Automatic Knowledge Extraction: Fusion of Human Expert Ratings and Biosignal Features for Fatigue Monitoring Applications

2008 ◽  
pp. 299-316
Author(s):  
Martin Golz ◽  
David Sommer
Keyword(s):  
Knowledge Extraction ◽  
Human Expert ◽  
Monitoring Applications ◽  
Fatigue Monitoring ◽  
Expert Ratings

Machine Learning of Motor Vehicle Accident Categories from Narrative Data

1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock
Keyword(s):  
Machine Learning ◽  
Bayesian Model ◽  
Keyword Search ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Computer Search ◽  
Vehicle Accident ◽  
Learning Technique ◽  
Expert Ratings ◽  
Keyword Searches

Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.

Comparison of Rheumatological Diagnoses by a Bayesian Program and by Physicians

1991 ◽  
Vol 30 (03) ◽  
pp. 187-193 ◽  
Author(s):  
H. J. Moens ◽  
J. K. van der Korst
Keyword(s):  
Receiver Operating Characteristics ◽  
Bayesian Decision ◽  
Operating Characteristics ◽  
Likelihood Ratios ◽  
Improve Performance ◽  
Mathematical Basis ◽  
Human Expert ◽  
Test Population ◽  
Sensitivity Specificity ◽  
Diagnostic Outcome

AbstractA Bayesian decision support system was developed for the diagnosis of rheumatic disorders. Knowledge in this system is represented as evidential weights of findings. Simple weights were calculated as the logarithm of likelihood ratios on the basis of 1,000 consecutive patients from a rheumatological clinic. The effect of various methods to improve performance of the system by modification of the weights was studied. Three methods had a mathematical basis; a fourth consisted of weights adapted by a human expert, which allowed inclusion of diagnostic rules such as defined in widely accepted criteria sets. The system’s performance was measured in a test population of 570 different cases from the same clinic and compared with predictions of diagnostic outcome made by rheumatologists. The weights from a human expert gave optimal results (sensitivity 65% and specificity 96%), that were close to the physicians’ predictions (sensitivity 64% and specificity 98%). The methods to measure the performance of the various models used in this study emphasize sensitivity, specificity and the use of receiver operating characteristics.

A Multi-Language System For Knowledge Extraction In E-Learning Videos

2006 ◽  
Vol 3 (1) ◽  
pp. 48-55
Author(s):  
Aparesh Sood ◽  
◽  
Ankush Mittal ◽  
Divya Sarthi ◽  
◽  
...  
Keyword(s):  
Knowledge Extraction ◽  
Language System ◽  
E Learning

scholarly journals Context-Aware Holographic Communication Based on Semantic Knowledge Extraction

2021 ◽  
Author(s):  
Agata Manolova ◽  
Krasimir Tonchev ◽  
Vladimir Poulkov ◽  
Sudhir Dixir ◽  
Peter Lindgren
Keyword(s):  
Deep Neural Networks ◽  
Data Gathering ◽  
Communication Technologies ◽  
Knowledge Extraction ◽  
Semantic Knowledge ◽  
Business Model Innovation ◽  
Context Aware ◽  
Human Body Model ◽  
Network Resource ◽  
Body Model

AbstractAugmented, mixed and virtual reality are changing the way people interact and communicate. Five dimensional communications and services, integrating information from all human senses are expected to emerge, together with holographic communications (HC), providing a truly immersive experience. HC presents a lot of challenges in terms of data gathering and transmission, demanding Artificial Intelligence empowered communication technologies such as 5G. The goal of the paper is to present a model of a context-aware holographic architecture for real time communication based on semantic knowledge extraction. This architecture will require analyzing, combining and developing methods and algorithms for: 3D human body model acquisition; semantic knowledge extraction with deep neural networks to predict human behaviour; analysis of biometric modalities; context-aware optimization of network resource allocation for the purpose of creating a multi-party, from-capturing-to-rendering HC framework. We illustrate its practical deployment in a scenario that can open new opportunities in user experience and business model innovation.

Real-Time Monitoring Applications for the Power Grid under Geomagnetic Disturbances

2020 ◽  
Author(s):  
Cecilia Klauber ◽  
Komal S. Shetye ◽  
Zeyu Mao ◽  
Thomas J. Overbye ◽  
Jennifer Gannon ◽  
...  
Keyword(s):  
Real Time ◽  
Power Grid ◽  
Real Time Monitoring ◽  
Geomagnetic Disturbances ◽  
Monitoring Applications

scholarly journals Knowledge extraction for assisted curation of summaries of bacterial transcription factor properties

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Carlos-Francisco Méndez-Cruz ◽  
Antonio Blanchet ◽  
Alan Godínez ◽  
Ignacio Arroyo-Fernández ◽  
Socorro Gama-Castro ◽  
...  
Keyword(s):  
Transcriptional Regulation ◽  
Knowledge Extraction ◽  
Biomedical Literature ◽  
Main Role ◽  
E Coli ◽  
Bacterial Transcription ◽  
Manual Curation ◽  
New Knowledge ◽  
Serovar Typhimurium ◽  
K 12

Abstract Transcription factors (TFs) play a main role in transcriptional regulation of bacteria, as they regulate transcription of the genetic information encoded in DNA. Thus, the curation of the properties of these regulatory proteins is essential for a better understanding of transcriptional regulation. However, traditional manual curation of article collections to compile descriptions of TF properties takes significant time and effort due to the overwhelming amount of biomedical literature, which increases every day. The development of automatic approaches for knowledge extraction to assist curation is therefore critical. Here, we show an effective approach for knowledge extraction to assist curation of summaries describing bacterial TF properties based on an automatic text summarization strategy. We were able to recover automatically a median 77% of the knowledge contained in manual summaries describing properties of 177 TFs of Escherichia coli K-12 by processing 5961 scientific articles. For 71% of the TFs, our approach extracted new knowledge that can be used to expand manual descriptions. Furthermore, as we trained our predictive model with manual summaries of E. coli, we also generated summaries for 185 TFs of Salmonella enterica serovar Typhimurium from 3498 articles. According to the manual curation of 10 of these Salmonella typhimurium summaries, 96% of their sentences contained relevant knowledge. Our results demonstrate the feasibility to assist manual curation to expand manual summaries with new knowledge automatically extracted and to create new summaries of bacteria for which these curation efforts do not exist. Database URL: The automatic summaries of the TFs of E. coli and Salmonella and the automatic summarizer are available in GitHub (https://github.com/laigen-unam/tf-properties-summarizer.git).

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