‘Ode to a Nightingale’(1819) is a typical poem of a Romantic poet like John Keats, but
‘The Nightingale’(1798) is an uncharacteristic poem of a Romantic poet like Coleridge.
The paper proposes a comparison between Coleridge’s ‘The Nightingale’ and Keats’
‘Ode to a Nightingale’.Coleridge’s poem diverges from the Romantic norm; it carries some
characteristics new to Romantic poetry like the realistic and objective portrayals of nature and
the nightingale, while Keats’ poem adhere to the characteristics of Romantic poetry; it
portrays nature and the nightingale subjectively and unrealistically. Coleridge’s poem is very
much influenced by the scientific approaches to environment, and natural history which have
emerged at the end of the 18th C., and this influence has led Coleridge to diverge from the
Romantic norm, while Keats’ Ode does not react to this emergence, it preserves the prominent
features of Romantic poetry, though it follows Coleridge’s poem in not more than twenty one
years.
The paper is divided into two sections, and a conclusion. The first section, ‘Coleridge’s
Divergence from the Romantic Norm’starts with presenting Coleridge as the conventional
Romantic poet who is not different from Keats nor any other major Romantic poet in
depicting nature and the nightingale in most of his poems, with the aim to highlight his
sudden divergence from his Romantic line in ‘The Nightingale’. The part moves to discuss
Coleridge’s abandonment of the characteristics previously mentioned of Romantic poetry in
his ‘The Nightingale’ through his realistic and objective approaches to the nightingale which
come as a reaction to the occurrence of natural and ornithological studies.
The second section, ‘Keats’ Adherence to the Romantic Norm’ discusses Keats’
preservation of the prominent characteristics of Romantic poetry in his ‘Ode to a Nightingale’
like escapism, allusion, imitation of Milton, the unrealistic and subjective approaches to
nature and the nightingale,and degenderization of the nightingale which all point out Keats’
disregard for the occurrence of scientific approaches to nature and the bird at the time.
Finally, the conclusion sums up the findings of the study.
Convolutional Neural Networks (CNN) have high performance in the fields of object recognition and classification. The strength of CNNs comes from the fact that they are able to extract information from raw-pixel content and learn features automatically. Feature extraction and classification algorithms can be either hand-crafted or Deep Learning (DL) based. DL detection approaches can be either two stages (region proposal approaches) detector or a single stage (non-region proposal approach) detector. Region proposal-based techniques include R-CNN, Fast RCNN, and Faster RCNN. Non-region proposal-based techniques include Single Shot Detector (SSD) and You Only Look Once (YOLO). We are going to compare the speed and accuracy of Faster RCNN,
... Show MoreAutorías: Muhammad Hamza Shihab, Nuha Mohsin Dhahi. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 4, 2022. Artículo de Revista en Dialnet.
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