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Engineers at the Tokyo Institute of Technology (Tokyo Tech) have demonstrated a simple computational approach for improving the way artificial intelligence classifiers, such as neural networks, can be trained based on limited amounts of sensor data. The emerging applications of the Internet of Things often require edge devices that can reliably classify behaviors and situations …

Read more “Making the most of quite little: Improving AI training for edge sensor time series”

In recent years, deep learning techniques have achieved remarkable results in numerous language and image-processing tasks. This includes visual speech recognition (VSR), which entails identifying the content of speech solely by analyzing a speaker’s lip movements.

Combining machine learning with multimodal electrochemical sensing can significantly improve the analytical performance of biosensors, according to new findings from a Penn State research team. These improvements may benefit noninvasive health monitoring, such as testing that involves saliva or sweat. The findings were published this month in Analytica Chimica Acta.

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Research finds using a large collection of simple, un-curated synthetic image generation programs to pretrain a computer vision model for image classification yields greater accuracy than employing other pretraining methods that are more costly and time consuming, and less scalable.