System for Automatic Assessment of Alzheimer’s Disease Diagnosis Based on Deep Learning Techniques

[Abstract] Automatic detection of Alzheimer’s disease is a very active area of research. This is due to its usefulness in starting the protocol to stop the inevitable progression of this neurodegenerative disease. This paper proposes a system for the detection of the disease by means of Deep Learning techniques in magnetic resonance imaging (MRI). As a solution, a model of neuronal networks (ANN) and two sets of reference data for training are proposed. Finally, the goodness of this system is verified within the domain of the application

Using Reinforcement Learning in the Path Planning of Swarms of UAVs for the Photographic Capture of Terrains

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021.[Abstract] The number of applications using unmanned aerial vehicles (UAVs) is increasing. The use of UAVs in swarms makes many operators see more advantages than the individual use of UAVs, thus reducing operational time and costs. The main objective of this work is to design a system that, using Reinforcement Learning (RL) and Artificial Neural Networks (ANNs) techniques, can obtain a good path for each UAV in the swarm and distribute the flight environment in such a way that the combination of the captured images is as simple as possible. To determine whether it is better to use a global ANN or multiple local ANNs, experiments have been done over the same map and with different numbers of UAVs at different altitudes. The results are measured based on the time taken to find a solution. The results show that the system works with any number of UAVs if the map is correctly partitioned. On the other hand, using local ANNs seems to be the option that can find solutions faster, ensuring better trajectories than using a single global network. There is no need to use additional map information other than the current state of the environment, like targets or distance maps.This research received no external funding

A review of artificial intelligence applied to path planning in UAV swarms

This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/ s00521-021-06569-4This is the accepted version of: A. Puente-Castro, D. Rivero, A. Pazos, and E. Fernández-Blanco, "A review of artificial intelligence applied to path planning in UAV swarms", Neural Computing and Applications, vol. 34, pp. 153–170, 2022. https://doi.org/10.1007/s00521-021-06569-4[Abstract]: Path Planning problems with Unmanned Aerial Vehicles (UAVs) are among the most studied knowledge areas in the related literature. However, few of them have been applied to groups of UAVs. The use of swarms allows to speed up the flight time and, thus, reducing the operational costs. When combined with Artificial Intelligence (AI) algorithms, a single system or operator can control all aircraft while optimal paths for each one can be computed. In order to introduce the current situation of these AI-based systems, a review of the most novel and relevant articles was carried out. This review was performed in two steps: first, a summary of the found articles; second, a quantitative analysis of the publications found based on different factors, such as the temporal evolution or the number of articles found based on different criteria. Therefore, this review provides not only a summary of the most recent work but it gives an overview of the trend in the use of AI algorithms in UAV swarms for Path Planning problems. The AI techniques of the articles found can be separated into four main groups based on their technique: reinforcement Learning techniques, Evolutive Computing techniques, Swarm Intelligence techniques, and, Graph Neural Networks. The final results show an increase in publications in recent years and that there is a change in the predominance of the most widely used techniques.This work is supported by Instituto de Salud Carlos III, grant number PI17/01826 (Collaborative Project in Genomic Data Integration (CICLOGEN) funded by the Instituto de Salud Carlos III from the Spanish National plan for Scientific and Technical Research and Innovation 2013–2016 and the European Regional Development Funds (FEDER)—“A way to build Europe.”. This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia ED431D 2017/16 and “Drug Discovery Galician Network” Ref. ED431G/01 and the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23). This work was also funded by the grant for the consolidation and structuring of competitive research units (ED431C 2018/49) from the General Directorate of Culture, Education and University Management of Xunta de Galicia, and the CYTED network (PCI2018_093284) funded by the Spanish Ministry of Ministry of Innovation and Science. This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia “PRACTICUM DIRECT” Ref. IN845D-2020/03.Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431C 2018/49Xunta de Galicia; IN845D-2020/0

UAV swarm path planning with reinforcement learning for field prospecting

[Abstract] There has been steady growth in the adoption of Unmanned Aerial Vehicle (UAV) swarms by operators due to their time and cost benefits. However, this kind of system faces an important problem, which is the calculation of many optimal paths for each UAV. Solving this problem would allow control of many UAVs without human intervention while saving battery between recharges and performing several tasks simultaneously. The main aim is to develop a Reinforcement Learning based system capable of calculating the optimal flight path for a UAV swarm. This method stands out for its ability to learn through trial and error, allowing the model to adjust itself. The aim of these paths is to achieve full coverage of an overflight area for tasks such as field prospection, regardless of map size and the number of UAVs in the swarm. It is not necessary to establish targets or to have any previous knowledge other than the given map. Experiments have been conducted to determine whether it is optimal to establish a single control for all UAVs in the swarm or a control for each UAV. The results show that it is better to use one control for all UAVs because of the shorter flight time. In addition, the flight time is greatly affected by the size of the map. The results give starting points for future research, such as finding the optimal map size for each situation

