scholarly journals Identification of Mesiodens Using Machine Learning Application in Panoramic Images

2021 ◽  
Vol 48 (2) ◽  
pp. 221-228
Author(s):  
Jaegook Seung ◽  
Jaegon Kim ◽  
Yeonmi Yang ◽  
Hyungbin Lim ◽  
Van Nhat Thang Le ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Dental Students ◽  
Mixed Dentition ◽  
Data Set ◽  
Average Accuracy ◽  
Testing Data ◽  
Panoramic Images ◽  
Resident Group ◽  
General Dentists

The aim of this study was to evaluate the use of easily accessible machine learning application to identify mesiodens, and to compare the ability to identify mesiodens between trained model and human.<br/>A total of 1604 panoramic images (805 images with mesiodens, 799 images without mesiodens) of patients aged 5 – 7 years were used for this study. The model used for machine learning was Google’s teachable machine. Data set 1 was used to train model and to verify the model. Data set 2 was used to compare the ability between the learning model and human group.<br/>As a result of data set 1, the average accuracy of the model was 0.82. After testing data set 2, the accuracy of the model was 0.78. From the resident group and the student group, the accuracy was 0.82, 0.69.<br/>This study developed a model for identifying mesiodens using panoramic radiographs of children in primary and early mixed dentition. The classification accuracy of the model was lower than that of the resident group. However, the classification accuracy (0.78) was higher than that of dental students (0.69), so it could be used to assist the diagnosis of mesiodens for non-expert students or general dentists.

scholarly journals Leveraging Road Characteristics and Contributor Behaviour for Assessing Road Type Quality in OSM

2021 ◽  
Vol 10 (7) ◽  
pp. 436
Author(s):  
Amerah Alghanim ◽  
Musfira Jilani ◽  
Michela Bertolotto ◽  
Gavin McArdle
Keyword(s):  
Machine Learning ◽  
Spatial Data ◽  
Classification Accuracy ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Semantic Inference ◽  
Road Type ◽  
The Impact

Volunteered Geographic Information (VGI) is often collected by non-expert users. This raises concerns about the quality and veracity of such data. There has been much effort to understand and quantify the quality of VGI. Extrinsic measures which compare VGI to authoritative data sources such as National Mapping Agencies are common but the cost and slow update frequency of such data hinder the task. On the other hand, intrinsic measures which compare the data to heuristics or models built from the VGI data are becoming increasingly popular. Supervised machine learning techniques are particularly suitable for intrinsic measures of quality where they can infer and predict the properties of spatial data. In this article we are interested in assessing the quality of semantic information, such as the road type, associated with data in OpenStreetMap (OSM). We have developed a machine learning approach which utilises new intrinsic input features collected from the VGI dataset. Specifically, using our proposed novel approach we obtained an average classification accuracy of 84.12%. This result outperforms existing techniques on the same semantic inference task. The trustworthiness of the data used for developing and training machine learning models is important. To address this issue we have also developed a new measure for this using direct and indirect characteristics of OSM data such as its edit history along with an assessment of the users who contributed the data. An evaluation of the impact of data determined to be trustworthy within the machine learning model shows that the trusted data collected with the new approach improves the prediction accuracy of our machine learning technique. Specifically, our results demonstrate that the classification accuracy of our developed model is 87.75% when applied to a trusted dataset and 57.98% when applied to an untrusted dataset. Consequently, such results can be used to assess the quality of OSM and suggest improvements to the data set.

Influence of Domain and Model Properties on the Reliability Estimates' Performance

2009 ◽  
Vol 5 (4) ◽  
pp. 58-76
Author(s):  
Zoran Bosnic ◽  
Igor Kononenko
Keyword(s):  
Machine Learning ◽  
Regression Models ◽  
Mean Squared Error ◽  
Noisy Data ◽  
Data Set ◽  
Squared Error ◽  
Reliability Estimates ◽  
Risk Sensitive ◽  
Average Accuracy ◽  
Applications Of Machine Learning

In machine learning, the reliability estimates for individual predictions provide more information about individual prediction error than the average accuracy of predictive model (e.g. relative mean squared error). Such reliability estimates may represent decisive information in the risk-sensitive applications of machine learning (e.g. medicine, engineering, and business), where they enable the users to distinguish between more and less reliable predictions. In the authors’ previous work they proposed eight reliability estimates for individual examples in regression and evaluated their performance. The results showed that the performance of each estimate strongly varies depending on the domain and regression model properties. In this paper they empirically analyze the dependence of reliability estimates’ performance on the data set and model properties. They present the results which show that the reliability estimates perform better when used with more accurate regression models, in domains with greater number of examples and in domains with less noisy data.

Assessing the Prevalence of Suspicious Activities in Asphalt Pavement Construction Using Algorithmic Logics and Machine Learning

2020 ◽  
Author(s):  
Mostofa Najmus Sakib
Keyword(s):  
Machine Learning ◽  
Construction Projects ◽  
Asphalt Pavement ◽  
Statistical Tests ◽  
Material Testing ◽  
Supervised Machine Learning ◽  
Test Results ◽  
Data Set ◽  
Testing Data ◽  
Pavement Construction

Quality Control (QC) and Quality Assurance (QA) is a planned systematic approach to secure the satisfactory performance of Hot mix asphalt (HMA) construction projects. Millions of dollars are invested by government and state highway agencies to construct large-scale HMA construction projects. QC/QA is statistical approach for checking the desired construction properties through independent testing. The practice of QC/QA has been encouraged by the Federal Highway Administration (FHWA) since the mid 60's. However, the standard QC/QA practice is often criticized on how effective such statistical tests and how representative the reported material tests are. Material testing data alteration in the HMA construction sector can render the QC/QA practice ineffective and shadow the performance of asphalt pavements. The American Society of Civil Engineers estimates that $340 billion is lost globally each year due to corruption in the construction industry. Asphalt pavement construction consists of several sectors, including construction and transportation, which are prone to potential suspicious activities. There is approximately 18 billion tons of asphalt pavement on American roads, which makes the costs of potential suspicious activities unacceptably large. The Idaho Transportation Department (ITD) relies on contractor-produced QC test results for the payment of the HMA pavement projects. In 2017, a case study by FHWA found some unnatural trends where 74% of the ITD test results didn't match with the contractor results. ITD's approach to track down the accuracy of mix design and volumetric test data set the off-stage of this research to mark out instances of suspicious activities in asphalt pavement projects. The first objective of this research was to develop algorithmic logics to recognize the patterns of discrepancies in agency- and contractor-produced QC/QA test results. This was possible with a unique dataset that ITD collected from several dozen HMA projects, in which all instances of data entry into the material testing report file was recorded in the background, without the operators' knowledge. My solution was bifurcated into development of an algorithm combining the logics to automatically detect and categorize suspicious instances when multiple data entries were observed. Modern data mining approaches were also used to explore the latent insights and screen out suspicious incidences to identify the chances of suboptimal materials used for paving and extra payment in HMA pavement projects. I have also successfully prompted supervised machine learning techniques to detect suspicious cases of data alterations. The second step of this research was to calculate the monetary losses due to data alteration. I replicated ITD's procedure for HMA payment calculation, and quantified payment-related parameters and associated payment for each project for two cases: 1. when the first parameter value categorized as Suspicious Alteration (S.A.) was used for payment calculation, and 2. when the last S.A. parameter value was used for payment. It was evident from my findings that there has been overpayment on construction projects across Idaho due to material testing data alterations. Overall, based on the available audit data, I found that overpayments have ranged from $14,000 to $360,000. Further analysis showed that alteration of each major material testing parameter's value can cause roughly $1,000 to $5,000 overpayment. I also note that data alteration did not always cause monetary gains. Other possible motives may include passing Percent Within Limit (PWL) criteria and precision criteria. Throughout the research, I strive to automate a suspicious activity detection system and calculate the associated excessive payment.

Reliability Estimates for Regression Predictions

2011 ◽  
pp. 320-338 ◽  
Author(s):  
Zoran Bosnic ◽  
Igor Kononenko
Keyword(s):  
Machine Learning ◽  
Prediction Error ◽  
Mean Squared Error ◽  
Noisy Data ◽  
Data Set ◽  
Squared Error ◽  
Reliability Estimates ◽  
Risk Sensitive ◽  
Average Accuracy ◽  
Applications Of Machine Learning

