Pixy

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Video Chapters: Soft Computing Module 2 - Neural Networks
00:00 Introduction
00:39 Topics Covered in Video
01:19 Neural Network Basics
05:12 Problem 01 - Calculate Net Input to Output Neuron
06:33 Artificial Neural Network Terminologies
06:54 Weights
07:59 Bias
08:13 Threshold
08:33 Learning Rate
08:49 Momemtum Factor
09:03 Problem 02 - Calculate Output of Neuron (Y) using Activation Function
09:30 Activation Function
12:42 Problem 02 - Solution
15:25 Neural Network Types
15:38 Simple Neural Network
16:08 Single Feedforward Neural Network
16:41 Multilayer Feedforward Neural Network
17:21 Single Layer Recurrent Network
17:50 Multilayer Recurrent Network
17:52 Perceptron
21:57 Multilayer Perceptron
22:15 Adaline -Its Training and Capabilities
22:59 Conclusion


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PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE

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Cuckoo Search Algorithm for Optimization Problems

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Particle Swarm Optimization (PSO) is a p opulation-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. PSO is a computational method that Optimizes a problem. PSO searches for Optima by updating generations. It is popular is an intelligent metaheuristic algorithm.  In Particle Swarm Optimization the solution of the problem is represented using Particles. [Flocking birds are replaced with particles for algorithm simplicity]. Objective Function is used for the performance evaluation for each particle / agent in the current population. After a number of iterations agents / particles will find out optimal solution in the search space. Q. What is PSO? A. PSO is a computational method that Optimizes a problem. Q. How PSO will optimize? A. By Improving a Candidate Solution. Q. How PSO Solve Problems? A. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to ...
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PSO (Particle Swarm Optimization) Example Step-by-Step

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 Particle swarm optimization (PSO) What is meant by PSO? PSO is a computational method that Optimizes a problem. It is a Population-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. n Particle Swarm Optimization the solution of the problem is represented using Particles. [Flocking birds are replaced with particles for algorithm simplicity]. Objective Function is used for the performance evaluation for each particle / agent in the current population. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to update particle position in each iteration. How PSO will optimize? By Improving a Candidate Solution. How PSO Solve Problems? PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). The population of Candidate Solutions (i.e., Particles). What is Search Space in PSO? It is the range in which the algorithm computes th...
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how is the LBP |Local Binary Pattern| values calculated? Step-by-Step with Example

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PSO Python Code || Particle Swarm Optimization in Python || ~xRay Pixy

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Particle Swarm Optimization Implementation in Python Video Chapters: 00:00 Introduction 02:01 Code 05:55 Position Initialization 08:06 PSO Main Loop 08:42 Velocity Calculation 10:02 Position Update 10:36 Fitness Evaluation 13:21 Objective Function 17:44 Result 19:00 Conclusion .....................................................SOURCE CODE......................................................................... import random import numpy as np from tkinter import messagebox #Define Class Particles class Particle: def __init__ (self,position): self.position=position self.velocity=np.zeros_like(position) self.best_position=position self.best_fitness=float('inf') def PSO(ObjF,Pop_Size,D,MaxT): swarm_best_position=None swarm_best_fitness=float('inf') particles=[] #Posotion Initialization position=np.random.uniform(-0.5,0.5,D) particle=Particle(position) particles.append(particle) #Fit...
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Grey Wolf Optimization Algorithm

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Grey Wolf Optimization Algorithm Numerical Example

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Bat algorithm Explanation Step by Step with example

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Whale Optimization Algorithm Code Implementation || WOA CODE || ~xRay Pixy

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