Titlebook: Computational Intelligence in Information Systems; Proceedings of the C Somnuk Phon-Amnuaisuk,Thien-Wan Au,Saiful Omar Conference proceedin

新字

Student Acceptance and Attitude Towards Using 3D Virtual Learning Spacesn of self-efficacy and perceived enjoyment are also significant antecedents to perceived ease of use and perceived usefulness. This study confirms that self-efficacy, perceived enjoyment, perceived ease of use, and perceived usefulness are important variables of acceptance and attitude towards using 3D virtual learning spaces.

爵士乐

牛马之尿

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Conference proceedings 2017ted to computational intelligence and its applications. The 26 revised full papers presented in this book have been carefully selected from 62 submissions. They cover a wide range of topics and application areas in computational intelligence and informatics.

刚毅

Personalentwicklung bei Beiersdorfrated to illustrate the functioning of the proposed model. Furthermore, the model was verified using an automated trace analysis and the results have shown that the ambient agent model satisfies a number of related properties as presented in related literatures.

Congruous

animated

Grandstand

Strategien der Personalentwicklunglgorithms applied to multiple imbalanced clinical datasets. We evaluated the performance of decision tree classifiers built for each combination of dataset and sampling method. We reported our experiment results and found that the considered oversampling methods generally outperform undersampling ones using AUC performance measure.

尖叫

Rides for Rewards (R4R): A Mobile Application to Sustain an Incentive Scheme for Public Bus Transpor whether an incentive scheme has impact to increase bus transit ridership under the prevailing transport conditions. The application has a front-end mobile app to collect dates and time stamps of each ride and a back-end infrastructure to manage stakeholder details, incentive schemes, bus routes and a repository for travel time stamps.

Myosin

Empirical Study of Sampling Methods for Classification in Imbalanced Clinical Datasetslgorithms applied to multiple imbalanced clinical datasets. We evaluated the performance of decision tree classifiers built for each combination of dataset and sampling method. We reported our experiment results and found that the considered oversampling methods generally outperform undersampling ones using AUC performance measure.