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Now showing items 31-40 of 52
Enhancing machine vision using human cognition from EEG analysis
(2022)
Visual classification is the perceptible/computational effort of arranging objects and visual
contexts into distinct labels. Humans and machines have mastered this advanced problem in
their own varied contexts. However, ...
Medical text simplification: bridging the gap between medical research and public understanding
(2023)
Text Simplification is a subdomain of Natural Language Processing that focuses on applying
computational techniques to modify the content and structure of the text to make it interpretable while retaining the main idea. ...
Preliminary identification and therapeutic support of depression in mental health using conversational AI
(2023)
World Health Organization statistics indicate that one out of every eight people suffers from
mental illness. Due to the fear of stigma and social discrimination, they start being resilient and
end up going through ...
Programming pedagogy in the age of accessible artificial intelligence
(2023)
In recent years, new teaching opportunities have emerged as artificial intelligence has gained
increasing attention in computational thinking education. However, to design effective pedagogy based on the present research ...
Light-weight federated learning with augmented knowledge distillation for human activity recognition
(2023)
The field of deep learning has experienced significant growth in recent years in various
domains where data can be collected and processed. However, as data plays a central role in
the deep learning revolution, there are ...
New paradigms of distributed AI for improving 5G-based network systems performance
(2023)
With the advent of 5G technology, there is an increasing need for efficient and effective
machine learning techniques to support a wide range of applications, from smart cities to
autonomous vehicles. The research question ...
Moreau envelopes-based personalized asynchronous federated learning: improving practicality in distributed machine learning
(2023)
Federated learning is a promising approach for training models on distributed data, driven by increasing demand in various industries. However, it faces several challenges, including communication bottlenecks and client ...
Vapnik-Chervonenkis dimension in neural networks
(2023)
This thesis aims to explore the potential of statistical concepts, specifically the
Vapnik-Chervonenkis Dimension (VCD)[33], in optimizing neural networks. With the
increasing use of neural networks in replacing human ...
User-centric clustering and pilot assignment in cell-free networks : a stochastic optimization approach
(2023)
Current 5G networks, primarily built on the cellular massive MIMO physical
layer technology, achieved significant improvement in spectral efficiency as compared to previous generations. Nevertheless, there is always an ...
Adding time-series data to enhance performance of naural language processing tasks
(2023)
In the past few decades, with the explosion of information, a large number of
computer scientists have devoted themselves to analyzing collected data and applying
these findings to many disciplines. Natural language ...