BDIOT 2019



Warm welcome to the official website of The 3rd International Conference on Big Data and Internet of Things (BDIOT2019). The Conference will be held in La Trobe University, Melbourne, Australia during August 22-24, 2019 co-located with VRIP 2019 (2019 International Conference on Virtual Reality and Image Processing). 

The rapid advancement and ubiquitous penetration of mobile network, web based information creation and sharing, and software defined networking technology have been enabling sensing, predicting and controlling of physical world with information technology. Every business process can be empowered, and therefore, various industries redesign their business models and processes along Internet of Things (IoT) paradigm.  The main purpose of BDIOT2019 is to provide an international platform for presenting and publishing the latest scientific research outcomes related to the topics of Big Data and Internet of Things.

Important Dates


Submission Deadline (Full Paper)

June 20th, 2019

Notification Date

July 10th, 2019

Registration Due

July 25th, 2019

Conference Dates

August 22-24, 2019

Conference Proceedings

All submissions will be peer reviewed 2-3 reviewers, and the accepted papers after registration will be published in the International Conference Proceedings Series by ACM, which will be archived in the ACM Digital Library, and indexed by Ei Compendex and Scopus and submitted to be reviewed by Thomson Reuters Conference Proceedings Citation Index (ISI Web of Science).

BDIOT 2019 - ACM, ISBN: 978-1-4503-7246-6

BDIOT 2018 - ACM, ISBN: 978-1-4503-6519-2 (Read More) | Ei-Compendex & Scopus (Ei核心检索和Scopus收录)

BDIOT 2017 - ACM, ISBN: 978-1-4503-5430-1(Read More| Ei-Compendex & Scopus (Ei核心检索和Scopus收录)

Submission System: https://easychair.org/conferences/?conf=bdiot2019, | bdiot2017@yeah.net  

 

Special Issues

Selected registered & presented papers with extension will be recommended to be published in a special issue of Springer Cluster Computing (Impact factor 1.601). 

Selected registered & presented papers with extension will be recommended to be published in a special issue of Healthcare Information Technology for the Extreme and Remote Environments with IEEE Access (SCIE-indexed Impact factor 3.557).   

Selected registered & presented papers with extension will be recommended to be published in a special issue of Big Data Processing and Analytics in the Era of Extreme Connectivity and Automation with the MDPI Future Internet (ESCI-indexed).   

 

Deep Learning - Workshop

Venue: GC-AD-211 (2:00 P.M - 4:30 P.M., Aug. 22nd, 2019) 

Presenters: Mr Rashmika Nawaratne and Mrs Achini Adikari
(Centre for Data Analytics and Cognition, La Trobe University, Australia)

Title: Hands-on Deep Learning for Big Data & IoT Applications

Abstract: Deep learning is a persistently maturing artificial intelligence paradigm in research and practice. It maintains a formidable evidence base and increasing potential for applications in Big Data and IoT environments in energy, manufacturing, transport, communication and human engagement. This workshop aims to develop essential knowledge of deep learning and key skills in industrial applications using Big Data and IoT, with hands-on tutorials in Python, using Google Colaboratory and Jupyter Notebook. The workshop will begin by exploring the structural elements of deep learning models, hyper-parameters, and comparison to standard machine learning algorithms, followed by the theory and application of deep neural networks (classification), convolutional neural networks (image processing), and deep recurrent neural networks (time-series prediction). Attendees will attempt hands-on experiments with each technique using a benchmark dataset, for training, testing and evaluation. Tutors will also demonstrate each technique in the context of separate real-life projects which accommodate Big Data and IoT data. Upon completion of the workshops, attendees will know theoretical foundations of deep learning, when to use and in which industrial settings, how to develop a deep learning model, implement, test and deploy the model as an algorithm in Python. 

 

 

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