Serviços web para aprendizagem automática

Deep learning has become increasingly popular over the years, having proved their efficiency in input-output functions for distinct types of data. However, their mathematical complexity and the training optimizations can be challenging. Moreover, access to dedicated hardware and the setup of a working environment is most of the times a barrier to its usage. This dissertation describes the design and implementation of a web platform to be used by students and advanced researchers. This platform aims to abstract the processes behind this technology and simplify its usage. It is a multi-user distributed platform that offers services, software and hardware, through a common web browser interface. The solution allows easy addition of new computational nodes, the upload of datasets, the visual design of models and datasets, the monitoring of hardware and training sessions, and also supports validation and test procedures.A utilização de deep learning tem vindo a aumentar em diferentes áreas aplicacionais devido à sua eficiência em processos de previsão e à capacidade de se adaptar a vários tipos de dados. No entanto, a sua complexidade matemática e as otimizações dos processos de treino são desafiantes. Mais ainda, o acesso a hardware dedicado e a configuração correcta de um ambiente de trabalho são muitas vezes barreiras à sua utilização. Esta dissertação propõe uma arquitetura e descreve a implementação de uma plataforma web de serviços de deep learning para utilização de estudantes e investigadores. Esta plataforma permite abstrair os processos complexos que suportam esta tecnologia, simplificando e democratizando o seu uso. Trata-se de uma solução distribuída e multiutilizador que oferece serviços, desde hardware a software, através de um navegador web comum. A solução permite a adição fácil de unidades computacionais de processamento, a inserção de dados de estudo, o desenho dos modelos com ferramentas visuais, a monitorização de sessões de treino, teste e validação destes modelos.Mestrado em Engenharia de Computadores e Telemátic

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries