ASPHALT PAVEMENT CRACK DETECTION USING IMAGE PROCESSING AND NA&Iuml;VE BAYES BASED MACHINE LEARNING APPROACH

Abstract Human taste perception is associated with the papillae on the tongue as they contain a large proportion of chemoreceptors for basic tastes and other chemosensation. Especially the density of fungiform papillae (FP) is considered as an index for responsiveness to oral chemosensory stimuli. The standard procedure for FP counting involves visual identification and manual counting of specific parts of the tongue by trained operators. This is a tedious task and automated image analysis methods are desirable. In this paper a machine learning image processing method based on a convolutional neural network is presented. This automated method was compared with three standard manual FP counting procedures using tongue pictures from 132 subjects. Automated FP counts, within the selected areas and the whole tongue, significantly correlated with the manual counting methods (all ρs ≥ 0.76). When comparing the images for gender and PROP status, the density of FP predicted from automated analysis was in good agreement with data from the manual counting methods, especially in the case of gender. Moreover, the present results reinforce the idea that caution should be applied in considering the relationship between FP density and PROP responsiveness since this relationship can be an oversimplification of the complexity of phenomena arising at the central and peripherical levels. Indeed, no significant correlations were found between FP and PROP bitterness ratings using the automated method for selected areas or the whole tongue. Besides providing estimates of the number of FP, the machine learning approach used a tongue coordinate system that normalizes the size and shape of an individual tongue and generated a heat map of the FP position and normalized area they cover. The present study demonstrated that the machine learning approach could provide similar estimates of FP on the tongue as compared to manual counting methods and provide estimates of more difficult-to-measure parameters, such as the papillae's areas and shape.

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Content-based Spam Email Detection Using N-gram Machine Learning Approach

10.20944/preprints202109.0236.v1 ◽

2021 ◽

Author(s):

Syed Md. Minhaz Hossain ◽

Iqbal H. Sarker

Keyword(s):

Machine Learning ◽

Support Vector ◽

Learning Approach ◽

Ve Bayes ◽

Spam Filter ◽

Email Address ◽

Machine Learning Approach ◽

Spam Filters ◽

N Gram ◽

Machine Learning Models

Recently, spam emails have become a significant problem with the expanding usage of the Internet. It is to some extend obvious to filter emails. A spam filter is a system that detects undesired and malicious emails and blocks them from getting into the users' inboxes. Spam filters check emails for something "suspicious" in terms of text, email address, header, attachments, and language. However, we have used different features such as word2vec, word n-grams, character n-grams, and a combination of variable length n-grams for comparative analysis in our proposed approach. Different machine learning models such as support vector machine (SVM), decision tree (DT), logistic regression (LR), and multinomial naïve bayes (MNB) are applied to train the extracted features. We use different evaluation metrics such as precision, recall, f1-score, and accuracy to evaluate the experimental results. Among them, SVM provides 97.6 \% of accuracy, 98.8\% of precision, and 94.9\% of f1-score using a combination of n-gram features.

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Machine learning approach for shaft crack detection through acoustical emission signals

2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA) ◽

10.1109/etfa.2015.7301416 ◽

2015 ◽

Cited By ~ 4

Author(s):

J. Wu ◽

X. Li ◽

S. Xu ◽

M.J. Er ◽

L. Wei ◽

...

Keyword(s):

Machine Learning ◽

Crack Detection ◽

Learning Approach ◽

Acoustical Emission ◽

Machine Learning Approach

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Ontology learning algorithm using weak functions

Open Physics ◽

10.1515/phys-2018-0112 ◽

2018 ◽

Vol 16 (1) ◽

pp. 910-916 ◽

Cited By ~ 2

Author(s):

Linli Zhu ◽

Gang Hua ◽

Adnan Aslam

Keyword(s):

Machine Learning ◽

Image Processing ◽

Learning Algorithm ◽

Point Of View ◽

Learning Approach ◽

Theoretical Point ◽

Ontology Learning ◽

Machine Learning Approach ◽

Similarity Calculation ◽

Weak Ontology

AbstractOntology is widely used in information retrieval, image processing and other various disciplines. This article discusses how to use machine learning approach to solve the most essential similarity calculation problem in multi-dividing ontology setting. The ontology function is regarded as a combination of several weak ontology functions, and the optimal ontology function is obtained by an iterative algorithm. In addition, the performance of the algorithm is analyzed from a theoretical point of view by statistical methods, and several results are obtained.

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Comparison of manual and machine learning image processing approaches to determine fungiform papillae on the tongue

10.1101/2020.07.05.187658 ◽

2020 ◽

Cited By ~ 1

Author(s):

Camilla Cattaneo ◽

Jing Liu ◽

Chenhao Wang ◽

Ella Pagliarini ◽

Jon Sporring ◽

...

Keyword(s):

Machine Learning ◽

Image Processing ◽

Taste Perception ◽

Automated Image Analysis ◽

Learning Approach ◽

Fungiform Papillae ◽

Automated Method ◽

Manual Counting ◽

Machine Learning Approach ◽

Counting Methods

AbstractHuman taste perception is associated with the papillae on the tongue as they contain a large proportion of chemoreceptors for basic tastes and other chemosensation. Especially the density of fungiform papillae (FP) is considered as an index for responsiveness to oral chemosensory stimuli. The standard procedure for FP counting involves visual identification and manual counting of specific parts of the tongue by trained operators. This is a tedious task and automated image analysis methods are desirable. In this paper a machine learning image processing method based on a convolutional neural network is presented. This automated method was compared with three standard manual FP counting procedures using tongue pictures from 132 subjects. Automated FP counts, within the selected areas and the whole tongue, significantly correlated with the manual counting methods (all ρs ≥ 0.76). When comparing the images for gender and PROP status, the density of FP predicted from automated analysis was in good agreement with data from the manual counting methods, especially in the case of gender. Moreover, the present results reinforce the idea that caution should be applied in considering the relationship between FP density and PROP responsiveness since this relationship can be an oversimplification of the complexity of phenomena arising at the central and peripherical levels. Indeed, no significant correlations were found between FP and PROP bitterness ratings using the automated method for selected areas or the whole tongue. Besides providing estimates of the number of FP, the machine learning approach used a tongue coordinate system that normalizes the size and shape of an individual tongue and generated a heat map of the FP position and normalized area they cover. The present study demonstrated that the machine learning approach could provide similar estimates of FP on the tongue as compared to manual counting methods and provide estimates of more difficult-to-measure parameters, such as the papillae’s areas and shape.

Download Full-text