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

2021 ◽  
Vol 226 ◽  
pp. 111228
Author(s):  
Wongi S. Na
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Training Data ◽  
Bolt Loosening ◽  
Non Destructive

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

Jurnal INFORM ◽  
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.

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

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.

Use of multiattribute transforms to predict log properties from seismic data

Geophysics ◽  
2001 ◽  
Vol 66 (1) ◽  
pp. 220-236 ◽  
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.

scholarly journals CAMPUS SENTIMENT ANALYSIS E-COMPLAINT USING PROBABILISTIC NEURAL NETWORK ALGORITHM

Kursor ◽  
2017 ◽  
Vol 8 (3) ◽  
pp. 135
Author(s):  
Mohammad Zoqi Sarwani
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Probabilistic Neural Network ◽  
Training Data ◽  
Electronic Systems ◽  
Testing Dataset ◽  
Testing Data ◽  
Neural Network Algorithm ◽  
Last Stage ◽  
Text Preprocessing

E-complaint is one of the technologies which is used to collect feedback from customers in the form of criticism and suggestions using electronic systems. For some companies or agencies, ecomplaint is used to provide better services to its customers. This study is aimed to perform sentiment analysis of an e-complaint service, with the case of Brawijaya University. There are three main stages for the proposed system, i.e. Text Preprocessing, Text Weighting, and PNN forthe classification. Tokenization, filtering, and stemming are done in the text preprocessing. Resulted text from the preprocessing stage is weighting using Term Inverse Document Frequent (TFIDF). To classify the negative or positive complaints, PNN are used in the last stage. For the experiments, 70 data are used as the training data, and 20 data are used as the testing data. The experimental results based on the combination of the number of training and testing dataset, showed that the accuracy achieved up to 90%.

scholarly journals UNCERTAINTY ESTIMATION FOR END-TO-END LEARNED DENSE STEREO MATCHING VIA PROBABILISTIC DEEP LEARNING

2020 ◽  
Vol V-2-2020 ◽  
pp. 161-169
Author(s):  
M. Mehltretter
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Stereo Matching ◽  
Probabilistic Neural Network ◽  
Uncertainty Estimation ◽  
Training Data ◽  
Stereo Image ◽  
Confidence Estimation ◽  
Extensive Evaluation ◽  
Dense Stereo Matching

Abstract. Motivated by the need to identify erroneous disparity assignments, various approaches for uncertainty and confidence estimation of dense stereo matching have been presented in recent years. As in many other fields, especially deep learning based methods have shown convincing results. However, most of these methods only model the uncertainty contained in the data, while ignoring the uncertainty of the employed dense stereo matching procedure. Additionally modelling the latter, however, is particularly beneficial if the domain of the training data varies from that of the data to be processed. For this purpose, in the present work the idea of probabilistic deep learning is applied to the task of dense stereo matching for the first time. Based on the well-known and commonly employed GC-Net architecture, a novel probabilistic neural network is presented, for the task of joint depth and uncertainty estimation from epipolar rectified stereo image pairs. Instead of learning the network parameters directly, the proposed probabilistic neural network learns a probability distribution from which parameters are sampled for every prediction. The variations between multiple such predictions on the same image pair allow to approximate the model uncertainty. The quality of the estimated depth and uncertainty information is assessed in an extensive evaluation on three different datasets.

Bio-Affective Computer Interface for Game Interaction

2014 ◽  
pp. 249-265
Author(s):  
Jorge Arroyo-Palacios ◽  
Daniela M. Romano
Keyword(s):  
Neural Network ◽  
Pattern Matching ◽  
Probabilistic Neural Network ◽  
Training Data ◽  
Physiological Signals ◽  
Data Sets ◽  
Independent Data ◽  
New Models ◽  
Affective Gaming ◽  
Matching Models

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.

Assessment and Monitoring Quality of Communications Network Based on Feature Selection and Probabilistic Neural Network

2013 ◽  
Vol 846-847 ◽  
pp. 836-839
Author(s):  
Cui Feng Du ◽  
Qiang Xu ◽  
Ji Hua Li
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Probabilistic Neural Network ◽  
Training Data ◽  
Feature Subset ◽  
Machine Learning Classification ◽  
Feature Vectors ◽  
Training Samples ◽  
Neural Network Prediction ◽  
Assessment And Monitoring

Using feature selection and neural networks to experiment the data, then we bring a warning model of user complaints. It is the core that using the known information of network index sample to analyze and discriminate. First, the training samples need to be extract, because there are too many features in training data will have an adverse impact on machine learning classification algorithm. Using extraction method to explore the feature subset with feature, feature subset is a set of feature vectors, then the feature vectors are input into the probability neural network prediction, find out the best features quantum set. This model can be achieved using the MATLAB software, and it is operational, and it can be extended to the network quality assessment and monitoring practice.

scholarly journals Dimensionality Reduction for Probabilistic Neural Network in Medical Data Classification Problems

2015 ◽  
Vol 61 (3) ◽  
pp. 289-300 ◽  
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.

