autoBioSeqpy: A Deep Learning Tool for the Classification of Biological Sequences

2020 ◽  
Vol 60 (8) ◽  
pp. 3755-3764
Author(s):  
Runyu Jing ◽  
Yizhou Li ◽  
Li Xue ◽  
Fengjuan Liu ◽  
Menglong Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Biological Sequences ◽  
Learning Tool

scholarly journals Protein Family Classification using Deep Learning

2018 ◽  
Author(s):  
K S Naveenkumar ◽  
Babu R Mohammed Harun ◽  
R Vinayakumar ◽  
KP Soman
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Protein Classification ◽  
Biological Sequences ◽  
Biological Sequence ◽  
Deep Learning Algorithm ◽  
N Gram ◽  
Family Classification ◽  
Selection Of

AbstractProtein classification is responsible for the biological sequence, we came up with an idea which deals with the classification of proteomics using deep learning algorithm. This algorithm focuses mainly to classify sequences of protein-vector which is used for the representation of proteomics. Selection of the type protein representation is challenging based on which output in terms of accuracy is depended on, The protein representation used here is n-gram i.e. 3-gram and Keras embedding used for biological sequences like protein. In this paper we are working on the Protein classification to show the strength and representation of biological sequence of the proteins.

Classification of Customer to Upgrade Profits in Retail Market with Deep Learning Methodology

2019 ◽  
Vol 7 (5) ◽  
pp. 188-191
Author(s):  
I.Gayathri Devi ◽  
G. Surya Kala Eswari ◽  
G. Kumari
Keyword(s):  
Deep Learning ◽  
Retail Market

A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 199-205
Author(s):  
Yuejun Liu ◽  
Yifei Xu ◽  
Xiangzheng Meng ◽  
Xuguang Wang ◽  
Tianxu Bai
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Region Of Interest ◽  
Classification Performance ◽  
Thyroid Diseases ◽  
Great Success ◽  
Learning Models ◽  
Good Classification Performance ◽  
Spect Images

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

scholarly journals Deep Learning Model Comparison for Vision-Based Classification of Full/Empty-Load Trucks in Earthmoving Operations

2019 ◽  
Vol 9 (22) ◽  
pp. 4871 ◽  
Author(s):  
Quan Liu ◽  
Chen Feng ◽  
Zida Song ◽  
Joseph Louis ◽  
Jian Zhou
Keyword(s):  
Deep Learning ◽  
Model Comparison ◽  
Surveillance Systems ◽  
Comparison Study ◽  
Learning Models ◽  
The Core ◽  
Dump Trucks ◽  
Deep Learning Model ◽  
Contact Field

Earthmoving is an integral civil engineering operation of significance, and tracking its productivity requires the statistics of loads moved by dump trucks. Since current truck loads’ statistics methods are laborious, costly, and limited in application, this paper presents the framework of a novel, automated, non-contact field earthmoving quantity statistics (FEQS) for projects with large earthmoving demands that use uniform and uncovered trucks. The proposed FEQS framework utilizes field surveillance systems and adopts vision-based deep learning for full/empty-load truck classification as the core work. Since convolutional neural network (CNN) and its transfer learning (TL) forms are popular vision-based deep learning models and numerous in type, a comparison study is conducted to test the framework’s core work feasibility and evaluate the performance of different deep learning models in implementation. The comparison study involved 12 CNN or CNN-TL models in full/empty-load truck classification, and the results revealed that while several provided satisfactory performance, the VGG16-FineTune provided the optimal performance. This proved the core work feasibility of the proposed FEQS framework. Further discussion provides model choice suggestions that CNN-TL models are more feasible than CNN prototypes, and models that adopt different TL methods have advantages in either working accuracy or speed for different tasks.

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