San Francisco Open Data

En este TFG se pretende realizar la integración de datos desde distintas fuentes, procesando y transformando los datos en información de la cual se nutrirá un sistema BI con tecnología Big Data. Dicho framework permitirá la computación en clúster mejorando la velocidad con respecto a otras herramientas tradicionales, permitiendo la exploración de datos ad hoc y una aplicación más sencilla de algoritmos machine learning y técnicas de minería de datos

Solar Energy Prediction Model Based on Artificial Neural Networks and Open Data

With climate change driving an increasingly stronger influence over governments and municipalities, sustainable development, and renewable energy are gaining traction across the globe. This is reflected within the EU 2030 agenda, that envisions a future where there is universal access to affordable, reliable and sustainable energy. One of the challenges to achieve this vision lies on the low reliability of certain renewable sources. While both particulars and public entities try to reach self-sufficiency through sustainable energy generation, it is unclear how much investment is needed to mitigate the unreliability introduced by natural factors such as varying wind speed and daylight across the year. In this sense, a tool that aids predicting the energy output of sustainable sources across the year for a particular location can aid greatly in making sustainable energy investments more efficient. In this paper, we make use of Open Data sources, Internet of Things (IoT) sensors and installations distributed across Europe to create such tool through the application of Artificial Neural Networks. We analyze how the different factors affect the prediction of energy production and how Open Data can be used to predict the expected output of sustainable sources. As a result, we facilitate users the necessary information to decide how much they wish to invest according to the desired energy output for their particular location. Compared to state-of-the-art proposals, our solution provides an abstraction layer focused on energy production, rather that radiation data, and can be trained and tailored for different locations using Open Data. Finally, our tests show that our proposal improves the accuracy of the forecasting, obtaining a lower mean squared error (MSE) of 0.040 compared to an MSE 0.055 from other proposals in the literature.This paper has been co-funded by the ECLIPSE-UA (RTI2018-094283-B-C32) project from the Spanish Ministry of Science, Innovation, and Universities; both Jose M. Barrera (I-PI 98/18) and Alejandro Reina (I-PI 13/20) hold an Industrial PhD Grants co-funded by the University of Alicante and the Lucentia Lab Spin-off Company

An extension of iStar for Machine Learning requirements by following the PRISE methodology

The rise of Artificial Intelligence (AI) and Deep Learning has led to Machine Learning (ML) becoming a common practice in academia and enterprise. However, a successful ML project requires deep domain knowledge as well as expertise in a plethora of algorithms and data processing techniques. This leads to a stronger dependency and need for communication between developers and stakeholders where numerous requirements come into play. More specifically, in addition to functional requirements such as the output of the model (e.g. classification, clustering or regression), ML projects need to pay special attention to a number of non-functional and quality aspects particular to ML. These include explainability, noise robustness or equity among others. Failure to identify and consider these aspects will lead to inadequate algorithm selection and the failure of the project. In this sense, capturing ML requirements becomes critical. Unfortunately, there is currently an absence of ML requirements modeling approaches. Therefore, in this paper we present the first i* extension for capturing ML requirements and apply it to two real-world projects. Our study covers two main objectives for ML requirements: (i) allows domain experts to specify objectives and quality aspects to be met by the ML solution, and (ii) facilitates the selection and justification of the most adequate ML approaches. Our case studies show that our work enables better ML algorithm selection, preprocessing implementation tailored to each algorithm, and aids in identifying missing data. In addition, they also demonstrate the flexibility of our study to adapt to different domains.This work has been co-funded by the AETHER-UA project (PID2020-112540RB-C43), a smart data holistic approach for context-aware data analytics: smarter machine learning for business modeling and analytics, funded by the Spanish Ministry of Science and Innovation. And the BALLADEER (PROMETEO/2021/088) project, a Big Data analytical platform for the diagnosis and treatment of Attention Deficit Hyperactivity Disorder (ADHD) featuring extended reality, funded by the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital (Generalitat Valenciana). A. Reina-Reina (I-PI 13/20) hold Industrial PhD Grants co-funded by the University of Alicante and the Lucentia Lab Spin-off Company

