Well logging data interpretation for appraising the performance of Alam El-Bueib reservoir in Safir Oil Field, Shushan Basin, Egypt
Sign Up to like & getrecommendations! Published in 2021 at "Journal of Petroleum Exploration and Production Technology"
DOI: 10.1007/s13202-021-01165-7
Abstract: This paper presents different well log data interpretation techniques for evaluating the reservoir quality for the sandstone reservoir of the Alam El-Bueib-3A Member in Safir-03 well, Shushan Basin, Egypt. The evaluation of the available well… read more here.
Keywords: reservoir; water; alam bueib; data interpretation ... See more keywords
The changing landscape of neuroimaging in frontotemporal lobar degeneration: from group-level observations to single-subject data interpretation
Sign Up to like & getrecommendations! Published in 2022 at "Expert Review of Neurotherapeutics"
DOI: 10.1080/14737175.2022.2048648
Abstract: ABSTRACT Introduction While the imaging signatures of frontotemporal lobar degeneration (FTLD) phenotypes and genotypes are well-characterized based on group-level descriptive analyses, the meaningful interpretation of single MRI scans remains challenging. Single-subject MRI classification frameworks rely… read more here.
Keywords: subject data; frontotemporal lobar; data interpretation; interpretation ... See more keywords
Errors in data interpretation from genetic variation of human analytes.
Sign Up to like & getrecommendations! Published in 2017 at "JCI insight"
DOI: 10.1172/jci.insight.94532
Abstract: In recent years, the extent of our vulnerability to misinterpretation due to poorly characterized reagents has become an issue of great concern. Antibody reagents have been identified as a major source of error, contributing to… read more here.
Keywords: variation human; variation; errors data; interpretation genetic ... See more keywords
A Collaborative Approach for the Development and Application of Machine Learning Solutions for CMR-Based Cardiac Disease Classification
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Cardiovascular Medicine"
DOI: 10.3389/fcvm.2022.829512
Abstract: The quality and acceptance of machine learning (ML) approaches in cardiovascular data interpretation depends strongly on model design and training and the interaction with the clinical experts. We hypothesize that a software infrastructure for the… read more here.
Keywords: application; machine; disease; classification ... See more keywords