Digital Commons Network^™

Critiquing The New Autonomy Of Immaterial Labour: An Analysis Of Work In The Artificial Intelligence Industry, James Steinhoff

Electronic Thesis and Dissertation Repository

Karl Marx theorized capitalism as a relation between labour, capital and machines. For Marx, capital, the process of self-augmenting value appropriated from human labour, is inherently driven by competition to replace labour in production with machines. Marx goes as far as to describe machines as capital’s “most powerful weapon” for suppressing working class revolt. Marx, however, could not have predicted the computing machines – such as artificial intelligence – which now form the basis for an increasingly cybernetic capital. Since Marx’s time, many Marxist thinkers have sought to apply or update his approach to the cybernetic era. The influential post-operaismo …

Parkinsonian Speech And Voice Quality: Assessment And Improvement, Amr Gaballah

Electronic Thesis and Dissertation Repository

Parkinson’s disease (PD) is the second most common neurodegenerative disease. Statistics show that nearly 90% of people impaired with PD develop voice and speech disorders. Speech production impairments in PD subjects typically result in hypophonia and consequently, poor speech signal-to-noise ratio (SNR) in noisy environments and inferior speech intelligibility and quality. Assessment, monitoring, and improvement of the perceived quality and intelligibility of Parkinsonian voice and speech are, therefore, paramount. In the first study of this thesis, the perceived quality of sustained vowels produced by PD patients was assessed through objective predictors. Subjective quality ratings of sustained vowels were collected from …

Data Analytics And Performance Enhancement In Edge-Cloud Collaborative Internet Of Things Systems, Tianqi Yu

Electronic Thesis and Dissertation Repository

Based on the evolving communications, computing and embedded systems technologies, Internet of Things (IoT) systems can interconnect not only physical users and devices but also virtual services and objects, which have already been applied to many different application scenarios, such as smart home, smart healthcare, and intelligent transportation. With the rapid development, the number of involving devices increases tremendously. The huge number of devices and correspondingly generated data bring critical challenges to the IoT systems. To enhance the overall performance, this thesis aims to address the related technical issues on IoT data processing and physical topology discovery of the subnets …

Classification With Measurement Error In Covariates Or Response, With Application To Prostate Cancer Imaging Study, Kexin Luo

Electronic Thesis and Dissertation Repository

The research is motivated by the prostate cancer imaging study conducted at the University of Western Ontario to classify cancer status using multiple in-vivo images. The prostate cancer histological image and the in-vivo images are subject to misalignment in the co-registration procedure, which can be viewed as measurement error in covariates or response. We investigate methods to correct this problem.

The first proposed method corrects the predicted class probability when the data has misclassified labels. The correction equation is derived from the relationship between the true response and the error-prone response. The probability for the observed class label is adjusted …

Towards Efficient Intrusion Detection Using Hybrid Data Mining Techniques, Fadi Salo

Electronic Thesis and Dissertation Repository

The enormous development in the connectivity among different type of networks poses significant concerns in terms of privacy and security. As such, the exponential expansion in the deployment of cloud technology has produced a massive amount of data from a variety of applications, resources and platforms. In turn, the rapid rate and volume of data creation in high-dimension has begun to pose significant challenges for data management and security. Handling redundant and irrelevant features in high-dimensional space has caused a long-term challenge for network anomaly detection. Eliminating such features with spectral information not only speeds up the classification process, but …

Machine Learning For Stock Prediction Based On Fundamental Analysis, Yuxuan Huang

Electronic Thesis and Dissertation Repository

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks’ historical data. Most of these existing approaches have focused on short term prediction using stocks’ historical price and technical indicators. In this thesis, we prepared 22 years’ worth of stock quarterly financial data and investigated three machine learning algorithms: Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS) for …

Applicability Of Recurrent Neural Networks To Player Data Analysis In Freemium Video Games, Jonathan Tan

Electronic Thesis and Dissertation Repository

We demonstrate the applicability and practicality of recurrent neural networks (RNNs), a machine learning methodology suited for sequential data, on player data from the mobile video game My Singing Monsters. Since this data comes in as a stream of events, RNNs are a natural solution for analyzing this data with minimal preprocessing. We apply RNNs to monitor and forecast game metrics, predict player conversion, estimate lifetime player value, and cluster player behaviours. In each case, we discuss why the results are interesting, how the trained models can be applied in a business setting, and how the preliminary work can …