scholarly journals Machine learning assisted Cameriere method for dental age estimation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shihui Shen ◽  
Zihao Liu ◽  
Jian Wang ◽  
Linfeng Fan ◽  
Fang Ji ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mean Square Error ◽  
Age Estimation ◽  
Estimation Method ◽  
Permanent Teeth ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Mean Square ◽  
Dental Age ◽  
Dental Age Estimation

Abstract Background Recently, the dental age estimation method developed by Cameriere has been widely recognized and accepted. Although machine learning (ML) methods can improve the accuracy of dental age estimation, no machine learning research exists on the use of the Cameriere dental age estimation method, making this research innovative and meaningful. Aim The purpose of this research is to use 7 lower left permanent teeth and three models [random forest (RF), support vector machine (SVM), and linear regression (LR)] based on the Cameriere method to predict children's dental age, and compare with the Cameriere age estimation. Subjects and methods This was a retrospective study that collected and analyzed orthopantomograms of 748 children (356 females and 392 males) aged 5–13 years. Data were randomly divided into training and test datasets in an 80–20% proportion for the ML algorithms. The procedure, starting with randomly creating new training and test datasets, was repeated 20 times. 7 permanent developing teeth on the left mandible (except wisdom teeth) were recorded using the Cameriere method. Then, the traditional Cameriere formula and three models (RF, SVM, and LR) were used to estimate the dental age. The age prediction accuracy was measured by five indicators: the coefficient of determination (R2), mean error (ME), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). Results The research showed that the ML models have better accuracy than the traditional Cameriere formula. The ME, MAE, MSE, and RMSE values of the SVM model (0.004, 0.489, 0.392, and 0.625, respectively) and the RF model (− 0.004, 0.495, 0.389, and 0.623, respectively) were lower with the highest accuracy. In contrast, the ME, MAE, MSE and RMSE of the European Cameriere formula were 0.592, 0.846, 0.755, and 0.869, respectively, and those of the Chinese Cameriere formula were 0.748, 0.812, 0.890 and 0.943, respectively. Conclusions Compared to the Cameriere formula, ML methods based on the Cameriere’s maturation stages were more accurate in estimating dental age. These results support the use of ML algorithms instead of the traditional Cameriere formula.

Development of Flood Forecasting System for Someshwari-Kangsa Sub-watershed of Bangladesh-India Using Different Machine Learning Techniques

2021 ◽  
Author(s):  
Md Hamidul Haque ◽  
Mushtari Sadia ◽  
Mashiat Mustaq
Keyword(s):  
Machine Learning ◽  
Water Level ◽  
Mean Square Error ◽  
Lead Time ◽  
Flood Forecasting ◽  
Data Driven ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Mean Square ◽  
Physically Based

