Mühendislik Fakültesi / Faculty of Engineering

Permanent URI for this collectionhttps://hdl.handle.net/11727/1401

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    Predicting Infections Using Computational Intelligence - A Systematic Review
    (2020) Baldominos, Alejandro; Puello, Adrian; Ogul, Hasan; Asuroglu, Tunc; Colomo-Palacios, Ricardo; 0000-0003-4153-0764
    Infections encompass a set of medical conditions of very diverse kinds that can pose a significant risk to health, and even death. As with many other diseases, early diagnosis can help to provide patients with proper care to minimize the damage produced by the disease, or to isolate them to avoid the risk of spread. In this context, computational intelligence can be useful to predict the risk of infection in patients, raising early alarms that can aid medical teams to respond as quick as possible. In this paper, we survey the state of the art on infection prediction using computer science by means of a systematic literature review. The objective is to find papers where computational intelligence is used to predict infections in patients using physiological data as features. We have posed one major research question along with nine specific subquestions. The whole review process is thoroughly described, and eight databases are considered which index most of the literature published in different scholarly formats. A total of 101 relevant documents have been found in the period comprised between 2003 and 2019, and a detailed study of these documents is carried out to classify the works and answer the research questions posed, resulting to our best knowledge in the most comprehensive study of its kind. We conclude that the most widely addressed infection is by far sepsis, followed by Clostridium difficile infection and surgical site infections. Most works use machine learning techniques, from which logistic regression, support vector machines, random forest and naive Bayes are the most common. Some machine learning works provide some ideas on the problems of small data and class imbalance, which can be of interest. The current systematic literature review shows that automatic diagnosis of infectious diseases using computational intelligence is well documented in the medical literature.
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    A review of power system planning and operational models for flexibility assessment in high solar energy penetration scenarios
    (2020) Emmanuel, Michael; Doubleday, Kate; Cakir, Burcin; Markovic, Marija; Hodge, Bri-Mathias
    This article reviews power system flexibility assessment, which is necessary to ensure both instantaneous stability and long-term security of supply under high penetrations of variable, uncertain, and asynchronous renewable energy resources, such as solar photovoltaics. This article reviews the concept of flexibility and summarizes metrics for evaluating power system flexibility, which is not yet available in the literature. Power system planning and operational models applicable for flexibility assessment, including net load analysis, capacity expansion, production cost, and dynamic models, are reviewed in a comprehensive literature survey, with a focus on high solar and other variable renewable energy penetrations. Each of these models applies different methodological approaches and feasibility criteria appropriate at different timescales. Finally, this article presents a conceptual integrated framework to combine these models for a holistic assessment of power system flexibility requirements across all timescales, from multiyear to sub-cycle.