Comparison of data mining algorithms for sex determination based on mastoid process measurements using cone-beam computed tomography

Farhadian, Maryam,Salemi, Fatemeh,Shokri, Abbas,Safi, Yaser,Rahimpanah, Shahin Korean Academy of Oral and Maxillofacial Radiology 2020 Imaging Science in Dentistry Vol.50 No.4

Purpose: The mastoid region is ideal for studying sexual dimorphism due to its anatomical position at the base of the skull. This study aimed to determine sex in the Iranian population based on measurements of the mastoid process using different data mining algorithms. Materials and Methods: This retrospective study was conducted on 190 3-dimensional cone-beam computed tomographic (CBCT) images of 105 women and 85 men between the ages of 18 and 70 years. On each CBCT scan, the following 9 landmarks were measured: the distance between the porion and the mastoidale; the mastoid length, height, and width; the distance between the mastoidale and the mastoid incision; the intermastoid distance (IMD); the distance between the lowest point of the mastoid triangle and the most prominent convex surface of the mastoid (MF); the distance between the most prominent convex mastoid point (IMSLD); and the intersecting angle drawn from the most prominent right and left mastoid point (MMCA). Several predictive models were constructed and their accuracy was compared using cross-validation. Results: The results of the t-test revealed a statistically significant difference between the sexes in all variables except MF and MMCA. The random forest model, with an accuracy of 97.0%, had the best performance in predicting sex. The IMSLD and IMD made the largest contributions to predicting sex, while the MMCA variable had the least significant role. Conclusion: These results show the possibility of developing an accurate tool using data mining algorithms for sex determination in the forensic framework.

Dental age estimation using the pulp-to-tooth ratio in canines by neural networks

Farhadian, Maryam,Salemi, Fatemeh,Saati, Samira,Nafisi, Nika Korean Academy of Oral and Maxillofacial Radiology 2019 Imaging Science in Dentistry Vol.49 No.1

Purpose: It has been proposed that using new prediction methods, such as neural networks based on dental data, could improve age estimation. This study aimed to assess the possibility of exploiting neural networks for estimating age by means of the pulp-to-tooth ratio in canines as a non-destructive, non-expensive, and accurate method. In addition, the predictive performance of neural networks was compared with that of a linear regression model. Materials and Methods: Three hundred subjects whose age ranged from 14 to 60 years and were well distributed among various age groups were included in the study. Two statistical software programs, SPSS 21 (IBM Corp., Armonk, NY, USA) and R, were used for statistical analyses. Results: The results indicated that the neural network model generally performed better than the regression model for estimation of age with pulp-to-tooth ratio data. The prediction errors of the developed neural network model were acceptable, with a root mean square error (RMSE) of 4.40 years and a mean absolute error (MAE) of 4.12 years for the unseen dataset. The prediction errors of the regression model were higher than those of the neural network, with an RMSE of 10.26 years and a MAE of 8.17 years for the test dataset. Conclusion: The neural network method showed relatively acceptable performance, with an MAE of 4.12 years. The application of neural networks creates new opportunities to obtain more accurate estimations of age in forensic research.

Dental age estimation using the pulp-to-tooth ratio in canines by neural networks

Maryam Farhadian,Fatemeh Salemi,Samira Saati,Nika Nafisi 대한영상치의학회 2019 Imaging Science in Dentistry Vol.49 No.1

Purpose: It has been proposed that using new prediction methods, such as neural networks based on dental data, could improve age estimation. This study aimed to assess the possibility of exploiting neural networks for estimating age by means of the pulp-to-tooth ratio in canines as a non-destructive, non-expensive, and accurate method. In addition, the predictive performance of neural networks was compared with that of a linear regression model. Materials and Methods: Three hundred subjects whose age ranged from 14 to 60 years and were well distributed among various age groups were included in the study. Two statistical software programs, SPSS 21 (IBM Corp., Armonk, NY, USA) and R, were used for statistical analyses. Results: The results indicated that the neural network model generally performed better than the regression model for estimation of age with pulp-to-tooth ratio data. The prediction errors of the developed neural network model were acceptable, with a root mean square error (RMSE) of 4.40 years and a mean absolute error (MAE) of 4.12 years for the unseen dataset. The prediction errors of the regression model were higher than those of the neural network, with an RMSE of 10.26 years and a MAE of 8.17 years for the test dataset. Conclusion: The neural network method showed relatively acceptable performance, with an MAE of 4.12 years. The application of neural networks creates new opportunities to obtain more accurate estimations of age in forensic research.

