Browsing by Subject "deep learning"
Now showing items 1-20 of 33
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(2023-04-13)The promise of deep learning is to discover rich hierarchical structure of the data that allows to circumvent manual feature extraction and engineering. In this study, we explore novel applications of the deep learning, ...
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As the volume of online information increases, recommender systems have been an effective strategy to overcome information overload by giving selective recommendations based on certain criteria such as user ratings and ...
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New theories and applications of deep learning have been discovered and implemented within the field of machine learning recently. The high degree of effectiveness of deep learning models span across many domains including ...
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(2021-12-01)Intrusion detection systems (IDS) play a critical role in cybersecurity and are used to identify malicious behaviour in network traffic. The weakness of modern approaches is that they are reactive responses reliant on ...
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(2023-04-21)Non-invasive wearable devices introduce more convenient and fashionable alternatives for around-the-clock remote health monitoring at the beat-to-beat resolution throughout the varying contexts of patients’ daily activities ...
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(2021-01-15)User reviews are ubiquitous. They power online review aggregators that influence our daily-based decisions, from what products to purchase (e.g., Amazon), movies to view (e.g., Netflix, HBO, Hulu), restaurants to patronize ...
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(2023-06-28)Chemical processes are complex and often characterized by nonlinearity, time-variation, and uncertainty. As a result, data-driven modeling approaches have gained widespread popularity in industry and academia for modeling ...
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(2020-01-16)With the advancement in deep learning research, neural networks have become one of the most powerful tools for artificial intelligence tasks. More specifically, recurrent neural networks (RNNs) have achieved state-of-the-art ...
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(2021-08-18)Modeling unconventional reservoirs has been an active area of research in response to significant reserves in the U.S. Various analytical and numerical models incorporating relevant physics at varying fidelity levels have ...
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(2024-01-02)The rise in popularity of AI-generated images has brought up concerns regarding the ethics of training AI using artists’ work without compensation. Because this is a relatively new phenomenon in the public eye, we have ...
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(2020-11-23)Image inpainting is the task of filling missing regions in a masked image. Modern approaches for inpainting are unable to effectively utilize contextual information present within the image which results in color, texture ...
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Graph Neural Networks are behind many pharmacological breakthroughs due to their innate ability to learn structural properties of molecules and accelerate high-throughput screening for favorable characteristics that could ...
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Neural Networks play an important role in real-time object detection. Several types of networks are being developed in order to perform such detections at a faster pace. One such neural network that can prove useful is the ...
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(2022-04-11)Software security is a crucial factor in software development and maintenance. Static analysis approaches can help secure software in different ways. First, it can help identify vulnerabilities ahead-of run. For example, ...
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(2020-11-11)This thesis is concerned with investigating super-resolution algorithms and solutions for handling electron microscopic images. Please note two main aspects differentiating the problem discussed here from those considered ...
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(2022-08-18)Artificial Intelligent and Machine Learning (AI/ML) systems have been widely adopted with the increasing availability of data in a variety of applications such as computer vision, activity recognition, autonomous driving, ...
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(2019-03-25)Inertial confinement fusion (ICF) experiments at the National Ignition Facility (NIF) and their corresponding computer simulations produce an immense amount of rich data. However, quantitatively interpreting that data remains ...
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(2022-08-17)With the rapid expansion of big data in all science and engineering domains, the potential that lays behind these massive data is undoubtedly significant. Leveraging such information in an optimal way requires innovative ...
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(2021-11-24)Numerical simulation of problems involving media with multiple spatial scales has important applications in many engineering and scientific areas, including material science, chemistry and unconventional reservoir simulation. ...
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(2020-03-12)Numerical modelling of flow problems in fractured porous media has important applications in many engineering areas, such as unconventional reservoir simulation and nuclear waste disposal. Simulation of the flow problems ...