Zimbabwe Tourism Authority (ZTA) is expansively using internet to promote destination Zimbabwe, few studies have been conducted to assess the effectiveness of ZTA website. The study applied the ICTRT (Information, Communication, Transaction, Relationship and Technical Merit) model the same as proposed by Li and Wang to evaluate the effectiveness of Zimbabwe Tourism Authority website through content analysis by expert evaluators. The objective of the research was to evaluate the effectiveness of ZTA website and investigate its functional features. The objective was achieved by content analysis of the website regarding its five functions (ICTRT) by expert evaluators. The results indicated that ZTA website was averagely effective and more on information and communication. The website is not effective on complex and sophisticated functions like transactions and relationship management. Website complexity was found to be the major determinant of effectiveness. Conclusion and recommendations were provided based on research findings and expert knowledge to improve the effectiveness of ZTA website.
Various machine learning techniques exist to perform regression on temporal data with concept drift occurring. However, there are numerous nonstationary environments where these techniques may fail to either track or detect the changes. This study develops a genetic programming-based predictive model for temporal data with a numerical target that tracks changes in a dataset due to concept drift. When an environmental change is evident, the proposed algorithm reacts to the change by clustering the data and then induce nonlinear models that describe generated clusters. Nonlinear models become terminal nodes of genetic programming model trees. Experiments were carried out using seven nonstationary datasets and the obtained results suggest that the proposed model yields high adaptation rates and accuracy to several types of concept drifts. Future work will consider strengthening the adaptation to concept drift and the fast implementation of genetic programming on GPUs to provide fast learning for high-speed temporal data.
Real-world nonstationary data are usually characterized by high nonlinearity and complex patterns due to the effects of different exogenous factors that make prediction a very challenging task. An ensemble strategically combines multiple techniques and tends to be robust and more precise compared to a single intelligent algorithmic model. In this work, a dynamic particle swarm optimization-based empirical mode decomposition ensemble is proposed for nonstationary data prediction. The proposed ensemble implements an environmental change detection technique to capture concept drift occurring and the intrinsic nonlinearity in time series, hence improving prediction accuracy. The proposed ensemble technique was experimentally evaluated on electric time series datasets. The obtained results show that the proposed technique improves prediction accuracy and it outperformed several stateof-the-art techniques in several cases. For future work direction, a detailed empirical analysis of the proposed technique can be considered such as the effect of the cost of prediction errors, and the technique's search capability.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.