In machine learning, the reliability estimates for individual predictions provide more information about individual prediction error than the average accuracy of predictive model (e.g. relative mean squared error). Such reliability estimates may represent decisive information in the risk-sensitive applications of machine learning (e.g. medicine, engineering, and business), where they enable the users to distinguish between more and less reliable predictions. In the atuhors’ previous work they proposed eight reliability estimates for individual examples in regression and evaluated their performance. The results showed that the performance of each estimate strongly varies depending on the domain and regression model properties. In this paper they empirically analyze the dependence of reliability estimates’ performance on the data set and model properties. They present the results which show that the reliability estimates perform better when used with more accurate regression models, in domains with greater number of examples and in domains with less noisy data.

scholarly journals Classification of caries in third molars on panoramic radiographs using deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shankeeth Vinayahalingam ◽  
Steven Kempers ◽  
Lorenzo Limon ◽  
Dionne Deibel ◽  
Thomas Maal ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Third Molar ◽  
Area Under The Curve ◽  
Third Molars ◽  
Data Set ◽  
Panoramic Radiographs ◽  
Carious Lesions ◽  
Panoramic Images

AbstractThe objective of this study is to assess the classification accuracy of dental caries on panoramic radiographs using deep-learning algorithms. A convolutional neural network (CNN) was trained on a reference data set consisted of 400 cropped panoramic images in the classification of carious lesions in mandibular and maxillary third molars, based on the CNN MobileNet V2. For this pilot study, the trained MobileNet V2 was applied on a test set consisting of 100 cropped PR(s). The classification accuracy and the area-under-the-curve (AUC) were calculated. The proposed method achieved an accuracy of 0.87, a sensitivity of 0.86, a specificity of 0.88 and an AUC of 0.90 for the classification of carious lesions of third molars on PR(s). A high accuracy was achieved in caries classification in third molars based on the MobileNet V2 algorithm as presented. This is beneficial for the further development of a deep-learning based automated third molar removal assessment in future.

Machine-Learning Models for Combinatorial Catalyst Discovery

2003 ◽  
Vol 804 ◽  
Author(s):  
Gregory A. Landrum ◽  
Julie Penzotti ◽  
Santosh Putta
Keyword(s):  
Machine Learning ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Homogeneous Catalyst ◽  
Validation Data ◽  
Data Set ◽  
Molecular Weights ◽  
Equivalent Models ◽  
Average Accuracy ◽  
Better Than

ABSTRACTStandard machine-learning algorithms were used to build models capable of predicting the molecular weights of polymers generated by a homogeneous catalyst. Using descriptors calculated from only the two-dimensional structures of the ligands, the average accuracy of the models on an external validation data set was approximately 70%. Because the models show no bias and perform significantly better than equivalent models built using randomized data, we conclude that they learned useful rules and did not overfit the data.

scholarly journals Machine Learning Model for IRIS Flower Classification using Tensor Flow and PyTorch

2021 ◽  
pp. 15-23
Author(s):  
Dr. Kalaivazhi Vijayaragavan ◽  
S. Prakathi ◽  
S. Rajalakshmi ◽  
M Sandhiya
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Classification Accuracy ◽  
Learning Model ◽  
Training Data ◽  
Data Set ◽  
Machine Learning Model ◽  
Learning Frameworks ◽  
Learning Concept ◽  
Iris Data

Machine learning is a subfield of artificial intelligence, which is learning algorithms to make decision-based on data and try to behave like a human being. Classification is one of the most fundamental concepts in machine learning. It is a process of recognizing, understanding, and grouping ideas and objects into pre-set categories or sub-populations. Using precategorized training datasets, machine learning concept use variety of algorithms to classify the future datasets into categories. Classification algorithms use input training data in machine learning to predict the subsequent data that fall into one of the predetermined categories. To improve the classification accuracy design of neural network is regarded as effective model to obtain better accuracy. However, design of neural network is usually consider scaling layer, perceptron layers and probabilistic layer. In this paper, an enhanced model selection can be evaluated with training and testing strategy. Further, the classification accuracy can be predicted. Finally by using two popular machine learning frameworks: PyTorch and Tensor Flow the prediction of classification accuracy is compared. Results demonstrate that the proposed method can predict with more accuracy. After the deployment of our machine learning model the performance of the model has been evaluated with the help of iris data set.