A Machine Learning Approach to Reduce Dimensional Space in Large Datasets

Large datasets computing is a research problem as well as a huge challenge due to massive amounts of data that are mined and crunched in order to successfully analyze these massive datasets because they constitute a valuable source of information over different and cross-folded domains, and therefore it represents an irreplaceable opportunity. Hence, the increasing number of environments that use data-intensive computations need more complex calculations than the ones applied to grid-based infrastructures. In this way, this paper analyzes the most commonly used algorithms regarding to this complex problem of handling large datasets whose part of research efforts are focused on reducing dimensional space. Consequently, we present a novel machine learning method that reduces dimensional space in large datasets. This approach is carried out by developing different phases: merging all datasets as a huge one, performing the Extract, Transform and Load (ETL) process, applying the Principal Component Analysis (PCA) algorithm to machine learning techniques, and finally displaying the data results by means of dashboards. The major contribution in this paper is the development of a novel architecture divided into five phases that presents an hybrid method of machine learning for reducing dimensional space in large datasets. In order to verify the correctness of our proposal, we have presented a case study with a complex dataset, specifically an epileptic seizure recognition database. The experiments carried out are very promising since they present very encouraging results to be applied to a great number of different domains.This work was partially funded by Grant RTI2018-094283-B-C32, ECLIPSE-UA (Spanish Ministry of Education and Science), and in part by the Lucentia AGI Grant. This work was partially funded by GENDER-NET Plus Joint Call on Gender an UN Sustainable Development Goals (European Commission - Grant Agreement 741874), funded in Spain by “La Caixa” Foundation (ID 100010434) with code LCF/PR/DE18/52010001 to MTH

Use of a i*extension for Machine Learning: a real case study

Capturing requirements in machine learning projects is a challenging task. It requires domain knowledge as well as experience in the machine learning field. The i* framework is a popular high abstraction-layer requirements capturing tool. However, the use of i* directly in the machine learning field (ML) is unfeasible due to it cannot capture all the restrictions and relationships of ML elements. In previous works we have extended i* to better capture machine learning requirements. In this paper, we apply the i* for machine learning extension to a real machine learning case study, in the context of a project focused on the diagnosis and treatment of Attention-Deficit/Hyperactivity Disorder (ADHD). The results show that the use of the i* for machine learning extension provides insights about the correct path to follow, aiding in the definition and selection of machine learning solutions that better fulfill the project requirements. Moreover, it facilitates faster development of the machine learning solution in a more structured way, avoiding errors and making the application of i* an effective tool for managing machine learning requirements

Machine learning model from a Spanish cohort for prediction of SARS-COV-2 mortality risk and critical patients

