scholarly journals LitCovid: an open database of COVID-19 literature

2020 ◽  
Vol 49 (D1) ◽  
pp. D1534-D1540 ◽  
Author(s):  
Qingyu Chen ◽  
Alexis Allot ◽  
Zhiyong Lu
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Information Needs ◽  
Healthcare Professionals ◽  
Information Overload ◽  
Evidence Synthesis ◽  
Scientific Information ◽  
Manual Curation ◽  
Text And Data Mining

Abstract Since the outbreak of the current pandemic in 2020, there has been a rapid growth of published articles on COVID-19 and SARS-CoV-2, with about 10 000 new articles added each month. This is causing an increasingly serious information overload, making it difficult for scientists, healthcare professionals and the general public to remain up to date on the latest SARS-CoV-2 and COVID-19 research. Hence, we developed LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/), a curated literature hub, to track up-to-date scientific information in PubMed. LitCovid is updated daily with newly identified relevant articles organized into curated categories. To support manual curation, advanced machine-learning and deep-learning algorithms have been developed, evaluated and integrated into the curation workflow. To the best of our knowledge, LitCovid is the first-of-its-kind COVID-19-specific literature resource, with all of its collected articles and curated data freely available. Since its release, LitCovid has been widely used, with millions of accesses by users worldwide for various information needs, such as evidence synthesis, drug discovery and text and data mining, among others.

scholarly journals Kreative Maschinen und Urheberrecht

2021 ◽  
Author(s):  
Lisa Käde
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Flow Chart ◽  
Special Focus ◽  
Computational Creativity ◽  
Database Protection ◽  
Text And Data Mining

The interdisciplinary analysis examines machine learning (ML) frameworks used in AI development and gives practical answers to copyright issues arising in this matter. A special focus lies on database protection of ML models. Furthermore, the thesis offers an opinion on text and data mining restrictions in the AI context. Regarding the production of potentially copyrightable works by means of ML, the author discusses the issue of copyright attribution, suggests a solution, and proposes a flow chart to identify the author in various scenarios. In view of a potentially increasing autonomy of AI, an introduction to the interconnection of the concepts of intelligence, creativity and Computational Creativity is provided.

scholarly journals PEMODELAN PREDIKSI KUAT TEKAN BETON UMUR MUDA MENGGUNAKAN H2O'S DEEP LEARNING

2020 ◽  
Vol 25 (1) ◽  
pp. 40
Author(s):  
Stefanus Santosa ◽  
Suroso Suroso ◽  
Marchus Budi Utomo ◽  
Martono Martono ◽  
Mawardi Mawardi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Deep Learning ◽  
Information Needs ◽  
Chemical Properties ◽  
Stochastic Gradient Descent ◽  
Design Data ◽  
Artificial Neural

Artificial Neural Network (ANN) is a Machine Learning (ML) algorithm which learn by itself and organize its thinking to solve problems. Although the learning process involves many hidden layers (Deep Learning) this algorithm still has weaknesses when faced with high noise data. Concrete mixture design data has a high enough noise caused by many unidentified / measurable aspects such as planning, design, manufacture of test specimens, maintenance, testing, diversity of physical and chemical properties, mixed formulas, mixed design errors, environmental conditions, and testing process. Information needs about the compressive strength of early age concrete (under 28 days) are often needed while the construction process is still ongoing. ANN has been tried to predict the compressive strength of concrete, but the results are less than optimal. This study aims to improve the ANN prediction model using an H2O’s Deep Learning based on a multi-layer feedforward artificial neural network that is trained with stochastic gradient descent using backpropagation. The H2O’s Deep Learning best model is achieved by 2 hidden layers- 50 hidden neurons and ReLU activation function with a RMSE value of 6,801. This Machine Learning model can be used as an alternative/ substitute for conventional mix designs, which are environmentally friendly, economical, and accurate. Future work with regard to the concrete industry, this model can be applied to create an intelligent Batching and Mixing Plants.

scholarly journals Data Mining, Machine Learning, Deep Learning, Chemometrics. Definitions, common points and Trends (Spoiler Alert: VALIDATE your models!)

