Titlebook: Smart City and Informatization; 7th International Co Guojun Wang,Abdulmotaleb El Saddik,Kim-Kwang Raymo Conference proceedings 2019 Springe

Affectation

FRFP: A Friend Recommendation Method Based on Fine-Grained Preferences are often fine-grained and can be better used to analyze the user’s fine-grained photography preferences for friend recommendation. However, the recommendation faces challenges because the latest related research works rarely pay attention to the user’s photography preferences are fine-grained, wh

舊石器

沉著

Assault

Securing Smart Offices Through an Intelligent and Multi-device Continuous Authentication Systemrise associated with the new capabilities of Smart Cities. One of the key challenges is the utilisation of continuous and non-invasive authentication mechanisms since traditional authentication methods have important limitations. Thus, to cover these limitations, the main contribution of this paper

楓樹

為現(xiàn)場

Isolate

Identification of Plant Leaf Diseases Based on Inception V3 Transfer Learning and Fine-Tuningchnology in the field of image classification and image recognition, and with the convenient acquisition of crop leaf images, it is possible to automatically identify crop disease through deep learning based on plant leaf disease images. This paper mainly completed the research and analysis of leaf

雪崩

Edge Computing-Enabled Resource Provisioning for Video Surveillance in Internet of Vehiclesdevices with image processing, circumstances analysis and events recognition. In spite of multiple advantages of IoV, preprocessing the huge data may demand abundant computation resources for video surveillance devices. Migrating tasks to remote servers for performing is efficient to solve this prob

Commonwealth

FORGO

Trustworthy Data Collection for Cyber Systems: A Taxonomy and Future Directionsta collected by Cyber-Physical System (CPS) is inherently noisy and may trigger many false alarms. These false or misleading data can lead to wrong decisions. Therefore, data trustworthiness (i.e., the data is free from error, up?to date, and originate from a reputable source) is always preferred. H