Gas-Liquid Two-Phase Flow Measurement Using Coriolis Flowmeters Incorporating Artificial Neural Network, Support Vector Machine, and Genetic Programming Algorithms

Index Terms: Regression model, House price prediction, machine learning, housing market, Arima model, artificial neural network, support vector machine, random forest, dataset.

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Vertical-Downward Two-Phase Flow Regime Identification by Probabilistic Neural Network (PNN) and Nonlinear Support Vector Machine (SVM)

10.1115/icone28-65467 ◽

2021 ◽

Author(s):

Wenyi Zhong ◽

Shouxu Qiao ◽

Sijia Hao ◽

Xupeng Li ◽

Sichao Tan

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Flow Regime ◽

Probabilistic Neural Network ◽

Low Frequency ◽

Support Vector ◽

Phase Flow ◽

Two Phase ◽

Flow Regime Identification ◽

Nonlinear Support

Abstract The present study proposes a new feature extraction method based on non-stationary conductivity probe signals. Two types of discriminative network models, i.e., the probabilistic neural network (PNN) and nonlinear support vector machine (SVM), are established for flow regime identification using small sample sets. The eigenvectors are composed of 16 feature quantities obtained by wavelet packet decomposition (WPD) and 8 feature quantities in the time-domain derived from the reconstructed low-frequency signals. The 8 features include maximum, minimum, standard deviation, arithmetic mean, kurtosis, peak factor, impulse factor and margin factor. The signals are normalized based on features rather than samples before flow regime identification. In the current study, WPD results show that the conductivity probe signals in two-phase flow are mostly in low frequency. The identification accuracy of the nonlinear SVM is 90.47%, which is better than 83.33% by the PNN method. This study verifies the superiority of nonlinear SVM in solving small samples and nonlinear flow regime classification problems. However, the accuracy of flow regime identification near flow regime transitional boundaries still remains questionable and needs further improvement.

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Performance analysis of static hand gesture recognition approaches using artificial neural network, support vector machine and two stream based transfer learning approach

International Journal of Information Technology ◽

10.1007/s41870-021-00831-7 ◽

2021 ◽

Author(s):

Anjali R. Patil ◽

S. Subbaraman

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Performance Analysis ◽

Transfer Learning ◽

Gesture Recognition ◽

Support Vector ◽

Hand Gesture ◽

Learning Approach ◽

Network Support

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Support Vector Machine and Artificial Neural Network Modelling of Orogenic Gold Prospectivity Mapping in the Swayze greenstone belt, Ontario, Canada

Ore Geology Reviews ◽

10.1016/j.oregeorev.2020.103968 ◽

2020 ◽

pp. 103968

Author(s):

Francisca Maepa ◽

Richard S. Smith ◽

Abera Tessema

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Greenstone Belt ◽

Orogenic Gold ◽

Support Vector ◽

Network Modelling ◽

Neural Network Modelling ◽

Artificial Neural Network Modelling ◽

Prospectivity Mapping

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Research on Automated Defect Classification Based on Visual Sensing and Convolutional Neural Network-Support Vector Machine for GTA-Assisted Droplet Deposition Manufacturing Process

Metals ◽

10.3390/met11040639 ◽

2021 ◽

Vol 11 (4) ◽

pp. 639

Author(s):

Chen Ma ◽

Haifei Dang ◽

Jun Du ◽

Pengfei He ◽

Minbo Jiang ◽

...

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Manufacturing Process ◽

Support Vector ◽

Defect Classification ◽

Droplet Deposition ◽

Network Support ◽

Visual Sensing ◽

Low Efficiency

This paper proposes a novel metal additive manufacturing process, which is a composition of gas tungsten arc (GTA) and droplet deposition manufacturing (DDM). Due to complex physical metallurgical processes involved, such as droplet impact, spreading, surface pre-melting, etc., defects, including lack of fusion, overflow and discontinuity of deposited layers always occur. To assure the quality of GTA-assisted DDM-ed parts, online monitoring based on visual sensing has been implemented. The current study also focuses on automated defect classification to avoid low efficiency and bias of manual recognition by the way of convolutional neural network-support vector machine (CNN-SVM). The best accuracy of 98.9%, with an execution time of about 12 milliseconds to handle an image, proved our model can be enough to use in real-time feedback control of the process.

