Towards an Intelligent Biomedical Engineering With Nature-Inspired Artificial Intelligence Techniques

Artificial intelligence has a remarkable effect on many different fields with its flexible and comprehensive solution approaches to solve real-world problems. In this context, the field of biomedical engineering has also been affected by employment of different artificial intelligence-based techniques. This chapter aims to give a theoretical discussion on using nature-inspired artificial intelligent techniques for obtaining intelligent applications within biomedical engineering. As it is known, techniques within the field of artificial intelligence are inspired from nature. So, it is a good approach to focus on nature-inspired techniques for discussing intelligent biomedical engineering research works. Readers will have a chance to understand some ways of using artificial intelligence for achieving better results in biomedical engineering and the related developments associated with this field.

Special issue HAIS 2015: Recent advancements in hybrid artificial intelligence systems and its application to real-world problems

Neurocomputing ◽

10.1016/j.neucom.2017.09.003 ◽

2019 ◽

Vol 326-327 ◽

pp. 69-70

Author(s):

Pablo García Bringas ◽

Igor Santos ◽

Enrique Onieva ◽

Eneko Osaba ◽

Héctor Quintián ◽

...

Keyword(s):

Artificial Intelligence ◽

Real World ◽

Special Issue ◽

Hybrid Artificial Intelligence ◽

Artificial Intelligence Systems ◽

Artificial Intelligence Techniques for Unbalanced Datasets in Real World Classification Tasks

Machine Learning ◽

10.4018/978-1-60960-818-7.ch304 ◽

2012 ◽

pp. 414-427 ◽

Cited By ~ 1

Author(s):

Marco Vannucci ◽

Valentina Colla ◽

Silvia Cateni ◽

Mirko Sgarbi

Keyword(s):

Artificial Intelligence ◽

Support Vector Machines ◽

Decision Trees ◽

Real World ◽

Support Vector ◽

Vector Machines ◽

Classification Tasks

In this chapter a survey on the problem of classification tasks in unbalanced datasets is presented. The effect of the imbalance of the distribution of target classes in databases is analyzed with respect to the performance of standard classifiers such as decision trees and support vector machines, and the main approaches to improve the generally not satisfactory results obtained by such methods are described. Finally, two typical applications coming from real world frameworks are introduced, and the uses of the techniques employed for the related classification tasks are shown in practice.

“Knowledge Sifting” for Preliminary Design

2nd International Conference on Design Theory and Methodology ◽

10.1115/detc1990-0136 ◽

1990 ◽

Author(s):

Victor Todd Miller ◽

Gale E. Nevill

Keyword(s):

Artificial Intelligence ◽

Real World ◽

Direct Synthesis ◽

Scaling Up ◽

Preliminary Design ◽

Associative Memories ◽

Constraint System ◽

Reasoning System ◽

Symbolic Reasoning ◽

Abstract This article describes the use of a new symbolic reasoning methodology, based on the use of matrix associative memories, in the domain of preliminary design. A candidate preliminary design is first formulated in terms of a symbolic constraint system. The symbolic reasoner can then derive information about free variables based on the constraints in the system. One of the major difficulties with current reasoning methodologies in artificial intelligence is the scalability problem; as a problem increases in size, interactions between the elements hinder the reasoning process. Initial investigation indicates that this new symbolic reasoning system provides a means to avoid some of the complications that AI search and logic techniques have when scaling up to real world problems. Also, the methodology appears applicable to both analysis and direct synthesis tasks.

Artificial intelligence and operations research: challenges and opportunities in planning and scheduling

The Knowledge Engineering Review ◽

10.1017/s0269888900001090 ◽

2000 ◽

Vol 15 (1) ◽

pp. 1-10 ◽

Cited By ~ 8

Author(s):

CARLA P. GOMES

Keyword(s):

Artificial Intelligence ◽

Real World ◽

Operations Research ◽

Expressive Power ◽

Combinatorial Problems ◽

The Other ◽

Planning And Scheduling ◽

Functional Programming Languages ◽

Other Hand ◽

Both the Artificial Intelligence (AI) and the Operations Research (OR) communities are interested in developing techniques for solving hard combinatorial problems, in particular in the domain of planning and scheduling. AI approaches encompass a rich collection of knowledge representation formalisms for dealing with a wide variety of real-world problems. Some examples are constraint programming representations, logical formalisms, declarative and functional programming languages such as Prolog and Lisp, Bayesian models, rule-based formalism, etc. The downside of such rich representations is that in general they lead to intractable problems, and we therefore often cannot use such formalisms for handling realistic size problems. OR, on the other hand, has focused on more tractable representations, such as linear programming formulations. OR-based techniques have demonstrated the ability to identify optimal and locally optimal solutions for well-defined problem spaces. In general, however, OR solutions are restricted to rigid models with limited expressive power. AI techniques, on the other hand, provide richer and more flexible representations of real-world problems, supporting efficient constraint-based reasoning mechanisms as well as mixed initiative frameworks, which allow the human expertise to be in the loop. The challenge lies in providing representations that are expressive enough to describe real-world problems and at the same time guaranteeing good and fast solutions.

