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In this Ph. D thesis, the main significant contributions are: formulation of a new non-linear membership function using fuzzy approach to capture and describe vagueness in the technological coefficients of constraints in...

"A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and exible methods of ...

"The amount of multidimensional data published on the semantic web (SW) is constantly increasing, due to initiatives such as Open Data and Open Government Data, among other ones. Models, languages, and tools, that allow ...

Many optimization problems in various fields have been solved using diverse optimization al gorithms. Traditional optimization techniques such as linear programming (LP), non-linear programming (NLP), and dynamic program...

Historically Functional Programming FP for short has been associated with a small scope of applications mainly academic The computer science community did not pay enough attention to its potential perhaps due t...

Harvesting topical content is a process that can be done by formulating topic-relevant queries and submitting them to a search engine. The quality of the material collected through this process is highly dependant on the...

1. Introduction. Control System Design Steps. Adaptive Control. A Brief History. 2. Models for Dynamic Systems. Introduction. State-Space Models. Input/Output Models. Plant Parametric Models. Problems. 3. Stability. Intr...

"Decision support systems for alert drug-drug interactions have been shown as valid strategy to reduce medical error. Even so the use of these systems has not been as expected, probably due to the lack of a suitable desi...

"We address the problem of querying data cubes for Online Analytical Processing (OLAP) analysis, directly on the Semantic Web (SW). We rst introduce CQL, a simple algebra for querying data cubes at a conceptual level. ...

This paper proposes a hybrid particle swarm approach called Simple Multi-Objective Particle Swarm Optimizer (SMOPSO) which incorporates Pareto dominance, an elitist policy, and two techniques to maintain diversity: a mut...

In this paper, we solve some benchmarks of Set Covering Problem and Equality Constrained Set Covering or Set Partitioning Problem. The resolution techniques used to solve them were Ant Colony Optimization algorithms and ...

"The steel industry faces increasing challenges in managing product diversity, maintaining competitiveness, and improving operational efficiency. Within this context, recommendation systems present a promising opportunit...

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals. In particular, ants have inspired a number of methods and techniques am...

Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficienc...

Data mining is the discovery of patterns and regularities from large amounts of data using machine learning algorithms. This can be applied to object recognition using image processing techniques. In fruits and vegetabl...

El Aprendizaje por Refuerzo (RL) se ha convertido en una herramienta crucial para alinear modelos generativos complejos, superando las limitaciones de los métodos de aprendizaje supervisado tradicionales. Su capacidad p...

Over the past few years, a continually increasing number of research efforts have investigated the application of evolutionary computation techniques for the solution of scheduling problems. Scheduling problems can pose...

A variety of optimization problems in fields such as production operations in manufacturing industry, parallel and distributed systems, logistics and traffic can be summarized within the general class of scheduling probl...

Two powerful metaheuristics being used successfully since their creation for the resolution of optimization problems are Cellular Genetic Algorithm (cGA) and Particle Swam Optimization (PSO). Over the last years, interes...

Evolutionary computation techniques, genetic algorithms, evolutionary strategies and genetic programming are motivated by the evolution of nature. A population of individuals, which encode the problem solutions are manip...

Evolutionary Computation is an emergent field, which provides new heuristics to function optimization where traditional approaches make the problem computationally intractable. Exploration and exploitation of solution i...

The main goal of this thesis was to design new parallel processing strategies specially conceived for distributed environments in order to solve numerical and structural problems from the field of process systems enginee...

The choice of a search algorithm can play a vital role in the success of a scheduling application. Evolutionary algorithms (EAs) can be used to solve this kind of combinatorial optimization problems. Compared to conventi...

In this paper two images compression techniques with loss based on the method used by the compression Standard Joint Photographic Experts Groups (JPEG) and on the Quadtree adaptive partitioning method are presented and c...