Browsing by Issue Date, starting with "2023-12-13"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
- Modelos de Previsão Aplicados no Setor AutomóvelPublication . Cardoso, Gonçalo Faceira Teixeira da Fonseca; Ramos, Sandra Cristina de FariaAtualmente, a previsão de valores de indicadores de gestão, tais como vendas, procura, rentabilidade, etc., desempenha um papel importantíssimo no mundo empresarial, contribuindo, de forma impactante, nas decisões estratégicas das organizações. As previsões tradicionais, baseadas em fórmulas simples ou na opinião de peritos, apenas, estão a ficar obsoletas e a ser substituídas por modelos de previsão automatizados e mais modernos. Isto porque, muitas vezes, existe complexidade nas tendências dos dados disponíveis que não são corretamente detetados pelos métodos mais simples. O presente trabalho teve como objetivo principal a aplicação de algoritmos de previsão baseados em modelos estatísticos e em métodos de aprendizagem automática para melhorar a precisão das previsões de matrículas futuras da Toyota Caetano Portugal. Os trabalhos iniciaram com uma revisão sobre métodos de previsão, seguindo-se uma análise exploratória exaustiva dos dados disponíveis e a modelação via modelos estatísticos e modelos de aprendizagem automática da procura e das vendas de automóveis da Toyota Caetano Portugal. A seleção do melhor método foi efetuada com base em métricas adequadas. Através da análise dos resultados, observou-se que o modelo baseado em redes neuronais foi o que produziu erros mais baixos. Além disso, as previsões obtidas com esse modelo apresentaram valores próximos dos valores reais e são mais precisas do que aquelas anteriormente realizadas pela Toyota Caetano Portugal. Como contribuição, este trabalho demonstra a viabilidade da aplicação de algoritmos de previsão no setor automóvel de forma a melhorar a eficácia das projeções de matrículas. Estes resultados têm uma importância prática tremenda, pois ajuda a empresa a tomar decisões mais corretas, e a realizar o plano de matrículas e os orçamentos com maior confiança.
- Multidisciplinary development and initial validation of a clinical knowledge base on chronic respiratory diseases for mHealth decision support systemsPublication . Pereira, Ana Margarida; Jácome, Cristina; Jacinto, Tiago; Amaral, Rita; Pereira, Mariana; Sá-Sousa, Ana; Couto, Mariana; Vieira-Marques, Pedro; Martinho, Diogo; Vieira, Ana; Almeida, Ana; Martins, Constantino; Marreiros, Goreti; Freitas, Alberto; Almeida, Rute; Fonseca, João A.Most mobile health (mHealth) decision support systems currently available for chronic obstructive respiratory diseases (CORDs) are not supported by clinical evidence or lack clinical validation. The development of the knowledge base that will feed the clinical decision support system is a crucial step that involves the collection and systematization of clinical knowledge from relevant scientific sources and its representation in a human-understandable and computer-interpretable way. This work describes the development and initial validation of a clinical knowledge base that can be integrated into mHealth decision support systems developed for patients with CORDs. A multidisciplinary team of health care professionals with clinical experience in respiratory diseases, together with data science and IT professionals, defined a new framework that can be used in other evidence-based systems. The knowledge base development began with a thorough review of the relevant scientific sources (eg, disease guidelines) to identify the recommendations to be implemented in the decision support system based on a consensus process. Recommendations were selected according to predefined inclusion criteria: (1) applicable to individuals with CORDs or to prevent CORDs, (2) directed toward patient self-management, (3) targeting adults, and (4) within the scope of the knowledge domains and subdomains defined. Then, the selected recommendations were prioritized according to (1) a harmonized level of evidence (reconciled from different sources); (2) the scope of the source document (international was preferred); (3) the entity that issued the source document; (4) the operability of the recommendation; and (5) health care professionals’ perceptions of the relevance, potential impact, and reach of the recommendation. A total of 358 recommendations were selected. Next, the variables required to trigger those recommendations were defined (n=116) and operationalized into logical rules using Boolean logical operators (n=405). Finally, the knowledge base was implemented in an intelligent individualized coaching component and pretested with an asthma use case. Initial validation of the knowledge base was conducted internally using data from a population-based observational study of individuals with or without asthma or rhinitis. External validation of the appropriateness of the recommendations with the highest priority level was conducted independently by 4 physicians. In addition, a strategy for knowledge base updates, including an easy-to-use rules editor, was defined. Using this process, based on consensus and iterative improvement, we developed and conducted preliminary validation of a clinical knowledge base for CORDs that translates disease guidelines into personalized patient recommendations. The knowledge base can be used as part of mHealth decision support systems. This process could be replicated in other clinical areas.
- Interpretable Classification of Wiki-Review StreamsPublication . García-Méndez, Silvia; Leal, Fátima; Malheiro, Benedita; Burguillo-Rial, Juan CarlosWiki articles are created and maintained by a crowd of editors, producing a continuous stream of reviews. Reviews can take the form of additions, reverts, or both. This crowdsourcing model is exposed to manipulation since neither reviews nor editors are automatically screened and purged. To protect articles against vandalism or damage, the stream of reviews can be mined to classify reviews and profile editors in real-time. The goal of this work is to anticipate and explain which reviews to revert. This way, editors are informed why their edits will be reverted. The proposed method employs stream-based processing, updating the profiling and classification models on each incoming event. The profiling uses side and content-based features employing Natural Language Processing, and editor profiles are incrementally updated based on their reviews. Since the proposed method relies on self-explainable classification algorithms, it is possible to understand why a review has been classified as a revert or a non-revert. In addition, this work contributes an algorithm for generating synthetic data for class balancing, making the final classification fairer. The proposed online method was tested with a real data set from Wikivoyage, which was balanced through the aforementioned synthetic data generation. The results attained near-90% values for all evaluation metrics (accuracy, precision, recall, and F-measure).