Neural network models based on small data sets

The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to compare the ability of the Mahalanobis-Taguchi System and a neural network to discriminate using small data sets. We examine the discriminant ability as a function of data set size using an application area where reliable data is publicly available. The study uses the Wisconsin Breast Cancer study with nine attributes and one class.

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A data-driven neural network architecture for sentiment analysis

Data Technologies and Applications ◽

10.1108/dta-03-2018-0017 ◽

2019 ◽

Vol 53 (1) ◽

pp. 2-19 ◽

Cited By ~ 1

Author(s):

Erion Çano ◽

Maurizio Morisio

Keyword(s):

Neural Network ◽

Sentiment Analysis ◽

Network Architecture ◽

Network Models ◽

Data Sets ◽

Feature Maps ◽

Neural Network Architecture ◽

Neural Network Models ◽

Content Type ◽

Max Pooling

Purpose The fabulous results of convolution neural networks in image-related tasks attracted attention of text mining, sentiment analysis and other text analysis researchers. It is, however, difficult to find enough data for feeding such networks, optimize their parameters, and make the right design choices when constructing network architectures. The purpose of this paper is to present the creation steps of two big data sets of song emotions. The authors also explore usage of convolution and max-pooling neural layers on song lyrics, product and movie review text data sets. Three variants of a simple and flexible neural network architecture are also compared. Design/methodology/approach The intention was to spot any important patterns that can serve as guidelines for parameter optimization of similar models. The authors also wanted to identify architecture design choices which lead to high performing sentiment analysis models. To this end, the authors conducted a series of experiments with neural architectures of various configurations. Findings The results indicate that parallel convolutions of filter lengths up to 3 are usually enough for capturing relevant text features. Also, max-pooling region size should be adapted to the length of text documents for producing the best feature maps. Originality/value Top results the authors got are obtained with feature maps of lengths 6–18. An improvement on future neural network models for sentiment analysis could be generating sentiment polarity prediction of documents using aggregation of predictions on smaller excerpt of the entire text.

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Intelligent Prediction of Refrigerant Amounts Based on Internet of Things

Complexity ◽

10.1155/2020/1743973 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Jincai Chang ◽

Qiuling Pan ◽

Zhihao Shen ◽

Hao Qin

Keyword(s):

Neural Network ◽

Internet Of Things ◽

Dimensionality Reduction ◽

Rbf Neural Network ◽

Network Models ◽

Practical Significance ◽

Data Sets ◽

Effective Parameters ◽

Neural Network Models ◽

Refrigeration Unit

In a refrigeration unit, the amount of refrigerant has a substantial influence on the entire refrigeration system. To predict the amount of refrigerant in refrigerators with the best performance, this study used refrigerator data collected in real time via the Internet of Things, which were screened to include only the effective parameters related to the compressor and refrigeration properties (based on their practical significance and the research background) and cleaned by applying longitudinal dimensionality reduction and transverse dimensionality reduction. Then, on the basis of an idealized model for refrigerator data, a model of the relationships between refrigerant amount (the dependent variable) and temperature variation, refrigerator compartment temperature, freezer temperature, and other relevant parameters (independent variables) was established. A refrigeration model based on a neural network was then established for predicting the amount of refrigerant and was used to predict five unknown amounts of refrigerant from data sets. BP neural network and RBF neural network models were used to compare the prediction results and analyze the loss functions. From the results, it was concluded that the unknown amount of refrigerant was most likely to be 32.5 g. It is of great practical significance for refrigerator production and maintenance to study the prediction of the amount of refrigerant remaining in a refrigerator.

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Neural Network Based Inverse System Identification from Small Data Sets

2019 International Joint Conference on Neural Networks (IJCNN) ◽

10.1109/ijcnn.2019.8851722 ◽

2019 ◽

Author(s):

Chathura Wanigasekara ◽

Akshya Swain ◽

Sing Kiong Nguang ◽

B. Gangadhara Prusty

Keyword(s):

Neural Network ◽

System Identification ◽

Inverse System ◽

Small Data ◽

Data Sets ◽

Small Data Sets

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Analysis of Algorithms for Searching Objects in Images Using Convolutional Neural Network

Advances in Cyber-Physical Systems ◽

10.23939/acps2021.02.128 ◽

2021 ◽

Vol 6 (2) ◽

pp. 128-133

Author(s):

