📮 Swarm Optimization for Biomedical Applications by Saurav Mallik(.PDF)+

Biomedical engineering is a rapidly growing interdisciplinary area that is providing solutions to biological and medical problems and improving the healthcare system. It is connected to various applications like protein structure prediction, computer-aided drug design, and computerized medical diagnosis based on image and signal data, which accomplish low-cost, accurate, and reliable solutions for improving healthcare services. With the recent advancements, Machine Learning (ML) and Deep Learning (DL) techniques are widely used in biomedical engineering to develop intelligent decision-making healthcare systems in real-time. However, accuracy and reliability in model performance can be a concern in tackling data generated from medical images and signals, making it challenging for researchers and practitioners. Therefore, optimized models can produce quality healthcare services to handle the complexities involved in biomedical research. The collective intelligence of a swarm is inspired by the way they interact with each other and the environment. The popular SI techniques are Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Cuckoo Search (CS), Firefly Algorithm (FA), Salp Swarm Algorithm (SSA), Grey Wolf Optimization (GWO), and Moth-Flame Optimizer (MFO).

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