scholarly journals Automatic detection of plan symbols in railway equipment engineering using a machine learning approach

2019 ◽  
Author(s):  
Deian Stoitchkov ◽  
Peer Breier ◽  
Martin Slepicka ◽  
Cengiz Genc ◽  
Felix Harmsen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Automatic Detection ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Redewiedergabe – Schritte zur automatischen Erkennung

2019 ◽  
Vol 47 (1) ◽  
pp. 216-248
Author(s):  
Annelen Brunner
Keyword(s):  
Machine Learning ◽  
Automatic Detection ◽  
Learning Approach ◽  
Manual Annotation ◽  
Narrative Texts ◽  
Rule Based ◽  
Machine Learning Approach ◽  
Different Types ◽  
Short Narrative ◽  
Over Time

Abstract This contribution presents a quantitative approach to speech, thought and writing representation (ST&WR) and steps towards its automatic detection. Automatic detection is necessary for studying ST&WR in a large number of texts and thus identifying developments in form and usage over time and in different types of texts. The contribution summarizes results of a pilot study: First, it describes the manual annotation of a corpus of short narrative texts in relation to linguistic descriptions of ST&WR. Then, two different techniques of automatic detection – a rule-based and a machine learning approach – are described and compared. Evaluation of the results shows success with automatic detection, especially for direct and indirect ST&WR.

scholarly journals A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images

2020 ◽  
Vol 6 ◽  
pp. e268 ◽  
Author(s):  
Abder-Rahman Ali ◽  
Jingpeng Li ◽  
Guang Yang ◽  
Sally Jane O’Shea
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Skin Lesion ◽  
Early Diagnosis ◽  
Convolutional Neural Network ◽  
Automatic Detection ◽  
Clinical Feature ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Sensitivity Specificity

Skin lesion border irregularity is considered an important clinical feature for the early diagnosis of melanoma, representing the B feature in the ABCD rule. In this article we propose an automated approach for skin lesion border irregularity detection. The approach involves extracting the skin lesion from the image, detecting the skin lesion border, measuring the border irregularity, training a Convolutional Neural Network and Gaussian naive Bayes ensemble, to the automatic detection of border irregularity, which results in an objective decision on whether the skin lesion border is considered regular or irregular. The approach achieves outstanding results, obtaining an accuracy, sensitivity, specificity, and F-score of 93.6%, 100%, 92.5% and 96.1%, respectively.

scholarly journals Automatic detection of rapid eye movements (REMs): A machine learning approach

2016 ◽  
Vol 259 ◽  
pp. 72-82 ◽  
Author(s):  
Benjamin D. Yetton ◽  
Mohammad Niknazar ◽  
Katherine A. Duggan ◽  
Elizabeth A. McDevitt ◽  
Lauren N. Whitehurst ◽  
...  
Keyword(s):  
Machine Learning ◽  
Eye Movements ◽  
Automatic Detection ◽  
Learning Approach ◽  
Rapid Eye Movements ◽  
Machine Learning Approach
Sign in / Sign up

Export Citation Format

Share Document