Titlebook: Artificial Intelligence Applications and Innovations; 16th IFIP WG 12.5 In Ilias Maglogiannis,Lazaros Iliadis,Elias Pimenidis Conference pr

Sentry

书目名称Artificial Intelligence Applications and Innovations影响因子(影响力)




书目名称Artificial Intelligence Applications and Innovations影响因子(影响力)学科排名




书目名称Artificial Intelligence Applications and Innovations网络公开度




书目名称Artificial Intelligence Applications and Innovations网络公开度学科排名




书目名称Artificial Intelligence Applications and Innovations被引频次




书目名称Artificial Intelligence Applications and Innovations被引频次学科排名




书目名称Artificial Intelligence Applications and Innovations年度引用




书目名称Artificial Intelligence Applications and Innovations年度引用学科排名




书目名称Artificial Intelligence Applications and Innovations读者反馈




书目名称Artificial Intelligence Applications and Innovations读者反馈学科排名

全国性

Meditative

1868-4238 entiment analysis - recommender systems.. .Part II: AI ethics - law; AI constraints; deep learning - LSTM; fuzzy algebra - fuzzy systems; machine learning; medical - health systems; and natural language..*The conference was held virtually due to the COVID-19 pandemic..978-3-030-49163-5978-3-030-49161-1Series ISSN 1868-4238 Series E-ISSN 1868-422X

myelography

Science of Data: A New Ladder for Causation, which is trained on the merchant names of the transactions. The other contribution is introducing a new strategy for training dataset generation employing the sliding window approach in a given time frame to adapt to the changes on the trends of fraudulent transactions. In the experiments, the feat

insincerity

Science of Data: A New Ladder for Causation,affic in a city. Our ensemble learns the functional behaviour of an application by training on logs from normal execution time. It can then detect deviations from normal behaviour and also be retrained on false positive cases found during validation. Anomaly detection in RAN shows that our ensemble

Melatonin

Geetanjali Bihani,Julia Taylor Rayz HRV features were extracted in a controlled study to determine three levels of CL i.e., S0: low CL, S1: normal CL and S2: high CL. To get the best classification accuracy with the ML algorithms, different optimizations such as kernel functions were chosen with different feature matrices both for bi

TIGER

DEBT

Leonida Gianfagna,Antonio Di Ceccodata ready for use. An innovative approach for analytics as a service is introduced and linked with a policy development toolkit, which is an integrated web-based environment to fulfil the requirements of the public policy ecosystem stakeholders.

attenuate

连累

Transparency: Motivations and Challengesion Challenge (BraTS) dataset, we demonstrate that it is able to outperform traditional interpolation methods by up to 20. on SSIM scores whilst retaining generalisability on brain MRI images. We show that performance across scales is not compromised, and that it is able to achieve competitive resul