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Two novel Natural Language Processing (NLP) classification techniques are applied to the analysis of corporate annual reports in the task of financial forecasting. The hypothesis is that textual content of annual reports contain vital information for assessing the performance of the stock over the next year. The first method is based on character n-gram(More)
In time series analysis research, there is a strong interest in discrete representations of real valued data streams. One approach still considered state-of-the-art is the Symbolic Aggregate Approximation (SAX) algorithm. The interest of this paper concerns the SAX assumption of data being highly Gaussian and the use of the standard normal curve to choose(More)
Educational data mining and learning analytics promise better understanding of student behavior and knowledge, as well as new information on the tacit factors that contribute to student actions. This knowledge can be used to inform decisions related to course and tool design and pedagogy, and to further engage students and guide those at risk of failure.(More)
This paper describes an investigation into the nature of the academic problems that face novice programming students. These learners are required to demonstrate competencies in high-level abstract principles of programming and logic, such as program design and OOP principles, which are conceptually difficult. During the programming task learners receive(More)
This paper uses an evaluation method for Information Communication Technology (ICT) in education adapted from Morgan (2007) to conduct a comparison between the Apple iPod, Nintendo DS and Nintendo Wii, in order to assess the potential cognitive impact on learners of the affordances of each ICT device. The ICT evaluation method uses the concept of(More)
Restoration and fuel treatments in the moist forests of the northern Rocky Mountains are complex and far different from those applicable to the dry ponderosa pine forests. In the moist forests, clearcuts are the favored method to use for growing early-seral western white pine and western larch. Nevertheless, clearcuts and their associated roads often affect(More)
In this paper, we attempt to make accurate predictions of the movement of the stock market with the aid of an evolutionary artificial neural network (EANN). To facilitate this objective we constructed an EANN for multi-objective optimization (MOO) that was trained with macro-economic data and its effect on market performance. Experiments were conducted with(More)
Data relating to university students' engagement is collected internationally via several large-scale student surveys such as the North American National Survey of Student Engagement. The instruments employed measure the extent to which students put their efforts into activities associated with effective learning. It is claimed that these process measures(More)
Artificial Immune System (AIS) to model and predict the movements of the stock market. To aid in this research the AIS models are compared with a k-Nearest Neighbors (kNN) algorithm, an artificial neural network (ANN) and a benchmark market portfolio to compare simulated trading results. The analysis shows that the AIS produced overall accuracy results of(More)
This paper introduces a novel forecasting algorithm that is a blend of micro and macro modelling perspectives when using Artificial Intelligence (AI) techniques. The micro component concerns the fine-tuning of technical indicators with population based optimization algorithms. This entails learning a set of parameters that optimize some economically(More)