Revolutionizing Disease Diagnosis with AR/VR: A Breakthrough in Medical Technology By C. Iwendi
Dr. Celestine Iwendi, a Reader at the University of Bolton under Creative Technologies, has recently published a groundbreaking research paper titled “Innovative Augmented and Virtual Reality Applications for Disease Diagnosis based on Integrated Genetic Algorithms.” The paper, featured in the International Journal of Cognitive Computing in Engineering (Volume 4, 2023, Pages 266-276), explores the use of animated video in conveying research findings. In this comprehensive work, Dr. Iwendi introduces a method for detecting various diseases within a short period using Audio Reality/Virtual Reality (AR/VR) techniques. The research incorporates three distinct algorithms—Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO)—into the AR/VR models for medical applications.
The entire operation is conducted within the search space, ensuring the proper functioning of these advanced models. The detection process in AR/VR models relies on various factors, emphasizing the need for minimum error functions. In the integrated technique, Absolute Errors (AE) and Time Errors (TE) are measured and compared with existing methods. The research highlights the significant improvement in detection performance with an enhanced search space, observing the fitness function of each algorithm as a maximization objective.
Furthermore, the paper addresses the complexity of AR/VR models in real-time detection processes. It identifies that highly complex detections can be transformed into simpler ones, enhancing the practicality of these applications. In a comparative analysis of the three algorithms, Ant Colony Optimization (ACO) emerges as particularly effective, minimizing errors while maximizing the fitness function.
The paper, with an impactful DOI link (https://lnkd.in/eu4npq2s), has been published in the International Journal of Cognitive Computing in Engineering and boasts an impressive Impact Factor of 8.35. Notably, it is categorized in Q1 for Computer Science Applications, Engineering (miscellaneous), Information Systems, and Information Systems and Management. Dr. Iwendi’s research promises to contribute significantly to the evolving landscape of augmented and virtual reality applications in disease diagnosis