Kanaan A. Faisal

Hybrid computational models for the characterization of oil and gas reservoirs

The process of combining multiple computational intelligence techniques to build a single hybrid model has become increasingly popular. As reported in the literature, the performance indices of these hybrid models have proved to be better than the individual components when used alone. Hybrid models are extremely useful for reservoir characterization in petroleum engineering, which requires high-accuracy predictions for efficient exploration and management of oil and gas resources. In this paper, we have utilized the capabilities of data mining and computational intelligence in the prediction of porosity and permeability, two important petroleum reservoir characteristics, based on the hybridization of Fuzzy Logic, Support Vector Machines, and Functional Networks, using several real-life well-logs. Two hybrid models have been built. In both, Functional Networks were used to select the best of the predictor variables for training directly from input data by using its functional approximation capability with least square fitting algorithm. In the first model (FFS), the selected predictor variables were passed to Type-2 Fuzzy Logic System to handle uncertainties and extract inference rules, while Support Vector Machines made the final predictions. In the second, the selected predictor variables were passed to Support Vector Machines for training by transforming them to a higher dimensional space, and then to Type-2 Fuzzy Logic to handle uncertainties, extract inference rules and make final predictions. The simulation results show that the hybrid models perform better than the individual techniques when used alone for the same datasets with their higher correlation coefficients. In terms of execution time, the hybrid models took less time for both training and testing than the Type-2 Fuzzy Logic, but more time than Functional Networks and Support Vector Machines. This could be the price for having a better and more robust model. The hybrid models also performed better than a combination of two of the individual components, Type-2 Fuzzy Logic and Support Vector Machines, in terms of higher correlation coefficients as well as lower execution times. This is due to the effective role of Functional Networks, as a best-variable selector in the hybrid models.

Iterative Least Squares Functional Networks Classifier

This paper proposes unconstrained functional networks as a new classifier to deal with the pattern recognition problems. Both methodology and learning algorithm for this kind of computational intelligence classifier using the iterative least squares optimization criterion are derived. The performance of this new intelligent systems scheme is demonstrated and examined using real-world applications. A comparative study with the most common classification algorithms in both machine learning and statistics communities is carried out. The study was achieved with only sets of second-order linearly independent polynomial functions to approximate the neuron functions. The results show that this new framework classifier is reliable, flexible, stable, and achieves a high-quality performance

Evaluation of Breast Cancer Tumor Classification with Unconstrained Functional Networks Classifier

This paper proposes functional networks as an unconstrained classifier scheme for multivariate data to diagnose the breast cancer tumor. The performance of this new technique is measured using two well known databases under the minimum description length criterion, the results are compared with the most common existing classifiers in both computer science and statistics literatures. This new classifier shown reliable and efficient results with better correct classification rate, and much less computational time.

Principles of curriculum design and revision: a case study in implementing computing curricula CC2001

Our department has recently revisited its computer science program in the light of IEEE/ACM Computing Curricula 2001 (CC2001) recommendations, taking into consideration the ABETs Criteria for Accrediting Computing programs (CAC 04-05). The effort resulted in a revised curriculum. This paper presents the different decisions we made with regard to the curriculum orientation, knowledge units coverage, transition management, and monitoring and assessment. The paper also sheds some light on challenges faced. Tables provided in the paper show that the curriculum successfully implements CC2001 recommendations while satisfying the CAC 04-05.

Rule-based training of neural networks

Abstract Rule-based expert systems either develop out of the direct involvement of a concerned expert or through the enormous efforts of intermediaries called knowledge engineers. In either case, knowledge engineering tools are inadequate in many ways to support the complex problem of expert system building. This article describes a set of experiments with adaptive neural networks which explore two types of learning, deductive and inductive, in the context of a rule-based, deterministic parser of Natural Language. Rule-based processing of Language is an important and complex domain. Experiences gained in this domain generalize to other rule-based domains. We report on those experiences and draw some general conclusions that are relevant to knowledge engineering activities and maintenance of rule-based systems.

Design of a hybrid deterministic parser

A deterministic parser is under development which represents a departure from traditional deterministic parsers in that it combines both symbolic and connectionist components. The connectionist component is trained either from patterns derived from the rules of a deterministic grammar. ~The development and evolution of such a hybrid architecture has lead to a parser which is superior to any known deterministic parser.

Overcoming Limitations of Rule-Based Systems: An Example of a Hybrid Deterministic Parser

The rule-based approach to building intelligent systems is prevalent throughout the enterprise of Artificial Intelligence. Many famous systems have succeeded because they rely on rules at least to some extent. Through good knowledge engineering, the representation and encodement of the elements required to find adequate problem solutions can be facilitated. But despite enormous efforts, rule-based systems are far from perfect in their performance. What are the limitations and how can they be overcome?

Determinism and Connectionism in a Rule-Based Natural Language System

The processing of Natural Language is, at the same time, natural symbolic and naturally symbolic and naturally sub-symbolic. It is symbolic because ultimately symbols play a critical role. Writing systems, for example, owe their existence to the symbolic nature of language. It is also sub-symbolic because of the nature of speech, the fuzziness of concepts, and the high degree of parallelism that is difficult to explain as a purely symbolic phenomenon. This report details a set of experiments which support the claim that Natural Language can be syntactically processed in a robust manner using a connectionist deterministic parser. The... Read complete abstract on page 2.