Automatic Assessment of Alzheimer’s Disease Diagnosis Based on Deep Learning Techniques

[Abstract] Early detection is crucial to prevent the progression of Alzheimer’s disease (AD). Thus, specialists can begin preventive treatment as soon as possible. They demand fast and precise assessment in the diagnosis of AD in the earliest and hardest to detect stages. The main objective of this work is to develop a system that automatically detects the presence of the disease in sagittal magnetic resonance images (MRI), which are not generally used. Sagittal MRIs from ADNI and OASIS data sets were employed. Experiments were conducted using Transfer Learning (TL) techniques in order to achieve more accurate results. There are two main conclusions to be drawn from this work: first, the damages related to AD and its stages can be distinguished in sagittal MRI and, second, the results obtained using DL models with sagittal MRIs are similar to the state-of-the-art, which uses the horizontal-plane MRI. Although sagittal-plane MRIs are not commonly used, this work proved that they were, at least, as effective as MRI from other planes at identifying AD in early stages. This could pave the way for further research. Finally, one should bear in mind that in certain fields, obtaining the examples for a data set can be very expensive. This study proved that DL models could be built in these fields, whereas TL is an essential tool for completing the task with fewer examples.This work is supported by the “Collaborative Project in Genomic Data Integration (CICLOGEN)” PI17/01826 funded by the Carlos III Health Institute in the context of the Spanish National Plan for Scientific and Technical Research and Innovation 2013–2016 and the European Regional Development Funds (FEDER)—”A way to build Europe”. This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431G/01, ED431D 2017/16), the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23), Competitive Reference Groups (Ref. ED431C 2018/49) and the Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER)Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431C 2018/4

Q-learning Based System for Path Planning with UAV Swarms in Obstacle Environments

Path Planning methods for autonomous control of Unmanned Aerial Vehicle (UAV) swarms are on the rise because of all the advantages they bring. There are more and more scenarios where autonomous control of multiple UAVs is required. Most of these scenarios present a large number of obstacles, such as power lines or trees. If all UAVs can be operated autonomously, personnel expenses can be decreased. In addition, if their flight paths are optimal, energy consumption is reduced. This ensures that more battery time is left for other operations. In this paper, a Reinforcement Learning based system is proposed for solving this problem in environments with obstacles by making use of Q-Learning. This method allows a model, in this particular case an Artificial Neural Network, to self-adjust by learning from its mistakes and achievements. Regardless of the size of the map or the number of UAVs in the swarm, the goal of these paths is to ensure complete coverage of an area with fixed obstacles for tasks, like field prospecting. Setting goals or having any prior information aside from the provided map is not required. For experimentation, five maps of different sizes with different obstacles were used. The experiments were performed with different number of UAVs. For the calculation of the results, the number of actions taken by all UAVs to complete the task in each experiment is taken into account. The lower the number of actions, the shorter the path and the lower the energy consumption. The results are satisfactory, showing that the system obtains solutions in fewer movements the more UAVs there are. For a better presentation, these results have been compared to another state-of-the-art approach

PRACTICUM DIRECT Simulator for Decision Making during Pandemics

Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021[Abstract] The past and current situation of the SARS-CoV-2 pandemic has put the entire society, and especially all hospital systems, worldwide to the test. It is essential that health system managers and decision makers optimize the management of resources, even being forced to improvise new units, divert resources usually destined to other functions and/or change the usual care modality by considerably enhancing aspects of telemedicine. Artificial Intelligence (AI) techniques and procedures are of great help in decision making in emergency environments due to severe pandemics because of their predictive capacity. This paper presents the PRACTICUM DIRECT project, which proposes the design and implementation of a tool to assist health system managers in making decisions on the early management of hospital resources. It makes use of AI techniques to identify the most critical variables in each case and build models capable of showing the possibilities and consequences of the decisions taken on resources at each moment of the emergency. It includes a simulator that shows how they would affect management. The current status is that of the selection of the most appropriate variables, taking into account those affected during the SARS-CoV-2 pandemic: infectious diseases, cardio-neuro-circulatory diseases, metabolic diseases and rehabilitative medicine.This research was funded by the General Directorate of Culture, Education and University Management of Xunta de Galicia “PRACTICUM DIRECT” Ref. IN845D-2020/03 and the GRANT FOR THE PROGRAM FOR CONSOLIDATION AND STRUCTURING OF COMPETITIVE RESEARCH UNITS Ref. ED431C 2018/49Xunta de Galicia; IN845D-2020/03Xunta de Galicia; ED431C 2018/4

