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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Leveraging Uncertainties in Softmax Decision-Making Models for Low-Power IoT Devices.

Chiwoo Cho1, Wooyeol Choi2, Taewoon Kim3

  • 1Hallym Institute for Data Science and Artificial Intelligence, Hallym University, Chuncheon 24252, Korea.

Sensors (Basel, Switzerland)
|August 23, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a lightweight framework to improve deep learning (DL) image classification for Internet of Things (IoT) devices. The method enhances decision-making accuracy without retraining models, significantly reducing errors in IoT applications.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Internet of Things (IoT)

Background:

Keywords:
Internet of Thingsclassificationdecision-makingdeep learningsensor datasoftmax

Related Experiment Videos

Last Updated: Dec 11, 2025

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  • Internet of Things (IoT) devices generate extensive sensor data, including environmental images.
  • Deep learning (DL) excels at image classification, commonly using softmax with cross-entropy loss.
  • Existing methods to enhance softmax performance are computationally intensive, unsuitable for low-power IoT devices.