To address this challenge, machine learning models that predict drug-drug interactions (DDIs) have actively been developed in recent years. In particular, the growing volume of drug datasets and the advances in machine learning have facilitated the model development. In this regard, this review ...
This approach can handle video scenes containing moving background, illumination variation, and also include into the background model shadow cast by moving objects.Shobha, GKumar, N. SatishInternational Association of Engineering and Technology (IAET)...
Every machine learning application has to consider the aspects of overfitting and underfitting. The reason for underfitting usually lies either in the model, which lacks the ability to express the complexity of the data, or in the features, which do not adequately describe the data. This inevitabl...
Oliynyk and co-workers recently used a set of elemental descriptors to train a machine-learning model, built on a random forest algorithm,36with an aim to accelerate the search for Heusler compounds. After training the model on available crystallographic data ...
the machine learning model, an optimized solid solution, (Ba0.5Ca0.5)TiO3–Ba(Ti0.7Zr0.3)O3, with piezoelectric properties was synthesized and characterized to show better temperature reliability than other BaTiO3-based piezoelectrics in the initial training data. ...
Machine Learning Journal 3 , 261{283. 11] Corlett, R. A. (1983). Explaining induced decision trees. In Expert Systems 83 , pp. 136{142. 12] Hammond, K. (1986). CHEF: a model of case-based planning. In AAAI-86 . 13] Hunt, E. B., Marin, J., and Stone, P. T. (1966)...
Machine learning and Deep Learning techniques may be also exploited to model the behavior of a number of MT components and structural parts. Interesting applications are related to the prediction of the process forces on the workpiece and the computation of coefficients to define the stability of th...
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Fuzzing Machine Learning Model TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing (2018) Paper Code Abstract:Machine learning models are notoriously difficult to interpret and debug. This is particularly true of neural networks. In this work, we introduce automated software testing techniqu...
By connecting Otto to a Bayesian optimizer, the machine-learning model directed the experimental execution on the basis of measurement feedback in real time to optimize the electrochemical window of aqueous sodium electrolyte in the design space of mixtures of NaNO3, NaClO4, Na2SO4, and NaBr and...