Aziz Guergachi

Mining sustainability indicators to classify hydrocarbon development

The role of energy in economic, social and ecological development of a country defines its significance in sustainable development. We propose here a method to classify a nations hydrocarbon development into one of five classes: (1) futuristic; (2) conforming; (3) sustainable; (4) unsustainable; or, (5) critical. K means clustering is a method of unsupervised classification in which the clusters cannot be labeled due to their lack of a class value. We propose a unique method to label unsupervised classes which is then used to divide the energy data of nations into five clusters. The labeled clusters are structured in an ID3 decision tree which provides a hierarchical structure to evaluate the hydrocarbon development in a given country. The results indicate some useful and interesting patterns in sustainability indicators.

Data mining applications in hydrocarbon exploration

This paper presents a review of the use of intelligent data analysis techniques in Hydrocarbon Exploration. The term “intelligent” is used in its broadest sense. The process of hydrocarbon exploration exploits data which have been collected from different sources. Different dimensions of data are analyzed by using Statistical Analysis, Data Mining, Artificial Neural Networks and Artificial Intelligence. This review is meant not only to describe the evolution of intelligent data analysis techniques used in different phases of hydrocarbon exploration but also signifying the growing use of Data Mining in various application domains; we avoided a general review of Data Mining and other intelligent data analysis techniques in this paper. The volume of general literature might affect the precision of our view regarding the application of these techniques in hydrocarbon exploration. The review reveals the suitability of existing techniques to data collected from diverse sources in addition to the use of analytical techniques for the process of hydrocarbon exploration.

EEG seizure detection and epilepsy diagnosis using a novel variation of Empirical Mode Decomposition

Epileptic seizure detection and epilepsy diagnosis based on feature extraction and classification using electroencephalography (EEG) signals is an important area of research. In this paper, we present a simple and effective approach based on signal decomposition, using a novel variation of the Empirical Mode Decomposition called Empirical Mode Decomposition-Modified Peak Selection (EMD-MPS). EMD-MPS allows time-scale based de-trending of signals, allowing signals to be separated directly into a de-trended component, and a trend, according to a frequency separation criterion. Features are extracted from the decomposed components, and a simple classifier, namely the 1-NN classifier is used for three classification tasks. The technique is tested on a publicly available EEG database, and a classification accuracy of 99% for epilepsy diagnosis task, and 100% and 98.2% for two seizure detection tasks is obtained. These results are better than, or comparable to previous results using the same EEG database, but have been obtained with a simpler and computationally fast signal analysis and classification method.

Application of Empirical Mode Decomposition and Teager energy operator to EEG signals for mental task classification

This paper presents a novel method for mental task classification from EEG signals using Empirical Mode Decomposition and Teager energy operator techniques on EEG data. The efficacy of these techniques for non-stationary and non-linear data has already been demonstrated, which therefore lend themselves well to EEG signals, which are also non-stationary and non-linear in nature. The method described in this paper decomposed the EEG signals (6 EEG signals per task per subject, for a total of 5 tasks over multiple trials) into their constituent oscillatory modes, called intrinsic mode functions, and separated out the trend from the signal. Teager energy operator was used to calculate the average energy of both the detrended signal and the trend. The average energy was used to construct separate feature vectors with a small number of parameters for the detrended signal and the trend. Based on these parameters, one-versus-one classification of mental tasks was performed using Linear Discriminant Analysis. Using both feature vectors, an average correct classification rate of more than 85% was achieved, demonstrating the effectiveness of the method used. Furthermore, this method used all the intrinsic mode functions, as opposed to similar studies, demonstrating that the trend of the EEG signal also contains important discriminatory information.

Chaotic time series prediction using knowledge based Green’s Kernel and least-squares support vector machines

This paper proposes a novel prior knowledge based Greens kernel for long term chaotic time series prediction. A mathematical framework is presented to obtain the domain knowledge about the magnitude of the Fourier transform of the function to be predicted and design a prior knowledge based Greens kernel that exhibits optimal regularization properties by using the concept of matched filters. The matched filter behavior of the proposed kernel function provides the optimal regularization. Simulation results on a chaotic benchmark time series indicate that the knowledge based Greens kernel shows good prediction performance compared to the other existing support vector kernels for the time series prediction task considered in this paper.

