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Intelligent Traffic Management Using || AI & Metaheuristics || ~xRay Pixy

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Hybrid Artificial Intelligence and Metaheuristics for Smart City TRafci Management Problem Video Chapters: 00:00 Introduction 00:40 Smart Cities 01:14 Traditional Methods for Traffic Management 02:12 Hybrid Approach AI and Metaheuristics 02:47 STEPS for Hybrid  Traffic Management System 08:40 Advantages of Smart Traffic Management System 09:33 Conclusion
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Loop Optimization Techniques in Compiler Design

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LOOP Optimization

Rules Every Programmer must follow before the optimization of any source code. 
1.) Loop transformation technique. 
2.) Help to reduce optimize the execution time of a program.
3.) Basically, remove the iterations. 
4.) Loop Unrolling increase the program speed by eliminating loop control instructions.

Loop Optimization is a Machine Independent Technique. We can do Code optimization in the loops of the program. In loop optimization, code optimization is performed on inner loops.

Methods of Loop Optimization
1.) Code Motion
2.) Loop Invariant Method
3.) Loop Jamming
4.) Loop Unrolling
5.) Strength Reduction
Code Motion: Technique that moves the code outside the loop.
Loop Jamming: Combining two or more loops in a single loop.
Code Strength: Expressions that consumes CPU time and memory should replace with cheaper expressions. Replace Expensive Expressions (i.e., *) with cheaper ones (i.e., +)

Vidoe: https://youtu.be/1cSnmjRPRTA
Loop Optimization Technique in Compiler Design

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PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE

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Cuckoo Search Algorithm for Optimization Problems

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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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how is the LBP |Local Binary Pattern| values calculated? Step-by-Step with Example

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Grey Wolf Optimization Algorithm Numerical Example

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

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