Titlebook: Artificial Intelligence XXXVIII; 41st SGAI Internatio Max Bramer,Richard Ellis Conference proceedings 2021 Springer Nature Switzerland AG 2

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Towards Explaining Metaheuristic Solution Quality by Data Mining Surrogate Fitness Models for Import. We show that two of the three surrogate models tested were able to generate variable rankings that agree with our understanding of variable importance rankings within the three common binary benchmark problems trialled.

lactic

Towards Explainable Metaheuristics: PCA for Trajectory Mining in Evolutionary Algorithmsolved by a Genetic Algorithm and a Univariate Estimation of Distribution Algorithm. We show that the new sub-space derived metrics can capture key learning steps in the algorithm run and how solution variable patterns that explain the fitness function may be captured in the principal component coefficients.

Thyroid-Gland

Detection of Brain Tumour Using Deep Learningically analyses the contributing factors to a CNN’s accuracy. The results from the proposed methodology show that the size of the dataset and data augmentation are some of the important factors which affect the accuracy rates of a CNN model.

Endearing

Modelling Emotion Dynamics in Chatbots with Neural Hawkes Processes preliminary model on two public datasets, each with different emotion taxonomies. Our preliminary results show promising emotion prediction accuracy over future conversation turns. Furthermore, our model captures meaningful excitation without being trained on explicit excitation ground-truths as practiced in earlier studies.

sperse

Conference proceedings 2021AI 2021, which was supposed to be held in Cambridge, UK, in December 2021. The conference was held virtually due to the COVID-19 pandemic..The 22 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 37 submissions. The volume includes technical papers pr

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Stephen Farrall,Colin Hay,Emily Gray We show how probabilistic inductive logic programming (PILP) can be applied in CBR systems to make transparent decisions combining logic and probabilities. Then, we demonstrate how our approach can be applied in scenarios presenting uncertainty.

corpus-callosum

https://doi.org/10.1007/b105829 for products based on a public e-commerce dataset. We qualitatively analyse the properties of the proposed model and how the learned representations capture semantic relatedness, similarity and entailment between products and text.