2021 ◽  
Vol 8 (32) ◽  
pp. 22-38
Author(s):  
José Manuel Amigo
Keyword(s):  
Analytical Chemistry ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Daily Life ◽  
Big Data Analysis ◽  
Mining Machine ◽  
Learning Data ◽  
Made In

Concepts like Machine Learning, Data Mining or Artificial Intelligence have become part of our daily life. This is mostly due to the incredible advances made in computation (hardware and software), the increasing capabilities of generating and storing all types of data and, especially, the benefits (societal and economical) that generate the analysis of such data. Simultaneously, Chemometrics has played an important role since the late 1970s, analyzing data within natural science (and especially in Analytical Chemistry). Even with the strong parallelisms between all of the abovementioned terms and being popular with most of us, it is still difficult to clearly define or differentiate the meaning of Machine Learning, Data Mining, Artificial Intelligence, Deep Learning and Chemometrics. This manuscript brings some light to the definitions of Machine Learning, Data Mining, Artificial Intelligence and Big Data Analysis, defines their application ranges and seeks an application space within the field of analytical chemistry (a.k.a. Chemometrics). The manuscript is full of personal, sometimes probably subjective, opinions and statements. Therefore, all opinions here are open for constructive discussion with the only purpose of Learning (like the Machines do nowadays).

Simulate, Optimise, Partition: Algorithmic Diagrams of Pattern Recognition from 1953 Onwards

2016 ◽  
Author(s):  
Adrian Mackenzie
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Pattern Recognition ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Deep Learning ◽  
Clustering Algorithms ◽  
Flow Through ◽  
The Cold War ◽  
Hand Writing

Contemporary attempts to find patterns in data, ranging from the now mundane technologies of hand-writing recognition through to mammoth infrastructure-heavy practices of deep learning conducted by major business and government actors, rely on a group of techniques intensively developed during the 1950-60s in physics, engineering and psychology. Whether we designate them as pattern recognition, data mining, or machine learning, these techniques all seek to uncover patterns in data that cannot appear directly to the human eye, either because there are too many items for anyone to look at, or because the patterns are too subtly woven through in the data. From the techniques in current use, three developed in the Cold War era iconify contemporary modes of pattern finding: Monte Carlo simulation, gradient descent, and clustering algorithms that search for groups or clusters in data. Each of these techniques implements a different mode of pattern, and these different modes of pattern recognition flow through into contemporary scientific, technological, business and governmental problematizations. The different perspectives on event, trajectory, and proximity they embody imbue many power relations, forms of value and the play of truth/falsehood today.

scholarly journals A Systematic Review of Deep Learning Approaches to Educational Data Mining

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-22 ◽  
Author(s):  
Antonio Hernández-Blanco ◽  
Boris Herrera-Flores ◽  
David Tomás ◽  
Borja Navarro-Colorado
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Language Processing ◽  
Educational Data Mining ◽  
Research Field ◽  
Machine Learning Techniques ◽  
Mining Machine ◽  
Learning Approaches ◽  
Learning Techniques

Educational Data Mining (EDM) is a research field that focuses on the application of data mining, machine learning, and statistical methods to detect patterns in large collections of educational data. Different machine learning techniques have been applied in this field over the years, but it has been recently that Deep Learning has gained increasing attention in the educational domain. Deep Learning is a machine learning method based on neural network architectures with multiple layers of processing units, which has been successfully applied to a broad set of problems in the areas of image recognition and natural language processing. This paper surveys the research carried out in Deep Learning techniques applied to EDM, from its origins to the present day. The main goals of this study are to identify the EDM tasks that have benefited from Deep Learning and those that are pending to be explored, to describe the main datasets used, to provide an overview of the key concepts, main architectures, and configurations of Deep Learning and its applications to EDM, and to discuss current state-of-the-art and future directions on this area of research.

scholarly journals Automatic interpretation of otoliths using deep learning

2018 ◽  
Author(s):  
Endre Moen ◽  
Nils Olav Handegard ◽  
Vaneeda Allken ◽  
Ole thomas Albert ◽  
Alf Harbitz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Object Recognition ◽  
Age Structure ◽  
Current Method ◽  
Fish Population ◽  
Population Fluctuations ◽  
Learning Models ◽  
Greenland Halibut ◽  
Manual Curation

AbstractThe age structure of a fish population has important implications for recruitment processes and population fluctuations, and is key input to fisheries assessment models. The current method relies on manually reading age from otoliths, and the process is labor intensive and dependent on specialist expertise.Advances in machine learning have recently brought forth methods that have been remarkably successful in a variety of settings, with potential to automate analysis that previously required manual curation. Machine learning models have previously been successfully applied to object recognition and similar image analysis tasks. Here we investigate whether deep learning models can also be used for estimating the age of otoliths from images.We adapt a standard neural network model designed for object recognition to the task of estimating age from otolith images. The model is trained and validated on a large collection of images of Greenland halibut otoliths. We show that the model works well, and that its precision is comparable to and may even surpass that of human experts.Automating this analysis will help to improve consistency, lower cost, and increase scale of age prediction. Similar approaches can likely be used for otoliths from other species as well as for reading fish scales. The method is therefore an important step forward for improving the age structure estimates of fish populations.

