scholarly journals Development of intra-oral automated landmark recognition (ALR) for dental and occlusal outcome measurements

2020 ◽  
Author(s):  
Brénainn Woodsend ◽  
Eirini Koufoudaki ◽  
Ping Lin ◽  
Grant McIntyre ◽  
Ahmed El-Angbawi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Geometric Mean ◽  
Software Tool ◽  
Automated System ◽  
Dental Arch ◽  
Learning Technology ◽  
Digital Models ◽  
Human Teeth ◽  
Landmark Recognition ◽  
Geometric Mean Error

SummaryPrevious studies embracing digital technology and automated methods of scoring dental arch relationships have shown that such technology is valid and accurate. To date, however there is no published literature on artificial intelligence and machine learning to completely automate the process of dental landmark recognition.This study aimed to develop and evaluate a fully automated system and software tool for the identification of landmarks on human teeth using geometric computing, image segmenting and machine learning technology.239 digital models were used in the automated landmark recognition (ALR) validation phase, 161 of which were digital models from cleft palate subjects aged 5 years. These were manually annotated to facilitate qualitative validation. Additionally, landmarks were placed on 20 adult digital models manually by three independent observers. The same models were subjected to scoring using the ALR software and the differences (in mm) were calculated. All the teeth from the 239 models were evaluated for correct recognition by the ALR with a breakdown to find which stages of the process caused the errors.The results revealed that 1526 out of 1915 teeth (79.7%) were correctly identified, and the accuracy validation gave 95% confidence intervals for the geometric mean error of [0.285, 0.317] for the humans and [0.269, 0.325] for ALR – a negligible difference.It is anticipated that ALR software tool will have applications throughout Dentistry and anthropology and in research will constitute an objective tool for handling large datasets without the need for time intensive employment of experts to place landmarks manually.

EFFICIENCY IMPROVEMENT OF ROAD SURFACE INSPECTION USING MACHINE LEARNING TECHNOLOGY

2017 ◽  
Vol 73 (2) ◽  
pp. I_409-I_415
Author(s):  
Naoko FUKUSHI ◽  
Daishiro KOBAYASHI ◽  
Seiji IWAO ◽  
Ryosuke KASAHARA ◽  
Nobuyoshi YABUKI
Keyword(s):  
Machine Learning ◽  
Road Surface ◽  
Surface Inspection ◽  
Learning Technology ◽  
Efficiency Improvement

scholarly journals Automobile tire life prediction based on image processing and machine learning technology

2021 ◽  
Vol 13 (3) ◽  
pp. 168781402110027
Author(s):  
Jianchen Zhu ◽  
Kaixin Han ◽  
Shenlong Wang
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Life Prediction ◽  
Traffic Accidents ◽  
Confusion Matrix ◽  
Small Sample ◽  
Image Feature ◽  
Engineering Practice ◽  
Learning Technology ◽  
K Nearest Neighbor

With economic growth, automobiles have become an irreplaceable means of transportation and travel. Tires are important parts of automobiles, and their wear causes a large number of traffic accidents. Therefore, predicting tire life has become one of the key factors determining vehicle safety. This paper presents a tire life prediction method based on image processing and machine learning. We first build an original image database as the initial sample. Since there are usually only a few sample image libraries in engineering practice, we propose a new image feature extraction and expression method that shows excellent performance for a small sample database. We extract the texture features of the tire image by using the gray-gradient co-occurrence matrix (GGCM) and the Gauss-Markov random field (GMRF), and classify the extracted features by using the K-nearest neighbor (KNN) classifier. We then conduct experiments and predict the wear life of automobile tires. The experimental results are estimated by using the mean average precision (MAP) and confusion matrix as evaluation criteria. Finally, we verify the effectiveness and accuracy of the proposed method for predicting tire life. The obtained results are expected to be used for real-time prediction of tire life, thereby reducing tire-related traffic accidents.

scholarly journals A New Method to Evaluate Trueness and Precision of Digital and Conventional Impression Techniques for Complete Dental Arch

