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

Sentry

書目名稱Artificial Intelligence Applications and Innovations影響因子(影響力)




書目名稱Artificial Intelligence Applications and Innovations影響因子(影響力)學(xué)科排名




書目名稱Artificial Intelligence Applications and Innovations網(wǎng)絡(luò)公開度




書目名稱Artificial Intelligence Applications and Innovations網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Artificial Intelligence Applications and Innovations被引頻次




書目名稱Artificial Intelligence Applications and Innovations被引頻次學(xué)科排名




書目名稱Artificial Intelligence Applications and Innovations年度引用




書目名稱Artificial Intelligence Applications and Innovations年度引用學(xué)科排名




書目名稱Artificial Intelligence Applications and Innovations讀者反饋




書目名稱Artificial Intelligence Applications and Innovations讀者反饋學(xué)科排名

全國性

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

連累

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