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RIS citation export for TUMBCMO15: Enhancing Electronic Logbooks Using Machine Learning

TY  - CONF
AU  - Maldonado, J.
AU  - Clark, S.L.
AU  - Fu, W.
AU  - Nemesure, S.
ED  - Schaa, Volker RW
ED  - Götz, Andy
ED  - Venter, Johan
ED  - White, Karen
ED  - Robichon, Marie
ED  - Rowland, Vivienne
TI  - Enhancing Electronic Logbooks Using Machine Learning
J2  - Proc. of ICALEPCS2023, Cape Town, South Africa, 09-13 October 2023
CY  - Cape Town, South Africa
T2  - International Conference on Accelerator and Large Experimental Physics Control Systems
T3  - 19
LA  - english
AB  - The electronic logbook (elog) system used at Brookhaven National Laboratory’s Collider-Accelerator Department (C-AD) allows users to customize logbook settings, including specification of favorite logbooks. Using machine learning techniques, customizations can be further personalized to provide users with a view of entries that match their specific interests. We will utilize natural language processing (NLP), optical character recognition (OCR), and topic models to augment the elog system. NLP techniques will be used to process and classify text entries. To analyze entries including images with text, such as screenshots of controls system applications, we will apply OCR. Topic models will generate entry recommendations that will be compared to previously tested language processing models. We will develop a command line interface tool to ease automation of NLP tasks in the controls system and create a web interface to test entry recommendations. This technique will create recommendations for each user, providing custom sets of entries and possibly eliminate the need for manual searching. 
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 382
EP  - 385
KW  - controls
KW  - interface
KW  - electron
KW  - database
KW  - power-supply
DA  - 2024/02
PY  - 2024
SN  - 2226-0358
SN  - 978-3-95450-238-7
DO  - doi:10.18429/JACoW-ICALEPCS2023-TUMBCMO15
UR  - https://jacow.org/icalepcs2023/papers/tumbcmo15.pdf
ER  -