<p>Floods are natural disasters caused mainly due to heavy or excessive rainfall. They induce massive economic losses in Bangladesh every year. Physically-based flood prediction models have been used over the years where simplified forms of physical laws are used to reduce calculations' complexity. It sometimes leads to oversimplification and inaccuracy in the prediction. Moreover, a physically-based model requires intensive monitoring datasets for calibration, accurate soil properties information, and a heavy computational facility, creating an impediment for quick, economical and precise short-term prediction. Researchers have tried different approaches like empirical data-driven models, especially machine learning-based models, to offer an alternative approach to the physically-based models but focused on developing only one machine learning (ML) technique at a time (i.e., ANN, MLP, etc.). There are many other techniques, algorithms, and models in machine learning (ML) technology that have the potential to be effective and efficient in flood forecasting. In this study, five different machine learning algorithms- exponent back propagation neural network (EBPNN), multilayer perceptron (MLP), support vector regression (SVR), DT Regression (DTR), and extreme gradient boosting (XGBoost) were used to develop total 180 independent models based on a different combination of time lags for input data and lead time in forecast. Models were developed for Someshwari-Kangsa sub-watershed of Bangladesh's North Central hydrological region with 5772 km<sup>2</sup> drainage area. It is also a data-scarce region with only three hydrological and hydro-meteorological stations for the whole sub-watershed. This region mostly suffers extreme meteorological events driven flooding. Therefore, satellite-based precipitation, temperature, relative humidity, wind speed data, and observed water level data from the Bangladesh Water Development Board (BWDB) were used as input and response variables.</p><p>For comparison, the accuracy of these models was evaluated using different statistical indices - coefficient of determination, mean square error (MSE), mean absolute error (MAE), mean relative error (MRE), explained variance score and normalized centred root mean square error (NCRMSE). Developed models were ranked based on the coefficient of determination (R<sup>2</sup>) value. All the models performed well with R<sup>2</sup> being greater than 0.85 in most cases. Further analysis of the model results showed that most of the models performed well for forecasting 24-hour lead time water level. Models developed using XGBoost algorithm outperformed other models in all metrics. Moreover, each of the algorithms' best-performed models was extended further up to 20 days lead time to generate forecasting horizon. Models demonstrated remarkable consistency in their performance with the coefficient of determination (R<sup>2</sup>) being greater than 0.70 at 20 days lead-time of forecasting horizon in most cases except the DTR-based model. For 10- and 5-days lead time of forecasting horizon, it was greater than 0.75 and 0.80 respectively, for all the model extended. This study concludes that the machine algorithm-based data-driven model can be a powerful tool for flood forecasting in data-scarce regions with excellent accuracy, quick building and running time, and economic feasibility.</p>

scholarly journals Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1166
Author(s):  
Bashir Musa ◽  
Nasser Yimen ◽  
Sani Isah Abba ◽  
Humphrey Hugh Adun ◽  
Mustafa Dagbasi
Keyword(s):  
Support Vector Regression ◽  
Mean Square Error ◽  
Prediction Accuracy ◽  
Demand Forecasting ◽  
Load Forecasting ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Hybrid Renewable Energy System

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

Comparison of radiological dental age estimation by means of the method by Cameriere and the modified European formula

2018 ◽  
Vol 23 (2) ◽  
Author(s):  
Katarzyna Różyło ◽  
Katarzyna Gruszka ◽  
Ingrid Różyło-Kalinowska
Keyword(s):  
Age Estimation ◽  
Treatment Options ◽  
Original Method ◽  
Permanent Teeth ◽  
Dental Age ◽  
Dental Age Estimation ◽  
Digital Radiographs ◽  
Conservative Dentistry ◽  
The Mean ◽  
Radiological Methods

Introduction. Dental age apart from skeletal age is an important factor in the estimation of biological age of patients. Its evaluation is crucial in making decisions concerning diagnostic algorithms and treatment options in such fields of medicine as paedodontics, conservative dentistry, orthodontics, paediatrics or endocrinology as well as for forensic purposes. There are various methods of radiological dental age estimation and their validity is related to the studied population. Aim. The aim of the paper is to estimate dental age by means of two radiological methods based on panoramic radiographs, i.e. the original method by Cameriere and the modified European formula. Material and methods. The material consisted of 2148 digital radiographs taken in patients of both genders, aged from 5 to 15 years, with visible germs of all permanent teeth, apart from third molars. Two methods by Cameriere were applied – the original one and the European formula. Statistical analysis was performed. Results. Dental age obtained by means of the two Cameriere’s methods was significantly different from chronological age (Wilcoxon’s test, p < 0.001). However, in the case of the original method the mean dental age was lower than the chronological one, while the European formula led to the overestimation of dental age. Conclusions. The European formula is more suitable for the evaluation of the Polish population than the original method by Cameriere.

