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 – Persistent infections can damage the eardrum and middle ear structures, leading to partial or permanent hearing loss....
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Chemical Reaction Optimization Algorithm step-by-step with example ~xRay...
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Chemical Reaction Optimization Algorithm
Chemical Reaction Optimization algorithm is a population-based metaheuristic algorithm. Chemical Reaction Optimization algorithm is inspired by Chemical reactions. Chemical Reaction Optimization algorithm is developed by Albert Y.S. Lam and Victor O.K. Li. In this algorithm, Molecular structure (sum all characteristics) is used to compute the solution. Chemical Reaction Optimization algorithm is used to solve optimization problems.
Chemistry Basic Fundamentals: Atom / Molecule / Chemical Bonding / Molecular Structure / Molecule Energy / Chemical Reations/ Elementary Reactions
Atom: According to Dalton (in 1808), an atom is the smallest part of an element that exists as the smallest entity. 3 important fundamental particles of an atom are Proton, Electron, and Neutron.
For Example Oxygen (O), Nitrogen (N), Hydrogen (H), etc.
Molecule: A molecule is composed of 2 or more atoms held together by chemical bonds. The molecule is always formed when it is more stable and has lower energy than individual atoms. The molecule is characterized by: Atom Type, Bond Length, Bond Energy, and Angle.
For Example Water (H2O), Nitrogen (N2), Ozone (O3), Carbon Dioxide (CO2).
Energy: Capacity to do any work. Different Energy Forms are:
Light Energy
Heat Energy
Electrical Energy
Mechanical Energy
Energy Principals:
1.) Potential Energy: Potential Energy Means energy due to Object Position/condition/composition.
2.) Kinetic Energy: Kinetic Energy means capacity for doing work directly (easily transferred between objects). For Example Body in Motion.
A chemical bond is the force of attraction that holds the atoms together in a molecule.
In any chemical reaction, molecules collide with each other or with the wall of the container.
Four Elementary reactions in Chemical Reaction Optimization algorithm:
Uni-molecular Reactions
On-Wall Ineffective Collision: Collision occurred when any molecule hits the wall of the container and bounce back
Decomposition: When any molecule hit the wall it decomposes into 2 molecules
Multi molecular Reactions
Inner molecular Ineffective Collision: Two molecules collide with each other and bounce away.
Synthesis: Two molecules collide together and fuse into one molecule.
Chemical Reaction Optimization Algorithm Step-by-Step
1.) Parameter Initialization Phase
Initialize all important parameters such as:
Population Size, [Molecular Structure]
Number of Variables, [Total number of characteristics of molecule]
KineticEnergyLoassRate,[% of an upper limit of K.E. loss in the environment (in the collision)]
Molecule Collision Fraction,
Initial Kinetic Energy,
2.) Initialize population Randomly in the search space.
Population = Set of Molecules
[1,2,3,4,5,6,…..Population Size]
3.) For each molecule do
Assign a Random Solution to the molecular structure.
Calculate the Potential Energy (P.E.) using the Objective function.
Assign Kinetic Energy as Initial Kinetic Energy.
End For
4.) Further Process in Flow Chart: Chemical Reaction Optimization Algorithm Flow Chart.
Chemical Reaction Optimization algorithm Advantage:
Chemical Reaction Optimization algorithms achieve the best solutions to solve different real-world problems.
PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE 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. Particle Swarm Optimization (PSO) Algorithm step-by-step explanation with Numerical Example and source code implementation. - PART 2 [Example 2] 1.) Initialize Population [Current Iteration (t) = 0] Population Size = 4; 𝑥𝑖 : (i = 1,2,3,4) and (t = 0) 𝑥1 =1.3; 𝑥2=4.3; 𝑥3=0.4; 𝑥4=−1.2 2.) Fitness Function u...
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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 ...
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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Grey Wolf Optimization Algorithm (GWO) Grey Wolf Optimization Grey Wolf Optimization Algorithm is a metaheuristic proposed by Mirjaliali Mohammad and Lewis, 2014. Grey Wolf Optimizer is inspired by the social hierarchy and the hunting technique of Grey Wolves. What is Metaheuristic? Metaheuristic means a High-level problem-independent algorithmic framework (develop optimization algorithms). Metaheuristic algorithms find the best solution out of all possible solutions of optimization. Who are the Grey Wolves? Wolf (Animal): Wolf Lived in a highly organized pack. Also known as Gray wolf or Grey Wolf, is a large canine. Wolf Speed is 50-60 km/h. Their Lifespan is 6-8 years (in the wild). Scientific Name: Canis Lupus. Family: Canidae (Biological family of dog-like carnivorans). Grey Wolves lived in a highly organized pack. The average pack size ranges from 5-12. 4 different ranks of wolves in a pack: Alpha Wolf, Beta Wolf, Delta Wolf, and Omega Wolf. How Grey Wolf Optimiza...
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