AI and Machine Learning in Engineering Conference
Data Science for Mining, Industrial Plants, Oil & Gas and Utilities

Are you involved with artificial intelligence (AI) and machine learning in an industrial setting? We are looking for a number of speakers to present papers at this important industry event. Join your peers, enhance your career, share your knowledge and strengthen your public profile while networking with your industry. 

This conference has been created to meet and exchange ideas with those who want to start with the basics of machine learning, develop their understanding of the technology involved, listen to up-to-date case studies, meet subject matter experts, gain actionable insights and finally set a clear and informed plan to implement and invest in predictive technology in their workplace.

“As artificial intelligence (AI) becomes a vital technology for all enterprises, its usage within enterprises has witnessed a tremendous growth. To reap the complete benefits of AI, organisations today need a deeper understanding of machine learning (ML) algorithms, data integration, business processes, and strategies. Although these tasks may seem uncomplicated, most companies lack time, resources, or ability to implement AI that aligns with their core business objectives, data management, and IT strategies.”


Mainstream examples of this technology are the self-driving Google car, automated recommendations on your favourite websites and fraud detection. What you may not realise is that this technology is used widely in the mining, oil & gas, manufacturing and utilities industries to analyse the huge swathes of data now encountered daily in workplaces. The mining industry is already taking advantage of algorithms by using real-time data to warn operators and maintenance crews of downtime hours in advance. Geologists are using data to discover minerals more effectively and automatically assessing ore fragmentation in less than a minute. Algorithms are being used to monitor machine health, automate tasks in a factory and improve workplace safety in all industrial settings. It is important to note that machine learning has moved on significantly and there is unprecedented value to be gained from quality data in mining and industrial plants.

What is machine learning?

Machine learning is a subset of artificial intelligence (AI) in the field of computer science that often uses statistical techniques and algorithms so computers have the ability to "learn" from data, identify patterns and make decisions with minimal human intervention and without being explicitly programmed. It’s not a new science, but one that has gained fresh momentum with many amazing advancements in data science platforms. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data is becoming easier. 

There is great value to be derived from machine-learning and artificial intelligence. Many Australian companies are embracing machine learning in their day-to-day operations enterprise wide. We now have access to an abundance of data and more computer power. Local industry needs to take advantage, as small improvements to large industrial operations can have a significant impact on profits and efficiencies. This is an opportunity to turn those large volumes of data into actionable insights. Some companies are in the early stages of implementation, others are exploring how to get started, and others have established on-going projects. At any stage, it is important that companies understand how they can maximise the value of machine learning applications in their operations. It is important to point out that there is a high rate of project failure when it comes to big data, as high as 85%. Without good data, up-to-date technology and company wide project support machine learning can be unsuccessful. 

Speaking at the conference 

As a speaker at the event, you will be sharing your experience and know-how with engineers, technicians and other technical professionals who are all eager to learn more about the benefits of Data Science, Machine Learning and Artificial Intelligence (DSMLAI). The conference will present an industry-wide forum to examine and discuss the latest local and international practices and standards in AI and machine learning. 

Presentations at this conference should allow attendees to:

•    Start with the basics and develop their understanding of the topic by the end of the conference
•    Learn how to apply machine learning techniques and get the most out of their data
•    Be exposed to real applications of machine learning
•    Discover how machine learning principles and predictive data can improve efficiencies, reduce downtimes, lengthen out maintenance times and increase safety. 
•    Learn how to train and educate their staff on the key elements of machine learning
•    Gain actionable insights of the emerging technology and learn real world applications
•    Hear industry case studies and learn from the trial and error and experiences of others
•    Network with experienced industry experts and your peers
The overall objective of this conference is to share best practices and new technologies in Data Science, Machine Learning and Artificial Intelligence. We are especially seeking papers on case studies and practical applications.  

This conference is emphatically not aimed at allowing vendors to “sell” their products but rather on real-life industry studies, practical applications and solutions – probably the best way to showcase your technologies and engineering skills.

Data Science, Machine Learning and Artificial Intelligence - Suggested technology, solutions, applications and case study topics:
•    Automation and robotics
•    Geology - 3D mapping, geophysics, hyperspectral imaging, geochemistry and mineralogy
•    Environmental monitoring
•    Asset management
•    Industrial inspections, assessment and maintenance planning
•    Facial recognition
•    Coding machine-learning algorithms into fixed plant control systems (e.g. DCS and PLCs)
•    IoT data used in industrial settings
•    Algorithms – Neural networks, decision trees, recommender systems, decision processes, multi-armed bandits, bayesian methods, graphical models
•    Potential problems with algorithms – can we blindly trust them?
•    Augmented reality/virtual reality
•    Automation safety – protecting workers from crush, trapped and caught injuries and fatalities
•    Measuring different industrial variables e.g. electricity and water consumption, waste flow, emissions, processing quantities.
•    Predictive maintenance
•    Cognitive processing
•    Image processing and computer vision
•    Deep learning / neural networks
•    Document recognition and understanding 
•    Intelligent information processing
•    Intelligent modeling and control theory
•    Intelligent vehicles – autonomous vehicles in mining
•    Intelligent video surveillance
•    Mass information processing/GPU processing / parallel computing / quantum computing
•    Natural language processing
•    Pattern recognition
•    Speech and character recognition
•    Signal processing
•    Unmanned aircraft/drones
•    Word recognition/text analytics 
•    Abnormality and data detection
•    Algorithms for new, structured, data types, such as arising in chemistry, biology, environment, and other scientific domains
•    Big data analytics and high performance implementations of data mining algorithms
•    Developing a unifying theory of data mining
•    Distributed data mining and mining multi-agent data
•    Mining high speed data streams
-    Real-time data analysis 
-    Productionising developed models
-    Intergrating models with dashboarding tools
-    Data fusion
•    Mining in networked settings: web, social and computer networks, and online communities
•    Mining sequences and sequential data
•    Mining sensor data
•    Mining spatial and temporal datasets
•    Mining textual and unstructured datasets
•    Novel data mining algorithms in traditional areas (such as classification, regression, clustering, probabilistic modeling, and association analysis)

All Submissions Welcome 

What is required from you?

-    A 100 word abstract, which outlines the topic you would like to present. This needs to be submitted electronically as soon as possible, to secure your place. 

-    Once your topic is approved, your technical paper and PowerPoint slides will be due six weeks prior to the event. 

-    Speaking slots are allocated on topic suitability and on a first come first served basis, so please register your interest today by emailing This email address is being protected from spambots. You need JavaScript enabled to view it. 

For further information on this event or to discuss sponsorship opportunities contact: 

Sarah Montgomery
Conference Manager
IDC Events  
T: 1300 138 522