scholarly journals Estudio de bases de datos para el reconocimiento automático de lenguas de signos

2020 ◽  
Vol 22 ◽  
pp. 145-160
Author(s):  
Darío Tilves Santiago ◽  
Carmén García Mateo ◽  
Soledad Torres Guijarro ◽  
Laura Docío Fernández ◽  
José Luis Alba Castro
Keyword(s):  
Machine Learning ◽  
Sign Language ◽  
Quality Data ◽  
Complex Task ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
High Quality ◽  
High Quality Data ◽  
Video Information ◽  
Language Technology

Automatic sign language recognition (ASLR) is quite a complex task, not only for the difficulty of dealing with very dynamic video information, but also because almost every sign language (SL) can be considered as an under-resourced language when it comes to language technology. Spanish sign language (LSE) is one of those under-resourced languages. Developing technology for SSL implies a number of technical challenges that must be tackled down in a structured and sequential manner. In this paper, some problems of machine-learning- based ASLR are addressed. A review of publicly available datasets is given and a new one is presented. It is also discussed the current annotations methods and annotation programs. In our review of existing datasets, our main conclusion is that there is a need for more with high-quality data and annotations.

scholarly journals Estudio de bases de datos para el reconocimiento automático de lenguas de signos

2020 ◽  
Vol 23 ◽  
pp. 145-160
Author(s):  
Darío Tilves Santiago ◽  
Carmén García Mateo ◽  
Soledad Torres Guijarro ◽  
Laura Docío Fernández ◽  
José Luis Alba Castro
Keyword(s):  
Machine Learning ◽  
Sign Language ◽  
Quality Data ◽  
Complex Task ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
High Quality ◽  
High Quality Data ◽  
Video Information ◽  
Language Technology

Automatic sign language recognition (ASLR) is quite a complex task, not only for the difficulty of dealing with very dynamic video information, but also because almost every sign language (SL) can be considered as an under-resourced language when it comes to language technology. Spanish sign language (LSE) is one of those under-resourced languages. Developing technology for SSL implies a number of technical challenges that must be tackled down in a structured and sequential manner. In this paper, some problems of machine-learning- based ASLR are addressed. A review of publicly available datasets is given and a new one is presented. It is also discussed the current annotations methods and annotation programs. In our review of existing datasets, our main conclusion is that there is a need for more with high-quality data and annotations.

Arabic Sign Language Recognition Using Deep Machine Learning

2021 ◽  
Author(s):  
Wael Suliman ◽  
Mohamed Deriche ◽  
Hamzah Luqman ◽  
Mohamed Mohandes
Keyword(s):  
Machine Learning ◽  
Sign Language ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Arabic Sign Language

scholarly journals Fluorescence Microscopy Datasets for Training Deep Neural Networks

2020 ◽  
Author(s):  
Guy M. Hagen ◽  
Justin Bendesky ◽  
Rosa Machado ◽  
Tram-Anh Nguyen ◽  
Tanmay Kumar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Fluorescence Microscopy ◽  
Quality Data ◽  
Short Exposure ◽  
Biological Research ◽  
High Quality ◽  
Learning Methods ◽  
High Quality Data

AbstractBackgroundFluorescence microscopy is an important technique in many areas of biological research. Two factors which limit the usefulness and performance of fluorescence microscopy are photobleaching of fluorescent probes during imaging, and, when imaging live cells, phototoxicity caused by light exposure. Recently developed methods in machine learning are able to greatly improve the signal to noise ratio of acquired images. This allows researchers to record images with much shorter exposure times, which in turn minimizes photobleaching and phototoxicity by reducing the dose of light reaching the sample.FindingsTo employ deep learning methods, a large amount of data is needed to train the underlying convolutional neural network. One way to do this involves use of pairs of fluorescence microscopy images acquired with long and short exposure times. We provide high quality data sets which can be used to train and evaluate deep learning methods under development.ConclusionThe availability of high quality data is vital for training convolutional neural networks which are used in current machine learning approaches.

Machine learning & deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry

2021 ◽  
Author(s):  
Sethu Arun Kumar ◽  
Thirumoorthy Durai Ananda Kumar ◽  
Narasimha M Beeraka ◽  
Gurubasavaraj Veeranna Pujar ◽  
Manisha Singh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Drug Discovery ◽  
Pharmaceutical Industry ◽  
Data Acquisition ◽  
Data Driven ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data

Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates and can be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review substantially discusses the recent updates in AI tools as cheminformatics application in medicinal chemistry for the data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry while improving small-molecule bioactivities and properties.

4. Computational systems biology

2020 ◽  
pp. 35-55
Author(s):  
Eberhard O. Voit
Keyword(s):  
Machine Learning ◽  
Systems Biology ◽  
Mathematical Models ◽  
Laboratory Experiments ◽  
Quality Data ◽  
Human Intelligence ◽  
Computational Systems Biology ◽  
High Quality ◽  
High Quality Data ◽  
Computational Systems

The new methods of —omics biology, combined with more traditional experiments, have the capacity of generating more high-quality data than ever before. So, why isn’t that sufficient? What is missing? The missing aspects arise from subtle, but important differences between data, information, knowledge, and understanding. ‘Computational systems biology’ explains how laboratory experiments generate data, whereas understanding additionally requires significant human intelligence and knowledge. Computational systems biology (CSB) attempts to bridge the gap between data and understanding. It uses a pipeline from data to understanding that consists of two toolsets: machine learning and mathematical models. The most useful of these models in CSB fall into two categories: static networks and dynamic biological systems.

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