Taste perception plays a pivotal role in guiding nutrient intake and aiding in the avoidance of potentially harmful substances through five basic tastes - sweet, bitter, umami, salty, and sour. Taste perception originates from molecular interactions in the oral cavity between taste receptors and chemical tastants. Hence, the recognition of taste receptors and the subsequent perception of taste heavily rely on the physicochemical properties of food ingredients. In recent years, several advances have been made towards the development of machine learning-based algorithms to classify chemical compounds’ tastes using their molecular structures. Despite the great efforts, there remains significant room for improvement in developing multi-class models to predict the entire spectrum of basic tastes. Here, we present a multi-class predictor aimed at distinguishing bitter, sweet, and umami, from other taste sensations. The development of a multi-class taste predictor paves the way for a comprehensive understanding of the chemical attributes associated with each fundamental taste. It also opens the potential for integration into the evolving realm of multi-sensory perception, which encompasses visual, tactile, and olfactory sensations to holistically characterize flavour perception. This concept holds promise for introducing innovative methodologies in the rational design of foods, including pre-determining specific tastes and engineering complementary diets to augment traditional pharmacological treatments.

The Internet of Things (IoT) plays a major role today in smart building infrastructures, from simple smart-home applications, to more sophisticated industrial type installations. The vast amounts of data generated from relevant systems can be processed in different ways revealing important information. This is especially true in the era of edge computing, when advanced data analysis and decision-making is gradually moving to the edge of the network where devices are generally characterised by low computing resources. In this context, one of the emerging main challenges is related to maintaining data analysis accuracy even with less data that can be efficiently handled by low resource devices. The present work focuses on correlation analysis of data retrieved from a pilot IoT network installation monitoring a small smart office by means of environmental and energy consumption sensors. The research motivation was to find statistical correlation between the monitoring variables that will allow the use of machine learning (ML) prediction algorithms for energy consumption reducing input parameters. For this to happen, a series of hypothesis tests for the correlation of three different environmental variables with the energy consumption were carried out. A total of ninety tests were performed, thirty for each pair of variables. In these tests, p-values showed the existence of strong or semi-strong correlation with two environmental variables, and of a weak correlation with a third one. Using the proposed methodology, we manage without examining the entire data set to exclude weak correlated variables while keeping the same score of accuracy.

In document categorization, the escalating volume of digital data underscores the critical need for advanced techniques in generating semantically rich document embedding, particularly for topic modelling applications. This article presents a novel methodology that leverages the capabilities of SentenceBERT (SBERT) for sentence embedding, aiming to address the challenges associated with embedding longer texts. The process begins by decomposing documents into individual sentences that can be processed using the SBERT model which generates an embedding for each sentence. Subsequently, a clustering algorithm is employed to select representative sentence embeddings for each document. These embeddings serve as the foundation for constructing comprehensive document embeddings through an autoencoder. This research exclusively explores the autoencoder approach, discarding alternative methods for brevity and focus. The autoencoder strategy showed promising results, outperforming the base model, SBERT, in every scenario. This highlights the method's potential effectiveness in document embedding tasks where understanding long context is important. Moreover, when the Transformer Autoencoder was fine-tuned on test data, it achieved even grater performance, underscoring the viability of this approach. The findings suggest considerable room for improvement in various aspects of the methodology, including the clustering process and the encoder architecture. This article underscores the potential of autoencoders in advancing the state-of-the-art in document embedding technologies, setting the stage for further refinement and exploration.

Visualization is a critical component across every software as it enables users to familiarize themselves with the environment and perform certain tasks with ease. Therefore, straightforward yet interactive and easy-to-understand tools let users’ complex demands be satisfied within minutes. The objective of this work is to give an optimized graphical user interface for the Apache Spark MLlib library to apply machine learning algorithms quickly, conveniently, and effectively. We introduce SparkReact, a responsive graphical user interface that allows users to apply clustering, classification, and regression techniques within just a few mouse clicks by implementing and evaluating a certain algorithm and pre-building the code ready for import to Spark. To evaluate the usefulness of our tool we performed crowdsourcing to two categories, computer experts and ordinary users. The results indicate that both populations were satisfied with the tool at a surprising 98 percent. As per the time required to construct and evaluate a machine learning model, it took approximately 4 minutes using SparkReact while with ordinary methods it took almost 4 times longer. Ultimately, future extensions will seek to provide more algorithmic choices.