Bio-Affective Computer Interface for Game Interaction

Affective bio-feedback can be an important instrument to enhance the game experience. Several studies have provided evidence of the usefulness of physiological signals for affective gaming; however, due to the limited knowledge about the distinctive autonomic signatures for every emotion, the pattern matching models employed are limited in the number of emotions they are able to classify. This paper presents a bio-affective gaming interface (BAGI) that can be used to customize a game experience according to the player’s emotional response. Its architecture offers important characteristics for gaming that are important because they make possible the reusability of previous findings and the inclusion of new models to the system. In order to prove the effectiveness of BAGI, two different types of neural networks have been trained to recognize emotions. They were incorporated into the system to customize, in real-time, the computer wallpaper according to the emotion experienced by the user. Best results were obtained with a probabilistic neural network with accuracy results of 84.46% on the training data and 78.38% on the validation for new independent data sets.

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Dimensionality Reduction for Probabilistic Neural Network in Medical Data Classification Problems

International Journal of Electronics and Telecommunications ◽

10.1515/eletel-2015-0038 ◽

2015 ◽

Vol 61 (3) ◽

pp. 289-300 ◽

Cited By ~ 4

Author(s):

Maciej Kusy

Keyword(s):

Neural Network ◽

Dimensionality Reduction ◽

Probabilistic Neural Network ◽

Extraction Procedure ◽

Principal Component ◽

Training Data ◽

Data Sets ◽

Classification Problems ◽

Prediction Ability ◽

Feature Selection Approach

Abstract This article presents the study regarding the problem of dimensionality reduction in training data sets used for classification tasks performed by the probabilistic neural network (PNN). Two methods for this purpose are proposed. The first solution is based on the feature selection approach where a single decision tree and a random forest algorithm are adopted to select data features. The second solution relies on applying the feature extraction procedure which utilizes the principal component analysis algorithm. Depending on the form of the smoothing parameter, different types of PNN models are explored. The prediction ability of PNNs trained on original and reduced data sets is determined with the use of a 10-fold cross validation procedure.

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Social Media Analysis Using Probabilistic Neural Network Algorithm to Know Personality Traits

Jurnal INFORM ◽

10.25139/inform.v6i1.3307 ◽

2021 ◽

Vol 6 (1) ◽

pp. 61-64

Author(s):

Mohammad Zoqi Sarwani ◽

Dian Ahkam Sani

Keyword(s):

Neural Network ◽

Social Media ◽

Probabilistic Neural Network ◽

Media Analysis ◽

Training Data ◽

The Internet ◽

Accuracy Score ◽

Network Algorithm ◽

The Social ◽

Neural Network Algorithm

The Internet creates a new space where people can interact and communicate efficiently. Social media is one type of media used to interact on the internet. Facebook and Twitter are one of the social media. Many people are not aware of bringing their personal life into the public. So that unconsciously provides information about his personality. Big Five personality is one type of personality assessment method and is used as a reference in this study. The data used is the social media status from both Facebook and Twitter. Status has been taken from 50 social media users. Each user is taken as a text status. The results of tests performed using the Probabilistic Neural Network algorithm obtained an average accuracy score of 86.99% during the training process and 83.66% at the time of testing with a total of 30 training data and 20 test data.

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The Application of Probabilistic Neural Network in Speech Recognition Based on Partition Clustering

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.263-266.2173 ◽

2012 ◽

Vol 263-266 ◽

pp. 2173-2178

Author(s):

Xin Guang Li ◽

Min Feng Yao ◽

Li Rui Jian ◽

Zhen Jiang Li

Keyword(s):

Neural Network ◽

Speech Recognition ◽

Clustering Algorithm ◽

Probabilistic Neural Network ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Data Sets ◽

Data Set ◽

Proposed Model ◽

Partition Clustering

A probabilistic neural network (PNN) speech recognition model based on the partition clustering algorithm is proposed in this paper. The most important advantage of PNN is that training is easy and instantaneous. Therefore, PNN is capable of dealing with real time speech recognition. Besides, in order to increase the performance of PNN, the selection of data set is one of the most important issues. In this paper, using the partition clustering algorithm to select data is proposed. The proposed model is tested on two data sets from the field of spoken Arabic numbers, with promising results. The performance of the proposed model is compared to single back propagation neural network and integrated back propagation neural network. The final comparison result shows that the proposed model performs better than the other two neural networks, and has an accuracy rate of 92.41%.

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Helicopter Motion Control Using a General Regression Neural Network

Computational Intelligence in Control ◽

10.4018/978-1-59140-037-0.ch003 ◽

2011 ◽

pp. 41-68 ◽

Cited By ~ 2

Author(s):

T. G.B. Amaral ◽

M. M. Crisostomo ◽

V. Fernao Pires

Keyword(s):

Neural Network ◽

Clustering Algorithm ◽

Training Data ◽

General Regression Neural Network ◽

Parallel Structure ◽

Data Sets ◽

Learning Capability ◽

Fast Learning ◽

Control Flight ◽

General Regression

This chapter describes the application of a general regression neural network (GRNN) to control the flight of a helicopter. This GRNN is an adaptive network that provides estimates of continuous variables and is a one-pass learning algorithm with a highly parallel structure. Even with sparse data in a multidimensional measurement space, the algorithm provides smooth transitions from one observed value to another. An important reason for using the GRNN as a controller is the fast learning capability and its non-iterative process. The disadvantage of this neural network is the amount of computation required to produce an estimate, which can become large if many training instances are gathered. To overcome this problem, it is described as a clustering algorithm to produce representative exemplars from a group of training instances that are close to one another reducing the computation amount to obtain an estimate. The reduction of training data used by the GRNN can make it possible to separate the obtained representative exemplars, for example, in two data sets for the coarse and fine control. Experiments are performed to determine the degradation of the performance of the clustering algorithm with less training data. In the control flight system, data training is also reduced to obtain faster controllers, maintaining the desired performance.

