Virtual Learning Environments (VLEs) can be used as a resource repository but also as an environment to encourage independent student learning. Customised online assignments that can be assembled by the lecturer can be found in teaching resources such as Mastering Biology, developed by Pearson Publishers. In this study, student engagement in both summative and formative assignments was measured...

Abstract: Incremental Net Pro (IncNet Pro) with local learning feature and statistically controlled growing and pruning of the network is introduced. The architecture of the net is based on RBF networks. Extended Kalman Filter algorithm and its new fast version is proposed and used as learning algorithm. IncNet Pro is similar to the Resource Allocation Network described by Platt in the main ide...

Purpose – A neuro-fuzzy system with a reinforcement learning algorithm (RL) for adaptive swarm behaviors acquisition is presented. The basic idea is that each individual (agent) has the same internal model and the same learning procedure, and the adaptive behaviors are acquired only by the reward or punishment from the environment. The formation of the swarm is also designed by RL, e.g., TD-err...

This study uses dynamic 5-bus and 30-bus test cases to explore the net surplus (congestion rents) collected and redistributed by ISOs in restructured wholesale power markets with grid congestion managed by locational marginal pricing. The price-sensitivity of demand and the learning capabilities of generators are taken as treatment factors. A key finding is that ISO net surplus substantially in...

A substantial body of research has been conducted on variables relating to students' mathematics achievement with TIMSS. However, most studies have employed conventional statistical methods, and have focused on selected few indicators instead of utilizing hundreds of variables TIMSS provides. This study aimed to find a prediction model for students' mathematics achievement using as many TIMSS s...

Recurrent networks can be used as associative memories where the stored memories represent fixed points to which the dynamics of the network converges. These networks, however, also can present continuous attractors, as limit cycles and chaotic attractors. The use of these attractors in recurrent networks for the construction of associative memories is argued. Here, we provide a training algori...

We explore three papers concerned with two methods for incorporating discrete, noisy, incomplete dissimilarity data into statistical/machine learning models for supervised, semisupervised or unsupervised machine learning. The two methods are RKE (Regularized Kernel Estimation), and RMU (Regularized Manifold Unfolding). Briefly put, the methods use dissimilarity information between objects in a ...

This paper proposes a generation method of a subject-specific Facial Expression Map (FEMap) using the Self-Organizing Maps (SOM) of unsupervised learning and Counter Propagation Networks (CPN) of supervised learning together. The proposed method consists of two steps. In the first step, the topological change of a face pattern in the expressional process of facial expression is learned hierarch...

In pedestrian detection methods, their high accuracy detection rates are always obtained at the cost of a large amount of false pedestrians. In order to overcome this problem, the authors propose an accurate pedestrian detection system based on two machine learning methods: cascade AdaBoost detector and random vector functional-link net. During the offline training phase, the parameters of a ca...

Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Following the recent success of neural networks, we developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME pro...