Research and improvement of deep learning tool chain for electric power applications

2021 ◽  
Author(s):  
He Yang ◽  
Long Lin ◽  
Cuncun Shi ◽  
Yue Wang
Keyword(s):  
Deep Learning ◽  
Electric Power ◽  
Learning Tool ◽  
Tool Chain

A Deep Learning Tool to Classify Vehicles in Real Time

2021 ◽  
Author(s):  
Humberto Farias ◽  
Mauricio Solar ◽  
Daniel Ortiz
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Learning Tool

scholarly journals SpineNet-6mA: A Novel Deep Learning Tool for Predicting DNA N6-Methyladenine Sites in Genomes

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 201450-201457 ◽  
Author(s):  
Zeeshan Abbas ◽  
Hilal Tayara ◽  
Kil to Chong
Keyword(s):  
Deep Learning ◽  
Learning Tool

scholarly journals DEEPrior: a deep learning tool for the prioritization of gene fusions

2020 ◽  
Vol 36 (10) ◽  
pp. 3248-3250
Author(s):  
Marta Lovino ◽  
Maria Serena Ciaburri ◽  
Gianvito Urgese ◽  
Santa Di Cataldo ◽  
Elisa Ficarra
Keyword(s):  
Deep Learning ◽  
Amino Acid ◽  
Gene Fusion ◽  
Supplementary Information ◽  
Gene Fusions ◽  
Supplementary Data ◽  
Learning Tool ◽  
Cancer Driver ◽  
Open Issue ◽  
Passenger Mutation

Abstract Summary In the last decade, increasing attention has been paid to the study of gene fusions. However, the problem of determining whether a gene fusion is a cancer driver or just a passenger mutation is still an open issue. Here we present DEEPrior, an inherently flexible deep learning tool with two modes (Inference and Retraining). Inference mode predicts the probability of a gene fusion being involved in an oncogenic process, by directly exploiting the amino acid sequence of the fused protein. Retraining mode allows to obtain a custom prediction model including new data provided by the user. Availability and implementation Both DEEPrior and the protein fusions dataset are freely available from GitHub at (https://github.com/bioinformatics-polito/DEEPrior). The tool was designed to operate in Python 3.7, with minimal additional libraries. Supplementary information Supplementary data are available at Bioinformatics online.

CapsCarcino: A novel sparse data deep learning tool for predicting carcinogens

2020 ◽  
Vol 135 ◽  
pp. 110921 ◽  
Author(s):  
Yi-Wei Wang ◽  
Lei Huang ◽  
Si-Wen Jiang ◽  
Kan Li ◽  
Jun Zou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sparse Data ◽  
Learning Tool

Social Media's Potential to Facilitate Dialogic Learning

2014 ◽  
pp. 909-921
Author(s):  
Jin Yang
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Knowledge Development ◽  
Learning Tool ◽  
Learning Materials ◽  
Learning Space ◽  
Discursive Space ◽  
Dialogic Learning ◽  
Use Of Social Media ◽  
Cognitive Dimensions

The chapter explores the use of social media in educational settings and assesses its potential as a learning tool in facilitating deep learning and knowledge development. Guided by Vygotsky and Bakhtin's theory of dialogic learning, the chapter argues, by discussion, that social media may facilitate deep learning and knowledge development due to social media's convenient discursive space and heightened interactivity. Specifically, social media's discursive space may provide a platform that is egalitarian and democratic to all who have access to it. The breakdown of traditional communication barriers in this discursive space can be significant in engaging students in dialogic learning. Social media's heightened interactivity embodied in social, procedural, expository, explanatory, and cognitive dimensions may shorten psychological distances, lighten class-managing load, expedite learning materials' delivery, expand the learning space without time constraint, and encourage cross-pollination of ideas and viewpoints. The chapter discusses the profound opportunity that social media may have to enhance knowledge development.

scholarly journals Predicting the Amount of Electric Power Transaction Using Deep Learning Methods

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6649
Author(s):  
Gwiman Bak ◽  
Youngchul Bae
Keyword(s):  
Deep Learning ◽  
Electric Power ◽  
Short Term Memory ◽  
Fuzzy Inference ◽  
Learning Algorithms ◽  
High Accuracy ◽  
Support Vector ◽  
Inference System ◽  
Power Company ◽  
Starting Date

The most important thing to operate a power system is that the power supply should be close to the power demand. In order to predict the amount of electric power transaction (EPT), it is important to choose and decide the variable and its starting date. In this paper, variables that could be acquired one the starting day of prediction were chosen. This paper designated date, temperature and special day as variables to predict the amount of EPT of the Korea Electric Power company. This paper also used temperature data from a year ago to predict the next year. To do this, we proposed single deep learning algorithms and hybrid deep learning algorithms. The former included multi-layer perceptron (MLP), convolution neural network (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), support vector machine regression (SVR), and adaptive network-based fuzzy inference system (ANFIS). The latter included LSTM + CNN and CNN + LSTM. We then confirmed the improvement of accuracy for prediction using pre-processed variables compared to original variables We also assigned two years of test data during 2017–2018 as variable data to measure high prediction accuracy. We then selected a high-accuracy algorithm after measuring root mean square error (RMSE) and mean absolute percent error (MAPE). Finally, we predicted the amount of EPT in 2018 and then measured the error for each proposed algorithm. With these acquired error data, we obtained a model for predicting the amount of EPT with a high accuracy.

scholarly journals Seeker: Alignment-free identification of bacteriophage genomes by deep learning

2020 ◽  
Author(s):  
Noam Auslander ◽  
Ayal B. Gussow ◽  
Sean Benler ◽  
Yuri I. Wolf ◽  
Eugene V. Koonin
Keyword(s):  
Deep Learning ◽  
Rapid Detection ◽  
Sequence Similarity ◽  
Biological Entity ◽  
Learning Tool ◽  
Alignment Free ◽  
User Friendly ◽  
Identification And Characterization ◽  
Python Package

SummaryAdvances in metagenomics enable massive discovery of diverse, distinct microbes and viruses. Bacteriophages, the most abundant biological entity on Earth, evolve rapidly, and therefore, detection of unknown bacteriophages in sequence datasets is a challenge. The existing methods rely on sequence similarity to known bacteriophage sequences, impeding the identification and characterization of distinct bacteriophage families. We present Seeker, a deep-learning tool for reference-free identification of phage sequences. Seeker allows rapid detection of phages in sequence datasets and clean differentiation of phage sequences from bacterial ones, even for phages with little sequence similarity to established phage families. We comprehensively validate Seeker’s ability to identify unknown phages and employ Seeker to detect unknown phages, some of which are highly divergent from known phage families. We provide a web portal (seeker.pythonanywhere.com) and a user-friendly python package (https://github.com/gussow/seeker) allowing researchers to easily apply Seeker in metagenomic studies, for the detection of diverse unknown bacteriophages.

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