Internationalization of the ClepiTO web platform

[Abstract] This adaptation consists of the translation from Spanish into Portuguese of the different contents offered by the ClepiTO web platform to be able to carry out a pilot test with a larger population in Portugal and thus be able to compare the results obtained among the Spanish and Portuguese populationMinisterio de Economía y Competitividad; IN852A 2016/10Xunta de Galicia; ED431D 2017/23Xunta de Galicia; ED431D 2017/16Xunta de Galicia; ED431G/0

Perfil fitoquímico, actividad antimicrobiana y antioxidante de extractos de Gnaphalium oxyphyllum y Euphorbia maculata nativas de Sonora, México

The use of synthetic chemical compounds to preserve foods or treat diseases of bacterial origin is limited because they can cause health damage. Therefore, the food and livestock industries seek natural strategies to preserve foods and preserve the health of animals intended for human consumption. In this sense, some extracts of plant from Sonora, Mexico could be an alternative due to the great diversity of plants and the fact that some of them are traditionally used to treat diseases. On the other hand, there are few studies that support the biological activity of ethanolic extracts of Gnaphalium oxyphyllum (E1) and Euphorbia maculata (E2). In this study, phytochemical content was determined by spectrophotometry, antimicrobial activity was determined by agar diffusion and antioxidant activity was evaluated by ABTS, DPPH and FRAP. The results showed that the E1 and E2 extracts had total phenols, total flavonoids, flavones and flavonols, total flavanones and dihydroflavonols, as well as total tannins, total chlorogenic acid and total polysaccharides. In addition, both extracts showed higher antimicrobial activity against Listeria monocytogenes ATCC 19115, Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 25922 and Salmonella enterica serovar Typhimurium ATCC 14028 when 1 mg ml-1 was used (P<0.05). In addition, they presented antioxidant activity by the methods of ABTS, DPPH and FRAP. Therefore, the antimicrobial and antioxidant potential of these plants represents a natural alternative to control some Gram-positive and Gram-negative bacteria in the livestock industry, as well as for food preservation.El uso de compuestos químicos sintéticos para conservar alimentos o tratar enfermedades de origen bacteriano está limitado debido a que pueden ocasionar daños en la salud. Por ello, las industrias alimentaria y pecuaria buscan estrategias naturales para conservar alimentos y mantener la salud de los animales destinados a consumo humano. En este sentido, algunos extractos de plantas provenientes de Sonora, México podrían ser una alternativa debido a la gran diversidad de plantas y que algunas de ellas se utilizan tradicionalmente para tratar enfermedades. Por otro lado, son pocos los estudios que sustentan la actividad biológica de los extractos etanólicos de Gnaphalium oxyphyllum (E1) y Euphorbia maculata (E2). En este estudio, el contenido de fitoquímicos se determinó por espectrofotometría, la actividad antimicrobiana se determinó por difusión en agar y la actividad antioxidante se evaluó por ABTS, DPPH y FRAP. Los resultados mostraron que los extractos E1 y E2 presentaron fenoles totales, flavonoides totales, flavonas y flavonoles, flavanonas y dihidroflavonoles totales, así como, taninos totales, ácido clorogénico total y polisacáridos totales. Además, ambos extractos mostraron mayor actividad antimicrobiana contra Listeria monocytogenes ATCC 19115, Staphylococcus aureus ATCC 25923, Escherichia coli ATCC 25922 y Salmonella entérica serovar Typhimurium ATCC 14028 cuando se utilizó 1 mg ml-1 (P<0.05). Además, presentaron actividad antioxidante por los métodos de ABTS, DPPH y FRAP. Por lo anterior, el potencial antimicrobiano y antioxidante de estas plantas representa una alternativa natural para controlar algunas bacterias Gram positivas y Gram negativas en la industria pecuaria, así como para la conservación de alimentos

Association of insularity and body condition to cloacal bacteria prevalence in a small shorebird

Do islands harbour less diverse disease communities than mainland? The island biogeography theory predicts more diverse communities on mainland than on islands due to more niches, more diverse habitats and availability of greater range of hosts. We compared bacteria prevalences ofCampylobacter,ChlamydiaandSalmonellain cloacal samples of a small shorebird, the Kentish plover (Charadrius alexandrinus) between two island populations of Macaronesia and two mainland locations in the Iberian Peninsula. Bacteria were found in all populations but, contrary to the expectations, prevalences did not differ between islands and mainland. Females had higher prevalences than males forSalmonellaand when three bacteria genera were pooled together. Bacteria infection was unrelated to bird's body condition but females from mainland were heavier than males and birds from mainland were heavier than those from islands. Abiotic variables consistent throughout breeding sites, like high salinity that is known to inhibit bacteria growth, could explain the lack of differences in the bacteria prevalence between areas. We argue about the possible drivers and implications of sex differences in bacteria prevalence in Kentish plovers