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Applied Artificial Intelligence

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Research Projects

AI System for IoT Devices Energy Management

This research presents an artificial intelligence based the energy management system for IoT devices which reduce the overall energy consumption by intelligently activating and deactivating the IoT devices while also managing the quality of service parameters. Both hardware and software aspects are considered for devising the efficient energy conservation models for IoT. Energy transparency has been achieved by modelling energy consumed during sensing, processing, memory access and communication. A multi-agent system has been introduced to model the IoT devices and their energy consumptions. Genetic algorithm is used to optimize the parameters of the multi-agent system. Finally, simulation tools such as MATLAB Simulink and OpenModelica are used to test the system. The results shows an 18.65% decrease in overall energy consumption by the implementation of decentralized intelligence of the multi-agent system for IoT.

IoT Energy Consumption Model

Report of sensor parameters and values

Nuclear Predictive Maintenance with Machine Learning

Nuclear infrastructure systems play an important role in national security. The functions and missions of nuclear infrastructure systems are vital to government, businesses, society and citizen's lives. It is crucial to design nuclear infrastructure for scalability, reliability and robustness. To do this, we can use machine learning, which is a state of the art technology used in various fields ranging from voice recognition, Internet of Things (IoT) device management and autonomous vehicles. In this research, we propose to design and develop a machine learning algorithm to perform predictive maintenance of nuclear infrastructure. Support vector machine and logistic regression algorithms will be used to perform the prediction.

Predicting Nuclear Machine Failure using Logistic Regression 

Predicting Nuclear Machine Failure using Logistic Regression

Comparison of rersults with other nuclear power plants preventative maintenence using machine learning

Security and Privacy Framework for Social Media

Social media technology provides a novel platform for faster, dynamic, manageable, cost-effective and adaptable professional network service provisioning. As such, social media is ideal for online outreach and getting traction with individuals, government organizations and business enterprises. However, as social media technology continues to provide an increasing number of functionalities to expand a social network, there is a growing need to design, develop and evaluate the security and privacy of personal and professional social media services. The proposed collaborative trustworthy security and privacy framework for social media provides a new avenue to develop improved security and privacy that can both verify and validate social media content before being made available online. It also monitors, assesses and reacts when online security and privacy is compromised.

collaborative trustworthy security and privacy framework for social media

Example of Security and Privacy Evaluation results