scholarly journals Machine learning in critical care: state-of-the-art and a sepsis case study

2018 ◽  
Vol 17 (S1) ◽  
Author(s):  
Alfredo Vellido ◽  
Vicent Ribas ◽  
Carles Morales ◽  
Adolfo Ruiz Sanmartín ◽  
Juan Carlos Ruiz Rodríguez
Keyword(s):  
Machine Learning ◽  
Critical Care ◽  
State Of The Art

scholarly journals Quantification of the suitable rooftop area for solar panel installation from overhead imagery using Convolutional Neural Networks

2021 ◽  
Vol 2042 (1) ◽  
pp. 012002
Author(s):  
Roberto Castello ◽  
Alina Walch ◽  
Raphaël Attias ◽  
Riccardo Cadei ◽  
Shasha Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Computer Vision ◽  
State Of The Art ◽  
Solar Panel ◽  
Post Processing ◽  
Processing Step ◽  
Recent Method

Abstract The integration of solar technology in the built environment is realized mainly through rooftop-installed panels. In this paper, we leverage state-of-the-art Machine Learning and computer vision techniques applied on overhead images to provide a geo-localization of the available rooftop surfaces for solar panel installation. We further exploit a 3D building database to associate them to the corresponding roof geometries by means of a geospatial post-processing approach. The stand-alone Convolutional Neural Network used to segment suitable rooftop areas reaches an intersection over union of 64% and an accuracy of 93%, while a post-processing step using building database improves the rejection of false positives. The model is applied to a case study area in the canton of Geneva and the results are compared with another recent method used in the literature to derive the realistic available area.

Machine Learning for Critical Care: An Overview and a Sepsis Case Study

2017 ◽  
pp. 15-30
Author(s):  
Alfredo Vellido ◽  
Vicent Ribas ◽  
Carles Morales ◽  
Adolfo Ruiz Sanmartín ◽  
Juan Carlos Ruiz-Rodríguez
Keyword(s):  
Machine Learning ◽  
Critical Care

scholarly journals Predict COVID-19 Spreading With C-SMOTE

2021 ◽  
pp. 27-38
Author(s):  
Alessio Bernardo ◽  
Emanuele Della Valle
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
High Impact ◽  
Statistical Evidence ◽  
The Other ◽  
Classification Algorithms ◽  
Minority Class ◽  
Art Methods ◽  
Concept Drifts

Data continuously gathered monitoring the spreading of the COVID-19 pandemic form an unbounded flow of data. Accurately forecasting if the infections will increase or decrease has a high impact, but it is challenging because the pandemic spreads and contracts periodically. Technically, the flow of data is said to be imbalanced and subject to concept drifts because signs of decrements are the minority class during the spreading periods, while they become the majority class in the contraction periods and the other way round. In this paper, we propose a case study applying the Continuous Synthetic Minority Oversampling Technique (C-SMOTE), a novel meta-strategy to pipeline with Streaming Machine Learning (SML) classification algorithms, to forecast the COVID-19 pandemic trend. Benchmarking SML pipelinesthat use C-SMOTE against state-of-the-art methods on a COVID-19 dataset, we bring statistical evidence that models learned using C-SMOTE are better.

scholarly journals A Survey on the Explainability of Supervised Machine Learning

2021 ◽  
Vol 70 ◽  
pp. 245-317
Author(s):  
Nadia Burkart ◽  
Marco F. Huber
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
State Of The Art ◽  
High Accuracy ◽  
Supervised Machine Learning ◽  
Future Directions ◽  
Survey Paper ◽  
Black Boxes ◽  
Highly Sensitive

Predictions obtained by, e.g., artificial neural networks have a high accuracy but humans often perceive the models as black boxes. Insights about the decision making are mostly opaque for humans. Particularly understanding the decision making in highly sensitive areas such as healthcare or finance, is of paramount importance. The decision-making behind the black boxes requires it to be more transparent, accountable, and understandable for humans. This survey paper provides essential definitions, an overview of the different principles and methodologies of explainable Supervised Machine Learning (SML). We conduct a state-of-the-art survey that reviews past and recent explainable SML approaches and classifies them according to the introduced definitions. Finally, we illustrate principles by means of an explanatory case study and discuss important future directions.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

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