2021 ◽  
Vol 11 (10) ◽  
pp. 4612
Author(s):  
KweonSoo Seo ◽  
Sunjai Kim
Keyword(s):  
Intraclass Correlation ◽  
Three Dimensional ◽  
New Method ◽  
Digital Impression ◽  
Dental Arch ◽  
Digital Models ◽  
Digital Impressions ◽  
Measuring Machine ◽  
Master Model ◽  
Conventional Impression

Purpose: The aim of this study was to present a new method to analyze the three-dimensional accuracy of complete-arch dental impressions and verify the reliability of the method. Additionally, the accuracies of conventional and intraoral digital impressions were compared using the new method. Methods: A master model was fabricated using 14 milled polyetheretherketone cylinders and a maxillary acrylic model. Each cylinder was positioned and named according to its corresponding tooth position. Twenty-five definitive stone casts were fabricated using conventional impressions of the master model. An intraoral scanner was used to scan the master model 25 times to fabricate 25 digital models. A coordinate measuring machine was used to physically probe each cylinder in the master model and definitive casts. An inspection software was used to probe cylinders of digital models. A three-dimensional part coordinate system was defined and used to compute the centroid coordinate of each cylinder. Intraclass correlation coefficient (ICC) was evaluated to examine the reliability of the new method. Independent two sample t-test was performed to compare the trueness and precision of conventional and intraoral digital impressions (α = 0.05). Results: ICC results showed that, the new method had almost perfect reliability for the measurements of the master model, conventional and digital impression. Conventional impression showed more accurate absolute trueness and precision than intraoral digital impression for most of the tooth positions (p < 0.05). Conclusions: The new method was reliable to analyze the three-dimensional deviation of complete-arch impressions. Conventional impression was still more accurate than digital intraoral impression for complete arches.

scholarly journals Implementation Framework for a Blockchain-Based Federated Learning Model for Classification Problems

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1116
Author(s):  
Zeba Mahmood ◽  
Vacius Jusas
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Global Perspective ◽  
Learning Technology ◽  
Classification Problems ◽  
Zero Knowledge ◽  
Privacy And Security ◽  
Implementation Framework ◽  
The Past

This paper introduces a blockchain-based federated learning (FL) framework with incentives for participating nodes to enhance the accuracy of classification problems. Machine learning technology has been rapidly developed and changed from a global perspective for the past few years. The FL framework is based on the Ethereum blockchain and creates an autonomous ecosystem, where nodes compete to improve the accuracy of classification problems. With privacy being one of the biggest concerns, FL makes use of the blockchain-based approach to ensure privacy and security. Another important technology that underlies the FL framework is zero-knowledge proofs (ZKPs), which ensure that data uploaded to the network are accurate and private. Basically, ZKPs allow nodes to compete fairly by only submitting accurate models to the parameter server and get rewarded for that. We have conducted an analysis and found that ZKPs can help improve the accuracy of models submitted to the parameter server and facilitate the honest participation of all nodes in FL.

Research on the expression of new visual intelligence system based on machine learning technology

2021 ◽  
pp. 1-9
Author(s):  
Zuoshan Li
Keyword(s):  
Machine Learning ◽  
Intelligent System ◽  
Science And Technology ◽  
Learning Technology ◽  
3D Animation ◽  
Motion Data ◽  
Intelligence System ◽  
New Science ◽  
Continuous Progress ◽  
Internet Of Things Technology

With the continuous progress of society, the level of science and technology of the country has made a leap forward development, the research energy of various industries on new science and technology continues to deepen, greatly promoting the promotion of science and technology. At the same time, with the increase in social pressure, more and more people pursue spiritual relaxation, and appropriate leisure and entertainment activities have gradually become a part of people’s life. Film plays an irreplaceable role in leisure and entertainment. Mainly from the background of the development of the film industry towards intelligent direction, and then use machine learning technology to study the application of film animation production and film virtual assets analysis and investigation. Based on the Internet of things technology, we also vigorously develop the ways and methods of visual expression of movies, and at the same time introduce new expression modes to promote the expression effect of the intelligent system. Finally, by comparing various algorithms in machine learning technology, the results of intelligent expression of random number forest algorithm in machine learning technology are more accurate. The system is also applied to 3D animation production to observe the measurement error of 3D motion data and facial expression data.

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