Adolescent age estimation using voice features

2020 ◽  
Vol 65 (4) ◽  
pp. 429-434
Author(s):  
Marcin D. Bugdol ◽  
Monika N. Bugdol ◽  
Maria J. Bieńkowska ◽  
Anna Lipowicz ◽  
Agata M. Wijata ◽  
...  
Keyword(s):  
Age Estimation ◽  
Cross Validation ◽  
Rapid Development ◽  
Absolute Error ◽  
Regression Method ◽  
Chronological Age ◽  
Height Measurement ◽  
Voice Signal ◽  
Selection Operator ◽  
Fold Cross Validation

AbstractIn this paper, a method for evaluating the chronological age of adolescents on the basis of their voice signal is presented. For every examined child, the vowels a, e, i, o and u were recorded in extended phonation. Sixty voice parameters were extracted from each recording. Voice recordings were supplemented with height measurement in order to check if it could improve the accuracy of the proposed solution. Predictor selection was performed using the LASSO (least absolute shrinkage and selection operator) algorithm. For age estimation, the random forest (RF) for regression method was employed and it was tested using a 10-fold cross-validation. The lowest absolute error (0.37 year ± 0.28) was obtained for boys only when all selected features were included into prediction. In all cases, the achieved accuracy was higher for boys than for girls, which results from the fact that the change of voice with age is larger for men than for women. The achieved results suggest that the presented approach can be employed for accurate age estimation during rapid development in children.

scholarly journals Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Feng Zhou ◽  
Meng-Meng Yin ◽  
Cui-Na Jiao ◽  
Zhen Cui ◽  
Jing-Xiu Zhao ◽  
...  
Keyword(s):  
Bipartite Graph ◽  
Matrix Factorization ◽  
Cross Validation ◽  
Rapid Development ◽  
Factorization Method ◽  
Computational Method ◽  
Human Diseases ◽  
Simulation Experiments ◽  
Disease Associations ◽  
Fold Cross Validation

Abstract Background With the rapid development of various advanced biotechnologies, researchers in related fields have realized that microRNAs (miRNAs) play critical roles in many serious human diseases. However, experimental identification of new miRNA–disease associations (MDAs) is expensive and time-consuming. Practitioners have shown growing interest in methods for predicting potential MDAs. In recent years, an increasing number of computational methods for predicting novel MDAs have been developed, making a huge contribution to the research of human diseases and saving considerable time. In this paper, we proposed an efficient computational method, named bipartite graph-based collaborative matrix factorization (BGCMF), which is highly advantageous for predicting novel MDAs. Results By combining two improved recommendation methods, a new model for predicting MDAs is generated. Based on the idea that some new miRNAs and diseases do not have any associations, we adopt the bipartite graph based on the collaborative matrix factorization method to complete the prediction. The BGCMF achieves a desirable result, with AUC of up to 0.9514 ± (0.0007) in the five-fold cross-validation experiments. Conclusions Five-fold cross-validation is used to evaluate the capabilities of our method. Simulation experiments are implemented to predict new MDAs. More importantly, the AUC value of our method is higher than those of some state-of-the-art methods. Finally, many associations between new miRNAs and new diseases are successfully predicted by performing simulation experiments, indicating that BGCMF is a useful method to predict more potential miRNAs with roles in various diseases.

scholarly journals Construction and Comprehensive Analysis of a Molecular Association Network via lncRNA–miRNA –Disease–Drug–Protein Graph

Cells ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 866 ◽  
Author(s):  
Guo ◽  
Yi ◽  
You
Keyword(s):  
Molecular Mechanisms ◽  
Cross Validation ◽  
Rapid Development ◽  
Molecular Association ◽  
Global Perspective ◽  
Association Network ◽  
Disease Treatment ◽  
Molecular Fingerprints ◽  
New Interactions ◽  
Fold Cross Validation

One key issue in the post-genomic era is how to systematically describe the associations between small molecule transcripts or translations inside cells. With the rapid development of high-throughput “omics” technologies, the achieved ability to detect and characterize molecules with other molecule targets opens the possibility of investigating the relationships between different molecules from a global perspective. In this article, a molecular association network (MAN) is constructed and comprehensively analyzed by integrating the associations among miRNA, lncRNA, protein, drug, and disease, in which any kind of potential associations can be predicted. More specifically, each node in MAN can be represented as a vector by combining two kinds of information including the attribute of the node itself (e.g., sequences of ncRNAs and proteins, semantics of diseases and molecular fingerprints of drugs) and the behavior of the node in the complex network (associations with other nodes). A random forest classifier is trained to classify and predict new interactions or associations between biomolecules. In the experiment, the proposed method achieved a superb performance with an area under curve (AUC) of 0.9735 under a five-fold cross-validation, which showed that the proposed method could provide new insight for exploration of the molecular mechanisms of disease and valuable clues for disease treatment.

