scholarly journals Algorithm based on normal coordinate vectors with 16 segments for the data fusion from hand-written Arabic text implemented with MATLAB

2021 ◽  
Vol 7 ◽  
pp. e705
Author(s):  
Said S. Saloum ◽  
Iván García-Magariño
Keyword(s):  
Machine Learning ◽  
Data Fusion ◽  
Handwriting Recognition ◽  
Text Recognition ◽  
Technical Equipment ◽  
Arabic Text ◽  
Normal Coordinate ◽  
Written Text ◽  
Machine Learning Approach ◽  
Processing Techniques

Hand-written text recognition is useful for interpreting records in different fields such as healthcare, surgery and police in which professionals may avoid technical equipment and prefer writing notes on paper. In order to perform data fusion from different data sources, handwriting automatic recognition involves barriers such as different ways of writing letters and deformation due to many reasons. This work presents a novel handwriting recognition approach based on the application of coordinate vectors to find similarities in different kinds of deformations. In particular, it has been implemented using 16 segments in order to distinguish all the particularities in matching the new text considering a dataset with a machine-learning approach. The implementation of this approach with MATLAB shows promising results with accuracy of 92.8% for with ensemble and bagged trees, after analyzing 22 possible combinations of machine learning and processing techniques.

A Machine Learning Approach to Hypothesis Decoding in Scene Text Recognition

2015 ◽  
pp. 169-180
Author(s):  
Jindřich Libovický ◽  
Lukáš Neumann ◽  
Pavel Pecina ◽  
Jiří Matas
Keyword(s):  
Machine Learning ◽  
Text Recognition ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Scene Text ◽  
Scene Text Recognition

scholarly journals Tracking COVID-19 vaccine hesitancy and logistical challenges: A machine learning approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252332
Author(s):  
Shantanu Dutta ◽  
Ashok Kumar ◽  
Moumita Dutta ◽  
Caolan Walsh
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Vaccine Hesitancy ◽  
Vaccine Production ◽  
Machine Learning Approach ◽  
Production And Distribution ◽  
The Usa ◽  
Physical Effects ◽  
Processing Techniques ◽  
Health Administrators

In this study, we use an effective word embedding model (word2vec) to systematically track ’vaccine hesitancy’ and ’logistical challenges’ associated with the Covid-19 vaccines, in the USA. To that effect, we use news articles from reputed media sources and create dictionaries to estimate different aspects of vaccine hesitancy and logistical challenges. Using machine learning and natural language processing techniques, we have developed (i) three sub-dictionaries that indicate vaccine hesitancy, and (ii) another dictionary for logistical challenges associated with vaccine production and distribution. Vaccine hesitancy dictionaries capture three aspects: (a) general vaccine related concerns, mistrusts, skepticisms, and hesitancy, (b) discussions on symptoms and side-effects, and (c) discussions on vaccine related physical effects. The dictionary on logistical challenges includes the words and phrases related to the production, storage, and distribution of vaccines. Our results show that over time, as vaccine developers complete different phase trials and get approval for their respective vaccines, the number of vaccine related news articles increases sharply. Accordingly, we also see a sharp increase in vaccine hesitancy related topics in news articles. However, in January 2021, there has been a decrease in the vaccine hesitancy score, which will give some relief to the health administrators and regulators. Our findings further show that as we get closer to the breakthrough of effective Covid-19 vaccines, new logistical challenges continue to rise, even in recent months.

Machine Learning in Handwritten Arabic Text Recognition

2013 ◽  
pp. 443-469 ◽  
Author(s):  
Utkarsh Porwal ◽  
Zhixin Shi ◽  
Srirangaraj Setlur
Keyword(s):  
Machine Learning ◽  
Text Recognition ◽  
Arabic Text ◽  
Handwritten Arabic

A Machine Learning Approach to Weighting Schemes in the Data Fusion of Similarity Coefficients

2009 ◽  
Vol 49 (2) ◽  
pp. 185-194 ◽  
Author(s):  
Jenny Chen ◽  
John Holliday ◽  
John Bradshaw
Keyword(s):  
Machine Learning ◽  
Data Fusion ◽  
Learning Approach ◽  
Similarity Coefficients ◽  
Weighting Schemes ◽  
Machine Learning Approach

A Computer Vision based Framework for Detecting Potholes on Asphalt-Road using Machine Learning Approach

2020 ◽  
Vol 1 (4) ◽  
pp. 1-5
Author(s):  
Abhishek Das ◽  
Sourav Saha
Keyword(s):  
Machine Learning ◽  
Local Binary Patterns ◽  
Road Surface ◽  
The Road ◽  
Machine Learning Approach ◽  
Asphalt Road ◽  
On The Road ◽  
Histogram Of Gradients ◽  
Processing Techniques ◽  
Pothole Detection

This paper proposes a Pothole Detection Framework which may assist the pedestrian in avoiding potholes on the roads by giving prior warnings. The basic idea of this framework is to detect the pothole on asphalt road by analyzing the image of the road-surface. This proposed framework combines image processing techniques with machine learning methods and primarily explores edges, Histogram of Gradients and Local Binary Patterns of an image frame for extracting features to detect the presence of potholes on the road surface. The experimental results indicate promising potential of the proposed framework for detection of potholes on asphalt-road.

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