Titlebook: Artificial Intelligence and Soft Computing; 20th International C Leszek Rutkowski,Rafał Scherer,Jacek M. Zurada Conference proceedings 2021

Osmosis

Diagnostik und Therapie – allgemeinion of the dissolution profile based on spectroscopic data is an alternative to the current destructive and time-consuming method. Raman and near infrared (NIR) spectroscopies are two complementary methods, that provide information on the physical and chemical properties of the tablets and can help

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Melanocytes

https://doi.org/10.1007/978-3-662-63137-9In general, textile products are structurally complex and highly varied in design, which makes the development of a generalized approach using conventional image processing methods impossible. Deep supervised machine learning models have been very successful on similar problems but cannot be applied

Paleontology

Fachwissen für Brandschutzhelferted on the basis of the data density and scattering in the input data space. The density and scattering are expressed by the values of the inner-cluster variances, which are obtained after the preliminary input data clustering. The experiments conducted on the two real datasets evaluated the propose

一致性

臭名昭著

Juliane Meyerhoff,Christoph Brühlpaper focuses on one such initiative contributing to attaining this goal, namely, the identification or prediction of disease in crops. More specifically the paper examines the automated quantification of the severity of common rust in maize. Previous work has focused on using standard image process

钻孔

cloture

Factor Augmented Artificial Neural Network vs Deep Learning for Forecasting Global Liquidity Dynamicology of the Factor Augmented Artificial Neural Network Model is applied to improve the predictive capacity of liquidity models compared to traditional econometric methodologies. This hybrid methodology based on dynamic factor models and neural networks is compared with Deep Learning methodologies s

遗忘

Integrate-and-Fire Neurons for Low-Powered Pattern Recognitionhe relationship between the data and the decision is complex and/or the amount of data to transmit is large (e.g. in biologgers). Artificial Neural Networks (ANNs) can efficiently detect patterns in the input data which makes them suitable for decision making or compression of information for data t

glisten

A New Variant of the GQR Algorithm for Feedforward Neural Networks Training on the QR decomposition. The paper describes mathematical background that needs to be considered during the application of the scaled Givens rotations in neural networks training. The paper concludes with sample simulation results.