Published in Microplastics, the study titled “Canonical Spectral Transformation for Raman Spectra Enables High Accuracy AI Identification of Marine Microplastics” introduces a novel data processing ...
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How do different data preprocessing techniques affect performance of machine learning models on prediction tasks?
Artificial intelligence (AI) and machine learning (ML) systems have become central to modern data-driven decision-making. They are now widely applied in fields as diverse as healthcare, finance, ...
Abstract: This work presents a very high-resolution land-cover dataset derived from historical grayscale aerial imagery over Norwegian hydropower catchments. Addressing the lack of publicly documented ...
Finally, another problem is class imbalance derived from basic and advanced transformations. To manage the resulting class imbalance, two further distinct balanced datasets are generated: i) one ...
Hi, thanks for your excellent work on TRELLIS. I’m currently preparing data for training and would like to confirm the expected preprocessing time. Could you share roughly: How long does the full ...
Hi, I'm trying to replicate the results of Table 3. The README states: Split the HumanEva part of the AMASS dataset into fragments using this script: Do you use the raw HumanEva dataset, and split it ...
ABSTRACT: This work contributes to the development of intelligent data-driven approaches to improve intrusion management in smart IoT environments. The proposed model combines a hybrid ...
Abstract: In recent years, the Vision-Language Models (VLMs) have significantly advanced the field of storytelling applications. Visual storytelling involves the use of Generative AI models to ...
A new technical paper titled “VerilogDB: The Largest, Highest-Quality Dataset with a Preprocessing Framework for LLM-based RTL Generation” was published by researchers at the University of Florida.
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