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...

Early residential fire detection is important for prompt extinguishing and reducing damages and life losses. To detect fire, one or a combination of sensors and a detection algorithm are needed. The sensors might be part of a wireless sensor network (WSN) or work independently. The previous research in the area of fire detection using WSN has paid little or no attention to investigate the optim...

In this paper, optimal control for stochastic nonlinear singular system with quadratic performance is obtained using neural networks. The goal is to provide optimal control with reduced calculus effort by comparing the solutions of the matrix Riccati differential equation (MRDE) obtained from the well-known traditional Runge–Kutta (RK) method and nontraditional neural network method. To obtain ...

Online-Recognition requires the acoustic model to provide posterior probabilities after a limited time delay given the online input audio data. This necessitates unidirectional modeling and the standard solution is to use unidirectional long short-term memory (LSTM) recurrent neural networks (RNN) or feedforward neural networks (FFNN). It is known that bidirectional LSTMs are more powerful and ...

Identifying dust aerosols from passive satellite images is of great interest for many applications. In this study, we developed five different machine-learning (ML) based algorithms, including Logistic Regression, K Nearest Neighbor, Random Forest (RF), Feed Forward Neural Network (FFNN), and Convolutional (CNN), to identify in the daytime Visible Infrared Imaging Radiometer Suite (VIIRS) under...

This paper proposes a new architecture — Attentive Tensor Product Learning (ATPL) — to represent grammatical structures in deep learning models. ATPL is a new architecture to bridge this gap by exploiting Tensor Product Representations (TPR), a structured neural-symbolic model developed in cognitive science, aiming to integrate deep learning with explicit language structures and rules. The key ...

In this paper we present a Feed-Foward Neural Networks Autoregressive (FFNN-AR) model with genetic algorithms training optimization in order to predict the gross domestic product growth of six countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final optimum weights from inpu...

Electrical power quality (PQ) disturbance has become an important issue in India. On a distribution network, it is mainly caused by various nonlinear loads. Due to the varying power produced, it is affected by penetration of solar PV system as well. Therefore it is necessary detect and classify PQ events in account of evaluating a PQ problem. In other side due to increase of smart meters in sma...

Alpha-galactosidase production in submerged fermentation by Acinetobacter sp. was optimized using feed forward neural networks and genetic algorithm (FFNN-GA). Six different parameters, pH, temperature, agitation speed, carbon source (raffinose), nitrogen source (tryptone), and K2HPO4, were chosen and used to construct 6-10-1 topology of feed forward neural network to study interactions between...

In this paper we investigate prediction based trading on financial time series assuming general AR(J) models. A suitable nonlinear estimator for predicting the future values will be provided by a properly trained FeedForward Neural Network (FFNN) which can capture the characteristics of the conditional expected value. In this way, one can implement a simple trading strategy based on the predict...