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

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Modification of Learning Feedforward Neural Networks with the BP Methodethod converges relatively slowly. In this paper a new approach to the backpropagation algorithm is presented. The proposed solution speeds up the BP method by using vector calculations. This modification of the BP algorithm was tested on a few standard examples. The obtained performance of both met

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Canine Behavior Interpretation Framework Using Deep Graph Modelior, it remains an unfamiliar concept to non-expertise. Therefore, in this paper, we introduce a framework for analyzing dog behavior, which defines the interrelationship between dog postures through a graph model without any additional devices but a camera. First of all, our framework classifies th

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Spectroscopy-Based Prediction of In Vitro Dissolution Profile Using Artificial Neural Networksion 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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Possibilistic Classification Learning Based on Contrastive Loss in Learning Vector Quantizer Network the learning vector quantization (LVQ) realizing a nearest prototype classifier model. Figuring out the problem of classifying based on possibilistic or probabilistic class labels (assignments) leads to the use of likelihood ratio to organize a sustainable approach. To this end, we start with a spe