Ihor Koval ◽

Keyword(s):

Neural Network ◽

Neural Networks ◽

Comparative Analysis ◽

Analysis Of Algorithms ◽

Network Models ◽

Data Sets ◽

Neural Network Models ◽

Network Algorithms ◽

Modern Computer ◽

The Neural Network

The problem of finding objects in images using modern computer vision algorithms has been considered. The description of the main types of algorithms and methods for finding objects based on the use of convolutional neural networks has been given. A comparative analysis and modeling of neural network algorithms to solve the problem of finding objects in images has been conducted. The results of testing neural network models with different architectures on data sets VOC2012 and COCO have been presented. The results of the study of the accuracy of recognition depending on different hyperparameters of learning have been analyzed. The change in the value of the time of determining the location of the object depending on the different architectures of the neural network has been investigated.

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Artificial neural network modeling of jatropha oil fueled diesel engine for emission predictions

Thermal Science ◽

10.2298/tsci0903091g ◽

2009 ◽

Vol 13 (3) ◽

pp. 91-102 ◽

Cited By ~ 4

Author(s):

Thirunavukkarasu Ganapathy ◽

Parkash Gakkhar ◽

Krishnan Murugesan

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Network Models ◽

Neural Network Modeling ◽

Data Sets ◽

Nox Emissions ◽

Jatropha Oil ◽

Neural Network Models ◽

Artificial Neural ◽

Artificial Neural Network Models

This paper deals with artificial neural network modeling of diesel engine fueled with jatropha oil to predict the unburned hydrocarbons, smoke, and NOx emissions. The experimental data from the literature have been used as the data base for the proposed neural network model development. For training the networks, the injection timing, injector opening pressure, plunger diameter, and engine load are used as the input layer. The outputs are hydrocarbons, smoke, and NOx emissions. The feed forward back propagation learning algorithms with two hidden layers are used in the networks. For each output a different network is developed with required topology. The artificial neural network models for hydrocarbons, smoke, and NOx emissions gave R2 values of 0.9976, 0.9976, and 0.9984 and mean percent errors of smaller than 2.7603, 4.9524, and 3.1136, respectively, for training data sets, while the R2 values of 0.9904, 0.9904, and 0.9942, and mean percent errors of smaller than 6.5557, 6.1072, and 4.4682, respectively, for testing data sets. The best linear fit of regression to the artificial neural network models of hydrocarbons, smoke, and NOx emissions gave the correlation coefficient values of 0.98, 0.995, and 0.997, respectively.

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Artificial Intelligent techniques for Flow Bottom Hole Pressure Prediction

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v15i12.4354 ◽

2016 ◽

Vol 15 (12) ◽

pp. 7263-7283

Author(s):

M Awadalla ◽

H Yousef ◽

A Al-Shidani ◽

A Al-Hinai

Keyword(s):

Neural Network ◽

Mean Square Error ◽

Correlation Coefficient ◽

Network Models ◽

Data Sets ◽

Mean Square ◽

Layer Model ◽

Neural Network Models ◽

Bottom Hole ◽

Hidden Layer

This paper proposes Radial Basis and Feed-forward Neural Networks to predict the flowing bottom-hole pressure in vertical oil wells. The developed neural network models rely on a large amount of available historical data measured from actual different oil fields. The unsurpassed number of neural network layers, the number of neurons per layer, and the number of trained samples required to get an outstanding performance have been obtained. Intensive experiments have been conducted and the standard statistical analysis has beenÂ accomplished on the achieved results to validate the modelsâ€™ prediction accuracy. For the sake of qualitative comparison, empirical modes have been developed. The obtained results show that the proposed Feed-Forward Neural Network models outperforms and capable of estimating the FBHPaccurately.The paper showed that the accuracy of FBHP estimation using FFNN with two hidden layer model is better than FFNN with single hidden layer model, Radial Basis neural network, and the empirical model in terms of data set used, mean square error, and the correlation coefficient error. With best results of 1.4 root mean square error (RMSE), 1.4 standard deviation of relative error (STD), correlation coefficient (R) 1.0 and 99.4% of the test data sets achieved less than 5% error. The minimum sufficient number of data sets used in training ANN model can be low as 375 sets only to give a 3.4Â RMES and 97% of the test data achieved 90% accuracy.

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