Greedy-AutoML: A novel greedy-based stacking ensemble learning framework for assessing soil liquefaction potential
Automated machine learning (AutoML) is a generic term for a specific approach to machine
learning (ML) area that tries to automate the end-to-end process of employing repetitive ML …
learning (ML) area that tries to automate the end-to-end process of employing repetitive ML …
A novel Adaptive Neural Network-Based Laplacian of Gaussian (AnLoG) classification algorithm for detecting diabetic retinopathy with colour retinal fundus images
MD Ramasamy, K Periasamy, S Periasamy… - Neural Computing and …, 2024 - Springer
Diabetic retinopathy (DR) is a human eye disease in which the eye's retina is damaged in
diabetics. Diabetic retinopathy can be diagnosed by manually interpreting retinal fundus …
diabetics. Diabetic retinopathy can be diagnosed by manually interpreting retinal fundus …
[PDF][PDF] Data quality for AI tool: exploratory data analysis on IBM API
A Jariwala, A Chaudhari, C Bhatt… - International Journal of …, 2022 - academia.edu
A huge amount of data is produced in every domain these days. Thus for applying
automation on any dataset, the appropriately trained data plays an important role in …
automation on any dataset, the appropriately trained data plays an important role in …
Detecting Customer Induced Damages in Motherboards with Deep Neural Networks
Identifying Customer Induced Damage (CID) is a key part in warranty programs of
electronics manufacturers. CID is defined as any damage in the unit performed by an …
electronics manufacturers. CID is defined as any damage in the unit performed by an …
Theoretical classification of exchange geometries from the perspective of NMR relaxation dispersion
FA Chao, Y Zhang, RA Byrd - Journal of Magnetic Resonance, 2021 - Elsevier
NMR relaxation dispersion experiments have been widely applied to probe important
conformational exchange of macro-molecules in many biological systems. The current …
conformational exchange of macro-molecules in many biological systems. The current …
Perform time-series predictions in the r development environment by combining statistical-based models with a decomposition-based approach
Z Pala, AF Pala - Muş Alparslan Üniversitesi Mühendislik Mimarlık …, 2020 - dergipark.org.tr
The analysis of a time-series (TS) measured or obtained by observing any area is an
important step in characterizing a desired system or a phenomenon and predicting its future …
important step in characterizing a desired system or a phenomenon and predicting its future …
IMPLEMENTATION OF THE INDICATOR SYSTEM IN MODELING OF COMPLEX TECHNICAL SYSTEMS
SD Leoshchenko, SA Subbotin, AO Oliinyk… - Radio electronics …, 2021 - ric.zntu.edu.ua
Context. The problem of determining the optimal topology of a neuromodel, which is
characterized by a high level of logical transparency in modeling complex technical systems …
characterized by a high level of logical transparency in modeling complex technical systems …
A produção do turismo mediada pela plataforma Airbnb: proposição de um instrumental/software para o levantamento de dados empíricos e teorizações introdutórias
RM Martoni, AS Nascimento, VH Martins… - Revista Brasileira de …, 2023 - SciELO Brasil
O estudo contempla a corporação Airbnb–plataforma de locação de imóveis para curtas
temporadas–, tanto em sua atuação na cidade colonial mineira de Ouro Preto, quanto em …
temporadas–, tanto em sua atuação na cidade colonial mineira de Ouro Preto, quanto em …
Augmented data deep learning model to prediction of S&P500 index: a case study including data of COVID-19 period
C Montenegro, R Armas - International Conference on Information …, 2022 - Springer
The forecasting of the financial markets, as the SP500 Index, is an arduous task because
their data is highly noisy, non-linear, complex, dynamic, non-parametric, and chaotic. In this …
their data is highly noisy, non-linear, complex, dynamic, non-parametric, and chaotic. In this …