This paper introduces the surface electromyogram (EMG) classification system based on statistical and entropy metrics. The system is intended for diagnostic use and enables classification of examined subject as normal, ...

This study compares the predictive performance of three neural network methods, namely the Learning Vector Quantization, Radial Basis Function, the Feedforward network that uses the conjugate gradient optimization algorithm, ...

Conformal Predictors (CPs) are Machine Learning algorithms that can provide reliable confidence measures to their predictions. In this work, we make use of the Conformal Prediction framework for the assessment of stroke ...

In this paper we present a framework for learning non-monotonic logic programs. The method is parametric on a classical learning algorithm whose generated rules are to be understood as default rules. This means that these ...

This paper investigates multiagent reinforcement learning (MARL) in a general-sum game where the payoffs' structure is such that the agents are required to exploit each other in a way that benefits all agents. The contradictory ...

The shapes and firing rates of motor unit action potentials (MUAPs) in an electromyographic (EMG) signal provide an important source of information for the diagnosis of neuromuscular disorders. In order to extract this ...

The design for continuous computer performance is increasingly becoming limited by the exponential increase in the power consumption. In order to improve the energy efficiency of multicore chips, we propose a novel global ...

Predictions on sequential data, when both the upstream and downstream information is important, is a difficult and challenging task. The Bidirectional Recurrent Neural Network (BRNN) architecture has been designed to deal ...

In this paper, we introduce a probabilistic modeling approach for addressing the problem of Web robot detection from Web-server access logs. More specifically, we construct a Bayesian network that classifies automatically ...