s – Mini Symposia 34 MS 6 (Monday 22, 15:45 – 17:15) Room C Indonesian PhD Students Minisymposium 1: Statistics and Neural Network Organizer: W.M. Kusumawinahyu (Dept. of Math., ITB, Indonesia) Brodjol Sutijo, Subanar, Suryo Guritno 1) Mathematics Department, Gadjah Mada University, Indonesia 2) Statistics Department, Sepuluh Nopember Institut of Technology, Indonesia Title: Construction and Tr...

This study proposes a novel approach based on multi-objective artificial bee colony (ABC) for feature selection, particularly for intrusion-detection systems. The approach is divided into two stages: generating the feature subsets of the Pareto front of non-dominated solutions in the first stage and using the hybrid ABC and particle swarm optimization (PSO) with a feed-forward neural network (F...

Feed forward neural networks (FFNN) with an unconstrained random number of hidden neurons deene exible non-parametric regression models. In M uller and Rios Insua (1998) we have argued that variable architecture models with random size hidden layer signiicantly reduce posterior mul-timodality typical for posterior distributions in neural network models. In this chapter we review the model propo...

production of highly viscous tar sand bitumen using steam assisted gravity drainage (sagd) with a pair of horizontal wells has advantages over conventional steam flooding. this paper explores the use of artificial neural networks (anns) as an alternative to the traditional sagd simulation approach. feed forward, multi-layered neural network meta-models are trained through the back-error-propaga...

In this paper the RWTH large vocabulary continuous speech recognition (LVCSR) systems developed for the IWSLT2016 evaluation campaign are described. This evaluation campaign focuses on transcribing spontaneous speech from Skype recordings. State-of-the-art bidirectional long shortterm memory (LSTM) and deep, multilingually boosted feed-forward neural network (FFNN) acoustic models are trained a...

CONTEXT is vital in formulating intelligent classifications and responses, especially under uncertainty. In a standard feed-forward neural network (FFNN), context comes in the form of information encoded in the input vector and trained in weight parameters. However, useful information can also be present in the temporal nature of the input vectors, or from past internal states of a network. Fut...

This paper presents short term load forecasting (STLF) in Java Island using recurrent neural network (RNN). The simple one of RNN is Elman, it has one hidden layer and suitable used in time series prediction. It can learn an input-output mapping which is nonlinear. The Elman RNN was proposed for one day a head forecasting, with interval time 30 minutes. Training model divided into weekday, week...

In an islanded microgrid, while considering the complex nature of line impedance, the generalized droop control fails to share the actual real/reactive power between the distributed generation (DG) units. To overcome this power sharing issue, in this paper a new approach based on feed forward neural network (FFNN) is proposed. Also, the proposed FFNN based droop control method simultaneously co...

In this paper, an automatic classifier has been developed using Feed Forward Neural Network (FFNN) to classify the ECG signals between different heartbeats. Here, the classifier is trained independently bymorphological, heartbeat interval features and temporal features using Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The trained classifier then classifies the be...

Brain is well protected inside the hard and bony skull that hampers the study of its functions as well as makes the diagnosis of brain diseases more difficult and challenging. In this paper we perform review study on brain tumor detection from Magnetic Resonance Image (MRI). Stages for brain tumor detection using MR image are Pre-processing, Segmentation, Feature Extraction, Classification. The...