In the recent years, the assessment and forecasting of flight performance based on pilot’s multiple physiological parameters has become an important theme of research. However, traditional forecasting and assessment of flight performance is mainly based on the manual assessment or explicit mathematical models, and rarely take the physiological parameters into consideration. Based on the complex...

We present the multiplicative recurrent neural network as a general model for compositional meaning in language, and evaluate it on the task of fine-grained sentiment analysis. We establish a connection to the previously investigated matrixspace models for compositionality, and show they are special cases of the multiplicative recurrent net. Our experiments show that these models perform compar...

In this paper two problems will be analyzed: First, the obtain the more accurate prediction than using traditional linear or nonlinear regression approach. For this purpose we shall use the tree layers NN based on Levenberg-Marquardt algorithm. Second, it is known that recurrent neural networks (RNN) are very useful in nonlinear process modeling. Consequently, the trend, i.e. a line of general ...

In this paper we describe how an actor critic rein forcement learning agent in a non Markovian domain nds an optimal sequence of actions in a totally model free fashion that is the agent neither learns transitional probabilities and associated rewards nor by how much the state space should be augmented so that the Markov prop erty holds In particular we employ an Elman type re current neural ne...

It is well known that artificial neural networks (ANNs) can learn deterministic automata. Learning nondeterministic automata is another matter. This is important because much of the world is nondeterministic, taking the form of unpredictable or probabilistic events that must be acted upon. If ANNs are to engage such phenomena, then they must be able to learn how to deal with nondeterminism. In ...

A novel prediction scheme is proposed for real-time MPEG video to predict the burst and long-range dependent traffic. The trend and periodic characteristics of MPEG video traffic are fully captured by a proposed stochastic state-space dynamic model. Then a recursive filtering algorithm is proposed to estimate traffic for long-range prediction. Simulation results based on real MPEG traffic data ...

Prediction is believed to be an important component of cognition, particularly in the processing of natural language. It has long been accepted that recurrent neural networks are best able to learn prediction tasks when trained on simple examples before incrementally proceeding to more complex sentences. Furthermore, the counter-intuitive suggestion has been made that networks and, by implicati...

This paper proposes the use of neural networks ensemble for predicting the cyclosporine A (CyA) concentration in kidney transplant patients. In order to optimize clinical outcomes and to reduce the cost associated with patient care, accurate prediction of CyA concentrations is the main objective of therapeutic drug monitoring. Thirty-two renal allograft patients and di erent factors (age, weigh...

Epilepsy is common neurological disorder disease in the world. Electroencephalogram (EEG) can provide significant information about epileptic activity in human brain. Since detection of the epileptic activity requires analyzing of very length EEG recordings by an expert, researchers tend to improve automated diagnostic systems for epilepsy in recent years. In this work, we try to automate detec...

Drug name recognition (DNR) is an essential step in the Pharmacovigilance (PV) pipeline. DNR aims to find drug name mentions in unstructured biomedical texts and classify them into predefined categories. State-of-the-art DNR approaches heavily rely on hand-crafted features and domain-specific resources which are difficult to collect and tune. For this reason, this paper investigates the effecti...