Groundwater Inflow Assessment to Karaj Water Conveyance Tunnel, Northern Iran

Hadi Farhadian,Arash Nikvar Hassani,Homayoon Katibeh 대한토목학회 2017 KSCE JOURNAL OF CIVIL ENGINEERING Vol.21 No.6

In this paper, groundwater inflow into Karaj Water Conveyance (KWC) tunnel was estimated using analytical and numerical methods in 12 different sections of the tunnel length. Further, these sections were rated from groundwater hazard point of view by means of Site Groundwater Rating (SGR) factor. Comparing results show a reasonable accordance between observed water ingress rate and those various methods. Since, KWC tunnel is excavated in fractured rocks with a high level of anisotropy, analytical methods provided highly overestimated water inflow rate. Furthermore all SGR, analytical and numerical results, show high levels of water inflow from fault zones. Maximum water inflow into tunnel was computed as 0.0536 and 0.0432 lit/sec/m using analytical and numerical methods, respectively. Based on SGR method, 11 out of 12 sections in KWC tunnel length are found to be in “No Risk” class with groundwater inflow of less than 0.04 lit/sec/m which are in agreement with analytical and numerical seepage values and also with the observed inflow rate.

Predicting 5-Year Survival Status of Patients with Breast Cancer based on Supervised Wavelet Method

Maryam Farhadian,Hossein Mahjub,Jalal Poorolajal,Abbas Moghimbeigi,Muharram Mansoorizadeh 질병관리본부 2014 Osong Public Health and Research Persptectives Vol.5 No.6

Objectives: Classification of breast cancer patients into different risk classes is very important in clinical applications. It is estimated that the advent of highdimensional gene expression data could improve patient classification. In this study, a new method for transforming the high-dimensional gene expression data in a low-dimensional space based on wavelet transform (WT) is presented. Methods: The proposed method was applied to three publicly available microarray data sets. After dimensionality reduction using supervised wavelet, a predictive support vector machine (SVM) model was built upon the reduced dimensional space. In addition, the proposed method was compared with the supervised principal component analysis (PCA). Results: The performance of supervised wavelet and supervised PCA based on selected genes were better than the signature genes identified in the other studies. Furthermore, the supervised wavelet method generally performed better than the supervised PCA for predicting the 5-year survival status of patients with breast cancer based on microarray data. In addition, the proposed method had a relatively acceptable performance compared with the other studies. Conclusion: The results suggest the possibility of developing a new tool using wavelets for the dimension reduction of microarray data sets in the classification framework.

Bio-based and self-catalyzed waterborne polyurethanes as efficient corrosion inhibitors for sour oilfield environment

Alireza Rahimi,Abdolreza Farhadian,Lei Guo,Esmaeil Akbarinezhad,Ruhollah Sharifi,Danial Iravani,Ali Asghar Javidparvar,Mohamed A. Deyab,Mikhail A. Varfolomeev 한국공업화학회 2023 Journal of Industrial and Engineering Chemistry Vol.123 No.-

Sunflower oil was used as environmentally friendly source to develop novel self-catalyzed waterbornepolyurethanes (WPUs) as efficient corrosion inhibitors for sour oilfield solution. A comprehensive experimentaland computational analysis was performed to evaluate the inhibition effect of WPUs. The resultsof electrochemical measurements indicated that 200 lM of WPUs were effectively protected mild steelfrom sour corrosion by 95% and 94.6% at 25 C and 60 C, respectively. Furthermore, it was found thatthe best inhibition efficiency was provided by WPU when pyrrolidine was included in its structure, particularlyat 60 C. Additionally, a smoother steel surface was observed in the presence of WPUs, indicatingthe adsorption of the polyurethane molecules on the metal surface. The results of X-ray photoelectronspectroscopy further confirmed the chemical adsorption of WPUs on the surface of mild steel. Moreover, scanning Kelvin probe microscopy revealed that the potential distribution of the steel surfacewas shifted to the negative values, which show the adsorption of the inhibitor on the surface and inhibitionof the corrosion process. Besides, high values of adsorption energy were achieved for WPUs usingmolecular dynamic simulation, indicating their spontaneous adsorption to the Fe (110) surface. The maximumadsorption energy of 794.9 kcal/mol was obtained for WPU3, which is consistent with experimentaldata. These results show that sunflower oil can be considered a potential source to developself-catalyzed polyurethanes under mild conditions as effective corrosion inhibitors for sourenvironment.

Accuracy of maxillofacial prototypes fabricated by different 3-dimensional printing technologies using multi-slice and cone-beam computed tomography

Yousefi, Faezeh,Shokri, Abbas,Farhadian, Maryam,Vafaei, Fariborz,Forutan, Fereshte Korean Academy of Oral and Maxillofacial Radiology 2021 Imaging Science in Dentistry Vol.51 No.1

Purpose: This study aimed to compare the accuracy of 3-dimensional(3D) printed models derived from multidetector computed tomography (MDCT) and cone-beam computed tomography (CBCT) systems with different fields of view (FOVs). Materials and Methods: Five human dry mandibles were used to assess the accuracy of reconstructions of anatomical landmarks, bone defects, and intra-socket dimensions by 3D printers. The measurements were made on dry mandibles using a digital caliper (gold standard). The mandibles then underwent MDCT imaging. In addition, CBCT images were obtained using Cranex 3D and NewTom 3G scanners with 2 different FOVs. The images were transferred to two 3D printers, and the digital light processing (DLP) and fused deposition modeling (FDM) techniques were used to fabricate the 3D models, respectively. The same measurements were also made on the fabricated prototypes. The values measured on the 3D models were compared with the actual values, and the differences were analyzed using the paired t-test. Results: The landmarks measured on prototypes fabricated using the FDM and DLP techniques based on all 4 imaging systems showed differences from the gold standard. No significant differences were noted between the FDM and DLP techniques. Conclusion: The 3D printers were reliable systems for maxillofacial reconstruction. In this study, scanners with smaller voxels had the highest precision, and the DLP printer showed higher accuracy in reconstructing the maxillofacial landmarks. It seemed that 3D reconstructions of the anterior region were overestimated, while the reconstructions of intra-socket dimensions and implant holes were slightly underestimated.

