Persona:
Castillo-Cara, Manuel

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manuelcastillo@dia.uned.es
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0000-0002-2990-7090
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Castillo-Cara
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Manuel
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2016 - 201962020 - 202521
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Mostrando 1 - 10 de 27
  • Miniatura
    Publicación
    Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach
    (Taylor & Francis, 2020-06-15) Carrillo Larco, Rodrigo M.; Castillo-Cara, Manuel
    Background: The COVID-19 pandemic has attracted the attention of researchers and clinicians whom have provided evidence about risk factors and clinical outcomes. Research on the COVID-19 pandemic benefiting from open-access data and machine learning algorithms is still scarce yet can produce relevant and pragmatic information. With country-level pre-COVID-19-pandemic variables, we aimed to cluster countries in groups with shared profiles of the COVID-19 pandemic. Methods: Unsupervised machine learning algorithms (k-means) were used to define data-driven clusters of countries; the algorithm was informed by disease prevalence estimates, metrics of air pollution, socio-economic status and health system coverage. Using the one-way ANOVA test, we compared the clusters in terms of number of confirmed COVID-19 cases, number of deaths, case fatality rate and order in which the country reported the first case. Results: The model to define the clusters was developed with 155 countries. The model with three principal component analysis parameters and five or six clusters showed the best ability to group countries in relevant sets. There was strong evidence that the model with five or six clusters could stratify countries according to the number of confirmed COVID-19 cases (p<0.001). However, the model could not stratify countries in terms of number of deaths or case fatality rate. Conclusions: A simple data-driven approach using available global information before the COVID-19 pandemic, seemed able to classify countries in terms of the number of confirmed COVID-19 cases. The model was not able to stratify countries based on COVID-19 mortality data.
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    BeeGOns!: A Wireless Sensor Node for Fog Computing in Smart City Applications
    (Institute of Electrical and Electronics Engineers, 2024-01) Vera Panez, Michael; Cuadros Claro, Kewin; Orozco Barbosa, Luis; Castillo-Cara, Manuel
    The widespread deployment of sensors interconnected by wireless links and the management and exploitation of the data collected have given rise to the Internet of Things (IoT) concept. In this article, we undertake the design and implementation of a wireless multisensor platform following the fog computing paradigm. Our main contributions are the integration of various off-the-shelf sensors smartly packaged into an air-flow module and the evaluation of the communications services offered implemented on top of two low-power radio communications technologies. Our study is complemented by evaluating the communi- cations services over a wired link. Our results show the superiority of LoRaWAN over ZigBee in terms of power consumption despite its slightly higher computational requirements and an estimation of the gap between the resource usage of the wired link and the two wireless radio technologies.
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    Comparative Study of Supervised Learning and Metaheuristic Algorithms for the Development of Bluetooth-Based Indoor Localization Mechanisms
    (Institute of Electrical and Electronics Engineers, 2019-02-15) Lovón Melgarejo, Jesús; Huarcaya Canal, Oscar; Orozco Barbosa, Luis; García Varea, Ismael; Castillo-Cara, Manuel
    The development of the Internet of Things (IoT) benefits from 1) the connections between devices equipped with multiple sensors; 2) wireless networks and; 3) processing and analysis of the gathered data. The growing interest in the use of IoT technologies has led to the development of numerous diverse applications, many of which are based on the knowledge of the end user's location and profile. This paper investigates the characterization of Bluetooth signals behavior using 12 different supervised learning algorithms as a first step toward the development of fingerprint-based localization mechanisms. We then explore the use of metaheuristics to determine the best radio power transmission setting evaluated in terms of accuracy and mean error of the localization mechanism. We further tune-up the supervised algorithm hyperparameters. A comparative evaluation of the 12 supervised learning and two metaheuristics algorithms under two different system parameter settings provide valuable insights into the use and capabilities of the various algorithms on the development of indoor localization mechanisms.
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    An Empirical Study of the Transmission Power Setting for Bluetooth-Based Indoor Localization Mechanisms
    (MDPI, 2017-06-07) Lovón Melgarejo, Jesús; Bravo Rocca, Gusseppe; Orozco Barbosa, Luis; García Varea, Ismael; Castillo-Cara, Manuel
    Nowadays, there is a great interest in developing accurate wireless indoor localization mechanisms enabling the implementation of many consumer-oriented services. Among the many proposals, wireless indoor localization mechanisms based on the Received Signal Strength Indication (RSSI) are being widely explored. Most studies have focused on the evaluation of the capabilities of different mobile device brands and wireless network technologies. Furthermore, different parameters and algorithms have been proposed as a means of improving the accuracy of wireless-based localization mechanisms. In this paper, we focus on the tuning of the RSSI fingerprint to be used in the implementation of a Bluetooth Low Energy 4.0 (BLE4.0) Bluetooth localization mechanism. Following a holistic approach, we start by assessing the capabilities of two Bluetooth sensor/receiver devices. We then evaluate the relevance of the RSSI fingerprint reported by each BLE4.0 beacon operating at various transmission power levels using feature selection techniques. Based on our findings, we use two classification algorithms in order to improve the setting of the transmission power levels of each of the BLE4.0 beacons. Our main findings show that our proposal can greatly improve the localization accuracy by setting a custom transmission power level for each BLE4.0 beacon.
