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COAR Access Rights: open accessPublisher: Springer London Ltd

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    Article
    Citation - WoS: 39
    Citation - Scopus: 49
    Effects of Working Memory, Attention, and Expertise on Pilots' Situation Awareness
    (Springer London Ltd, 2020) Cak, Serkan; Say, Bilge; Misirlisoy, Mine
    The current study investigates individual differences that predict situation awareness (SA) in professional pilots. The aim of the study is twofold: to examine the roles of divided attention, inhibition, working memory, and expertise in predicting SA, and to demonstrate the relative contributions of these individual differences to online (Situation Awareness Present Method, SPAM) and offline (Situation Awareness Global Assessment Technique, SAGAT) SA measures. Thirty-six professional pilots completed a challenging flight scenario in a full-flight simulator. Divided attention, inhibition, working memory span, and expertise were measured using choice reaction time with dichotic listening, Stroop, and Automated Operation Span tasks, and flight hours in a full-flight simulator, respectively. Results indicated that offline and online SA measure were not correlated, supporting their concurrent use to obtain a comprehensive measure of SA. Offline SA scores were best predicted by working memory and level of expertise, while online SA scores were predicted by expertise, divided attention and inhibition. Results are discussed focusing on both theoretical contributions for defining and measuring SA and applications. Findings have implications for operators of critical domains and their interactions with automated systems, in which SA is crucial for performance and safety.
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    Article
    Citation - WoS: 13
    Citation - Scopus: 23
    Evaluation of Efficientnet Models for Covid-19 Detection Using Lung Parenchyma
    (Springer London Ltd, 2023) Kurt, Zuhal; Isik, Sahin; Kaya, Zeynep; Anagun, Yildiray; Koca, Nizameddin; Cicek, Suemeyye
    When the COVID-19 pandemic broke out in the beginning of 2020, it became crucial to enhance early diagnosis with efficient means to reduce dangers and future spread of the viruses as soon as possible. Finding effective treatments and lowering mortality rates is now more important than ever. Scanning with a computer tomography (CT) scanner is a helpful method for detecting COVID-19 in this regard. The present paper, as such, is an attempt to contribute to this process by generating an open-source, CT-based image dataset. This dataset contains the CT scans of lung parenchyma regions of 180 COVID-19-positive and 86 COVID-19-negative patients taken at the Bursa Yuksek Ihtisas Training and Research Hospital. The experimental studies show that the modified EfficientNet-ap-nish method uses this dataset effectively for diagnostic purposes. Firstly, a smart segmentation mechanism based on the k-means algorithm is applied to this dataset as a preprocessing stage. Then, performance pretrained models are analyzed using different CNN architectures and with our Nish activation function. The statistical rates are obtained by the various EfficientNet models and the highest detection score is obtained with the EfficientNet-B4-ap-nish version, which provides a 97.93% accuracy rate and a 97.33% F1-score. The implications of the proposed method are immense both for present-day applications and future developments.