Machine learning

Bonetto, Riccardo; Latzko, Vincent

doi:10.1016/b978-0-12-820488-7.00021-9

Cited by 22 publications

(8 citation statements)

References 6 publications

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“…Machine learning is a proven approach to predictive analytics, leveraging large data sets with complex interrelationships ( Bonetto & Latzko, 2020 ). Our application is a multi-class classification approach to specify and predict earthworks, ADEs, and other archaeological sites.…”

Section: Methodsmentioning

confidence: 99%

Predicting the geographic distribution of ancient Amazonian archaeological sites with machine learning

Walker

Ferguson

Olmeda

et al. 2023

PeerJ

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Amazonia has as least two major centers of ancient human social complexity, but the full geographic extents of these centers remain uncertain. Across the southern rim of Amazonia, over 1,000 earthwork sites comprised of fortified settlements, mound villages, and ditched enclosures with geometric designs known as geoglyphs have been discovered. Qualitatively distinct and densely located along the lower stretches of major river systems and the Atlantic coast are Amazonian Dark Earth sites (ADEs) with deep anthropogenic soils enriched by long-term human habitation. Models predicting the geographic extents of earthworks and ADEs can assist in their discovery and preservation and help answer questions about the full degree of indigenous landscape modifications across Amazonia. We classify earthworks versus ADEs versus other non-earthwork/non-ADE archaeological sites with multi-class machine learning algorithms using soils, climate, and distances to rivers of different types and sizes as geospatial predictors. Model testing is done with spatial cross-validation, and the best model at the optimal spatial scale of 1 km has an Area Under the Curve of 0.91. Our predictive model has led to the discovery of 13 new geoglyphs, and it pinpoints specific areas with high probabilities of undiscovered archaeological sites that are currently hidden by rainforests. The limited, albeit impressive, predicted extents of earthworks and ADEs means that other non-ADE/non-earthwork sites are expected to predominate most of Western and Northern Amazonia.

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Section: Methodsmentioning

confidence: 99%

Predicting the geographic distribution of ancient Amazonian archaeological sites with machine learning

Walker

Ferguson

Olmeda

et al. 2023

PeerJ

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“…However, the computer needs more examples to learn than humans do [ 41 ]. Machine Learning allows the introduction of intelligent decision-making in many areas and applications where developing algorithms would be complex and excellent results are needed [ 42 ].…”

Section: Fundamentals and Concepts Of Machine Learning And Deep Learn...mentioning

confidence: 99%

An Insight of Deep Learning Based Demand Forecasting in Smart Grids

Aguiar-Pérez

Pérez-Juárez

2023

Sensors

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Smart grids are able to forecast customers’ consumption patterns, i.e., their energy demand, and consequently electricity can be transmitted after taking into account the expected demand. To face today’s demand forecasting challenges, where the data generated by smart grids is huge, modern data-driven techniques need to be used. In this scenario, Deep Learning models are a good alternative to learn patterns from customer data and then forecast demand for different forecasting horizons. Among the commonly used Artificial Neural Networks, Long Short-Term Memory networks—based on Recurrent Neural Networks—are playing a prominent role. This paper provides an insight into the importance of the demand forecasting issue, and other related factors, in the context of smart grids, and collects some experiences of the use of Deep Learning techniques, for demand forecasting purposes. To have an efficient power system, a balance between supply and demand is necessary. Therefore, industry stakeholders and researchers should make a special effort in load forecasting, especially in the short term, which is critical for demand response.

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“…In this type of algorithm, regression or classification is based on successive binary divisions of subsets of the training set until the final division generates the desired classification with the lowest possible impurity. The decision tree structure consists of a root node with no parent, each node having only one parent, and each node with children representing a binary decision with branches that are divided into new decisions until they become leaves which are the classes in which the dataset is classified [25].…”

Section: Random Forest -A Supervised Machine Learningmentioning

confidence: 99%

Newborn Cystic Fibrosis Diagnosis Made Accurate and Efficient with Machine Learning to Reduce False Positives in IRT-Trypsinogen Immunoreactive Screening Program

Custódio,

Klausner,

de Almeida

2023

Preprint

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This paper presents a methodology for developing a predictive model using random forests to identify true positive cases of cystic fibrosis in neonatal screening, aiming to improve early detection and care for patients. The current heel prick test used in Brazilian neonatal screening has a high incidence of false positives, leading to unnecessary anxiety and medical interventions for patients and their families. Our methodology, developed using synthetic data and varied model parameters, showed promising results in improving the sensitivity of the model for identifying true positive cases. Our methodology prioritizes sensitivity in the model and uses general indices from the neonatal screening laboratory APAE in São Luís do Maranhão. In conclusion, our approach offers a promising avenue for developing a predictive model for cystic fibrosis in neonatal screening and highlights the potential benefits of improving early detection and care for patients.

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Machine learning

Cited by 22 publications

References 6 publications

Predicting the geographic distribution of ancient Amazonian archaeological sites with machine learning

Predicting the geographic distribution of ancient Amazonian archaeological sites with machine learning

An Insight of Deep Learning Based Demand Forecasting in Smart Grids

Newborn Cystic Fibrosis Diagnosis Made Accurate and Efficient with Machine Learning to Reduce False Positives in IRT-Trypsinogen Immunoreactive Screening Program

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