Titlebook: Methods of Mathematical Modelling; Continuous Systems a Thomas Witelski,Mark Bowen Textbook 2015 Springer Nature Switzerland AG 2015 Asympt

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Modelling in Applied Fluid Dynamicsows in wedges, build on a common core model called lubrication theory, which is derived in the beginning of the chapter. The case studies differ in many physical aspects (compressible versus incompressible flows, and free-surface versus squeeze film flows) and mathematical techniques employed (simil

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vical cancer. However, not all women are eligible for this ablative treatment due to their cervical characteristics. In our previous study, we presented a deep learning network that used pyramidal features to determine if a cervix is eligible for ablative treatment based on visual characteristics pr

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Thomas Witelski,Mark Boweng the description of the Osteoarthritis Initiative (OAI) nowadays, not only older but also younger people are also suffering from knee OA. The early diagnosis and prognosis of OA are necessary to reduce the pains. Manual detection and accurate severity level identification from an X-ray image is a c

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Thomas Witelski,Mark Bowengorithms. Regarding optimization for algorithms, adam optimization technique tune the learning parameters by utilising the exponential decay of past gradients and their squares. However, the optimizer requires the engagement of frequently occurring features from the datasets that play a significant

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Thomas Witelski,Mark Bowen the primary standard for finding severity of abnormality in many cancer diagnoses and also plays the key role in diagnostic assessments. Recently, computerized techniques have been evolving in diagnosing digital pathology for emerging applications related to nuclei detection, segmentation, and clas

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Thomas Witelski,Mark Boweniologists, resulting in delayed detection and diagnosis. To overcome this challenge, machine learning-based computer-aided detection methods are being explored. However, due to the lack of publicly available large databases of silicosis images, highly accurate deep-learning models for silicosis dete