Analisis de la producción científica basado en las tendencias en temas de investigación. Un estudio de caso sobre inteligencia artificial

Scientific documentation research leads to the computation of large amounts of information from published works of the scientific community. It is necessary to explain these processes and create application frameworks. This paper provides the following: a) An Information System designed to extract scientific information from published papers, b) Accurate explanations of the main processing stages including data mining, natural language processing, and machine learning, and c) Categorized and explained results coming from the Artificial Intelligence case study. The results in this paper include the following: a) Topics and research area rankings and b) Quantity versus quality comparisons of topics and research areas.La investigación en el campo de la documentación científica nos lleva hacia un procesamiento automático de grandes cantidades de información proveniente de los trabajos publicados por la comunidad científica. Resulta necesario explicar estos procesos y crear sistemas que los lleven a cabo. En este artículo se proporciona: a) Un Sistema de Información diseñado para extraer información científica a partir del texto que proporcionan los artículos publicados, b) Explicaciones de las etapas fundamentales de procesamiento: minería de datos, procesamiento del lenguaje natural, aprendizaje automático, y c) Resultados categorizados y explicados de nuestro caso de estudio: el área Artificial Intelligence. Los resultados de este artículo incluyen: a) Ranking de temas y ranking de áreas de investigación, y b) Comparativa entre cantidad y calidad de los temas y de las áreas de investigación

A machine learning approach for mapping and accelerating multiple sclerosis research

The medical field, as many others, is overwhelmed with the amount of research-related information available, such as journal papers, conference proceedings and clinical trials. The task of parsing through all this information to keep up to date with the most recent research findings on their area of expertise is especially difficult for practitioners who must also focus on their clinical duties. Recommender systems can help make decisions and provide relevant information on specific matters, such as for these clinical practitioners looking into which research to prioritize. In this paper, we describe the early work on a machine learning approach, which through an intelligent reinforcement learning approach, maps and recommends research information (papers and clinical trials) specifically for multiple sclerosis research. We tested and evaluated several different machine learning algorithms and present which one is the most promising in developing a complete and efficient model for recommending relevant multiple sclerosis research.info:eu-repo/semantics/publishedVersio

Documenting use cases in the affective computing domain using Unified Modeling Language

The study of the ethical impact of AI and the design of trustworthy systems needs the analysis of the scenarios where AI systems are used, which is related to the software engineering concept of "use case" and the "intended purpose" legal term. However, there is no standard methodology for use case documentation covering the context of use, scope, functional requirements and risks of an AI system. In this work, we propose a novel documentation methodology for AI use cases, with a special focus on the affective computing domain. Our approach builds upon an assessment of use case information needs documented in the research literature and the recently proposed European regulatory framework for AI. From this assessment, we adopt and adapt the Unified Modeling Language (UML), which has been used in the last two decades mostly by software engineers. Each use case is then represented by an UML diagram and a structured table, and we provide a set of examples illustrating its application to several affective computing scenarios.Comment: 8 pages, 5 figures, 2 table

Development of a recommender system based on life and health sciences literature

Tese de Mestrado, Bioinformática e Biologia Computacional , 2022, Universidade de Lisboa, Faculdade de CiênciasOs sistemas de recomendação têm evoluído rapidamente e transformado o nosso diaa-dia ao usar grandes quantidades de informação para obter recomendações personalizadas em áreas como música, filmes ou vendas online. No entanto, nas ciências da vida e da saúde, apesar da necessidade de novas formas de explorar a crescente quantidade de informação digital, há um obstáculo que tem impedido esta evolução: a privacidade dos dados. É preciso ter acesso às preferências dos utilizadores para testar e evoluir os sistemas de recomendação em saúde. O objetivo deste trabalho é criar um conjunto de dados de acesso aberto com preferências de utilizadores obtidas implicitamente a partir de literatura das ciências da vida e da saúde, e testá-lo utilizando sistemas de recomendação de filtragem colaborativa. Utilizando a metodologia LIBRETTI, criámos um conjunto de dados (DisRM) a partir de artigos científicos do PubMed. O DisRM está no formato <utilizador, item, classificação> onde os utilizadores são autores de artigos e os itens são doenças, tendo um total de 2 309 190 classificações. Foram criados dois conjuntos de dados adicionais, DisRM10 e DisRM20, que incluem apenas os utilizadores que têm um número de classificações igual ou superior a 10 e 20, respetivamente. Ao aplicar um algoritmo de filtragem colaborativa k-vizinhos mais próximos baseado em memória aos conjuntos de dados DisRM10 e DisRM20, o objetivo era otimizar o recall e o ganho cumulativo com desconto normalizado (nDCG) para garantir que a maioria dos itens relevantes eram recomendados e apareciam primeiro na lista de recomendações. Os melhores resultados de recomendações foram alcançados utilizando a medida de similaridade PIP, obtendo um recall de 0.81 e um nDCG de 0.87 para o DisRM10. Comparando o DisRM com outros conjuntos de dados padronizados, este obteve resultados semelhantes ou melhores o que valida a qualidade do nosso conjunto de dados.Recommender systems are quickly evolving and transforming our daily life by being used to explore large amounts of information and delivering personalized recommendations on several areas like streaming and e-commerce. But in the life and health sciences field, although there is a growing need of new ways to explore information due to the increase of digital information, there is one major issue that is preventing its evolution: the privacy of data. It is necessary to have data about users’ preferences to test and evolve health recommender systems. The main objective of this work is to create an open-source implicit feedback dataset based on life and health sciences literature and test it using a collaborative filtering recommender system. Using the LIBRETTI methodology, we created the dataset, called DisRM, using research articles from PubMed. The dataset is in the format where the users are authors of research articles and the items are diseases, and it has 2 309 190 ratings. Two additional datasets were created, DisRM10 and DisRM20, including only the users who have a number of ratings equal to or greater than 10 and 20, respectively. When applying a memory-based CF K-Nearest Neighbors algorithm to DisRM10 and DisRM20 we had the goal of optimizing the recall and the normalized discounted cumulative gain (nDCG), to ensure that most of the relevant items are being recommended and ranked high. We achieved the best recommendation results using the similarity measure PIP, obtaining a recall of 0.81 and a nDCG of 0.87 for DisRM10. When comparing DisRM with other baseline datasets, it performed similarly or better for recall and nDCG. This validates the quality of our dataset

Deep Learning Algorithm Recommender

Deep learning contains a set of algorithms that are based on the functioning of human brain i.e. neural networks. These algorithms require a lot of computation power and time along with complex setup to get good results. The project contains several artificial neural network implementation for a variety of tasks like data classification, image classification, natural language processing and more. The project contains an exploratory analysis of hyperparameters of deep learning algorithms in domain of deep learning applications to prove that it is possible to achieve a good accuracy with less resources