scholarly journals Rainfall Prediction using Machine Learning and Deep Learning Algorithms

2021 ◽  
Vol 10 (4) ◽  
pp. 251-254
Author(s):  
B.Meena Preethi ◽  
◽  
R. Gowtham ◽  
S. Aishvarya ◽  
S. Karthick ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Missing Values ◽  
Binary Classification ◽  
Learning Algorithms ◽  
Gradient Boosting ◽  
Rainfall Prediction ◽  
The Neural Network

The project entitled as “Rainfall Prediction using Machine Learning & Deep Learning Algorithms” is a research project which is developed in Python Language and dataset is stored in Microsoft Excel. This prediction uses various machine learning and deep learning algorithms to find which algorithm predicts with most accurately. Rainfall prediction can be achieved by using binary classification under Data Mining. Predicting the rainfall is very important in several aspects of one’s country and can help from preventing serious natural disasters. For this prediction, Artificial Neural Network using Forward and Backward Propagation, Ada Boost, Gradient Boosting and XGBoost algorithms are used in this model for predicting the rainfall. There are totally five modules used in this project. The Data Analysis Module will analyse the datasets and finding the missing values in the dataset. The Data Pre-processing includes Data Cleaning which is the process of filling the missing values in the dataset. The Feature Transformation Module is used to modify the features of the dataset. The Data Mining Module is used to train the dataset to models using any algorithm for learning the pattern. The Model Evaluation Module is used to measure the performance of the model and finalize the overall best accuracy for the prediction. Dataset used in this prediction is for the country Australia. This main aim of the project is to compare the various boosting algorithms with the neural network and find the best algorithm among them. This prediction can be major advantage to the farmers in order to plant the types of crops according to the needy of water. Overall, we analyse the algorithm which is feasible for qualitatively predicting the rainfall.

Técnicas de inteligência artificial para reconhecimento de sepse em ambientes hospitalares: revisão integrativa

2020 ◽  
Vol 9 (1) ◽  
pp. 15-31
Author(s):  
Everton Osnei Cesario ◽  
Cristiane Yumi Nakamura ◽  
Yohan Bonescki Gumiel ◽  
Deborah Ribeiro Carvalho
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning

A sepse é uma inflamação generalizada com elevada morbidade e mortalidade, cujo reconhecimento e tratamento precoce são fatores essenciais para uma melhor qualidade de vida para o paciente; caso não seja identificada e tratada rapidamente, poderá levar a óbito. Este artigo de revisão integrativa objetiva identificar as técnicas baseadas em inteligência artificial adotadas, sua respectiva acurácia, sensibilidade e especificidade para a identificação precoce nos casos de sepse em ambiente hospitalar. A pesquisa, adaptada do método PRISMA, foi realizada em cinco bases de dados indexadas a partir dos seguintes descritores: sepse, septic, sepsis, forecasting, predict, prediction, detection, predicting, diagnosis, assessment, machine learning, artificial intelligence, data mining e deep learning. Foram identificados 333 artigos, sendo 21 com referência ao reconhecimento precoce da sepse por meio de 16 técnicas. Os resultados demonstram que as redes neurais tiveram melhor desempenho, variando a acurácia entre 76% e 93%, as árvores de decisão entre 69,0% e 91,5% e os métodos estatísticos entre 56% e 89%. Conclui-se que o fator mais influente na identificação precoce do diagnóstico são a variedade e a qualidade dos dados. Também se evidencia o desafio em relação ao pré-processamento, visto que os dados em geral são oriundos de fontes heterogêneas, coletados com critérios, métodos e objetivos distintos.

scholarly journals Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey

2019 ◽  
Vol 52 (1) ◽  
pp. 77-124 ◽  
Author(s):  
Giang Nguyen ◽  
Stefan Dlugolinsky ◽  
Martin Bobák ◽  
Viet Tran ◽  
Álvaro López García ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Large Scale ◽  
Large Scale Data ◽  
Learning Frameworks ◽  
Scale Data
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