A Review on the use of Artificial Intelligence Techniques in Brain MRI Analysis

Journal of Informatics Electrical and Electronics Engineering (JIEEE) ◽

10.54060/jieee/002.02.010 ◽

2021 ◽

Vol 2 (2) ◽

pp. 1-15

Author(s):

Shruti Agarwal ◽

Keyword(s):

Artificial Intelligence ◽

Knowledge Engineering ◽

Brain Mri ◽

Research Community ◽

Research Subject ◽

Engineering Research ◽

The Past ◽

History Of ◽

Medical Domain

Over the past 20 years, the global research going on in Artificial Intelligence in applications in medication is a venue internationally, for medical trade and creating an energetic research community. The Artificial Intelligence in Medicine magazine has posted a massive amount. This paper gives an overview of the history of AI applications in brain MRI analysis to research its effect at the wider studies discipline and perceive de-manding situations for its destiny. Analysis of numerous articles to create a taxonomy of research subject matters and results was done. The article is classed which might be posted between 2000 and 2018 with this taxonomy. Analyzed articles have excessive citations. Efforts are useful in figuring out popular studies works in AI primarily based on mind MRI analysis throughout specific issues. The biomedical prognosis was ruled by way of knowledge engineering research in its first decade, whilst gadget mastering, and records mining prevailed thereafter. Together these two topics have contributed a lot to the latest medical domain.

Special issue HAIS 2012: Recent advancements in hybrid artificial intelligence systems and its application to real-world problems

Neurocomputing ◽

10.1016/j.neucom.2015.02.077 ◽

2015 ◽

Vol 163 ◽

pp. 1-2

Author(s):

Héctor Quintián ◽

Emilio Corchado ◽

Ajith Abraham ◽

André C.P.L.F. de Carvalho ◽

Michal Wozniak ◽

...

Keyword(s):

Artificial Intelligence ◽

Real World ◽

Special Issue ◽

Hybrid Artificial Intelligence ◽

Artificial Intelligence Systems ◽

LEARNING STEPPINGSTONES FOR PROBLEM SOLVING

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001493000273 ◽

1993 ◽

Vol 07 (03) ◽

pp. 527-540

Author(s):

DAVID RUBY ◽

DENNIS KIBLER

Keyword(s):

Artificial Intelligence ◽

Problem Solving ◽

General Problem ◽

Real World ◽

Logic Synthesis ◽

Vlsi Design ◽

Problem Solver ◽

Problem Difficulty ◽

Learning Problem ◽

One goal of Artificial Intelligence is to develop and understand computational mechanisms for solving difficult real-world problems. Unfortunately, domains traditionally used in general problem-solving research lack important characteristics of real-world domains, making it difficult to apply the techniques developed. Most classic AI domains require satisfying a set of Boolean constraints. Real-world problems require finding a solution that meets a set of Boolean constraints and performs well on a set of real-valued constraints. In addition, most classic domains are static while domains from the real world change. In this paper we demonstrate that SteppingStone, a general learning problem solver, is capable of solving problems with these characteristics. SteppingStone heuristically decomposes a problem into simpler subproblems, and then learns to deal with the interactions that arise between the subproblems. In lieu of an agreed upon metric for problem difficulty, we choose significant problems that are difficult for both people and programs as good candidates for evaluating progress. Consequently we adopt the domain of logic synthesis from VLSI design to demonstrate SteppingStone’s capabilities.

Artificial Intelligence Techniques for Unbalanced Datasets in Real World Classification Tasks

Computational Modeling and Simulation of Intellect ◽

10.4018/978-1-60960-551-3.ch021 ◽

2011 ◽

pp. 551-565 ◽

Cited By ~ 4

Author(s):

Marco Vannucci ◽

Valentina Colla ◽

Silvia Cateni ◽

Mirko Sgarbi

Keyword(s):

Artificial Intelligence ◽

Support Vector Machines ◽

Decision Trees ◽

Real World ◽

Support Vector ◽

Vector Machines ◽

Classification Tasks

In this chapter a survey on the problem of classification tasks in unbalanced datasets is presented. The effect of the imbalance of the distribution of target classes in databases is analyzed with respect to the performance of standard classifiers such as decision trees and support vector machines, and the main approaches to improve the generally not satisfactory results obtained by such methods are described. Finally, two typical applications coming from real world frameworks are introduced, and the uses of the techniques employed for the related classification tasks are shown in practice.