Survey of Applied Soft Computing Methods and Applications

EasyChair Preprint no. 11636

Abstract

This paper presents a comprehensive survey of applied soft computing methods and their diverse applications across various fields. Soft computing, a branch of computer science, offers flexible problem-solving approaches that are adaptable to real-world complexities. The survey explores the integration of fuzzy logic, neural networks, evolutionary algorithms, and probabilistic reasoning, highlighting their collective contributions to addressing intricate problems where precise solutions are challenging to attain. Through a systematic review of literature and case studies, this survey outlines the practical applications of soft computing techniques in domains such as pattern recognition, image processing, optimization, and decision-making processes. It examines how these methods effectively handle uncertainty, imprecision, and incomplete information, showcasing their utility in solving complex problems encountered in engineering, medicine, finance, and other diverse fields. Additionally, the survey discusses the strengths and limitations of each method while emphasizing their collective impact in advancing solutions for real-world challenges.

Keyphrases: Applied soft computing, machine learning, Soft Computing

@Booklet{EasyChair:11636,
  author = {Smith Miller},
  title = {Survey of Applied Soft Computing Methods and Applications},
  howpublished = {EasyChair Preprint no. 11636},

  year = {EasyChair, 2023}}