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Master of Science in Information Technology Project Reports (Concordia University of Edmonton)
Items in this Collection
- 6machine learning
- 1Artificial intelligence
- 1COVID-19
- 1Computer Vision
- 1Frechet inception distance
- 1GAN
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A unified view of epidemiological modelling and artificial intelligence-based approach to understanding the COVID-19 disease spread for supporting public policy decisions
Download2022
The Covid-19 epidemic has emerged as one of the most concerning global public health catastrophes of the twenty-first century, highlighting the critical need for robust forecasting approaches for disease identification, alleviation, and prevention, among other things. Forecasting is one of the...
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2023
Chat-bots could be considered as one of widely used technologies since it increases the business efficiency and throughput. Proposed project is to develop a web based chat-bot that is capable of booking appointments via the website Telecare Plus. Machine learning techniques are used to provide an...
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2022
Every living species has cells and based on those cells scientists observe and make some predictions or observations. The identification and classification of cells is a highly crucial part of medical research and involves human efforts to a huge extent. Due to human involvement, the research may...
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2022
Many people have been interested in music recognition. The automated transcription of musical compositions and the identification of sound sources, such as the sort of instruments used, have taken a lot of time and work. With the rise of personal computers and multimedia systems in recent years,...
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2021-09-01
Lately, technological advancement has made it possible with the accessibility of enormous annotated datasets and artificial intelligence breakthroughs to have sparked a spectacular rise of precise object recognition and analysis. This thesis specifies the development and implementation...
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2023
Synthetic image generation using Generative Adversarial Networks (GANs) has emerged as a promising technique to address the challenge of limited datasets in the field of garbage classification. With supervised machine learning algorithms relying on labeled data and larger training examples, the...