Newfoundland & Labrador__Memorial University of Newfoundland

A $5,000 contribution from the academic supervisor or university is required in lieu of provincial funding.
Fellowships will be awarded competitively.

Investigation and development of strategies for performing constrained inversions of geophysical data for mineral exploration

Geophysical inversion is the process of determining a 3D computer model of the Earth’s subsurface from measurements made in a geophysical survey. Geophysical data are sensitive to the presence, location and size of certain rock types including ore bodies. However, many different rock types can give similar measurements, and the measurements are usually sensitive to only large-scale variations in the subsurface. To mitigate these shortcomings, it is possible to incorporate additional information into the inversion process.

Maximizing Oil Recovery from the Hibernia Oil Field

The project focuses on screening and testing state-of-art Enhanced Oil Recovery techniques. It is carried out through both experiment study in core scale and simulation research in field scale. The new knowledge that will be accrued will be the culmination of a truly collaborative approach. First and foremost, Canada and Newfoundland and Labrador will gain a holistic perspective recovering ultimate oil from its offshore east coast reserves.

Comparative assessment of Machine Learning methods for fraud detection and improving the interpretability of the best model

Machine learning algorithms are being used in a wide range of applications. It is a branch of computer science where the system can learn from the data and make decisions. Financial fraud is an increasing hazard in the financial industry, and it is important to detect a fraudulent transaction. Machine learning algorithms can be used to decide whether the transaction is fraud or not. After the system makes its prediction, it is important for users to understand the reason behind the prediction in such cases.

Detecting Credit Transaction Fraudulent Behavior Using Recurrent Neural Networks

Fraudulent activities are hard to detect, but they cost financial institutions millions of dollars in monetary losses and legal costs every year. Millions of dollars are being lost in credit transactions as criminals are finding new, more sophisticated ways to conduct financial crime. This research project examines novel ways of detecting fraudulent behavior using powerful tools such as Recurrent Neural Networks, a type of machine learning model that is well suited for sequence or historical data.

Design of autonomous robotic system for removal of Porcupine Crab spine

Porcupine Crab (Neolithodes grimaldii) inhabits the seabed off the Coast of Newfoundland and Labrador and in the eastern Arctic as a by-catch in the turbot gillnet fishery. This research project focuses on developing automatic robotic technology for the removal of Porcupine Crab spines to ease the crab processing for potential future development of a Porcupine Crab fishery.

Application of Different Machine Learning and Data Mining Algorithms in the Detection of Financial Fraud

Detection of financial fraud is a priority for financial institutions. There are a variety of techniques and models that can be used to address the problem of financial fraud. However, as fraudsters are becoming more inventive and adaptive, they have been able to penetrate the conventional protective methods. This is one of the main reasons for the growth in financial fraud activity, regardless of the efforts of financial institutions and government and law enforcement agencies.

Deep Fraud Detection

Financial fraud is a serious issue that is taking place globally and causing considerable damage at great expense. Statistical analysis and machine learning tools can help financial institutions detect different types of fraud. In some cases however, mislabeling and the cost of classification may actually increase the volume of ‘false positives’ for supervised methods. As the number of normal transactions in financial domains far outweigh the number of anomalous transactions, it is challenging to classify the anomaly labels.

New Approaches to Mine Closure in Nunavik, Québec

Mine closure is the final stage of a mine’s lifecycle and can have complex social, economic, and cultural impacts on nearby communities. These impacts include population decline, reduced services, household stress, ecological change, and reduced access to land-based activities. These impacts become more likely when communities are not engaged with during the closure planning process. Glencore Raglan is attempting to mitigate these issues through the Raglan Mine Closure Sub-committee, which is made up of both company employees and community representatives from Salluit and Kangiqsujuaq.

Industrial application of genomics derived biomarkers of salmon performance

The Atlantic salmon aquaculture industry is becoming a strategic economic sector for Canada. Aquaculture in Canada employs approximately 25,000 people with a total economic impact of over $5B. While farmed salmon is already Canada’s top aquaculture export, salmon aquaculture has significant capacity for growth in this country. Atlantic salmon farmed in sea cages on the Canadian coasts face multiple environmental stressors that can impair their growth performance and immune status.

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