posted by organizer: kragab || 7250 views || tracked by 4 users: [display]

Call for Book Chapter 2020 : Deep Learning and Big Data for Intelligent Transportation: Enabling Technologies and Future Trends

FacebookTwitterLinkedInGoogle

Link: https://www2.cs.siu.edu/~kahmed/downloads//BookChapter_CFP.pdf
 
When N/A
Where N/A
Abstract Registration Due Mar 12, 2020
Submission Deadline May 11, 2020
Notification Due Jul 14, 2020
Final Version Due Aug 4, 2020
Categories    deep learning   big data   intelligent transportation   artificial intelligence
 

Call For Papers

Call for Book Chapters

Book Title: Deep Learning and Big Data for Intelligent Transportation: Enabling Technologies and Future Trends

Published by Springer, Studies in Computational Intelligence series in Year 2020

Deep learning and big data are very dynamic, grooming and important research topics of today’s technology. They are contributing to the progress towards intelligent transportation such as fully autonomous vehicles. Transportation generated massive amount of data collected from multiple sources including road sensors, UAVs, probe, GPS, CCTV and incident reports. The collected data are highly needed to make serious traffic decisions such as rerouting, safe-driving decision, etc. With this rich volume and velocity of data, it is challenging to build reliable prediction models based on traditional relational database and machine learning methods. Recently, big data, deep learning and reinforcement learning are new state-of-the-art data management and machine learning approaches which have been of great interest in both academic research and industrial applications.
In general, the use of big data, deep learning and reinforcement learning in transportation is still limited. The main aim of this book is to encourage recent studies of deep learning and reinforcement learning for intelligent transportation and focus on popular topics including processing traffic data, transportation network representation, traffic flow forecasting, traffic signal control, automatic vehicle detection, traffic incident processing, travel demand prediction, autonomous driving and driver behaviors. It is expected the research submitted to this book will answer the following question: How big data and deep learning should be used to build intelligent transportation systems to achieve safety and optimize performance and economy?
Target Audience:
This book would serve a broad audience including researchers, academicians, students and working professional in the field of utilities, manufacturing, health, environmental services, government, defense and networking companies.
Recommended Topics:
 Big data Technologies
Deep Learning Techniques
Big data, Deep learning, Safety in surveillance applications
IoT-driven intelligence and incorporate deep learning models
 Big data and autonomous vehicles
 Deep learning for transportation models
 Reinforcement learning for intelligent transportation
 Detection of Vulnerable Road Users and Animals Air, Road, and Rail
 Deep learning models for achieving pedestrians and cyclist safety
 Practical issues in building Safe transports applications
 Vision, Image Processing and Environment Perception
 Vehicle localization and autonomous navigation
 Vehicle Platooning and Automated Highways
 Performance and Traffic Management Issues
 Intelligent Automation
 Operational and Policy issues in Automation
 Cyber-physical transportation systems
 Advanced Public Transportation Management
 Air, Road, and Rail Traffic Management
 Smart Driver and Traveler Support Systems
 Big Data & Vehicle Analytics
 Big Data Analytics for Intelligent Transportation
 Big Data and Naturalistic Datasets
 Infrastructure and Platform for Big Data and Intelligent transportation

Submission Procedure
All book chapters proposal must be electronically submitted by using Easychair link below, following these guidelines:
• Researchers and practitioners are kindly invited to submit full chapter.
• The length of the book chapter should be between 15 to 20 pages (including reference).
• All submitted chapters will be reviewed by at least two reviewers on a double-blind review basis
• Submission link:


https://easychair.org/conferences/?conf=dlits2020
Important Dates:
August 31, 2020: Full chapter submission due
Sept 16, 2020: Review results including notification of acceptance of chapter
Sept 30, 2020: Camera ready submission due




Related Resources

Federated Learning in IOT Cybersecurity 2021   PeerJ Computer Science - Federated Learning for Cybersecurity in Internet of Things
IEEE COINS 2022   IEEE COINS 2022: Hybrid (3 days on-site | 2 days virtual)
IJCAI 2022   31st International Joint Conference on Artificial Intelligence
Edited Book in Springer-Verlag 2022   Call for Book Chapters-Machine Learning and Deep Learning for Time Series Processing and Analysis
ICDM 2022   22th Industrial Conference on Data Mining
dlmia_ii 2022   Deep Learning in Medical Image Analysis, Volume II
FAIML 2022   2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML 2022)
IEEE COINS 2022   Internet of Things IoT | Artificial Intelligence | Machine Learning | Big Data | Blockchain | Edge & Cloud Computing | Security | Embedded Systems |
MTAP Special Issue on CBIR 2022   Special Issue on Content-Based Image Retrieval: where have we been, and where are we going
DL-ASAP 2022   Pattern Recognition Letters | Deep Learning for Acoustic Sensor Array Processing