Innovative augmented and virtual reality applications for disease diagnosis based on integrated genetic algorithms

Abstract

In this comprehensive paper the method of detecting various diseases within short period of time using Audio Reality/ Virtual Reality (AR/VR) techniques is proposed. For proper functioning of AR/VR models in medical applications three distinct algorithms such Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) is integrated where the entire operation is performed with respect to search space. Moreover, the detection process in AR/VR models depends on several factors where minimum error functions must be ensured. Hence in the integrated technique both Absolute Errors (AE) and Time Errors (TE) are measured and compared with existing methods. As the performance of detection is greatly improved with search space the fitness function of each algorithm is observed and it is considered as maximization objective in the proposed method. Furthermore, the complexity of AR/VR models in real time detection process is detected and it is realistic that high complex detections are converted to simple detections. In the comparative analysis of three algorithms ACO proves to be much better as errors are minimized with maximization of fitness function.

Keywords

• Genetic algorithm (GA)
• Ant colony optimization (ACO)
• Particle swarm optimization (PSO)
• Virtual reality
• Audio reality