Aboul Ella Hassanien (Abo)

A compilation of the latest trends in the field, Computational Intelligence in Medical Imaging: Techniques and Applications explores how intelligent computing can bring enormous benefit to existing technology in medical image processing as well as improve medical imaging research. The contributors also cover state-of-the-art research toward integrating medical image processing with artificial intelligence and machine learning approaches.

Due to their excellent performance in solving combinatorial optimization problems, metaheuristics algorithms such as Genetic Algorithms GA [35], [18], [5], Simulated Annealing SA [34], [13] and Tabu Search TS make up another class of search methods that has been adopted to efficiently solve dynamic optimization problem. Most of these methods are confined to the population space and in addition the solutions of nonlinear problems become quite difficult especially when they are heavily constrained. They do not make full use of the historical information and lack prediction about the search space. Besides the knowledge that individuals inherited "genetic code" from their ancestors, there is another component called Culture. In this paper, a novel culture-based GA algorithm is proposed and is tested against multidimensional and highly nonlinear real world applications.

Time series clustering is an important topic, particularly for similarity search amongst long time series such as those arising in bioinformatics, in marketing research, software engineering and management. This chapter discusses the state-of-the-art methodology for some mining time series databases and presents a new evolutionary algorithm for times series clustering an input time series data set. The data mining methods presented include techniques for efficient segmentation, indexing, and clustering time series.

The purpose of this book is to provide an overview of powerful state-of-the-art methodologies that are currently utilized for biomedicine and/ or bioinformatics-oriented applications, so that researchers working in those fields could learn of new methods to help them tackle their problems. On the other hand, the CI community will find this book useful by discovering a new and intriguing area of applications. In order to help fill the gap between the scientists on both sides of this spectrum, the editors have solicited contributions from researchers actively applying computational intelligence techniques to important problems in biomedicine and bioinformatics.

For the last decades Multimedia processing has emerged as an important technology to generate content based on images, video, audio, graphics, and text. Furthermore, the recent new development represented by High Definition Multimedia content and Interactive television will generate a huge volume of data and important computing problems connected with the creation, processing and management of Multimedia content. "Computational Intelligence in Multimedia Processing: Recent Advances" is a compilation of the latest trends and developments in the field of computational intelligence in multimedia processing. This edited book presents a large number of interesting applications to intelligent multimedia processing of various Computational Intelligence techniques, such as rough sets, Neural Networks; Fuzzy Logic; Evolutionary Computing; Artificial Immune Systems; Swarm Intelligence; Reinforcement Learning and evolutionary computation.

This chapter presents a broad overview of Computational Intelligence (CI) techniques including Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), Genetic Algorithms (GA), Fuzzy Sets (FS), and Rough Sets (RS). We review a number of applications of computational intelligence to problems in bioinformatics and computational biology, including gene expression, gene selection, cancer classification, protein function prediction, multiple sequence alignment, and DNA fragment assembly. We discuss some representative methods to provide inspiring examples to illustrate how CI could be applied to solve bioinformatic problems and how bioinformatics could be analyzed, processed, and characterized by computational intelligence. Challenges to be addressed and future directions of research are presented. An extensive bibliography is also included.