Background: Alzheimer’s is a degenerative dementing disorder that starts with a mild memory impairment and progresses to a total loss of mental and physical faculties. The sooner the diagnosis is made, the better for the patient, as preventive actions and treatment can be started. Although tests such as the Mini-Mental State Tests Examination are usually used for early identification, diagnosis relies on magnetic resonance imaging (MRI) brain analysis. Methods: Public initiatives such as the OASIS (Open Access Series of Imaging Studies) collection provide neuroimaging datasets openly available for research purposes. In this work, a new method based on deep learning and image processing techniques for MRI-based Alzheimer’s diagnosis is proposed and compared with previous literature works. Results: Our method achieves a balance accuracy (BAC) up to 0.93 for image-based automated diagnosis of the disease, and a BAC of 0.88 for the establishment of the disease stage (healthy tissue, very mild and severe stage). Conclusions: Results obtained surpassed the state-of-the-art proposals using the OASIS collection. This demonstrates that deep learning-based strategies are an effective tool for building a robust solution for Alzheimer’s-assisted diagnosis based on MRI data.

This paper introduces Lynx, an intelligent system for personal safety at home environments, oriented to elderly people living independently, which encompasses a decision support machine for automatic home risk prevention, tested in real-life environments to respond to real time situations. The automatic system described in this paper prevents such risks by an advanced analytic methods supported by an expert knowledge system. It is minimally intrusive, using plug-and-play sensors and machine learning algorithms to learn the elder's daily activity taking into account even his health records. If the system detects that something unusual happens (in a wide sense) or if something is wrong relative to the user's health habits or medical recommendations, it sends at real-time alarm to the family, care center, or medical agents, without human intervention. The system feeds on information from sensors deployed in the home and knowledge of subject physical activities, which can be collected by mobile applications and enriched by personalized health information from clinical reports encoded in the system. The system usability and reliability have been tested in real-life conditions, with an accuracy larger than 81%.

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Quantum Computing is considered as the next frontier in computing, and it is attracting a lot of attention from the current scientific community. This kind of computation provides to researchers with a revolutionary paradigm for addressing complex optimization problems, offering a significant speed advantage and an efficient search ability. Anyway, despite hopes placed in this field are high, Quantum Computing is still in an incipient stage of development. For this reason, present architectures show certain limitations in terms of computational capabilities and performance. These limitations have motivated the carrying out of this paper. With this paper, we contribute to the field introducing a novel solving scheme coined as hybrid Quantum Computing -Tabu Search Algorithm. Main pillars of operation of the proposed method are a greater control over the access to quantum resources, and a considerable reduction of non-profitable accesses. For assessing the quality of our method, we have used the well-known TSP as benchmarking problem. Furthermore, the performance of QTA has been compared with QBSolv -a state-of-the-art decomposing solver -on a set of 7 different TSP instances. The obtained experimental outcomes support the preliminary conclusion that QTA is an approach which offers promising results for solving partitioning problems, while it drastically reduces the access to QC resources. Furthermore, we also contribute in this paper to the field of Transfer Optimization by developing and using a evolutionary multiform multitasking algorithm as initialization method for the introduced hybrid Quantum Computing -Tabu Search Algorithm. Concretely, the evolutionary multitasking algorithm implemented is a multiform variant of the recently published Coevolutionary Variable Neighborhood Search Algorithm for Discrete Multitasking.

INTRODUCTION: We present early results of an ongoing investigation into the use of sound for real-time monitoring of anomalous behaviour in digital and digital/physical systems. OBJECTIVES: We aim to define design guidelines to both support authors in the process of creating sonifications that are both efficient and engaging and the transition of sonification into a mass medium for the representation of data in everyday life. METHODS: Through two Design Actions, we apply Design Research to the definition of the use case, the interaction paradigm and the experimental protocol for real-world evaluation of sonification tools. RESULTS: Two Design Actions are described. Methodologies and results of the first experimental phase are presented in detail along with their influence on the second phase, currently ongoing. CONCLUSION: We sketch a tentative design-driven process for sonifications for the real-time monitoring of anomalous behaviour in digital and digital/physical systems.