Analysis, Discussion, and Evaluations for the Case Studies

2021 ◽  
pp. 142-150
Keyword(s):  
Colon Cancer ◽  
Case Studies ◽  
Classification Accuracy ◽  
Experimental Results ◽  
Full Data ◽  
Data Set ◽  
Evaluation Measures ◽  
Full Dataset ◽  
Average Accuracy

The purpose of this chapter is to discuss and analyse the results produced in Chapter 5. To evaluate the proposed models, this chapter compares the models with others existing in the literature. Additionally, the chapter discusses the evaluation measures used to validate the experimental results of Chapter 5. For example, from experiments, GA/DT demonstrated the highest average accuracy (92%) for classifying colon cancer, compared with other algorithms. PSO/DT presented 89%, PSO/SVM presented 89%, and IG/DT presented 89%, demonstrating very good classification accuracy. PSO/NB presented 57% and GA/NB presented 58%: less classification accuracy. Table ‎6.1 lists all accuracies resulting from experiments of case study one, as applied to the full data set. There are 45 algorithmic incorporation methods that have accuracy above 80% when applied to the full dataset. One algorithm presents an accuracy of 92%. Nine others scored below 60%.

scholarly journals A machine learning approach to aerosol classification for single-particle mass spectrometry

2018 ◽  
Vol 11 (10) ◽  
pp. 5687-5699 ◽  
Author(s):  
Costa D. Christopoulos ◽  
Sarvesh Garimella ◽  
Maria A. Zawadowicz ◽  
Ottmar Möhler ◽  
Daniel J. Cziczo
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Single Particle ◽  
Classification Accuracy ◽  
Machine Learning Algorithms ◽  
Particle Mass ◽  
Training Set ◽  
Data Set ◽  
Analytical Technique ◽  
Single Particle Mass Spectrometry

Abstract. Compositional analysis of atmospheric and laboratory aerosols is often conducted via single-particle mass spectrometry (SPMS), an in situ and real-time analytical technique that produces mass spectra on a single-particle basis. In this study, classifiers are created using a data set of SPMS spectra to automatically differentiate particles on the basis of chemistry and size. Machine learning algorithms build a predictive model from a training set for which the aerosol type associated with each mass spectrum is known a priori. Our primary focus surrounds the growing of random forests using feature selection to reduce dimensionality and the evaluation of trained models with confusion matrices. In addition to classifying ∼20 unique, but chemically similar, aerosol types, models were also created to differentiate aerosol within four broader categories: fertile soils, mineral/metallic particles, biological particles, and all other aerosols. Differentiation was accomplished using ∼40 positive and negative spectral features. For the broad categorization, machine learning resulted in a classification accuracy of ∼93 %. Classification of aerosols by specific type resulted in a classification accuracy of ∼87 %. The “trained” model was then applied to a “blind” mixture of aerosols which was known to be a subset of the training set. Model agreement was found on the presence of secondary organic aerosol, coated and uncoated mineral dust, and fertile soil.

scholarly journals PseUdeep: RNA Pseudouridine Site Identification with Deep Learning Algorithm

2021 ◽  
Vol 12 ◽  
Author(s):  
Jujuan Zhuang ◽  
Danyang Liu ◽  
Meng Lin ◽  
Wenjing Qiu ◽  
Jinyang Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Biological Processes ◽  
Rna Sequences ◽  
Frequency Pattern ◽  
Data Set ◽  
Testing Data

Background: Pseudouridine (Ψ) is a common ribonucleotide modification that plays a significant role in many biological processes. The identification of Ψ modification sites is of great significance for disease mechanism and biological processes research in which machine learning algorithms are desirable as the lab exploratory techniques are expensive and time-consuming.Results: In this work, we propose a deep learning framework, called PseUdeep, to identify Ψ sites of three species: H. sapiens, S. cerevisiae, and M. musculus. In this method, three encoding methods are used to extract the features of RNA sequences, that is, one-hot encoding, K-tuple nucleotide frequency pattern, and position-specific nucleotide composition. The three feature matrices are convoluted twice and fed into the capsule neural network and bidirectional gated recurrent unit network with a self-attention mechanism for classification.Conclusion: Compared with other state-of-the-art methods, our model gets the highest accuracy of the prediction on the independent testing data set S-200; the accuracy improves 12.38%, and on the independent testing data set H-200, the accuracy improves 0.68%. Moreover, the dimensions of the features we derive from the RNA sequences are only 109,109, and 119 in H. sapiens, M. musculus, and S. cerevisiae, which is much smaller than those used in the traditional algorithms. On evaluation via tenfold cross-validation and two independent testing data sets, PseUdeep outperforms the best traditional machine learning model available. PseUdeep source code and data sets are available at https://github.com/dan111262/PseUdeep.

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