Network attacks are becoming more complex every day. It is crucial that we use tools that can detect these sophisticated attacks on networks so that we can identify malicious behavior and prevent attacks and intrusions. The use of machine learning to create intrusion detection engines is great, and we need enough data to train these engines. The purpose of this project is to analyze the problems of existing public datasets and the challenges involved in finding the right machine learning techniques and settings for them.
The beer industry is one of the oldest and high demand industries in the world. The major limitation associated with beer can recycling with its content is that it produces large volume of liquid waste that cannot be discharged to water bodies. The expired beer is unsuitable for to be converted as animal feed due to the health safety aspects. There is a need for recycling this liquid waste as it contains valuable nutrients and energy. There is potential for using the waste beer as substrate for biogas production thereby recovering energy as biogas and nutrients as organic fertilizer.
This research focuses on solving the reporting problem in online proctoring platforms by introducing “behavioral reports” from data collected during an online test. We propose a Machine Learning and artificial intelligence-powered “clarity” report module. A state-of-the-art reporting module that generates behavioral patterns that will help employers make unbiased and informed decisions. Reveal to educators their students’ strengths and weaknesses as well. The behavioral report will be paired with student performance data to help the employer/educators make smarter decisions.
For marine autonomous surface ships (MASS), a more acceptable operation is ‘constrained autonomous operation’ where the ship operates fully autonomously. Most of the existing systems have strictly defined operational constraints or limited available decision spaces; therefore, autonomous decisions are only allowed for some predefined scenarios. However, marine environment is dynamic; the environmental disturbances (wind, wave, current, ice), surrounding obstacles (ship, ice) can change quickly.
The challenges and problems concerning the energy crisis, environmental protection and also global warming force human societies to use alternative green and renewable energy resources instead of conventional resources. In this regard, hydrogen gas as an important energy carrier has been noticed seriously. This project aims to concentrate on the CFD simulation of a solar photocatalytic reactor for hydrogen production by water splitting. The results of the model can be used to optimal design of photoreactors for efficient hydrogen production.
One of the major problems listed by many energy companies is flow assurance which involves significant efforts and costs to prevent hydrate blockages in the oil and gas facilities. Gas hydrate forms so rapidly and without warning in the offshore pipelines relative to waxes, scales, or asphaltenes. Although the hydrate plug formation rate is fast, remediation may take days or months. This research may help to understand further the hydrate formation behavior in the pipes. Conducting tests on the experimental setup is the best way to see hydrate significantly where and when it will be formed.
This research project aims to employ advanced machine learning and data science models for Developing Proprietary Natural Resource Business Models for Identification of Feedstocks Susceptible to a By-Product First Solution.
The increasing concentration of CO2 has caused various adverse environmental effects on the Earth’s oceans, land, and atmosphere, leading to a worldwide consensus on the necessity of action and commitment to emission reduction. Carbon Capture and Storage (CCS), which involves capturing CO2 resulting from industrial processes for storage in underground geological formations, has proven to be an effective method for CO2 emission mitigation. CO2 absorption using chemical solvents such as amines is a prevailing but technically challenging and rather low-efficiency method of carbon capture.
This project will look at if and how transcranial magnetic stimulation can be used to influence or delay either observed or perceived muscle fatigue in either continuous or non continuous exhaustive physical activity. The project also will conclude whether or not it would make financial senes, based on observed performance gains, to include transcranial magnetic stimulation devices to pair with with any future brain-machine-interfaces that are developed.
The objective of this collaborative research project, between American Bureau of Shipping and Memorial University, is to improve the safety of ships in Polar waters. ABS is continuously updating information to its clients on practices for the safe operation of ships. The goal of this research is to provide technology and operational guidelines so that ships can operate safely in regions where ice is present. This requires protecting the ship, its passengers and crew and the marine environment.
