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Prediction of relative response factors for flame ionization and photoionization detection using self-training artificial neural networks
, Article Journal of Chromatography A ; Volume 950, Issue 1-2 , 2002 , Pages 183-194 ; 00219673 (ISSN) ; Garkani Nejad, Z ; Sharif University of Technology
2002
Abstract
The relative response factors (RRFs) of a flame ionization detection (FID) system and two pulsed discharge photoionization detection (PID) systems with different discharge gases are predicted for a set of organic compounds containing various functional groups. As a first step, numerical descriptors were calculated based on the molecular structures of compounds. Then, multiple linear regression (MLR) was employed to find informative subsets of descriptors that can predict the RRFs of these compounds. The selected MLR model for the FID system includes seven descriptors and two selected MLR models for the PID systems with argon- and krypton-doped helium as the discharge gases, respectively,...
Use of self-training artificial neural networks in modeling of gas chromatographic relative retention times of a variety of organic compounds
, Article Journal of Chromatography A ; Volume 945, Issue 1-2 , 2002 , Pages 173-184 ; 00219673 (ISSN) ; Garkani Nejad, Z ; Sharif University of Technology
2002
Abstract
A quantitative structure-activity relationship study based on multiple linear regression (MLR), artificial neural network (ANN), and self-training artificial neural network (STANN) techniques was carried out for the prediction of gas chromatographic relative retention times of 13 different classes of organic compounds. The five descriptors appearing in the selected MLR model are molecular density, Winer number, boiling point, polarizability and square of polarizability. A 5-6-1 ANN and a 5-4-1 STANN were generated using the five descriptors appearing in the MLR model as inputs. Comparison of the standard errors and correlation coefficients shows the superiority of ANN and STANN over the MLR...
Deep Semi-Supervised Text Classification
, M.Sc. Thesis Sharif University of Technology ; Semati, Hossein (Supervisor)
Abstract
Large data sources labeled by experts at cost are essential for deep learning success in various domains. But, when labeling is expensive and labeled data is scarce, deep learning generally does not perform well. The goal of semi-supervised learning is to leverage abundant unlabeled data that one can easily collect. New semi-supervised algorithms based on data augmentation techniques have reached new advances in this field. In this work, by studying different textual augmentation techniques, a new approach is proposed that can obtain effective information signals from unlabeled data. The method encourages the model to generate the same representation vectors for different augmented versions...
3D Image segmentation with sparse annotation by self-training and internal registration
, Article IEEE Journal of Biomedical and Health Informatics ; 2020 ; Nikdan, M ; Soleymanibaghshah, M ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2020
Abstract
Anatomical image segmentation is one of the foundations for medical planning. Recently, convolutional neural networks (CNN) have achieved much success in segmenting volumetric (3D) images when a large number of fully annotated 3D samples are available. However, rarely a volumetric medical image dataset containing a sufficient number of segmented 3D images is accessible since providing manual segmentation masks is monotonous and time-consuming. Thus, to alleviate the burden of manual annotation, we attempt to effectively train a 3D CNN using a sparse annotation where ground truth on just one 2D slice of the axial axis of each training 3D image is available. To tackle this problem, we propose...