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Full-Text Articles in Engineering
Ai-Synthesized Speech : Generation And Detection, Ehab Alsayed Albadawy Abdrabuh
Ai-Synthesized Speech : Generation And Detection, Ehab Alsayed Albadawy Abdrabuh
Legacy Theses & Dissertations (2009 - 2024)
From speech to images, and videos, advances in machine learning have led to dramatic improvements in the quality and realism of so-called AI-synthesized content. While there are many exciting and interesting applications, this type of content can also be used to create convincing and dangerous fakes. We seek to develop forensic techniques that can distinguish a real human voice from a synthesized voice. We observe that deep neural networks used to synthesize speech introduce specific and unusual artifacts not typically found in human speech. Although not necessarily audible, we develop various detection algorithms to measure these artifacts and be able …
Dynamic Instance-Wise Decision-Making For Machine Learning, Yasitha Warahena Liyanage
Dynamic Instance-Wise Decision-Making For Machine Learning, Yasitha Warahena Liyanage
Legacy Theses & Dissertations (2009 - 2024)
In a typical supervised machine learning setting, the predictions on all test instances are based on a common subset of features discovered during model training. However, using a different subset of features that are most informative for each test instance individually may improve not only the quality of prediction but also the overall interpretability of the model. To this end, in this dissertation, we study the problem of optimizing the trade-off between instance-level sparsity and the quality of prediction using a dynamic instance-wise decision-making approach. Specifically, this approach sequentially reviews features one at a time for each data instance given …
Efficient Detection Of Diseases By Feature Engineering Approach From Chest Radiograph, Avishek Mukherjee
Efficient Detection Of Diseases By Feature Engineering Approach From Chest Radiograph, Avishek Mukherjee
Legacy Theses & Dissertations (2009 - 2024)
Deep Learning is the new state-of-the-art technology in Image Processing. We applied Deep Learning techniques for identification of diseases from Radiographs made publicly available by NIH. We applied some Feature Engineering approach to augment the data from Anterior-Posterior position to Posterior-Anterior position and vice-versa for all the diseases, at the same point we suppressed ‘No Finding’ radiographs which contributed to more than 50% (approximately 60,000) of the dataset to top 1000 images. We also prepared a model by adding a huge amount of noise to the augmented data, which if need be can be deployed at rural locations which lack …
Communications Using Deep Learning Techniques, Priti Gopal Pachpande
Communications Using Deep Learning Techniques, Priti Gopal Pachpande
Legacy Theses & Dissertations (2009 - 2024)
Deep learning (DL) techniques have the potential of making communication systems