Titlebook: Nanotechnology for Biomedical Applications; Sreerag Gopi,Preetha Balakrishnan,Nabisab Mujawar Book 2022 The Editor(s) (if applicable) and

Buren

書目名稱Nanotechnology for Biomedical Applications影響因子(影響力)




書目名稱Nanotechnology for Biomedical Applications影響因子(影響力)學(xué)科排名




書目名稱Nanotechnology for Biomedical Applications網(wǎng)絡(luò)公開度




書目名稱Nanotechnology for Biomedical Applications網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Nanotechnology for Biomedical Applications被引頻次




書目名稱Nanotechnology for Biomedical Applications被引頻次學(xué)科排名




書目名稱Nanotechnology for Biomedical Applications年度引用




書目名稱Nanotechnology for Biomedical Applications年度引用學(xué)科排名




書目名稱Nanotechnology for Biomedical Applications讀者反饋




書目名稱Nanotechnology for Biomedical Applications讀者反饋學(xué)科排名

jeopardize

砍伐

控訴

rectocele

入會

Souravi Bardhan,Shubham Roy,Sukhen Dasuire vast amounts of data. Since the theoretical sample complexity is nearly impossible to evaluate, these amounts of examples are usually estimated with crude rules of thumb. However, these rules only suggest when the networks should work, but do not relate to the traditional methods. In particular

祖先

Sarjana Raikwar,Pritish Kumar Panda,Pooja Das Bidla,Shivani Saraf,Ankit Jain,Sanjay K. Jainine and especially deep learning. Purely data-driven methods, however, come with the drawback of disregarding prior knowledge of the problem even though it has shown to be beneficial to incorporate this knowledge into the problem-solving process..We thus introduce an unpaired learning approach for l

避開

Malihe Pooresmaeil,Hassan Namaziine and especially deep learning. Purely data-driven methods, however, come with the drawback of disregarding prior knowledge of the problem even though it has shown to be beneficial to incorporate this knowledge into the problem-solving process..We thus introduce an unpaired learning approach for l

amplitude

Hayretin Tonbul,Y?lmaz Capanbetween first- and second-order regularisation, they are applicable to scenes with both fronto-parallel and ego-motion. So far, however, existing order-adaptive methods have a decisive drawback. While the involved first- and second-order smoothness terms already make use of anisotropic concepts, the

合唱團(tuán)

Mamta Bishnoi,Deepika,Nishi Mody,Ankit Jaine decomposition model providing a hierarchical decomposition of a given image into the sum of scale-varying components. In line with this framework, we extend the approach to the case of registration, task which consists of mapping salient features of an image onto the corresponding ones in another,