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Using Artificial Neural Network to Discriminate Parkinson’s Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images

Biomedicines ◽

10.3390/biomedicines9010012 ◽

2020 ◽

Vol 9 (1) ◽

pp. 12

Author(s):

Chung-Yao Chien ◽

Szu-Wei Hsu ◽

Tsung-Lin Lee ◽

Pi-Shan Sung ◽

Chou-Ching Lin

Keyword(s):

Neural Network ◽

Parkinson’S Disease ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Parkinson's Disease ◽

Dopamine Transporter ◽

Support Vector ◽

Dat Spect ◽

Ann Classifier ◽

Spect Images

Background: The challenge of differentiating, at an early stage, Parkinson’s disease from parkinsonism caused by other disorders remains unsolved. We proposed using an artificial neural network (ANN) to process images of dopamine transporter single-photon emission computed tomography (DAT-SPECT). Methods: Abnormal DAT-SPECT images of subjects with Parkinson’s disease and parkinsonism caused by other disorders were divided into training and test sets. Striatal regions of the images were segmented by using an active contour model and were used as the data to perform transfer learning on a pre-trained ANN to discriminate Parkinson’s disease from parkinsonism caused by other disorders. A support vector machine trained using parameters of semi-quantitative measurements including specific binding ratio and asymmetry index was used for comparison. Results: The predictive accuracy of the ANN classifier (86%) was higher than that of the support vector machine classifier (68%). The sensitivity and specificity of the ANN classifier in predicting Parkinson’s disease were 81.8% and 88.6%, respectively. Conclusions: The ANN classifier outperformed classical biomarkers in differentiating Parkinson’s disease from parkinsonism caused by other disorders. This classifier can be readily included into standalone computer software for clinical application.

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A feature weighted support vector machine and artificial neural network algorithm for academic course performance prediction

Neural Computing and Applications ◽

10.1007/s00521-021-05962-3 ◽

2021 ◽

Author(s):

Chenxi Huang ◽

Junsheng Zhou ◽

Jinling Chen ◽

Jane Yang ◽

Kathy Clawson ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Performance Prediction ◽

Support Vector ◽

Course Performance ◽

Network Algorithm ◽

Neural Network Algorithm ◽

Artificial Neural ◽

Artificial Neural Network Algorithm

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Pioneer dataset and recognition of Handwritten Pashto characters using Convolution Neural Networks

Measurement and Control ◽

10.1177/0020294020964826 ◽

2020 ◽

pp. 002029402096482

Author(s):

Sulaiman Khan ◽

Abdul Hafeez ◽

Hazrat Ali ◽

Shah Nazir ◽

Anwar Hussain

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Support Vector Machine ◽

Research Work ◽

Recognition Rate ◽

Recognition Task ◽

Convolution Neural Network ◽

Medium Size ◽

Support Vector ◽

Artificial Neural

This paper presents an efficient OCR system for the recognition of offline Pashto isolated characters. The lack of an appropriate dataset makes it challenging to match against a reference and perform recognition. This research work addresses this problem by developing a medium-size database that comprises 4488 samples of handwritten Pashto character; that can be further used for experimental purposes. In the proposed OCR system the recognition task is performed using convolution neural network. The performance analysis of the proposed OCR system is validated by comparing its results with artificial neural network and support vector machine based on zoning feature extraction technique. The results of the proposed experiments shows an accuracy of 56% for the support vector machine, 78% for artificial neural network, and 80.7% for the proposed OCR system. The high recognition rate shows that the OCR system based on convolution neural network performs best among the used techniques.

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