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    Development, validation, and application of a machine learning model to estimate salt consumption in 54 countries
    (eLife Sciences Publications, 2022-01-25) Guzman Vilca, Wilmer Cristobal; Carrillo Larco, Rodrigo M.; Castillo-Cara, Manuel
    Global targets to reduce salt intake have been proposed, but their monitoring is challenged by the lack of population-based data on salt consumption. We developed a machine learning (ML) model to predict salt consumption at the population level based on simple predictors and applied this model to national surveys in 54 countries. We used 21 surveys with spot urine samples for the ML model derivation and validation; we developed a supervised ML regression model based on sex, age, weight, height, and systolic and diastolic blood pressure. We applied the ML model to 54 new surveys to quantify the mean salt consumption in the population. The pooled dataset in which we developed the ML model included 49,776 people. Overall, there were no substantial differences between the observed and ML-predicted mean salt intake (p<0.001). The pooled dataset where we applied the ML model included 166,677 people; the predicted mean salt consumption ranged from 6.8 g/day (95% CI: 6.8–6.8 g/day) in Eritrea to 10.0 g/day (95% CI: 9.9–10.0 g/day) in American Samoa. The countries with the highest predicted mean salt intake were in the Western Pacific. The lowest predicted intake was found in Africa. The country-specific predicted mean salt intake was within reasonable difference from the best available evidence. An ML model based on readily available predictors estimated daily salt consumption with good accuracy. This model could be used to predict mean salt consumption in the general population where urine samples are not available.
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    Phenotypes of non-alcoholic fatty liver disease (NAFLD) and all-cause mortality: unsupervised machine learning analysis of NHANES III
    (BMJ Publishing Group, 2022-11-23) Carrillo Larco, Rodrigo M.; Guzman Vilca, Wilmer Cristobal; Alvizuri Gómez, Claudia; Alqahtani, Saleh; Garcia Larsen, Vanessa; Castillo-Cara, Manuel
    Objectives Non- alcoholic fatty liver disease (NAFLD) is a non-communicable disease with a rising prevalence worldwide and with large burden for patients and health systems. To date, the presence of unique phenotypes in patients with NAFLD has not been studied, and their identification could inform precision medicine and public health with pragmatic implications in personalised management and care for patients with NAFLD. Design Cross-sectional and prospective (up to 31 December 2019) analysis of National Health and Nutrition Examination Survey III (1988–1994). Primary and secondary outcomes measures NAFLD diagnosis was based on liver ultrasound. The following predictors informed an unsupervised machine learning algorithm (k-means): body mass index, waist circumference, systolic blood pressure (SBP), plasma glucose, total cholesterol, triglycerides, liver enzymes alanine aminotransferase, aspartate aminotransferase and gamma glutamyl transferase. We summarised (means) and compared the predictors across clusters. We used Cox proportional hazard models to quantify the all-cause mortality risk associated with each cluster. Results 1652 patients with NAFLD (mean age 47.2 years and 51.5% women) were grouped into 3 clusters: anthro-SBP- glucose (6.36%; highest levels of anthropometrics, SBP and glucose), lipid-liver (10.35%; highest levels of lipid and liver enzymes) and average (83.29%; predictors at average levels). Compared with the average phenotype, the anthro-SBP- glucose phenotype had higher all-cause mortality risk (aHR=2.88; 95% CI: 2.26 to 3.67); the lipid-liver phenotype was not associated with higher all-cause mortality risk (aHR=1.11; 95% CI: 0.86 to 1.42). Conclusions There is heterogeneity in patients with NAFLD, whom can be divided into three phenotypes with different mortality risk. These phenotypes could guide specific interventions and management plans, thus advancing precision medicine and public health for patients with NAFLD.
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    Spatial statistical analysis for the design of indoor particle-filter-based localization mechanisms
    (SAGE, 2016-08-24) Martínez Gómez, Jesús; Martínez del Horno, Miguel; Brea Luján, Victor Manuel; Orozco Barbosa, Luis; García Varea, Ismael; Castillo-Cara, Manuel
    The accurate localization of end-users and resources is seen as one of the main pillars toward the successful implementation of context-based applications. While current outdoor localization mechanisms fulfill most application requirements, the design of accurate indoor localization mechanisms is still an open issue. Most research efforts are focusing on the design of mechanisms making use of the receiver signal strength indications generated by WLAN (wireless local area network) devices. However, the accuracy and robustness of such mechanisms can be severely compromised due to the random and unpredictable nature of radio channels. In this article, we develop a methodology incorporating various algorithms capable of coping with the unpredictable nature of radio channels. Following a holistic approach, we start by identifying the wireless equipment parameter setting, better meeting the implementation requirements of a robust indoor localization mechanism. We then make use of RANdom SAmple Consensus paradigm: a robust model-fitting mechanism capable of smoothing the data captured during the space survey. Using an experimental setup, we evaluate the benefits of integrating the floor plan and an ordinary Kriging interpolation algorithm in the estimation process. Our main findings show that our proposal can greatly improve the quality of the information to be used in the development of particle-filter-based indoor localization mechanisms.
