IMPROVEMENT OF DOSE ESTIMATION PROCESS USING ARTIFICIAL NEURAL NETWORKS

Radiation Protection Dosimetry ◽

10.1093/rpd/ncy185 ◽

2018 ◽

Vol 184 (1) ◽

pp. 36-43 ◽

Author(s):

Gal Amit ◽

Hanan Datz

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Support Vector ◽

Physical Parameters ◽

Machine Method ◽

Algorithm Performance ◽

Artificial Neural ◽

Automatic Algorithm

Abstract We present here for the first time a fast and reliable automatic algorithm based on artificial neural networks for the anomaly detection of a thermoluminescence dosemeter (TLD) glow curves (GCs), and compare its performance with formerly developed support vector machine method. The GC shape of TLD depends on numerous physical parameters, which may significantly affect it. When integrated into a dosimetry laboratory, this automatic algorithm can classify ‘anomalous’ (having any kind of anomaly) GCs for manual review, and ‘regular’ (acceptable) GCs for automatic analysis. The new algorithm performance is then compared with two kinds of formerly developed support vector machine classifiers—regular and weighted ones—using three different metrics. Results show an impressive accuracy rate of 97% for TLD GCs that are correctly classified to either of the classes.

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Intelligent Prediction of Differential Pipe Sticking by Support Vector Machine Compared With Conventional Artificial Neural Networks: An Example of Iranian Offshore Oil Fields

SPE Drilling & Completion ◽

10.2118/163062-pa ◽

2012 ◽

Vol 27 (04) ◽

pp. 586-595 ◽

Author(s):

Reza Jahanbakhshi ◽

Reza Keshavarzi ◽

Mahdi Aliyari Shoorehdeli ◽

Abolqasem Emamzadeh

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Support Vector ◽

Artificial Neural ◽

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Predicting Students Grades Using Artificial Neural Networks and Support Vector Machine

Advanced Methodologies and Technologies in Modern Education Delivery - Advances in Educational Technologies and Instructional Design ◽

10.4018/978-1-5225-7365-4.ch059 ◽

2019 ◽

pp. 751-766

Author(s):

Sajid Umair ◽

Muhammad Majid Sharif

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Student Performance ◽

Semantic Analysis ◽

Support Vector ◽

Data Set ◽

Important Research Topic ◽

Artificial Neural

Prediction of student performance on the basis of habits has been a very important research topic in academics. Studies show that selection of the correct data set also plays a vital role in these predictions. In this chapter, the authors took data from different schools that contains student habits and their comments, analyzed it using latent semantic analysis to get semantics, and then used support vector machine to classify the data into two classes, important for prediction and not important. Finally, they used artificial neural networks to predict the grades of students. Regression was also used to predict data coming from support vector machine, while giving only the important data for prediction.

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Robustness Analysis of Artificial Neural Networks and Support Vector Machine in Making Prediction

2011 IEEE Ninth International Symposium on Parallel and Distributed Processing with Applications ◽

10.1109/ispa.2011.64 ◽

2011 ◽

Author(s):

Saiful Anwar ◽

Rifki Ismal

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Robustness Analysis ◽

Support Vector ◽

Artificial Neural

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Comparison of Support Vector Machine, Artificial Neural Networks and Spectral Angle Mapper Classifiers on Fused Hyperspectral Data for Improved LULC Classification

2019 8th International Conference on Modeling Simulation and Applied Optimization (ICMSAO) ◽

10.1109/icmsao.2019.8880336 ◽

2019 ◽

Author(s):

Veeramallu Satya Sahithi ◽

Stalin Subbiah ◽

Shefali Agrawal

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Hyperspectral Data ◽

Support Vector ◽

Spectral Angle Mapper ◽

Artificial Neural ◽

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Comparison of artificial neural networks (ANN), support vector machine (SVM) and gene expression programming (GEP) approaches for predicting TBM penetration rate

SN Applied Sciences ◽

10.1007/s42452-020-03767-y ◽

2020 ◽

Vol 2 (12) ◽

Author(s):

Alireza Afradi ◽

Arash Ebrahimabadi

Keyword(s):

Gene Expression ◽

Neural Networks ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Penetration Rate ◽

Gene Expression Programming ◽

Support Vector ◽

Artificial Neural ◽

Tbm Penetration Rate

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Comparison of artificial neural networks and support vector machine classifiers for land cover classification in Northern China using a SPOT-5 HRG image

International Journal of Remote Sensing ◽

10.1080/01431161.2011.568531 ◽

2011 ◽

Vol 33 (10) ◽

pp. 3301-3320 ◽

Author(s):

Xianfeng Song ◽

Zheng Duan ◽

Xiaoguang Jiang

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Northern China ◽

Land Cover Classification ◽

Support Vector ◽

Artificial Neural ◽

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A comparative trend in forecasting ability of artificial neural networks and regressive support vector machine methodologies for energy dissipation modeling of off-road vehicles

Energy ◽

10.1016/j.energy.2014.01.022 ◽

2014 ◽

Vol 66 ◽

pp. 569-576 ◽

Author(s):

Hamid Taghavifar ◽

Aref Mardani

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Energy Dissipation ◽

Support Vector ◽

Road Vehicles ◽

Artificial Neural

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Predicting Lipase Specific Activity and Bacterial Growth of Aneurinibacillus thermoaerophilus Strain AFNA via Artificial Neural Networks and Support Vector Machine

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2016.5573 ◽

2016 ◽

Vol 13 (9) ◽

pp. 6382-6386

Author(s):

Zhichen Yang ◽

Hong Li ◽

Wei Wei Zhang ◽

Jinshun Bai

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Bacterial Growth ◽

Specific Activity ◽

Support Vector ◽

Artificial Neural

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Modeling river discharge time series using support vector machine and artificial neural networks

Environmental Earth Sciences ◽

10.1007/s12665-016-5435-6 ◽

2016 ◽

Vol 75 (8) ◽

Author(s):

Mohammad Ali Ghorbani ◽

Rahman Khatibi ◽

Arun Goel ◽

Mohammad Hasan FazeliFard ◽

Atefeh Azani

Keyword(s):

Neural Networks ◽

Time Series ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

River Discharge ◽

Support Vector ◽

Discharge Time ◽

Artificial Neural

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New model for prediction binary mixture of antihistamine decongestant using artificial neural networks and least squares support vector machine by spectrophotometry method

Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy ◽

10.1016/j.saa.2017.04.001 ◽

2017 ◽

Vol 182 ◽

pp. 105-115 ◽

Author(s):

Shirin Mofavvaz ◽

Mahmoud Reza Sohrabi ◽

Alireza Nezamzadeh-Ejhieh

Keyword(s):

Neural Networks ◽

Support Vector Machine ◽

Artificial Neural Networks ◽

Binary Mixture ◽

Least Squares ◽

Support Vector ◽

New Model ◽

Artificial Neural

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