scholarly journals Machine learning meets pKa

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 113 ◽  
Author(s):  
Marcel Baltruschat ◽  
Paul Czodrowski
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
External Validation ◽  
Absolute Error ◽  
Source Model ◽  
Squared Error ◽  
Fold Cross Validation ◽  
Better Than

We present a small molecule pKa prediction tool entirely written in Python. It predicts the macroscopic pKa value and is trained on a literature compilation of monoprotic compounds. Different machine learning models were tested and random forest performed best given a five-fold cross-validation (mean absolute error=0.682, root mean squared error=1.032, correlation coefficient r2 =0.82). We test our model on two external validation sets, where our model performs comparable to Marvin and is better than a recently published open source model. Our Python tool and all data is freely available at https://github.com/czodrowskilab/Machine-learning-meets-pKa.

Discovering Emotion-Inducing Music Features Using EEG Signals

2013 ◽  
Vol 17 (3) ◽  
pp. 362-370 ◽  
Author(s):  
Rafael Cabredo ◽  
◽  
Roberto Legaspi ◽  
Paul Salvador Inventado ◽  
Masayuki Numao ◽  
...  
Keyword(s):  
Cross Validation ◽  
Absolute Error ◽  
Window Length ◽  
Analysis Method ◽  
Eeg Signals ◽  
Eeg Data ◽  
Windowing Technique ◽  
High Level ◽  
Fold Cross Validation ◽  
Emotion Models

Music induces different kinds of emotions in listeners. Previous research on music and emotions discovered that different music features can be used for classifying how certain music can induce emotions in an individual. We propose a method for collecting electroencephalograph (EEG) data from subjects listening to emotion-inducing music. The EEG data is used to continuously label high-level music features with continuous-valued emotion annotations using the emotion spectrum analysis method. The music features are extracted fromMIDI files using a windowing technique. We highlight the results of two emotion models for stress and relaxation which were constructed using C4.5. Evaluations of the models using 10-fold cross validation give promising results with an average relative absolute error of 6.54% using a window length of 38.4 seconds.

scholarly journals Construction and Comprehensive Analysis of a Molecular Associations Network via lncRNA-miRNA -disease-drug-protein Graph

2019 ◽  
Author(s):  
Zhen-Hao Guo ◽  
Hai-Cheng Yi ◽  
Zhu-Hong You
Keyword(s):  
Molecular Mechanisms ◽  
Cross Validation ◽  
Rapid Development ◽  
Comprehensive Analysis ◽  
Global Perspective ◽  
Disease Treatment ◽  
Molecular Fingerprints ◽  
Molecular Associations ◽  
New Interactions ◽  
Fold Cross Validation

The key issue in the post-genomic era is how to systematically describe the association between small molecule transcripts or translations inside cells. With the rapid development of high-throughput “omics” technologies, the achieved ability to detect and characterize molecules with other molecule targets opens up the possibility of investigating the relationships between different molecules from a global perspective. In this article, a Molecular Associations Network(MAN) is constructed and comprehensively analyzed by integrating the associations among miRNA, lncRNA, protein, drug, and disease, in which any kind of potential associations can be predicted. More specifically, each node in MAN can be represented as a vector by combining two kinds of information including the attributes of the node itself (e.g. sequences of ncRNAs and proteins, semantics of diseases and molecular fingerprints of drugs) and the manner of the node in the complex network (associations with other nodes). Random Forest classifier is trained to classify and predict new interactions or associations between biomolecules. In the experiment, the proposed method achieves a superb performance with 0.9735 AUC in 5-fold cross-validation, which show that the proposed method can provide new insight for exploration of the molecular mechanisms of disease and valuable clues for disease treatment.

scholarly journals Automated age estimation of young individuals based on 3D knee MRI using deep learning

2020 ◽  
Author(s):  
Markus Auf der Mauer ◽  
Eilin Jopp-van Well ◽  
Jochen Herrmann ◽  
Michael Groth ◽  
Michael M. Morlock ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Age Estimation ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Chronological Age ◽  
Computer Based ◽  
Young Subjects ◽  
Magnetic Resonance Imaging Mri ◽  
Age Range

AbstractAge estimation is a crucial element of forensic medicine to assess the chronological age of living individuals without or lacking valid legal documentation. Methods used in practice are labor-intensive, subjective, and frequently comprise radiation exposure. Recently, also non-invasive methods using magnetic resonance imaging (MRI) have evaluated and confirmed a correlation between growth plate ossification in long bones and the chronological age of young subjects. However, automated and user-independent approaches are required to perform reliable assessments on large datasets. The aim of this study was to develop a fully automated and computer-based method for age estimation based on 3D knee MRIs using machine learning. The proposed solution is based on three parts: image-preprocessing, bone segmentation, and age estimation. A total of 185 coronal and 404 sagittal MR volumes from Caucasian male subjects in the age range of 13 and 21 years were available. The best result of the fivefold cross-validation was a mean absolute error of 0.67 ± 0.49 years in age regression and an accuracy of 90.9%, a sensitivity of 88.6%, and a specificity of 94.2% in classification (18-year age limit) using a combination of convolutional neural networks and tree-based machine learning algorithms. The potential of deep learning for age estimation is reflected in the results and can be further improved if it is trained on even larger and more diverse datasets.

