This paper presents an Artificial Intelligence approach to mining context and emotions related to olfactory cultural heritage narratives, particularly to fairy tales. We provide an overview of the role of smell and emotions in literature, as well as highlight the importance of olfactory experience and emotions from psychology and linguistic perspectives. We introduce a methodology for extracting smells and emotions from text, as well as demonstrate the context-based visualizations related to smells and emotions implemented in a novel smell tracker tool. The evaluation is performed using a collection of fairy tales from Grimm and Andersen. We find out that fairy tales often connect smell with the emotional charge of situations. The experimental results show that we can detect smells and emotions in fairy tales with an F1 score of 91.62 and 79.2, respectively.

In this paper, a novel feature selection algorithm for inference from high-dimensional data (FASTENER) is presented. With its multi-objective approach, the algorithm tries to maximize the accuracy of a machine learning algorithm with as few features as possible. The algorithm exploits entropy-based measures, such as mutual information in the crossover phase of the iterative genetic approach. FASTENER converges to a (near) optimal subset of features faster than other multi-objective wrapper methods, such as POSS, DT-forward and FS-SDS, and achieves better classification accuracy than similarity and information theory-based methods currently utilized in earth observation scenarios. The approach was primarily evaluated using the earth observation data set for land-cover classification from ESA’s Sentinel-2 mission, the digital elevation model and the ground truth data of the Land Parcel Identification System from Slovenia. For land cover classification, the algorithm gives state-of-the-art results. Additionally, FASTENER was tested on open feature selection data sets and compared to the state-of-the-art methods. With fewer model evaluations, the algorithm yields comparable results to DT-forward and is superior to FS-SDS. FASTENER can be used in any supervised machine learning scenario.

Refineries execute a series of interlinked processes. Given the output of a certain unit serves as the input of another one, the failures within these processes affect the quality of the end products and the revenue of the entire refinery. The implementation of realtime quality monitoring helps with accurate decision-making regarding process operations. In this paper, multiple machine-learning models aimed at forecasting the amount of ethylene (C2) and pentane (C5) content in the final product mixture are compared. In particular, strategies to deal with zero-inflated data are explored. The presence of zeroes in the dependent variable usually affects the models' learning and may result in poor performance. Furthermore, the presence of zeroes also affects the metrics used to compute the regressor's prediction quality, making it hard to assess their quality. The research results show a two-fold machine learning model leads to the best outcomes. The approach was validated with a real-world use case from a refinery.