Investigation the activity and stability of lysozyme on presence of magnetic nanoparticles

B. Shareghi,S. Farhadian,N. Zamani,M. Salavati-Niasari,H. Moshtaghi,S. Gholamrezaei 한국공업화학회 2015 Journal of Industrial and Engineering Chemistry Vol.21 No.1

In this work the influence of Fe2O3 magnetic nanoparticle on lysozyme activity and thermal stabilitywere investigated. The effect of nano-Fe2O3 on lysozyme activity was studied by UV–visspectrophotometry at 35 8C in pH = 7.25 using sodium phosphate as a buffer. Measurements werecarried out using 6 10 8 M (0.1 mg/ml) of lysozyme and a range of nano-Fe2O3 concentrationsbetween 0 and 0.045 mg/ml. It was found that by increasing the concentration of nano-Fe2O3, the rate ofMicrococcus lysodeikticus lyses (lysozyme activity) will be increased. On the other hand, the thermalstability of lysozyme was investigated in the presence of nano-Fe2O3 over the temperature range (from293 K to 363 K). Results indicate that by increasing of nano-Fe2O3, the thermal stability of lysozyme willbe increased.

Sex determination from lateral cephalometric radiographs using an automated deep learning convolutional neural network

Khazaei, Maryam,Mollabashi, Vahid,Khotanlou, Hassan,Farhadian, Maryam Korean Academy of Oral and Maxillofacial Radiology 2022 Imaging Science in Dentistry Vol.52 No.-

Purpose: Despite the proliferation of numerous morphometric and anthropometric methods for sex identification based on linear, angular, and regional measurements of various parts of the body, these methods are subject to error due to the observer's knowledge and expertise. This study aimed to explore the possibility of automated sex determination using convolutional neural networks(CNNs) based on lateral cephalometric radiographs. Materials and Methods: Lateral cephalometric radiographs of 1,476 Iranian subjects (794 women and 682 men) from 18 to 49 years of age were included. Lateral cephalometric radiographs were considered as a network input and output layer including 2 classes(male and female). Eighty percent of the data was used as a training set and the rest as a test set. Hyperparameter tuning of each network was done after preprocessing and data augmentation steps. The predictive performance of different architectures (DenseNet, ResNet, and VGG) was evaluated based on their accuracy in test sets. Results: The CNN based on the DenseNet121 architecture, with an overall accuracy of 90%, had the best predictive power in sex determination. The prediction accuracy of this model was almost equal for men and women. Furthermore, with all architectures, the use of transfer learning improved predictive performance. Conclusion: The results confirmed that a CNN could predict a person's sex with high accuracy. This prediction was independent of human bias because feature extraction was done automatically. However, for more accurate sex determination on a wider scale, further studies with larger sample sizes are desirable.

Sex determination from lateral cephalometric radiographs using an automated deep learning convolutional neural network

Khazaei Maryam,Mollabashi Vahid,Khotanlou Hassan,Farhadian Maryam 대한영상치의학회 2022 Imaging Science in Dentistry Vol.52 No.3

Purpose: Despite the proliferation of numerous morphometric and anthropometric methods for sex identification based on linear, angular, and regional measurements of various parts of the body, these methods are subject to error due to the observer’s knowledge and expertise. This study aimed to explore the possibility of automated sex determination using convolutional neural networks(CNNs) based on lateral cephalometric radiographs. Materials and Methods: Lateral cephalometric radiographs of 1,476 Iranian subjects (794 women and 682 men) from 18 to 49 years of age were included. Lateral cephalometric radiographs were considered as a network input and output layer including 2 classes(male and female). Eighty percent of the data was used as a training set and the rest as a test set. Hyperparameter tuning of each network was done after preprocessing and data augmentation steps. The predictive performance of different architectures (DenseNet, ResNet, and VGG) was evaluated based on their accuracy in test sets. Results: The CNN based on the DenseNet121 architecture, with an overall accuracy of 90%, had the best predictive power in sex determination. The prediction accuracy of this model was almost equal for men and women. Furthermore, with all architectures, the use of transfer learning improved predictive performance. Conclusion: The results confirmed that a CNN could predict a person’s sex with high accuracy. This prediction was independent of human bias because feature extraction was done automatically. However, for more accurate sex determination on a wider scale, further studies with larger sample sizes are desirable.