scholarly journals Age estimation based on 3D post-mortem computed tomography images of mandible and femur using convolutional neural networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251388
Author(s):  
Cuong Van Pham ◽  
Su-Jin Lee ◽  
So-Yeon Kim ◽  
Sookyoung Lee ◽  
Soo-Hyung Kim ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Age Estimation ◽  
Cross Validation ◽  
Absolute Error ◽  
Post Mortem ◽  
Age Assessment ◽  
Computed Tomography Images ◽  
Forensic Service ◽  
Domain Specific Knowledge ◽  
Prediction Approach

Age assessment has attracted increasing attention in the field of forensics. However, most existing works are laborious and requires domain-specific knowledge. Modern computing power makes it is possible to leverage massive amounts of data to produce more reliable results. Therefore, it is logical to use automated age estimation approaches to handle large datasets. In this study, a fully automated age prediction approach was proposed by assessing 3D mandible and femur scans using deep learning. A total of 814 post-mortem computed tomography scans from 619 men and 195 women, within the age range of 20–70, were collected from the National Forensic Service in South Korea. Multiple preprocessing steps were applied for each scan to normalize the image and perform intensity correction to create 3D voxels that represent these parts accurately. The accuracy of the proposed method was evaluated by 10-fold cross-validation. The initial cross-validation results illustrated the potential of the proposed method as it achieved a mean absolute error of 5.15 years with a concordance correlation coefficient of 0.80. The proposed approach is likely to be faster and potentially more reliable, which could be used for age assessment in the future.

Age Estimation from Facial Images using Transfer Learning and K-fold Cross-Validation

2021 ◽  
Author(s):  
S.M. Shihab Uddin ◽  
Md. Samin Morshed ◽  
Mahruf Islam Prottoy ◽  
A.B.M. Ashikur Rahman
Keyword(s):  
Transfer Learning ◽  
Age Estimation ◽  
Cross Validation ◽  
Facial Images ◽  
Fold Cross Validation

scholarly journals Machine learning meets pKa

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 113
Author(s):  
Marcel Baltruschat ◽  
Paul Czodrowski
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
External Validation ◽  
Absolute Error ◽  
Source Model ◽  
Squared Error ◽  
Fold Cross Validation ◽  
Better Than

We present a small molecule pKa prediction tool entirely written in Python. It predicts the macroscopic pKa value and is trained on a literature compilation of monoprotic compounds. Different machine learning models were tested and random forest performed best given a five-fold cross-validation (mean absolute error=0.682, root mean squared error=1.032, correlation coefficient r2 =0.82). We test our model on two external validation sets, where our model performs comparable to Marvin and is better than a recently published open source model. Our Python tool and all data is freely available at https://github.com/czodrowskilab/Machine-learning-meets-pKa.

scholarly journals Expert cancer model using supervised algorithms with a LASSO selection approach

2021 ◽  
Vol 11 (3) ◽  
pp. 2631
Author(s):  
Pronab Ghosh ◽  
Asif Karim ◽  
Syeda Tanjila Atik ◽  
Saima Afrin ◽  
Mohd. Saifuzzaman
Keyword(s):  
Breast Cancer ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Disease Model ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Cancer Disease ◽  
Critical Issues ◽  
Selection Operator ◽  
Fold Cross Validation

One of the most critical issues of the mortality rate in the medical field in current times is breast cancer. Nowadays, a large number of men and women is facing cancer-related deaths due to the lack of early diagnosis systems and proper treatment per year. To tackle the issue, various data mining approaches have been analyzed to build an effective model that helps to identify the different stages of deadly cancers. The study successfully proposes an early cancer disease model based on five different supervised algorithms such as logistic regression (henceforth LR), decision tree (henceforth DT), random forest (henceforth RF), Support vector machine (henceforth SVM), and K-nearest neighbor (henceforth KNN). After an appropriate preprocessing of the dataset, least absolute shrinkage and selection operator (LASSO) was used for feature selection (FS) using a 10-fold cross-validation (CV) approach. Employing LASSO with 10-fold cross-validation has been a novel steps introduced in this research. Afterwards, different performance evaluation metrics were measured to show accurate predictions based on the proposed algorithms. The result indicated top accuracy was received from RF classifier, approximately 99.41% with the integration of LASSO. Finally, a comprehensive comparison was carried out on Wisconsin breast cancer (diagnostic) dataset (WBCD) together with some current works containing all features.

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