AI-Based Transportation Planning and Operation

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Date: 19 Jun 2021 03:50:04

AI-Based Transportation Planning and Operation by Keemin Sohn
English | PDF | 2021 | 126 Pages | ISBN : 3036503641 | 13.5 MB

The purpose of this Special Issue is to create an an academic platform whereby high-quality research papers are published on the applications of innovative AI algorithms to transportation planning and operation. The authors present their original research articles related to the applications of AI or machine-learning techniques to transportation planning and operation. The topics of the articles encompass traffic surveillance, traffic safety, vehicle emission reduction, congestion management, traffic speed forecasting, and ride sharing strategy.

unsupervised learning congestion management reinforcement learning Bayesian modeling planning Transportation Planning Mathematics Electronics Development Transportation Engineering traffic safety traffic surveillance Reinforced Learning vehicle emission reduction AI Science decision-making Programming modeling Physics traffic speed forecasting supervised learning nonlinearity Communication complexity Planning ride sharing strategy estimation Technology

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Machine Learning, Optimization, and Data Science

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Date: 14 Mar 2021 11:34:59

This two-volume set, LNCS 12565 and 12566, constitutes the refereed proceedings of the 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, held in Siena, Italy, in July 2020.

Development computational optimization Communication methods Mathematics InfoSec Programming ideas reinforcement learning IT Security Patterns Engineering algorithms ePub IT Science Algorithms technologies machine learning Technology artificial intelligence

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Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability

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Date: 11 Jan 2021 15:07:27

Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability by Marcus Hutter
English | PDF | 2005 | 294 Pages | ISBN : 3540221395 | 32.7 MB

This book presents sequential decision theory from a novel algorithmic information theory perspective. While the former is suited for active agents in known environments, the latter is suited for passive prediction in unknown environments.

Development information information theory Science algorithmic information theory uncertainty Programming reinforcement learning IT Patterns Engineering algorithms artificial intelligence Mathematics Algorithms Technology decision theory

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Machine Learning, Optimization, and Data Science

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Date: 9 Jan 2021 23:23:11

computational optimization methods reinforcement learning Mathematics Electronics Web development Development ideas artificial intelligence Web dev Science Programming IT Patterns Algorithms Databases technologies Communication machine learning Hardware Engineering algorithms SQL Technology

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Computer Vision – ECCV 2020

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Date: 13 Nov 2020 14:43:49

Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XX by Andrea Vedaldi
English | PDF | 2020 | 839 Pages | ISBN : 3030585646 | 140.2 MB

The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic.

deep neural networks neural networks InfoSec reinforcement learning Mathematics Neural Network Development image reconstruction computer vision artificial intelligence Science Programming IT Patterns Algorithms Databases object recognition image coding Communication computational photography machine learning IT Security Engineering motion estimation SQL Technology

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Computer Vision – ECCV 2020

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Date: 13 Nov 2020 14:40:55

Computer Vision – ECCV 2020: 6th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXI by Andrea Vedaldi
English | PDF | 2020 | 832 Pages | ISBN : 3030585883 | 158. MB

deep neural networks Development image reconstruction Mathematics InfoSec SQL Programming reinforcement learning Science IT IT Security Patterns Engineering Algorithms motion estimation Databases image classification Artificial Intalligence Communication Technology Neural Network

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Qualitative Spatial Abstraction in Reinforcement Learning (Repost)

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Date: 20 Sep 2020 06:21:59

Qualitative Spatial Abstraction in Reinforcement Learning (Repost)

Qualitative Spatial Abstraction in Reinforcement Learning by Lutz Frommberger
English | PDF | 2010 | 186 Pages | ISBN : 3642165893 | 13.73 MB

Reinforcement learning has developed as a successful learning approach for domains that are not fully understood and that are too complex to be described in closed form. However, reinforcement learning does not scale well to large and continuous problems. Furthermore, acquired knowledge specific to the learned task, and transfer of knowledge to new tasks is crucial.

Development Spatial Abstraction Science robotics Programming reinforcement learning State Space Representation Patterns Engineering cognitive science Algorithms Mathematics Technology artificial intelligence

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Deep Reinforcement Learning for Wireless Networks

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Date: 18 Sep 2020 13:45:54

This Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance. Particularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wireless networks and mobile social networks. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme.

Development Deep reinforcement learning deep learning Programming reinforcement learning Wireless Patterns Mobile Social Networks Algorithms Science Mathematics Wireless Systems Communication Technology Engineering ePub

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Genetic Algorithms for Machine Learning

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Date: 19 Jul 2020 16:27:00

Genetic Algorithms for Machine Learning by John J. Grefenstette
English | PDF | 1994 | 167 Pages | ISBN : 1461361826 | 19.34 MB

The articles presented here were selected from preliminary versions presented at the International Conference on Genetic Algorithms in June 1991, as well as at a special Workshop on Genetic Algorithms for Machine Learning at the same Conference.

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