Titlebook: Intelligent Computing; Proceedings of the 2 Kohei Arai Conference proceedings 2022 The Editor(s) (if applicable) and The Author(s), under e

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书目名称Intelligent Computing影响因子(影响力)




书目名称Intelligent Computing影响因子(影响力)学科排名




书目名称Intelligent Computing网络公开度




书目名称Intelligent Computing网络公开度学科排名




书目名称Intelligent Computing被引频次




书目名称Intelligent Computing被引频次学科排名




书目名称Intelligent Computing年度引用




书目名称Intelligent Computing年度引用学科排名




书目名称Intelligent Computing读者反馈




书目名称Intelligent Computing读者反馈学科排名

FID

,A Stochastic Modified Limited Memory BFGS for Training Deep Neural Networks, neural networks. We consider the limited memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) update in the framework of a trust-region approach. We provide an almost comprehensive overview of recent improvements in quasi-Newton based training algorithms, such as accurate selection of the initial Hessian

责任

Ringworm

,Towards Tackling QSAT Problems with Deep Learning and Monte Carlo Tree Search,rting from zero knowledge, can gradually approximate a winning strategy for certain two-player games after an amount of training. In this paper, we present a proof-of-concept to solve small instances of Quantified Boolean Formula Satisfaction (QSAT) problems by leveraging the computational power fro

antenna

Laplacian Pyramid-like Autoencoder,Signal Processing. LPAE decomposes an image into the approximation image and the detail image in the encoder part and then tries to reconstruct the original image in the decoder part using the two components. We use LPAE for experiments on classifications and super-resolution areas. Using the detail

大都市

,Autonomous Vision-Based UAV Landing with Collision Avoidance Using Deep Learning,system denied. There is a risk of collision when multiple UAVs land simultaneously without communication on the same platform. This work accomplishes vision-based autonomous landing and uses a deep-learning-based method to realize collision avoidance during the landing process. Specifically, the lan

nonradioactive

SPASM

,Deep Convolutional Neural Networks for COVID-19 Detection from Chest X-Ray Images Using ResNetV2,s this as a very dangerous disease and has been marked as a global pandemic by the world health organization. Existing COVID-19 testing methods, such as RT-PCR are not completely reliable or convenient. Since the virus affects the respiratory tract, manual analysis of chest X-rays could be a more re

来就得意

,Deep Neural Networks for Remote Sensing Image Classification,useful for change detection and monitoring studies of hydro-geomorphological high risk areas on the Earth surface. Following this framework, several Convolutional Neural Networks (CNNs) have been trained to test an original dataset of images acquired by UAV missions along the Basento River (in Basil

malapropism

,Linear Block and Convolutional MDS Codes to Required Rate, Distance and Type,es are presented. Algorithms are given to design codes to required rate and required error-correcting capability and required types. Infinite series of block codes with rate approaching a given rational . with . and relative distance over length approaching . are designed. These can be designed over