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    An Analysis of Computational Resources of Event-Driven Streaming Data Flow for Internet of Things: A Case Study
    (['Oxford University Press', 'BCS, The Chartered Institute for IT'], 2021-10-06) Tenorio Trigoso, Alonso; Mondragón Ruiz, Giovanny; Carrión, Carmen; Caminero, Blanca; Castillo-Cara, Manuel
    Information and communication technologies backbone of a smart city is an Internet of Things (IoT) application that combines technologies such as low power IoT networks, device management, analytics or event stream processing. Hence, designing an efficient IoT architecture for real-time IoT applications brings technical challenges that include the integration of application network protocols and data processing. In this context, the system scalability of two architectures has been analysed: the first architecture, named as POST architecture, integrates the hyper text transfer protocol with an Extract-Transform-Load technique, and is used as baseline; the second architecture, named as MQTT-CEP, is based on a publish-subscribe protocol, i.e. message queue telemetry transport, and a complex event processor engine. In this analysis, SAVIA, a smart city citizen security application, has been deployed following both architectural approaches. Results show that the design of the network protocol and the data analytic layer impacts highly in the Quality of Service experimented by the final IoT users. The experiments show that the integrated MQTT-CEP architecture scales properly, keeps energy consumption limited and thereby, promotes the development of a distributed IoT architecture based on constraint resources. The drawback is an increase in latency, mainly caused by the loosely coupled communication pattern of MQTT, but within reasonable levels which stabilize with increasing workloads.
  • Miniatura
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    On the Significance of Graph Neural Networks With Pretrained Transformers in Content-Based Recommender Systems for Academic Article Classification
    (Wiley, 2025-05-27) Liu, Jiayun; Castillo-Cara, Manuel; García Castro, Raúl; CYTED Ciencia y Tecnología para el Desarrollo and Comunidad de Madrid.
    Recommender systems are tools for interacting with large and complex information spaces by providing a personalised view of such spaces, prioritising items that are likely to be of interest to the user. In addition, they serve as a significant tool in academic research, helping authors select the most appropriate journals for their academic articles. This paper presents a comprehensive study of various journal recommender systems, focusing on the synergy of graph neural networks (GNNs) with pretrained transformers for enhanced text classification. Furthermore, we propose a content-based journal recommender system that combines a pretrained Transformer with a Graph Attention Network (GAT) using title, abstract and keywords as input data. The proposed architecture enhances text representation by forming graphs from the Transformers' hidden states and attention matrices, excluding padding tokens. Our findings highlight that this integration improves the accuracy of the journal recommendations and reduces the transformer oversmoothing problem, with RoBERTa outperforming BERT models. Furthermore, excluding padding tokens from graph construction reduces training time by 8%–15%. Furthermore, we offer a publicly available dataset comprising 830,978 articles.
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    From cloud and fog computing to federated-fog computing: A comparative analysis of computational resources in real-time IoT applications based on semantic interoperability
    (ELSEVIER, 2024-05-10) Huaranga, Edgar; González Gerpe, Salvador; Castillo-Cara, Manuel; Cimmino, Andrea; García Castro, Raúl; https://orcid.org/0000-0002-8087-0940; https://orcid.org/0000-0003-1550-0430; https://orcid.org/0000-0002-1823-4484; https://orcid.org/0000-0002-0421-452X
    In contemporary computing paradigms, the evolution from cloud computing to fog computing and the recent emergence of federated-fog computing have introduced new challenges pertaining to semantic interoperability, particularly in the context of real-time applications. Fog computing, by shifting computational processes closer to the network edge at the local area network level, aims to mitigate latency and enhance efficiency by minimising data transfers to the cloud. Building upon this, federated-fog computing extends the paradigm by distributing computing resources across diverse organisations and locations, while maintaining centralised management and control. This research article addresses the inherent problematics in achieving semantic interoperability within the evolving architectures of cloud computing, fog computing, and federated-fog computing. Experimental investigations are conducted on a diverse node-based testbed, simulating various end-user devices, to emphasise the critical role of semantic interoperability in facilitating seamless data exchange and integration. Furthermore, the efficacy of federated-fog computing is rigorously evaluated in comparison to traditional fog and cloud computing frameworks. Specifically, the assessment focuses on critical factors such as latency time and computational resource utilisation while processing real-time data streams generated by Internet of Things (IoT) devices. The findings of this study underscore the advantages of federated-fog computing over conventional cloud and fog computing paradigms, particularly in the realm of real-time IoT applications demanding high performance (lowering CPU usage to 20%) and low latency (with picks up to 300ms). The research contributes valuable insights into the optimisation of processing architectures for contemporary computing paradigms, offering implications for the advancement of semantic interoperability in the context of emerging federated-fog computing for IoT applications.