Titlebook: Intelligent Systems and Applications; Proceedings of the 2 Kohei Arai Conference proceedings 2022 The Editor(s) (if applicable) and The Aut

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書目名稱Intelligent Systems and Applications影響因子(影響力)




書目名稱Intelligent Systems and Applications影響因子(影響力)學科排名




書目名稱Intelligent Systems and Applications網(wǎng)絡公開度




書目名稱Intelligent Systems and Applications網(wǎng)絡公開度學科排名




書目名稱Intelligent Systems and Applications被引頻次




書目名稱Intelligent Systems and Applications被引頻次學科排名




書目名稱Intelligent Systems and Applications年度引用




書目名稱Intelligent Systems and Applications年度引用學科排名




書目名稱Intelligent Systems and Applications讀者反饋




書目名稱Intelligent Systems and Applications讀者反饋學科排名

DEVIL

有花

A Framework for Adaptive Mobile Ecological Momentary Assessments Using Reinforcement Learning,compliance is triggering mEMAs at inopportune moments. In this work, we propose a framework for implementing adaptive mEMAs using reinforcement learning (RL) to address the timing and context challenge, aiming to improve long term response compliance. To effectively model user state, we also propose

NAUT

配置

Multi-GPU-based Convolutional Neural Networks Training for Text Classification,e promising results in many NLP tasks such as text classification. However, training CNNs is a computationally intensive task, which limits their use in large scale applications. In this paper, we are focusing on the improvement of the training cost of the text classification CNN. We implement a dis

MIME

Observe

Gratulate

,A Vision Based Deep Reinforcement Learning Algorithm for UAV Obstacle?Avoidance,rs. An important part focuses on obstacle detection and avoidance for UAVs navigating through an environment. Exploration in an unseen environment can be tackled with Deep Q-Network (DQN). However, value exploration with uniform sampling of actions may lead to redundant states, where often the envir

Indigence

,Detecting and Fixing Nonidiomatic Snippets in Python Source Code with?Deep Learning,thm is divided into three subtasks: (i) the snippets are localized, then for each snippet (ii) the type of the nonidiomatic pattern, and (iii) the key variables are determined. The subtasks of localizing patterns and extracting variables are solved as Sequence Tagging tasks with LSTM networks. Deter

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