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Bolt loosening detection using impedance based non-destructive method and probabilistic neural network technique with minimal training data

Engineering Structures ◽

10.1016/j.engstruct.2020.111228 ◽

2021 ◽

Vol 226 ◽

pp. 111228

Author(s):

Wongi S. Na

Keyword(s):

Neural Network ◽

Probabilistic Neural Network ◽

Training Data ◽

Bolt Loosening ◽

Non Destructive

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METHOD OF FORMULATING INPUT PARAMETERS OF NEURAL NETWORK FOR DIAGNOSING GAS-TURBINE ENGINES

Aviation ◽

10.3846/16487788.2013.805868 ◽

2013 ◽

Vol 17 (2) ◽

pp. 52-56 ◽

Cited By ~ 1

Author(s):

Mykola Kulyk ◽

Sergiy Dmitriev ◽

Oleksandr Yakushenko ◽

Oleksandr Popov

Keyword(s):

Neural Network ◽

Gas Turbine ◽

Gas Turbine Engine ◽

Training Data ◽

Turbine Engines ◽

Data Sets ◽

Gas Turbine Engines ◽

Turbine Engine ◽

Wide Range ◽

And Training

A method of obtaining test and training data sets has been developed. These sets are intended for training a static neural network to recognise individual and double defects in the air-gas path units of a gas-turbine engine. These data are obtained by using operational process parameters of the air-gas path of a bypass turbofan engine. The method allows sets that can project some changes in the technical conditions of a gas-turbine engine to be received, taking into account errors that occur in the measurement of the gas-dynamic parameters of the air-gas path. The operation of the engine in a wide range of modes should also be taken into account.

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Deep Neural Network Application for 4D Seismic Inversion to Pressure and Saturation: Enhancing Training Data Sets

10.3997/2214-4609.202010961 ◽

2020 ◽

Author(s):

G. Corte ◽

J. Dramsch ◽

C. MacBeth ◽

H. Amini

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Seismic Inversion ◽

Training Data ◽

Data Sets ◽

Network Application ◽

4D Seismic

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Klasifikasi Data Berat Bayi Lahir Menggunakan Weighted Probabilistic Neural Network (WPNN) (Studi Kasus di Rumah Sakit Islam Sultan Agung Semarang)

MEDIA STATISTIKA ◽

10.14710/medstat.10.1.61-70 ◽

2017 ◽

Vol 10 (1) ◽

pp. 61

Author(s):

Hasbi Yasin ◽

Dwi Ispriyansti

Keyword(s):

Neural Network ◽

Low Birthweight ◽

Probabilistic Neural Network ◽

High Accuracy ◽

Training Data ◽

World Health ◽

Model Accuracy ◽

Health Organization ◽

Very High ◽

Length Of Gestation

Low Birthweight (LBW) is one of the causes of infant mortality. Birthweight is the weight of babies who weighed within one hour after birth. Low birthweight has been defined by the World Health Organization (WHO) as weight at birth of less than 2,500 grams (5.5 pounds). There are several factors that influence the BWI such as maternal age, length of gestation, body weight, height, blood pressure, hemoglobin and parity. This study uses a Weighted Probabilistic Neural Network (WPNN) to classify the birthweight in RSI Sultan Agung Semarang based on these factors. The results showed that the birthweight classification using WPNN models have a very high accuracy. This is shown by the model accuracy of 98.75% using the training data and 94.44% using the testing data.Keywords:Birthweight, Classification, LBW, WPNN.

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Use of multiattribute transforms to predict log properties from seismic data

Geophysics ◽

10.1190/1.1444899 ◽

2001 ◽

Vol 66 (1) ◽

pp. 220-236 ◽

Cited By ~ 313

Author(s):

Daniel P. Hampson ◽

James S. Schuelke ◽

John A. Quirein

Keyword(s):

Neural Network ◽

Seismic Data ◽

Prediction Error ◽

Probabilistic Neural Network ◽

Real Data ◽

Analysis Data ◽

Training Data ◽

Average Error ◽

Validation Data ◽

Linear Mode

We describe a new method for predicting well‐log properties from seismic data. The analysis data consist of a series of target logs from wells which tie a 3-D seismic volume. The target logs theoretically may be of any type; however, the greatest success to date has been in predicting porosity logs. From the 3-D seismic volume a series of sample‐based attributes is calculated. The objective is to derive a multiattribute transform, which is a linear or nonlinear transform between a subset of the attributes and the target log values. The selected subset is determined by a process of forward stepwise regression, which derives increasingly larger subsets of attributes. An extension of conventional crossplotting involves the use of a convolutional operator to resolve frequency differences between the target logs and the seismic data. In the linear mode, the transform consists of a series of weights derived by least‐squares minimization. In the nonlinear mode, a neural network is trained, using the selected attributes as inputs. Two types of neural networks have been evaluated: the multilayer feedforward network (MLFN) and the probabilistic neural network (PNN). Because of its mathematical simplicity, the PNN appears to be the network of choice. To estimate the reliability of the derived multiattribute transform, crossvalidation is used. In this process, each well is systematically removed from the training set, and the transform is rederived from the remaining wells. The prediction error for the hidden well is then calculated. The validation error, which is the average error for all hidden wells, is used as a measure of the likely prediction error when the transform is applied to the seismic volume. The method is applied to two real data sets. In each case, we see a continuous improvement in predictive power as we progress from single‐attribute regression to linear multiattribute prediction to neural network prediction. This improvement is evident not only on the training data but, more importantly, on the validation data. In addition, the neural network shows a significant improvement in resolution over that from linear regression.

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