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Nationalistic bias among internationalexperts : evidence from professional skijumping
Get PDFSki jumping competitions involve subjective evaluations by judges from different countries. This may lead to nationalistic bias, according to which judges assign higher scores to their compatriots. To test this claim empirically, we exploit within-performance variation of scores from all World Cup, World Championship, and Olympic Games competitions between the 2010/11 and 2016/17 seasons. Our findings confirm that judges assign significantly higher scores to their compatriots. The magnitude of this nationalistic bias is significantly higher in more corrupt countries. We do not find that judges assign significantly different scores to jumpers whose compatriots are present on the judging panel.publishedVersio
Deep Learning-Based Computer-Aided Diagnosis (CAD) : applications for medical image datasets
Get PDFComputer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors. Keywords: deep learning, CNN, auto-encoder, ant colony optimization, COVID-19, brain tumorpublishedVersio
