A soft computing decision support framework to improve the e-learning experience

In this paper an e-learning decision support framework based on a set of soft computing techniques is presented. The framework is mainly based on the FIR methodology and two of its key extensions: a set of Causal Relevance approaches (CR-FIR), that allow to reduce uncertainty during the forecast stage; and a Rule Extraction algorithm (LR-FIR), that extracts comprehensible, actionable and consistent sets of rules describing the student learning behavior. The data set analyzed was gathered from the data generated from user’s interaction with an e-learning environment. The introductory course data set was analyzed with the proposed framework with the goal to help virtual teachers to understand the underlying relations between the actions of the learners, and make more interpretable the student learning behavior. The results obtained improve system understanding and provide valuable knowledge to teachers about the course performance.Postprint (author’s final draft

Fuzzy rules model for global warming decision suport

Ponencia presentada en: VI Congreso Internacional de la Asociación Española de Climatología celebrado en Tarragona del 8 al 11 de octubre de 2008.[EN]In this work a simple box model of the ocean-atmosphere is used to asses the response of the coupled system to the projected increase in the amount of carbon dioxide, by varying internal model parameters, within plausible ranges, as well as the thermal forcing associated with the greenhouse gases. The values of temperature increase are used to build fuzzy logic models based on the Fuzzy Inductive Reasoning (FIR) methodology that are able to deal with the uncertainties associated to the box model parameters. FIR is a data driven methodology that uses fuzzy and pattern recognition techniques to infer system models and to predict their future behavior.[ES]En este trabajo se utiliza un modelo simple del sistema océano-atmósfera para obtener valores de temperatura promediados globalmente. En este modelo, la temperatura es una función del calor agregado al sistema, de la sensitividad de la atmósfera y de la difusividad del océano. A partir de los campos de temperatura obtenidos, se construye un modelo basado en lógica difusa, concretamente usando la metodología del Razonamiento Inductivo Difuso (FIR, por las siglas en inglés), el cual es capaz de predecir el cambio de temperatura global con una gran precisión. Sin embargo, el modelo FIR no permite una interpretación suficientemente sencilla de la dinámica del sistema para que sea útil a los tomadores de decisiones

Encapsulation of Soft Computing Approaches within Itemset Mining a A Survey

Data Mining discovers patterns and trends by extracting knowledge from large databases. Soft Computing techniques such as fuzzy logic, neural networks, genetic algorithms, rough sets, etc. aims to reveal the tolerance for imprecision and uncertainty for achieving tractability, robustness and low-cost solutions. Fuzzy Logic and Rough sets are suitable for handling different types of uncertainty. Neural networks provide good learning and generalization. Genetic algorithms provide efficient search algorithms for selecting a model, from mixed media data. Data mining refers to information extraction while soft computing is used for information processing. For effective knowledge discovery from large databases, both Soft Computing and Data Mining can be merged. Association rule mining (ARM) and Itemset mining focus on finding most frequent item sets and corresponding association rules, extracting rare itemsets including temporal and fuzzy concepts in discovered patterns. This survey paper explores the usage of soft computing approaches in itemset utility mining

Complexity, BioComplexity, the Connectionist Conjecture and Ontology of Complexity\ud

This paper develops and integrates major ideas and concepts on complexity and biocomplexity - the connectionist conjecture, universal ontology of complexity, irreducible complexity of totality & inherent randomness, perpetual evolution of information, emergence of criticality and equivalence of symmetry & complexity. This paper introduces the Connectionist Conjecture which states that the one and only representation of Totality is the connectionist one i.e. in terms of nodes and edges. This paper also introduces an idea of Universal Ontology of Complexity and develops concepts in that direction. The paper also develops ideas and concepts on the perpetual evolution of information, irreducibility and computability of totality, all in the context of the Connectionist Conjecture. The paper indicates that the control and communication are the prime functionals that are responsible for the symmetry and complexity of complex phenomenon. The paper takes the stand that the phenomenon of life (including its evolution) is probably the nearest to what we can describe with the term “complexity”. The paper also assumes that signaling and communication within the living world and of the living world with the environment creates the connectionist structure of the biocomplexity. With life and its evolution as the substrate, the paper develops ideas towards the ontology of complexity. The paper introduces new complexity theoretic interpretations of fundamental biomolecular parameters. The paper also develops ideas on the methodology to determine the complexity of “true” complex phenomena.\u