Emotion Recognition Using Novel Speech Signal Features

Automatic emotion recognition (AER) is a very recent research topic in the human-computer interaction (HCI) field which still has much room to grow. In this contribution a set of novel acoustic features and least square-support vector machines (LS-SVMs) are proposed to set up a speaker-independent automatic human emotion recognition system. Six discrete emotional states are classified throughout this work: happiness, sadness, anger, surprise, fear, and disgust. Different multi-class SVM methods are implemented in order to get the best result. The result achieved by LS-SVM is then compared by that of a linear classifier. We achieved an overall accuracy of 81.3%.

Negative and Positive Association Rules Mining from Text Using Frequent and Infrequent Itemsets

Association rule mining research typically focuses on positive association rules (PARs), generated from frequently occurring itemsets. However, in recent years, there has been a significant research focused on finding interesting infrequent itemsets leading to the discovery of negative association rules (NARs). The discovery of infrequent itemsets is far more difficult than their counterparts, that is, frequent itemsets. These problems include infrequent itemsets discovery and generation of accurate NARs, and their huge number as compared with positive association rules. In medical science, for example, one is interested in factors which can either adjudicate the presence of a disease or write-off of its possibility. The vivid positive symptoms are often obvious; however, negative symptoms are subtler and more difficult to recognize and diagnose. In this paper, we propose an algorithm for discovering positive and negative association rules among frequent and infrequent itemsets. We identify associations among medications, symptoms, and laboratory results using state-of-the-art data mining technology.

Empirical mode decomposition based sparse dictionary learning with application to signal classification

This paper will present a novel empirical framework for dictionary learning where the dictionary is learned from the data to be analyzed, rather than using a pre-defined basis. A dictionary formation and learning algorithm is presented, which learns sparse dictionaries, where sparsity is understood in terms of the small number of dictionary atoms compared to the signal dimensions. An initial dictionary is formed using training signals of different classes, where the dictionary atoms consist of intrinsic mode functions obtained as a result of decomposing the training signals using empirical mode decomposition. A dictionary learning algorithm trains this dictionary which results in a significant reduction in the size of the learned dictionary. The learned dictionary can be applied to signal classification, whereby coefficients of orthogonal projections of test signals against the learned dictionary are used as features to classify the test signals into different classes. We also show that the learned dictionary allows calculation of the coefficient vector based on sparse representation of test signals, which can also be used as a feature vector. Although the framework is not formulated as reconstructive, or combined reconstructive and discriminative dictionary learning, its efficacy in signal classification is demonstrated using real-life EEG signals.

Knowledge-Based Green's Kernel for Support Vector Regression

This paper presents a novel prior knowledge-based Greens kernel for support vector regression (SVR). After reviewing the correspondence between support vector kernels used in support vector machines (SVMs) and regularization operators used in regularization networks and the use of Greens function of their corresponding regularization operators to construct support vector kernels, a mathematical framework is presented to obtain the domain knowledge about magnitude of the Fourier transform of the function to be predicted and design a prior knowledge-based Greens kernel that exhibits optimal regularization properties by using the concept of matched filters. The matched filter behavior of the proposed kernel function makes it suitable for signals corrupted with noise that includes many real world systems. We conduct several experiments mostly using benchmark datasets to compare the performance of our proposed technique with the results already published in literature for other existing support vector kernel over a variety of settings including different noise levels, noise models, loss functions, and SVM variations. Experimental results indicate that knowledge-based Greens kernel could be seen as a good choice among the other candidate kernel functions.

Knowledge management as a holistic tool for superior project management

The challenges encountered in project-based organisations have been addressed by many strategies. This paper intends to provide an empirical insight of knowledge and its application within project environs. This would instigate learning and innovation within Knowledge Management (KM) in project-based organisations. Based on two case studies, a simple understanding of knowledge, Knowledge Creation (KC) and its management are proposed. It further underlines the humanistic core of KM and a framework that can be utilised to align knowledge paths. Finally, the paper concludes with suggestions and recommendations for future research on KM in the realm of project management.