Название: Swarm Intelligence and its Applications in Biomedical InformaticsАвтор: Dr. A. Sheik Abdullah
Издательство: CRC Press
Год: 2024
Страниц: 175
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB
Swarm Intelligence and Its Applications in Biomedical Informatics discusses Artificial Intelligence (AI) applications in medicine and biology, as well as challenges and opportunities presented in these arenas. It covers healthcare Big Data analytics, mobile health, personalized medicine, and clinical trial data management. This book shows how AI can be used for early disease diagnosis, prediction, and prognosis, and it offers healthcare case studies that demonstrate the application of AI and Machine Learning.
The term Swarm Intelligence was first coined by Gerardo Beni and Jing Wang toward the perspective of cellular robotic system. Swarm intelligence corresponds to a collection of quantified algorithms that simulate natural learning behavior in a system of individuals that synchronize using self-organizing and distributed control mechanism. The working principle of the algorithms focuses toward the collective behavior inference, which results from interactions among individuals in the group and with the environment.
Algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bee-based Harmony Search (BHS), Stochastic Diffusion Search, Bat Algorithm, Multi-Swarm Optimization (MSO), and Cuckoo Search work according to the behavior of nature. A typical swarm intelligence system has some eminent properties such as:
• Group of individuals.
• Homogeneity among the group with respect to the environment.
• Learning ability and interaction among the group of individuals (local information) with respect to the environment.
• Global learning behavior as a result of the learning factor through local information with respect to the environment.
The research and application of swarm intelligence algorithms focus toward the collective behavior of birds resembling the character of flocking, pheromone set by the ants, schooling by fish, and microbiological intelligence.
Swarm Intelligence algorithms are widely used in a number of applications such as physical science, medical informatics, imaging, engineering, and data management services. The major consequence of Swarm Intelligence is to choose the best feasible solution, which results in the decision-making process. The process in swarm intelligence has two major steps. First, problem formulation involves the assignment of decision variable, problem constraints, and objective function with a complete analysis over the problem domain. Second, it provides the solution to the observed problem by selecting appropriate mathematical function and test cases with an intention toward proper decision-making.
The art of swarm intelligence falls under metaheuristic approach with the intention of following an iterative approach. A nearoptimal solution will be obtained with the process of assigning various learning strategies over the objective function. The following are some of the widely used swarm intelligence techniques used for many sorts of real-time applications:
• Particle Swarm Optimization Algorithm
• Ant Colony Optimization Algorithm
• Bee Colony Algorithm
• Fish Swarm Algorithm
• Stochastic Diffusion Search Algorithm
Key Features:
• Covers all major topics of swarm intelligence research and development such as novel-based search methods and novel optimization algorithm: applications of swarm intelligence to management problems and swarm intelligence for real-world application.
• Provides a unique insight into the complex problems of bioinformatics and the innovative solutions which make up ‘intelligent bioinformatics’.
• Covers a wide range of topics on the role of AI, Machine Learning, and Big Data for healthcare applications and deals with the ethical issues and concerns associated with it.
• Explores applications in different areas of healthcare and highlights the current research.
This book is designed as a reference text, and it aims primarily at advanced undergraduates and postgraduate students studying computer science and bioinformatics. Researchers and professionals will find this book useful.
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