The mining industry has traditionally been a laggard when it comes to innovation. The 21st-century economy, however, dominated by emerging technologies like electric vehicles, green energy sources and ever-more advanced mobile devices, is demanding creative approaches to efficiently delivering the raw materials that will fuel modern economies. Canadian mining is seeing a real drive to innovate that is bolstering our ability to get materials to global markets as efficiently and cost-effectively as possible. And it's changing what it means to work in mining. New technologies are enabling workers to make quicker, more informed decisions at the front lines of operations.

Advanced Data Mining Projects with R takes you one step ahead in understanding the most complex data mining algorithms and implementing them in the popular R language. Follow up to our course Data Mining Projects in R, this course will teach you how to build your own recommendation engine. You will also implement dimensionality reduction and use it to build a real-world project. Going ahead, you will be introduced to the concept of neural networks and learn how to apply them for predictions, classifications, and forecasting. Finally, you will implement ggplot2, plotly and aspects of geomapping to create your own data visualization projects.By the end of this course, you will be well-versed with all the advanced data mining techniques and how to implement them using R, in any real-world scenario.

Those who dream of getting an Amazon package, a prescription drug, or even a beer delivered to their doorsteps via drone might have their wishes fulfilled sooner than expected.

Tue 13 Feb 2018 11.00 EST Last modified on Tue 13 Feb 2018 11.01 EST Drones invoke varying perceptions, from fun gadget to fly in the park to deadly military weapons. In the future, they may even be viewed as a handy tool in the battle to fight climate change. Greenhouse gas emissions from the tra...

A key phase in the bridge design process is the selection of the structural system. Due to budget and time constraints, engineers typically rely on engineering judgment and prior experience when selecting a structural system, often considering a limited range of design alternatives. The objective of this study was to explore the suitability of supervised machine learning as a preliminary design aid that provides guidance to engineers with regards to the statistically optimal bridge type to choose, ultimately improving the likelihood of optimized design, design standardization, and reduced maintenance costs. In order to devise this supervised learning system, data for over 600,000 bridges from the National Bridge Inventory database were analyzed. Key attributes for determining the bridge structure type were identified through three feature selection techniques. Potentially useful attributes like seismic intensity and historic data on the cost of materials (steel and concrete) were then added from the US Geological Survey (USGS) database and Engineering News Record. Decision tree, Bayes network and Support Vector Machines were used for predicting the bridge design type. Due to state-to-state variations in material availability, material costs, and design codes, supervised learning models based on the complete data set did not yield favorable results. Supervised learning models were then trained and tested using 10-fold cross validation on data for each state. Inclusion of seismic data improved the model performance noticeably. The data was then resampled to reduce the bias of the models towards more common design types, and the supervised learning models thus constructed showed further improvements in performance. The average recall and precision for the state models was 88.6% and 88.0% using Decision Trees, 84.0% and 83.7% using Bayesian Networks, and 80.8% and 75.6% using SVM.

The ability to instantly gather and analyse environmental and equipment data, and carry out real time risk and area assessments is a big benefit to large scale operations, particularly those like mining, when operatives are working in a compact, changing and potentially dangerous environment. From increasing automation and removing human operatives from dangerous environments, to real time atmospheric monitoring and safety alerts, to more efficient operations through equipment monitoring, new connected technology is having a massive impact on the future of the mining sectors. But are IoT and AI technologies really the beneficial platforms they appear to be? And what exactly can they do for mining operations? The mining industry is a large and diverse eco-system.

One of the primary activities that companies pursue with analytics and data is to plan and optimize operations; this has been a long-term focus of the "operations research" approach to analytics. It has always been done on a relatively small scale, however, using individual models with only a few variables. Cognitive tools--and machine learning in particular--can take this activity to the next level in breadth and depth. AI may not be known for its role in manufacturing and operations, but there is an opportunity to use these tools to dramatically improve the efficiency and effectiveness of these important industries. Take, for example, the steel manufacturing startup Big River Steel, which is attempting a major transformation in this most industrial of industries.

Edmonton, Alberta, Feb. 07, 2018 (GLOBE NEWSWIRE) -- OneSoft Solutions Inc. (the --Company-- or --OneSoft--) (TSX-V:OSS, OTC:OSSIF)--is pleased to announce that its wholly owned subsidiary, OneBridge Solutions, Inc. (--OneBridge--), has entered into a Pilot Program agreement with another U.S.-based, Fortune 500 natural gas, oil and petrochemical company (the --Client--). The Client, whose operations include natural gas gathering, treating, processing, transportation and storage, primarily in the United States, will evaluate OneBridge--s Cognitive Integrity ManagementTM (--CIM--) SaaS solution.

The classical view of a robot as a mechanical body with a central "brain" that controls its behavior could soon be on its way out. The authors of a recent article in Science Robotics argue that future robots will have intelligence distributed throughout their bodies. The concept, and the emerging discipline behind it, are variously referred to as "material robotics" or "robotic materials" and are essentially a synthesis of ideas from robotics and materials science. Proponents say advances in both fields are making it possible to create composite materials capable of combining sensing, actuation, computation, and communication and operating independently of a central processing unit. Much of the inspiration for the field comes from nature, with practitioners pointing to the adaptive camouflage of the cuttlefish's skin, the ability of bird wings to morph in response to different maneuvers, or the banyan tree's ability to grow roots above ground to support new branches.