Personality Classification through Social Media Using Probabilistic Neural Network Algorithms

2019 ◽  
Vol 1 (1) ◽  
pp. 9
Author(s):  
Mohammad Zoqi Sarwani ◽  
Dian Ahkam Sani ◽  
Fitria Chabsah Fakhrini
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Digital Media ◽  
Text Processing ◽  
Probabilistic Neural Network ◽  
Training Data ◽  
Modern Culture ◽  
Network Algorithm ◽  
The Status ◽  
Neural Network Algorithm

Today the internet creates a new generation with modern culture that uses digital media. Social media is one of the popular digital media. Facebook is one of the social media that is quite liked by young people. They are accustomed to conveying their thoughts and expression through social media. Text mining analysis can be used to classify one's personality through social media with the probabilistic neural network algorithm. The text can be taken from the status that is on Facebook. In this study, there are three stages, namely text processing, weighting, and probabilistic neural networks for determining classification. Text processing consists of several processes, namely: tokenization, stopword, and steaming. The results of the text processing in the form of text are given a weight value to each word by using the Term Inverse Document Frequent (TF / IDF) method. In the final stage, the Probabilistic Neural Network Algorithm is used to classify personalities. This study uses 25 respondents, with 10 data as training data, and 15 data as testing data. The results of this study reached an accuracy of 60%.

scholarly journals PENERAPAN METODE RBPNN UNTUK KLASIFIKASI KANKER PAYUDARA

2016 ◽  
Vol 3 (2) ◽  
pp. 135
Author(s):  
Fairudz Shahura ◽  
Oni Soesanto ◽  
Fatma Indriani
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Fractal Dimension ◽  
Fine Needle Aspiration ◽  
Probabilistic Neural Network ◽  
Needle Aspiration ◽  
Training Data ◽  
Fine Needle ◽  
Radial Basis ◽  
Malignant Breast

<p><em>Breast cancer is the most commonly diagnosed cancer in women. Breast cancer cases are increasing each year. Therefore, early detection of breast cancer plays an important role in anticipating the spread of cancer. Fine-needle aspiration (FNA) biopsy is one way to detect breast cancer. FNA is a method of taking the majority of tissue with a syringe that is intended to aid in the diagnosis of various tumor diseases. The FNA samples that have been studied generate ten characteristics, namely radius, texture, perimeter, area, compactness, smoothness, concavity, concave points, symmetry, and fractal dimension. These characteristics are used to classify benign and malignant breast cancer. To classify breast cancer, Radial Basis Probabilistic Neural Network (RBPNN) required. This study aims to determine how the performance of the method of Radial Basis Probabilistic Neural Network for classifying breast cancer. The accuracy was found to be equal 93.19% for training data, and 90.35% for testing data.</em><em></em></p><p><strong><em>Keywords:</em></strong><em> Radial Basis Probabilistic Neural Network, Classification, Breast Cancer.</em></p><p><em>Kanker payudara merupakan penyakit yang paling banyak menyerang kaum wanita. Penderita penyakit kanker payudara semakin meningkat pada tiap tahunnya. Oleh karena itu deteksi dini kanker payudara memegang peranan penting dalam mengantisipasi penyebaran kanker. Salah satu cara untuk mendeteksi kanker payudara adalah  dengan fine-needle aspiration (FNA) biopsy. FNA merupakan suatu metode pengambilan sebagian jaringan tubuh manusia dengan jarum suntik yang bertujuan untuk membantu diagnosis berbagai penyakit tumor. Sampel FNA yang telah diteliti menghasilkan sepuluh karakteristik, yaitu radius, texture, perimeter, area, compactness, smoothness, concavity, concave points, symmetry, dan fractal dimension. Kesepuluh karakteristik tersebut digunakan untuk mengklasifikasikan kanker payudara jinak dan ganas. Untuk mengklasifikasi tingkat keganasan dari kanker payudara dapat dilakukan dengan metode Radial Basis Probabilistic Neural Network (RBPNN). Penelitian ini bertujuan untuk mengetahui bagaimana performansi metode Radial Basis Probabilistic Neural Network untuk mengklasifikasikan kanker payudara. Dari hasil penelitian didapat akurasi 93.19% untuk data training, serta 90.35% untuk data testing.</em><em></em></p><strong><em>Kata kunci :</em></strong><em> Radial Basis Probabilistic Neural Network, Klasifikasi, Breast Cancer.</em>

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