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Feature learning boosts network performance
(2020)
Features are an important part of machine learning. Features are often the
reduced-dimensional representation of input data, feature calculation, extraction, and
fusion directly affect the final result of the network. ...
Online sequential learning with non-iterative strategy for feature extraction, classification and data augmentation
(2020)
Network aims to optimize for minimizing the cost function and provide better
performance. This experimental optimization procedure is widely recognized as gradient descent, which is a form of iterative learning that starts ...
Developing machine learning coding similarity indicators for C & C++ corpora
(2020)
The digital data in this modern world is vulnerable to copying, altering and claiming
someone else’s work as their own. Performing the same activity in programming
assignments can be referred to as source-code theft or ...
Towards machine learning enabled future-generation wireless network optimization
(2020)
We anticipate that there will be an enormous amount of wireless devices connected
to the Internet through the future-generation wireless networks. Those wireless devices vary
from self-driving vehicles to smart wearable ...
Towards designing AI-aided lightweight solutions for key challenges in sensing, communication and computing layers of IoT: smart health use-cases
(2021)
The advent of the 5G and Beyond 5G (B5G) communication system, along with the
proliferation of the Internet of Things (IoT) and Artificial Intelligence (AI), have started to
evolve the vision of the smart world into a ...
Identification of cracks in pipelines based on machine learning and deep learning
(2022)
Pipelines are important long-distance transportation structures in modern industry, and because
many are buried deep underground, pipeline health monitoring is critical to industry; however,
inspecting underground pipelines ...
Boosting feature extraction performance on the aspect of representation learning efficiency
(2022)
Machine learning is famous for its automatic data handling. While there is a
slow growth in the performance of the state-of-the-art models in the most recent
well-known learning frameworks, the number of parameters and ...
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 ...