LBS hosted the 3rd International Conference on Big Data and Internet of Things (BDIOT 2019) and provided a workshop on deep learning for big data and internet of things (IoT) applications.

About BDIOT 2019

The main purpose of BDIOT 2019 was to provide an international platform for presenting and publishing the latest scientific research outcomes related to the topics of big data and IoT. 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 the physical world with information technology. Every business process can be empowered, and therefore, various industries redesign their business models and processes along the paradigm.

Deep-learning workshop

Rashmika Nawaratne and Achini Adikari provided a workshop on deep learning for big data and internet of things (IoT) applications. The workshop demonstrated how to use deep learning theories in practical applications such as transport, health and energy. Around 25 participants from diverse backgrounds, such as IoT, Business, Sports, Data Mining, Computer Science and Geography, took part in the workshop. Participants came from countries such as Japan, Germany, China, Thailand, India and Pakistan.

The workshop conveners

Rashmika and Achini are LBS PhD candidates and researchers at our Centre for Data Analytics and Cognition (CDAC). Rashmika is pursing research on brain inspired Artificial Intelligent (AI) algorithms. During his PhD, he plans to conceptualize, design and develop a brain inspired self-learning AI algorithm to comprehend video and IoT data that can be used in application areas such as national security, smart cities and smart homes. Achini is engaged in multiple research projects involving text analysis in public health forums and social media data, with a particular interest in human emotions analysis using self-learning AI. Her PhD focuses on modelling emotions from digital data in social media conversations using novel AI techniques. Prior to their PhD, both Rashmika and Achini have worked as Technical Team Leads at Software Product Engineering Organizations.

Rashmika during the deep learning workshop
Rashmika during the deep learning workshop

What is deep learning?

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. According to Rashmika it is essential to showcase the practical use of these AI techniques in real-world scenarios rather than only focusing on theories and concepts.

The workshop

The workshop aimed to develop essential knowledge of deep learning and key skills in industrial applications using big data and IoT, and incorporated hands-on tutorials in Python, using Google Collaboratory and Jupyter Notebook.

Rashmika and Achini started with 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). Participants then attempted hands-on experiments with each technique using a benchmark dataset, for training, testing and evaluation. Rashmika and Achini also demonstrated each technique in the context of separate real-life projects which accommodate big data and IoT data. One of these real-life projects was vehicular traffic prediction using IoT smart sensor data setup of arterial road networks. The real-life scenario contains over 190 million records of smart sensor network traffic data generated by 545,851 commuters.

After completing the workshop, participants walked away with solid theoretical foundations of deep learning, when to use it and in which industrial settings, how to design, implement, validate and deploy deep learning models in industrial settings. Feedback from participants has been very positive.

“Most of the workshops on deep learning focus on theoretical aspects, but this workshop focused on practical aspects of using deep learning for industry applications on Big Data and IoT.”

“Easy to understand for a beginner. For a person who do not have a background in AI, it was quite easy to capture the essence of what deep learning means and its hype.”

“Was able to understand what deep learning is and completely implement an AI solution for a business problem within 3 hours.”