Time series forecasting is an important and widely popular topic in the research of system modeling. This paper describes how to use the hybrid PSO-RLSE neuro-fuzzy learning approach to the problem of time series forecasting. The PSO algorithm is used to update the premise parameters of the proposed prediction system, and the RLSE is used to update the consequence parameters. Thanks to the hybr...

Event extraction is a popular and interesting research field in the area of Natural Language Processing (NLP). In this paper, we propose a hybrid approach for event extraction within the TimeML framework. Initially, we develop a machine learning based system based on Conditional Random Field (CRF). But most of the deverbal event nouns are not correctly identified by this machine learning approa...

This paper presents a hybrid method using machine learning approach for Named Entity Recognition (NER). A system built based on this method is able to achieve reasonable performance with minimal training data and gazetteers. The hybrid machine learning approach differs from previous machine learning-based systems in that it uses Maximum Entropy Model (MEM) and Hidden Markov Model (HMM) successi...

Traffic assignment is fundamentally a tool for transportation planning. It allocates trips within the traffic network. However, modern uses of traffic assignment also include shorter time horizons and even real-time use (e.g., for route recommendation). In the latter case, it is interesting to recommend routes that are as close as possible to the system optimum. To compute an approximation of t...

This paper explores the contribution of virtual tools to student learning within fulltime management programmes. More specifically, the paper focuses on asynchronous courseware, considering the scope it offers for group-based collaborative learning outside the classroom. We report on the effectiveness of this approach for an economics course on International Money and Finance, which was deliver...

Researchers have embraced a variety of machine learning (ML) techniques in their efforts to improve the quality of learning programs. The recent evolution of hybrid architectures for machine learning systems has resulted in several approaches that combine rule-induction methods with case-based reasoning techniques to engender performance improvements over moretraditional one-representation arch...

This paper describes an approach for image segmentation that relies on learning from experience to adapt and improve the segmentation performance. The adaptive image segmentation system incorporates a feedback loop consisting of a machine learning subsystem, an image segmentation algorithm, and an evaluation component which determines segmentation quality. The machine learning component is base...

This study proposes design concepts for a comprehensive home financial learning environment that individual investors can use as a reference in establishing web-based learning and investment platforms. This study also introduces a hybrid approach that demonstrates a data mining function of the financial learning environment. Known as Fuzzy BPN, this approach is comprised of backpropagation neur...