A comparison of neural and non-neural machine learning models for food safety risk prediction with European Union RASFF data.

European Union launched the RASFF portal in 1977 to ensure cross-border monitoring and a quick reaction when public health risks are detected in the food chain. There are not enough resources available to guarantee a comprehensive inspection policy, but RASFF data has enormous potential as a preventive tool. However, there are few studies of food and feed risk issues prediction and none with RASFF data. Although deep learning models are good prediction systems, it must be confirmed whether in this field they behave better than other machine learning techniques. The importance of categorical variables encoding as input for numerical models should be specially studied. Results in this paper show that deep learning with entity embedding is the best combination, with accuracies of 86.81%, 82.31%, and 88.94% in each of the three stages of the simplified RASFF process in which the tests were carried out. However, the random forest models with one hot encoding offer only slightly worse results, so it seems that in the quality of the results the coding has more weight than the prediction technique. Our work also demonstrates that the use of probabilistic predictions (an advantage of neural models) can also be used to optimize the number of inspections that can be carried out.pre-print301 K

Using Game Learning Analytics for Validating the Design of a Learning Game for Adults with Intellectual Disabilities.

Serious Games, defined as a game in which education (in its various forms) is the primary goal rather than entertainment, have been proven as an effective educational tool for engaging and motivating students (Michael & Chen, 2006). However, more research is needed to sustain the suitability of these games to train users with cognitive impairments. This empirical study addresses the use of a Serious Game for training students with Intellectual Disabilities in traveling around the subway as a complement to traditional training. Fifty-one (51) adult people with Down Syndrome, mild cognitive disability or certain types of Autism Spectrum Disorder, all conditions classified as intellectual disabilities, played the learning game Downtown, A Subway Adventure which was designed ad-hoc considering their needs and cognitive skills. We used standards-based Game Learning Analytics techniques (i.e. Experience API –xAPI), to collect and analyze learning data both off-line and in near-real time while the users were playing the videogame. This article analyzes and assesses the evidence data collected using analytics during the game sessions, like time completing tasks, inactivity times or the number of correct/incorrect stations while traveling. Based on a multiple baseline design, the results validated both the game design and the tasks and activities proposed in Downtown as a supplementary tool to train skills in transportation. Differences between High-Functioning and Medium-Functioning users were found and explained in this paper, but the fact that almost all of the students completed at least one route without mistakes, the general improvement trough sessions and the low-mistake ratio are good indicators about the appropriateness of the game design.pre-print311 K

A domain categorisation of vocabularies based on a deep learning classifier.

The publication of large amounts of open data has become a major trend nowadays. This is a consequence of pro-jects like the Linked Open Data (LOD) community, which publishes and integrates datasets using techniques like Linked Data. Linked Data publishers should follow a set of principles for dataset design. This information is described in a 2011 document that describes tasks as the consideration of reusing vocabularies. With regard to the latter, another project called Linked Open Vocabularies (LOV) attempts to compile the vocabularies used in LOD. These vocabularies have been classified by domain following the subjective criteria of LOV members, which has the inherent risk introducing personal biases. In this paper, we present an automatic classifier of vocabularies based on the main categories of the well-known knowledge source Wikipedia. For this purpose, word-embedding models were used, in combination with Deep Learning techniques. Results show that with a hybrid model of regular Deep Neural Network (DNN), Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN), vocabularies could be classified with an accuracy of 93.57 per cent. Specifically, 36.25 per cent of the vocabularies belong to the Culture category.pre-print304 K

Machine learning approaches for detecting parkinson’s disease from EEG analysis: a systematic review.

Background: Diagnosis of Parkinson’s disease (PD) is mainly based on motor symptoms and can be supported by imaging techniques such as the single photon emission computed tomography (SPECT) or M-iodobenzyl-guanidine cardiac scintiscan (MIBG), which are expensive and not always available. In this review, we analyzed studies that used machine learning (ML) techniques to diagnose PD through resting state or motor activation electroencephalography (EEG) tests. Methods: The review process was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. All publications previous to May 2020 were included, and their main characteristics and results were assessed and documented. Results: Nine studies were included. Seven used resting state EEG and two motor activation EEG. Subsymbolic models were used in 83.3% of studies. The accuracy for PD classification was 62–99.62%. There was no standard cleaning protocol for the EEG and a great heterogeneity in the characteristics that were extracted from the EEG. However, spectral characteristics predominated. Conclusions: Both the features introduced into the model and its architecture were essential for a good performance in predicting the classification. On the contrary, the cleaning protocol of the EEG, is highly heterogeneous among the different studies and did not influence the results. The use of ML techniques in EEG for neurodegenerative disorders classification is a recent and growing field.post-print1,30 M

Survey of Machine Learning Techniques in the Analysis of EEG Signals for Parkinson’s Disease: A Systematic Review.