Patients affected by SARS-COV-2 have collapsed healthcare systems around the world. Consequently, different challenges arise regarding the prediction of hospital needs, optimization of resources, diagnostic triage tools and patient evolution, as well as tools that allow us to analyze which are the factors that determine the severity of patients. Currently, it is widely accepted that one of the problems since the pandemic appeared was to detect (i) who patients were about to need Intensive Care Unit (ICU) and (ii) who ones were about not overcome the disease. These critical patients collapsed Hospitals to the point that many surgeries around the world had to be cancelled. Therefore, the aim of this paper is to provide a Machine Learning (ML) model that helps us to prevent when a patient is about to be critical. Although we are in the era of data, regarding the SARS-COV-2 patients, there are currently few tools and solutions that help medical professionals to predict the evolution of patients in order to improve their treatment and the needs of critical resources at hospitals. Moreover, most of these tools have been created from small populations and/or Chinese populations, which carries a high risk of bias. In this paper, we present a model, based on ML techniques, based on 5378 Spanish patients’ data from which a quality cohort of 1201 was extracted to train the model. Our model is capable of predicting the probability of death of patients with SARS-COV-2 based on age, sex and comorbidities of the patient. It also allows what-if analysis, with the inclusion of comorbidities that the patient may develop during the SARS-COV-2 infection. For the training of the model, we have followed an agnostic approach. We explored all the active comorbidities during the SARS-COV-2 infection of the patients with the objective that the model weights the effect of each comorbidity on the patient’s evolution according to the data available. The model has been validated by using stratified cross-validation with k = 5 to prevent class imbalance. We obtained robust results, presenting a high hit rate, with 84.16% accuracy, 83.33% sensitivity, and an Area Under the Curve (AUC) of 0.871. The main advantage of our model, in addition to its high success rate, is that it can be used with medical records in order to predict their diagnosis, allowing the critical population to be identified in advance. Furthermore, it uses the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD 9-CM) standard. In this sense, we should also emphasize that those hospitals using other encodings can add an intermediate layer business to business (B2B) with the aim of making transformations to the same international format.This paper has been partially funded by the AETHER-UA (PID2020-112540RB-C43) project by the Ministry of Science and Innovation, the BALLADEER (PROMETEO/2021/088) project by the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital. Both Jose M. Barrera (I-PI 98/18) and Alejandro Reina (I-PI 13/20) hold an Industrial PhD Grants co-funded by the University of Alicante and the Lucentia Lab Spin-off Company

Residuo cromático el color para el residuo sólido

Por medio de este proyecto se aborda la influencia que tiene el color en la disposición de los residuos según la clasificación y normatividad establecida en la ciudad de Bogotá, con su programa "Bogotá Basura Cero", y las normas internacionales y nacionales legisladas y aplicadas por el INSTITUTO COLOMBIANO DE NORMAS TÉCNICAS Y CERTIFICACIÓN ICONTEC. Se proponen algunos colores que se utilizan para la identificación de residuos de manera general y particular, en tres sectores de la economía de la ciudad: salud, industria, y educación. Se realiza una distinción del residuo por medio de la identificación de un color del elemento donde debe ser depositada correctamente

Residuo cromático el color para el residuo sólido

Por medio de este proyecto se aborda la influencia que tiene el color en la disposición de los residuos según la clasificación y normatividad establecida en la ciudad de Bogotá, con su programa "Bogotá Basura Cero", y las normas internacionales y nacionales legisladas y aplicadas por el INSTITUTO COLOMBIANO DE NORMAS TÉCNICAS Y CERTIFICACIÓN ICONTEC. Se proponen algunos colores que se utilizan para la identificación de residuos de manera general y particular, en tres sectores de la economía de la ciudad: salud, industria, y educación. Se realiza una distinción del residuo por medio de la identificación de un color del elemento donde debe ser depositada correctamente

Plan estratégico de comunicación con base en marketing sensorial, para contribuir al fortalecimiento de la imagen corporativa, caso: Draft Bar

Draft se fundó en el año 2015 en Bogotá como el primer bar con un sistema de autoservicio. El establecimiento cuenta con ocho grifos de cerveza en los que se encuentran variedades de cervezas colombianas comerciales y artesanales. Su sistema único en Colombia, funciona con las tarjetas personalizadas pre-pago para poder realizar cualquier compra. En la primera visita, se realiza la activación de la tarjeta y se recarga con el valor que el cliente desee.Comunicador (a) SocialPregrad

Producción y lanzamiento del disco Caminos

Este trabajo trata acerca de la producción musical, composición y arreglos musicales de mi primer trabajo discográfico. Contiene una breve investigación sobre la historia del Pop y la industria discográfica en Norteamérica, además de un análisis musical de cada canción.This work is about the musical production composition and music arrangement of my first discographic album. It contains a short investigation about the Pop history and the recording industry in America, with a music analysis of each song contained on the record.Maestro (a) en MúsicaPregrad