Neurules are a kind of hybrid rules integrating neurocomputing and production rules. Each neurule is represented as an adaline unit. Thus, the corresponding neurule base consists of a number of autonomous adaline units (neurules). Due to this fact, a modular and natural knowledge base is constructed, in contrast to existing connectionist knowledge bases. In this paper, we present a method for g...

Two experiments assessed the contributions of implicit and explicit learning to base-rate sensitivity. Using a factorial design that included both implicit and explicit learning disruptions, we tested the hypothesis that implicit learning underlies base-rate sensitivity from experience (and that explicit learning contributes comparatively little). Participants learned to classify two categories...

The study objective is to develop a motivation-based training model in household waste management which will result the development of conceptual model, procedural and physical model. Research methods (R D), combination Borg Gall Dick Carey learning Keller Motivation Model. instrument validated by validator, then that has been be used for feasibility product tested experts consisting instructio...

Cardiologists undertaking percutaneous coronary intervention (PCI) are excited by the combination of patient and physician satisfaction and technological advance occurring on the background of the necessary manual dexterity. Progress and applicability of percutaneous techniques since their inception in 1977 have been remarkable; a sound evidence base coupled with the enthusiasm and ingenuity of...

Using ensemble methods is one of the general strategies to improve the accuracy of classifier and predictor. Bagging is one of the suitable ensemble learning methods. Ensemble learning is a simple, useful and effective metaclassification methodology that combines the predictions from multiple base classifiers (or learners). In this paper we show a comparative study of different classifiers (Dec...

In this paper, we present a novel deep learning approach, deeply-fused nets. The central idea of our approach is deep fusion, i.e., combine the intermediate representations of base networks, where the fused output serves as the input of the remaining part of each base network, and perform such combinations deeply over several intermediate representations. The resulting deeply fused net enjoys s...