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Who needs rewrites? This metadata-powered architecture fuses AI and ETL so smoothly, it turns pipelines into self-evolving ...
Interpreting a deep Convolutional Neural Network (CNN) involves identifying the features in a hierarchy of layers that contribute to recognition. Although the current approaches serve as methods to ...
With the widespread application of deep learning and Convolutional Neural Networks (CNN) in image classification, how to effectively improve model performance and reduce its complexity has become a ...
The method combines refined CAN traffic features with a lightweight Deep Learning (DL) network. The time interval series, ID and the CAN message payload are extracted by a T-shaped window, vectorized ...
This paper studies the computational offloading of CNN inference in dynamic multi-access edge computing (MEC) networks. To address the uncertainties in communication time and edge servers’ available ...
Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning ...
Abstract: A staple crop, rice feeds a substantial chunk of the world's population. However, several diseases adversely affect productivity and quality; therefore, timely diagnosis and management are ...
This paper discusses the use of the VGG-16 model and CNN architecture for classifying brain tumors in MRI images. The focus is enhancing the existing accuracy attained in the diagnosis of tumors ...
In recent advancements in cancer diagnosis from MRI imaging, pre-trained deep learning models have shown promise for improving accuracy, efficiency, and scalability. By employing transfer learning, ...
Mental health diseases like depression, anxiety, and schizophrenia affect worldwide healthcare systems. Traditional diagnostic methods are subjective, thus data-driven methods are needed. EEG, a ...
With large-scale adoption of Electric Vehicles (EVs), forecasting of charging loads is becoming crucial for utilities and network operators to understand the impact, plan for infrastructure upgrades, ...
A new technical paper titled “Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems” was published by researchers at National ...
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