Identifying phase information of high-entropy alloys (HEAs) can be helpful as it provides useful information such as anticipated mechanical properties. Recently, machine learning methods are attracting interest to predict phases of HEAs, which could reduce the effort for designing new HEAs. As research direction is in its infancy, there is still pl...
Published in Pharmaceutical statistics
In phase I trials, the main goal is to identify a maximum tolerated dose under an assumption of monotonicity in dose-response relationships. On the other hand, such monotonicity is no longer applied to biologic agents because a different mode of action from that of cytotoxic agents potentially draws unimodal or flat dose-efficacy curves. Therefore,...
Published in Chaos, Solitons, and Fractals
COVID-19 virus has encountered people in the world with numerous problems. Given the negative impacts of COVID-19 on all aspects of people's lives, especially health and economy, accurately forecasting the number of cases infected with this virus can help governments to make accurate decisions on the interventions that must be taken. In this study,...
Published in Advanced Modeling and Simulation in Engineering Sciences
This article addresses the problem of constrained derivative-free optimization in a multi-fidelity (or variable-complexity) framework using Bayesian optimization techniques. It is assumed that the objective and constraints involved in the optimization problem can be evaluated using either an accurate but time-consuming computer program or a fast lo...
Published in SN Computer Science
Bayesian optimization, which offers efficient parameter search, suffers from high computation cost if the parameters have high dimensionality because the search space expands and more trials are needed. One existing solution is an embedding method that enables the search to be restricted to a low-dimensional subspace, but this method works well onl...
Bayesian optimization methods have been successfully applied to black box optimization problems that are expensive to evaluate. In this paper, we adapt the so-called super efficient global optimization algorithm to solve more accurately mixed constrained problems. The proposed approach handles constraints by means of upper trust bound, the latter e...
Published in BioMedical Engineering OnLine
BackgroundIntracranial aneurysm is a common type of cerebrovascular disease with a risk of devastating subarachnoid hemorrhage if it is ruptured. Accurate computer-aided detection of aneurysms can help doctors improve the diagnostic accuracy, and it is very helpful in reducing the risk of subarachnoid hemorrhage. Aneurysms are detected in 2D or 3D ...
A challenging problem in both engineering and computer science is that of minimising a function for which we have no mathematical formulation available, that is expensive to evaluate, and that contains continuous and integer variables, for example in automatic algorithm configuration. Surrogate-based algorithms are very suitable for this type of pr...
Published in Proceedings on Privacy Enhancing Technologies
Differential privacy is a mathematical framework for privacy-preserving data analysis. Changing the hyperparameters of a differentially private algorithm allows one to trade off privacy and utility in a principled way. Quantifying this trade-off in advance is essential to decision-makers tasked with deciding how much privacy can be provided in a pa...
Published in Frontiers in neuroscience
In resource-constrained environments, such as low-power edge devices and smart sensors, deploying a fast, compact, and accurate intelligent system with minimum energy is indispensable. Embedding intelligence can be achieved using neural networks on neuromorphic hardware. Designing such networks would require determining several inherent hyperparame...
