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    Detecting COVID-19 from Respiratory Sound Recordings with Transformers
    (2022) Aytekin, Idil; Dalmaz, Onat; Ankishan, Haydar; Saritas, Emine U.; Bagci, Ulas; Cukur, Tolga; Celik, Haydar; Drukker, K; Iftekharuddin, KM
    Auscultation is an established technique in clinical assessment of symptoms for respiratory disorders. Auscultation is safe and inexpensive, but requires expertise to diagnose a disease using a stethoscope during hospital or office visits. However, some clinical scenarios require continuous monitoring and automated analysis of respiratory sounds to pre-screen and monitor diseases, such as the rapidly spreading COVID-19. Recent studies suggest that audio recordings of bodily sounds captured by mobile devices might carry features helpful to distinguish patients with COVID-19 from healthy controls. Here, we propose a novel deep learning technique to automatically detect COVID-19 patients based on brief audio recordings of their cough and breathing sounds. The proposed technique first extracts spectrogram features of respiratory recordings, and then classifies disease state via a hierarchical vision transformer architecture. Demonstrations are provided on a crowdsourced database of respiratory sounds from COVID-19 patients and healthy controls. The proposed transformer model is compared against alternative methods based on state-of-the-art convolutional and transformer architectures, as well as traditional machine-learning classifiers. Our results indicate that the proposed model achieves on par or superior performance to competing methods. In particular, the proposed technique can distinguish COVID-19 patients from healthy subjects with over 94% AUC.
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    A Deep LSTM Approach for Activity Recognition
    (2019) Guney, Selda; Erdas, Cagatay Berke; 0000-0002-0573-1326; 0000-0003-3467-9923; AAC-7404-2020
    Since 1990s, activity recognition effectual field in machine learning literature. Most of studies that relevant activity recognition, use feature extraction method to achieve higher classification performance. Moreover, these studies mostly use traditional machine learning algorithms for classification. In this paper, we focus on a deep (Long Short Term Memory) LSTM neural network for feature free classification of seven daily activities by using raw data that collected from three-dimensional accelerometer. Based on the results, the proposed deep LSTM approach can classify raw data with high performance. The results show that the proposed deep LSTM approach achieved 91.34, 96.91, 88.78, 87.58 as percent classification performance in terms of accuracy, sensitivity, specificity, F-measure respectively.
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    Explaining intrapreneurial behaviors of employees with perceived organizational climate and testing the mediating role of organizational identification: A research study among employees of Turkish innovative firms
    (2014) Tastan, Secil Bal; Gucel, Cem
    This study examines perceived organizational climate and organizational identification as potential antecedents of employees' intrepreneurial behaviors. In particular, the study suggests positive relationships between perceived organizational climate components-structural support and recognition-and intrepreneurial behaviors construct. In addition, employees' organizational identification is suggested to have a mediating role on the relationship between organizational climate and intrepreneurial behaviors. The survey of this study is performed among employees working in high performing and innovative firms operating in White Good Manufacturing, Food and Drink, Telecommunication, and Textile industries in Turkey. The obtained data from the questionnaires are analyzed through the SPSS statistical packaged software. Analyses results revealed that both dimensions of organizational climate (structural support and organizational recognition) significantly and positively related to intrepreneurial behaviors and perceived organizational identification mediate the effects of the organizational climate on the intrepreneurial behaviors construct. (C) 2014 The Authors. Published by Elsevier Ltd.