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Constructing a metadata knowledge graph as an atlas for demystifying AI pipeline optimization.
Front Big Data
January 2025
AI Institute, University of South Carolina, Columbia, SC, United States.
The emergence of advanced artificial intelligence (AI) models has driven the development of frameworks and approaches that focus on automating model training and hyperparameter tuning of end-to-end AI pipelines. However, other crucial stages of these pipelines such as dataset selection, feature engineering, and model optimization for deployment have received less attention. Improving efficiency of end-to-end AI pipelines requires metadata of past executions of AI pipelines and all their stages.
View Article and Find Full Text PDFMatched pairs demonstrate robustness against inter-assay variability.
J Cheminform
January 2025
Drug Discovery Data Sciences, Janssen Pharmaceutica NV, Turnhoutseweg 30, 2340, Beerse, Belgium.
Machine learning models for chemistry require large datasets, often compiled by combining data from multiple assays. However, combining data without careful curation can introduce significant noise. While absolute values from different assays are rarely comparable, trends or differences between compounds are often assumed to be consistent.
View Article and Find Full Text PDFMapping Pituitary Neuroendocrine Tumors: An Annotated MRI Dataset Profiling Tumor and Carotid Characteristics.
Sci Data
January 2025
Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK.
Pituitary neuroendocrine tumors remain one of the most common intracranial tumors. While radiomic research related to pituitary tumors is progressing, public data sets for external validation remain scarce. We introduce an open dataset comprising high-resolution T1 contrast-enhanced MR scans of 136 patients with pituitary tumors, annotated for tumor segmentation and accompanied by clinical, radiological and pathological metadata.
View Article and Find Full Text PDFBridging the Gap Between Computational Efficiency and Segmentation Fidelity in Object-Based Image Analysis.
Animals (Basel)
December 2024
Graduate Program in Production Engineering, Universidade Paulista, Rua Dr. Bacelar 1212, São Paulo 04026-002, SP, Brazil.
A critical issue in image analysis for analyzing animal behavior is accurate object detection and tracking in dynamic and complex environments. This study introduces a novel preprocessing algorithm to bridge the gap between computational efficiency and segmentation fidelity in object-based image analysis for machine learning applications. The algorithm integrates convolutional operations, quantization strategies, and polynomial transformations to optimize image segmentation in complex visual environments, addressing the limitations of traditional pixel-level and unsupervised methods.
View Article and Find Full Text PDFA graph neural architecture search approach for identifying bots in social media.
Front Artif Intell
December 2024
Decision Support Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
Social media platforms, including X, Facebook, and Instagram, host millions of daily users, giving rise to bots automated programs disseminating misinformation and ideologies with tangible real-world consequences. While bot detection in platform X has been the area of many deep learning models with adequate results, most approaches neglect the graph structure of social media relationships and often rely on hand-engineered architectures. Our work introduces the implementation of a Neural Architecture Search (NAS) technique, namely Deep and Flexible Graph Neural Architecture Search (DFG-NAS), tailored to Relational Graph Convolutional Neural Networks (RGCNs) in the task of bot detection in platform X.
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