The adaptation of Demirjian’s dental age estimation method on North German children

2019 ◽  
Vol 303 ◽  
pp. 109927
Author(s):  
Nadiajda Khdairi ◽  
Talal Halilah ◽  
Mohannad N. Khandakji ◽  
Paul-Georg Jost-Brinkmann ◽  
Theodosia Bartzela
Keyword(s):  
Age Estimation ◽  
Estimation Method ◽  
Dental Age ◽  
Dental Age Estimation ◽  
German Children

scholarly journals Using Multi-Temporal MODIS NDVI Data to Monitor Tea Status and Forecast Yield: A Case Study at Tanuyen, Laichau, Vietnam

2020 ◽  
Vol 12 (11) ◽  
pp. 1814
Author(s):  
Phamchimai Phan ◽  
Nengcheng Chen ◽  
Lei Xu ◽  
Zeqiang Chen
Keyword(s):  
Remote Sensing ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Vegetation Index ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Multi Temporal

Tea is a cash crop that improves the quality of life for people in the Tanuyen District of Laichau Province, Vietnam. Tea yield, however, has stagnated in recent years, due to changes in temperature, precipitation, the age of the tea bushes, and diseases. Developing an approach for monitoring tea bushes by remote sensing and Geographic Information Systems (GIS) might be a way to alleviate this problem. Using multi-temporal remote sensing data, the paper details an investigation of the changes in tea health and yield forecasting through the normalized difference vegetation index (NDVI). In this study, we used NDVI as a support tool to demonstrate the temporal and spatial changes in NDVI through the extract tea NDVI value and calculate the mean NDVI value. The results of the study showed that the minimum NDVI value was 0.42 during January 2013 and February 2015 and 2016. The maximum NDVI value was in August 2015 and June 2017. We indicate that the linear relationship between NDVI value and mean temperature was strong with R 2 = 0.79 Our results confirm that the combination of meteorological data and NDVI data can achieve a high performance of yield prediction. Three models to predict tea yield were conducted: support vector machine (SVM), random forest (RF), and the traditional linear regression model (TLRM). For period 2009 to 2018, the prediction tea yield by the RF model was the best with a R 2 = 0.73 , by SVM it was 0.66, and 0.57 with the TLRM. Three evaluation indicators were used to consider accuracy: the coefficient of determination ( R 2 ), root-mean-square error (RMSE), and percentage error of tea yield (PETY). The highest accuracy for the three models was in 2015 with a R 2 ≥ 0.87, RMSE < 50 kg/ha, and PETY less 3% error. In the other years, the prediction accuracy was higher in the SVM and RF models. Meanwhile, the RF algorithm was better than PETY (≤10%) and the root mean square error for this algorithm was significantly less (≤80 kg/ha). RMSE and PETY showed relatively good values in the TLRM model with a RMSE from 80 to 100 kg/ha and a PETY from 8 to 15%.

scholarly journals Comparing Four Types Methods for Karst NDVI Prediction Based on Machine Learning

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1341
Author(s):  
Yuju Ma ◽  
Liyuan Zuo ◽  
Jiangbo Gao ◽  
Qiang Liu ◽  
Lulu Liu
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Human Activities ◽  
Vegetation Index ◽  
Terrestrial Ecosystem ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Response Factors

As a link for energy transfer between the land and atmosphere in the terrestrial ecosystem, karst vegetation plays an important role. Karst vegetation is not only affected by environmental factors but also by intense human activities. The nonlinear characteristics of vegetation growth are induced by the interaction mechanism of these factors. Previous studies of this relationship were not comprehensive, and it is necessary to further explore it using a suitable method. In this study, we selected climate, human activities, topography, and soil texture as the response factors; a nonlinear relationship model between the karst normalized difference vegetation index (NDVI) and these factors was established by applying a back propagation neural network (BPNN), a radial basis function neural network (RBFNN), the random forest (RF) algorithm, and support vector regression (SVR); and then, the karst NDVI was predicted. The coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the obtained results were calculated, and the mean R2 values of the BPNN, RBFNN, RF, and SVR models were determined to be 0.77, 0.86, 0.89, and 0.91, respectively. Compared with the BPNN, RBFNN, and RF models, the SVR model had the lowest errors, with mean MSE, RMSE, and MAPE values of 0.001, 0.02, and 2.77, respectively. The results show that the BPNN, RBFNN, RF, and SVR models are within acceptable ranges for karst NDVI prediction, but the overall performance of the SVR model is the best, and it is more suitable for karst vegetation prediction.