Background: Parkinson’s disease (PD) affects 7–10 million people worldwide. Its diagnosis is clinical and can be supported by image-based tests, which are expensive and not always accessible. Electroencephalograms (EEG) are non-invasive, widely accessible, low-cost tests. However, the signals obtained are difficult to analyze visually, so advanced techniques, such as Machine Learning (ML), need to be used. In this article, we review those studies that consider ML techniques to study the EEG of patients with PD. Methods: The review process was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, which are used to provide quality standards for the objective evaluation of various studies. All publications before February 2022 were included, and their main characteristics and results were evaluated and documented through three key points associated with the development of ML techniques: dataset quality, data preprocessing, and model evaluation. Results: 59 studies were included. The predominating models were Support Vector Machine (SVM) and Artificial Neural Networks (ANNs). In total, 31 articles diagnosed PD with a mean accuracy of 97.35 ± 3.46%. There was no standard cleaning protocol for EEG and a great heterogeneity in EEG characteristics was shown, although spectral features predominated by 88.37%. Conclusions: Neither the cleaning protocol nor the number of EEG channels influenced the classification results. A baseline value was provided for the PD diagnostic problem, although recent studies focus on the identification of cognitive impairment.post-print1392 K

Game Analytics Evidence-Based Evaluation of a Learning Game for Intellectual Disabled Users.

Learning games are becoming popular among teachers as educational tools. However, despite all the game development quality processes (e.g., beta testing), there is no total assurance about the game design appropriateness to the students' cognitive skills until the games are used in the classroom. Furthermore, games designed speci cally for Intellectual Disabled (ID) users are even harder to evaluate because of the communication issues that this type of players have. ID users' feedback about their learning experience is complex to obtain and not always fully reliable. To address this problem, we use an evidence-based approach for evaluating the game design of Downtown, A Subway Adventure, a game created to improve independent living in users with ID. In this paper we exemplify the whole process of applying Game Analytics techniques to gather actual users' gameplay interaction data in real settings for evaluating the design. Following this process, researchers were able to validate different game aspects (e.g., mechanics) and could also identify game aws that may be dif cult to detect using formative evaluation or other observational-based methods. Results showed that the proposed evidence-based approach using Game Analytics information is an effective way to evaluate both the game design and the implementation, especially in situations where other types of evaluations that require users' involvement are limited.post-print1129 K

Network analysis for food safety: Quantitative and structural study of data gathered through the RASFF system in the European Union.

This paper reports a quantitative and structural analysis of data gathered on the food issues reported by the European Union members over the last forty years. The study applies statistical measures and network analysis techniques. For this purpose, a graph was constructed of how different contaminated products have been distributed through countries. The work aims to leverage insights into the structure formed by the involvement of European countries in the exchange of goods that can cause problems for populations. The results obtained show the roles of different countries in the detection of sensitive routes. In particular, the analysis identifies problematic origin countries, such as China or Turkey, whereas European countries, in general, do have good border control policies for the import/export of food.pre-print1210 K

Genetic and Swarm Algorithms for Optimizing the Control of Building HVAC Systems Using Real Data: A Comparative Study.

Buildings consume a considerable amount of electrical energy, the Heating, Ventilation, and Air Conditioning (HVAC) system being the most demanding. Saving energy and maintaining comfort still challenge scientists as they conflict. The control of HVAC systems can be improved by modeling their behavior, which is nonlinear, complex, and dynamic and works in uncertain contexts. Scientific literature shows that Soft Computing techniques require fewer computing resources but at the expense of some controlled accuracy loss. Metaheuristics-search-based algorithms show positive results, although further research will be necessary to resolve new challenging multi-objective optimization problems. This article compares the performance of selected genetic and swarmintelligence- based algorithms with the aim of discerning their capabilities in the field of smart buildings. MOGA, NSGA-II/III, OMOPSO, SMPSO, and Random Search, as benchmarking, are compared in hypervolume, generational distance, ε-indicator, and execution time. Real data from the Building Management System of Teatro Real de Madrid have been used to train a data model used for the multiple objective calculations. The novelty brought by the analysis of the different proposed dynamic optimization algorithms in the transient time of an HVAC system also includes the addition, to the conventional optimization objectives of comfort and energy efficiency, of the coefficient of performance, and of the rate of change in ambient temperature, aiming to extend the equipment lifecycle and minimize the overshooting effect when passing to the steady state. The optimization works impressively well in energy savings, although the results must be balanced with other real considerations, such as realistic constraints on chillers’ operational capacity. The intuitive visualization of the performance of the two families of algorithms in a real multi-HVAC system increases the novelty of this proposal.post-print888 K

A survey of deep learning models in medical therapeutic areas.

pre-print1407 K

Autonomic Management Architecture for Multi-HVAC Systems in Smart Buildings.

This article proposes a self-managing architecture for multi-HVAC systems in buildings, based on the “Autonomous Cycle of Data Analysis Tasks” concept. A multi-HVAC system can be plainly seen as a set of HVAC subsystems, made up of heat pumps, chillers, cooling towers or boilers, among others. Our approach is used for improving the energy consumption, as well as to maintain the indoor comfort, and maximize the equipment performance, by means of identifying and selecting of a possible multi-HVAC system operational mode. The multi-HVAC system operational modes are the different combinations of the HVAC subsystems. The proposed architecture relies on a set of data analysis tasks that exploit the data gathered from the system and the environment to autonomously manage the multi-HVAC system. Some of these tasks analyze the data to obtain the optimal operational mode in a given moment, while others control the active HVAC subsystems. The proposed model is based on standard standard HVAC mathematical models, that are adapted on the fly to the contextual data sensed from the environment. Finally, two case studies, one with heterogeneous and another with homogeneous HVAC equipment, show the generality of the proposed autonomous management architecture for multi-HVAC systems.post-print4413 K