Results 151 to 160 of about 2,998 (192)

Deep learning-based gait phase detection using shank-mounted IMU data: Classification approach. [PDF]

PLoS One
Choi W, Choi MT.
europepmc   +1 more source

Diabetic retinopathy as the primary predictor of mild cognitive impairment in type 2 diabetes: Insights from machine learning models. [PDF]

PLoS One
Rhmari Tlemçani FZ, Aitlahbib F, Laidi S, Alidrissi N, Aasfara J, Elamari S, Chadli A, Motaib I.   +7 more
europepmc   +1 more source

A Holistic Nursing Surveillance Decision Support System for Postoperative Pulmonary Complications After Abdominal Surgery: A Retrospective Cohort Study. [PDF]

Healthcare (Basel)
Kim SY, Lim DH, Kim DH, Jeong OR.
europepmc   +1 more source

scMarkerGene: an interpretable neural network framework for cell-type-specific marker gene discovery. [PDF]

Brief Bioinform
Zhang J, Kou SH, Zhao J, Li X, Zhao Y.
europepmc   +1 more source

The Use of Machine Learning Models with Optuna in Disease Prediction

Electronics (Switzerland)
Effectively and equitably allocating medical resources, particularly for minority groups, is a critical issue that warrants further investigation in rural hospitals. Machine learning techniques have gained significant traction and demonstrated strong performance across various fields in recent years.
Ying-Lei Lin, Lai Jung-Pin, Ping-Feng Pai   +2 more
exaly   +3 more sources

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Predicting scour depth in the presence of aprons using XGBoost-Optuna

Applied Soft Computing Journal
Chonoor Abdi Chooplou, Masoud Ghodsian, Mohammad Vaghefi   +2 more
exaly   +2 more sources

LSTM with forget gates optimized by Optuna for lithofacies prediction

2022
One of major technical competitions in energy industry relates to how optimally deep-learning architectures we can design. Optimization of hyperparameters is treated as labor-intensive. However, it is important to tune the parameters especially when we deal with relatively small targets, yet high-impact consequences can be resulted.
Yohei Nishitsuji, Jalil Nasseri
openaire   +1 more source

Automating hyperparameter optimization in geophysics with Optuna: A comparative study

Geophysical Prospecting
AbstractDeep learning has gained attraction amongst geophysicists for solving complex longstanding problems. Nevertheless, proper hyperparameter optimization methodologies remain critically underexplored in geophysical deep learning research. This paper attempts to first highlight the importance of hyperparameter optimization and then showcase two ...
Hussain Almarzooq, Umair Bin Waheed
exaly   +2 more sources

Optuna and AutoML

2020
We can now create an efficient model using the techniques that were discussed in the previous chapters. Bayesian optimization goes a long way in finding hyperparameters. This chapter provides an overview of the Optuna framework and discusses further the role of hyperparameter optimization in automated machine learning.
openaire   +1 more source