scholarly journals Prakiraan Usia Gigi Menggunakan Standar Blenkin (Modifikasi Metode Demirjian) pada Anak-Anak Etnik Jawa di Kabupaten Jember

2020 ◽  
Vol 17 (2) ◽  
pp. 60
Author(s):  
Dwi Kartika Apriyono
Keyword(s):  
Age Estimation ◽  
Accurate Estimate ◽  
Permanent Teeth ◽  
Chronological Age ◽  
Age Difference ◽  
Dental Age ◽  
Dental Age Estimation ◽  
Panoramic Radiographs ◽  
Significant Difference ◽  
Age Range

Chronological and dental age are necessary aspects of dental age estimation. Both have a close relationship. Chronological age reflects the age of the tooth, and vice versa. Dental age estimation aims to provide the data in the field of dentistry with an accurate dental age range. In order to get the value of an accurate estimate of dental age, needed a method of estimation that has a standard deviation as low as possible and validated in a specific population groups of an individual. Demirjian method is a method frequently used in the dental age estimation. It uses the classification stages of the seven permanent teeth of mandibular left side using panoramic radiographs. Application of its method in some countries showed vary results so it needed adjustment. Blenkin standard is an adjustment of its method that changes the score of maturity stages 0-H to 1-8 and calculate the dental age by regression formula. The study aimed to assess the dental age estimation using Blenkin standard on children of Javanese ethnic in Jember region. This was an analytic descriptive study design. The samples were panoramic radiographs. The subjects were 70 samples consisting of 29 boys and 41 girls with an age range 6-12 years, and they were divided into 7 groups based on chronological age. Each tooth of the sample was calculated using Blenkin standard. The Blenkin standard showed non-significant difference with the age difference in the amount of approximately -0.22 years for boys and -0.03 years for girls (underestimation).

PEMODELAN PREDIKTIF KONSUMSI ENERGI BANGUNAN GEDUNG KOMERSIAL DENGAN ALGORITMA SUPPORT VECTOR MACHINE

2018 ◽  
Vol 14 (2) ◽  
pp. 225
Author(s):  
Indriyanti Indriyanti ◽  
Agus Subekti
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Support Vector ◽  
Mean Square

Konsumsi energi bangunan yang semakin meningkat mendorong para peneliti untuk membangun sebuah model prediksi dengan menerapkan metode machine learning, namun masih belum diketahui model yang paling akurat. Model prediktif untuk konsumsi energi bangunan komersial penting untuk konservasi energi. Dengan menggunakan model yang tepat, kita dapat membuat desain bangunan yang lebih efisien dalam penggunaan energi. Dalam tulisan ini, kami mengusulkan model prediktif berdasarkan metode pembelajaran mesin untuk mendapatkan model terbaik dalam memprediksi total konsumsi energi. Algoritma yang digunakan yaitu SMOreg dan LibSVM dari kelas Support Vector Machine, kemudian untuk evaluasi model berdasarkan nilai Mean Absolute Error dan Root Mean Square Error. Dengan menggunakan dataset publik yang tersedia, kami mengembangkan model berdasarkan pada mesin vektor pendukung untuk regresi. Hasil pengujian kedua algoritma tersebut diketahui bahwa algoritma SMOreg memiliki akurasi lebih baik karena memiliki nilai MAE dan RMSE sebesar 4,70 dan 10,15, sedangkan untuk model LibSVM memiliki nilai MAE dan RMSE sebesar 9,37 dan 14,45. Kami mengusulkan metode berdasarkan algoritma SMOreg karena kinerjanya lebih baik.

Permanent teeth development in a Spanish sample. Application to dental age estimation

2012 ◽  
Vol 214 (1-3) ◽  
pp. 213.e1-213.e6 ◽  
Author(s):  
Gonzalo Feijóo ◽  
Elena Barbería ◽  
Joaquín De Nova ◽  
Jose Luis Prieto
Keyword(s):  
Age Estimation ◽  
Permanent Teeth ◽  
Dental Age ◽  
Dental Age Estimation ◽  
Sample Application ◽  
Teeth Development
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