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Feature learning boosts network performance
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
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
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
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
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 ...