ANNIIP 2012 Abstracts

Full Papers
Paper Nr: 9

GPU-based Parallel Implementation of a Growing Self-organizing Network


Giacomo Parigi, Angelo Stramieri, Danilo Pau and Marco Piastra

Abstract: Self-organizing systems are characterized by an inherently local behavior, as their configuration is almost exclusively determined by the union of the states of each of the units composing the system. Moreover, all state changes are mutually independent and governed by the same laws. In this work we study the parallel implementation of a specific subset of this broader family, namely that of growing self-organizing networks, in relation to parallel computing hardware devices based on Graphic Processing Units (GPUs), which are increasingly gaining popularity due to their favourable cost/performance ratio. In order to do so, we first define a new version of the standard, sequential algorithm, where the intrinsic parallelism of the execution is made more explicit and then we perform comparative experiments with the standard algorithm, together with an optimized variant of the latter, where an hash index is used for speed. Our experiments demonstrates that the parallel version outperforms both variants of the sequential algorithm but also reveals a few interesting differences in the overall behavior of the system, that might be relevant for further investigations.

Paper Nr: 12

Parallel Batch Pattern Training of Recirculation Neural Network


Volodymyr Turchenko, Vladimir Golovko and Anatoly Sachenko

Abstract: The development of a parallel batch pattern back propagation training algorithm of a recirculation neural network is presented in this paper. The model of a recirculation neural network and usual sequential batch pattern algorithm of its training are theoretically described. An algorithmic description of the parallel version of the batch pattern training method is presented. The parallelization efficiency of the developed parallel algorithm is investigated on the example of data compression and principal component analysis. The results of the experimental researches show that the developed parallel algorithm provides high parallelization efficiency on a parallel symmetric multiprocessor computer system. It allows applying the developed parallel software for the facilitation of scientific research of neural network-based intrusion detection system for computer networks.

Short Papers
Paper Nr: 3

Forecasting Financial Success of Hollywood Movies - A Comparative Analysis of Machine Learning Methods


Dursun Delen and Ramesh Sharda

Abstract: Forecasting financial success of a particular movie has intrigued many scholars and industry leaders as a worthy but challenging problem. In this study, we explore the use of machine learning methods to forecast the financial performance of a movie at the box-office before its theatrical release. In our models, we convert the forecasting problem into a multinomial classification problem—rather than forecasting the point estimate of box-office receipts; we classify a movie based on its box-office receipts in one of nine categories, ranging from a “flop” to a “blockbuster.” Herein, we present our comparative prediction results along with variable importance measures (using sensitivity analysis on trained prediction models).

Paper Nr: 8

Comparison the Performance of Hybrid HMM/MLP and RBF/LVQ ANN Models - Application for Speech and Medical Pattern Classification


Lilia Lazli, Mounir Boukadoum, Abdennasser Chebira and Kurosh Madani

Abstract: In the last several years, the hybrid models have become increasingly popular. We use involves multi-network RBF/LVQ structure and hybrid HMM/MLP model for speech recognition and medical diagnosis.