scholarly journals A Hierarchical Framework for Facial Age Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yuyu Liang ◽  
Xianmei Wang ◽  
Li Zhang ◽  
Zhiliang Wang
Keyword(s):  
Age Estimation ◽  
Nearest Neighbor ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Age Group ◽  
K Nearest Neighbor ◽  
Complex Issue ◽  
Ordinal Information ◽  
Hierarchical Framework ◽  
Estimation System

Age estimation is a complex issue of multiclassification or regression. To address the problems of uneven distribution of age database and ignorance of ordinal information, this paper shows a hierarchic age estimation system, comprising age group and specific age estimation. In our system, two novel classifiers, sequence k-nearest neighbor (SKNN) and ranking-KNN, are introduced to predict age group and value, respectively. Notably, ranking-KNN utilizes the ordinal information between samples in estimation process rather than regards samples as separate individuals. Tested on FG-NET database, our system achieves 4.97 evaluated by MAE (mean absolute error) for age estimation.

Prediction of cavitation damage on spillway using K-nearest neighbor modeling

2014 ◽  
Vol 71 (3) ◽  
pp. 347-352 ◽  
Author(s):  
E. Fadaei Kermani ◽  
G. A. Barani ◽  
M. Ghaeini-Hessaroeyeh
Keyword(s):  
Present Model ◽  
Nearest Neighbor ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
K Nearest Neighbor ◽  
Flow Rates ◽  
Cavitation Damage ◽  
Using Data ◽  
The Stability ◽  
Observed Damage

Cavitation is a common and destructive process on spillways that threatens the stability of the structure and causes damage. In this study, based on the nearest neighbor model, a method has been presented to predict cavitation damage on spillways. The model was tested using data from the Shahid Abbaspour dam spillway in Iran. The level of spillway cavitation damage was predicted for eight different flow rates, using the nearest neighbor model. Moreover, based on the cavitation index, five damage levels from no damage to major damage have been determined. Results showed that the present model predicted damage locations and levels close to observed damage during past floods. Finally, the efficiency and precision of the model was quantified by statistical coefficients. Appropriate values of the correlation coefficient, root mean square error, mean absolute error and coefficient of residual mass show the present model is suitable and efficient.

Optimized Collaborative Filtering Recommendation Based on User’s Social Relationships

2014 ◽  
Vol 1044-1045 ◽  
pp. 1484-1488
Author(s):  
Yue Kun Fan ◽  
Xin Ye Li ◽  
Meng Meng Cao
Keyword(s):  
Collaborative Filtering ◽  
Social Relationships ◽  
Digital Libraries ◽  
Social Relationship ◽  
Nearest Neighbor ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Personalized Recommendation ◽  
Recommendation Algorithm

Currently collaborative filtering is widely used in e-commerce, digital libraries and other areas of personalized recommendation service system. Nearest-neighbor algorithm is the earliest proposed and the main collaborative filtering recommendation algorithm, but the data sparsity and cold-start problems seriously affect the recommendation quality. To solve these problems, A collaborative filtering recommendation algorithm based on users' social relationships is proposed. 0n the basis of traditional filtering recommendation technology, it combines with the interested objects of user's social relationship and takes the advantage of the tags to projects marked by users and their interested objects to improve the methods of recommendation. The experimental results of MAE ((Mean Absolute Error)) verify that this method can get better quality of recommendation.

Machine learning and Grad-Cam based vascular aging assessment using photoplethysmogram (Preprint)

2021 ◽  
Author(s):  
Hangsik Shin
Keyword(s):  
Machine Learning ◽  
Correlation Coefficient ◽  
Age Estimation ◽  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Vascular Aging ◽  
Squared Error ◽  
Vascular Age

BACKGROUND Arterial stiffness due to vascular aging is a major indicator for evaluating cardiovascular risk. OBJECTIVE In this study, we propose a method of estimating age by applying machine learning to photoplethysmogram for non-invasive vascular age assessment. METHODS The machine learning-based age estimation model that consists of three convolutional layers and two-layer fully connected layers, was developed using segmented photoplethysmogram by pulse from a total of 752 adults aged 19–87 years. The performance of the developed model was quantitatively evaluated using mean absolute error, root-mean-squared-error, Pearson’s correlation coefficient, coefficient of determination. The Grad-Cam was used to explain the contribution of photoplethysmogram waveform characteristic in vascular age estimation. RESULTS Mean absolute error of 8.03, root mean squared error of 9.96, 0.62 of correlation coefficient, and 0.38 of coefficient of determination were shown through 10-fold cross validation. Grad-Cam, used to determine the weight that the input signal contributes to the result, confirmed that the contribution to the age estimation of the photoplethysmogram segment was high around the systolic peak. CONCLUSIONS The machine learning-based vascular aging analysis method using the PPG waveform showed comparable or superior performance compared to previous studies without complex feature detection in evaluating vascular aging. CLINICALTRIAL 2015-0104

scholarly journals Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2424 ◽  
Author(s):  
Md Atiqur Rahman Ahad ◽  
Thanh Trung Ngo ◽  
Anindya Das Antar ◽  
Masud Ahmed ◽  
Tahera Hossain ◽  
...  
Keyword(s):  
Deep Learning ◽  
Age Estimation ◽  
Prediction Error ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Training Dataset ◽  
Wearable Sensor ◽  
Age And Gender ◽  
Test Dataset ◽  
And Gender

Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the 12th IAPR International Conference on Biometrics (ICB), 2019. In this competition, 18 teams initially registered from 14 countries. The goal of this challenge was to find some smart approaches to deal with age and gender estimation from sensor-based gait data. For this purpose, we employed a large wearable sensor-based gait dataset, which has 745 subjects (357 females and 388 males), from 2 to 78 years old in the training dataset; and 58 subjects (19 females and 39 males) in the test dataset. It has several walking patterns. The gait data sequences were collected from three IMUZ sensors, which were placed on waist-belt or at the top of a backpack. There were 67 solutions from ten teams—for age and gender estimation. This paper extensively analyzes the methods and achieved-results from various approaches. Based on analysis, we found that deep learning-based solutions lead the competitions compared with conventional handcrafted methods. We found that the best result achieved 24.23% prediction error for gender estimation, and 5.39 mean absolute error for age estimation by employing angle embedded gait dynamic image and temporal convolution network.

Foot Age Estimation System from Walking Dynamics Based on Fuzzy Logic

2014 ◽  
Vol 18 (4) ◽  
pp. 489-498 ◽  
Author(s):  
Takahiro Takeda ◽  
◽  
Yoshitada Sakai ◽  
Syoji Kobashi ◽  
Kei Kuramoto ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Pressure Distribution ◽  
Age Estimation ◽  
Age Groups ◽  
Absolute Error ◽  
Elderly Age ◽  
Age Related ◽  
Gait Features ◽  
Walking Dynamics ◽  
Estimation System

This paper describes a foot-age estimation system based on fuzzy logic. The foot-age is one of age related indexes, and it shows the degree of aging by the gait condition. The system estimates the foot-age from sole pressure distribution change during walking. The sole pressure distribution is acquired by a mat-type load distribution sensor. Our estimation system extracts four gait features from sole pressure data, and calculates fuzzy degrees for young age,middle age and elderly age groups from these gait features. The footage of the walking person on the sensor is calculated by fuzzy MIN-MAX center of gravity method. In our experiment, we employed 93 male and 132 female volunteers, and the system estimated their foot-ages with low mean absolute error for their true ages. Additionally, we developed a diagnosis system based on estimated foot-age.

scholarly journals Regional Forest Volume Estimation by Expanding LiDAR Samples Using Multi-Sensor Satellite Data

2020 ◽  
Vol 12 (3) ◽  
pp. 360 ◽  
Author(s):  
Bo Xie ◽  
Chunxiang Cao ◽  
Min Xu ◽  
Barjeece Bashir ◽  
Ramesh P. Singh ◽  
...  
Keyword(s):  
Satellite Data ◽  
Nearest Neighbor ◽  
Field Measurements ◽  
Absolute Error ◽  
Timber Harvesting ◽  
Volume Estimation ◽  
Accurate Information ◽  
K Nearest Neighbor ◽  
Entire Area ◽  
Regional Forest

Accurate information regarding forest volume plays an important role in estimating afforestation, timber harvesting, and forest ecological services. Traditionally, operations on forest growing stock volume using field measurements are labor-intensive and time-consuming. Recently, remote sensing technology has emerged as a time-cost efficient method for forest inventory. In the present study, we have adopted three procedures, including samples expanding, feature selection, and results generation and evaluation. Extrapolating the samples from Light Detection and Ranging (LiDAR) scanning is the most important step in satisfying the requirement of sample size for nonparametric methods operation and result in accuracy improvement. Besides, mean decrease Gini (MDG) methodology embedded into Random Forest (RF) algorithm served as a selector for feature measure; afterwards, RF and K-Nearest Neighbor (KNN) were adopted in subsequent forest volume prediction. The results show that the retrieval of Forest volume in the entire area was in the range of 50–360 m3/ha, and the results from the two models show a better consistency while using the sample combination extrapolated by the optimal threshold value (2 × 10−4), leading to the best performances of RF (R2 = 0.618, root mean square error, RMSE = 43.641 m3/ha, mean absolute error, MAE = 33.016 m3/ha), followed by KNN (R2 = 0.617, RMSE = 43.693 m3/ha, MAE = 32.534 m3/ha). The detailed analysis that is discussed in the present paper clearly shows that expanding image-derived LiDAR samples helps in refining the prediction of regional forest volume while using satellite data and nonparametric models.

scholarly journals Cross-Racial Automatic Age Estimation from Facial Images using Deep Learning

2021 ◽  
Vol 9 (9) ◽  
pp. 1288-1294
Keyword(s):  
Deep Learning ◽  
Age Estimation ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Learning Approach ◽  
Racial Groups ◽  
Human Beings ◽  
The Mean ◽  
Facial Images ◽  
Facial Age

This paper presents a deep learning approach for age estimation of human beings using their facial images. The different racial groups based on skin colour have been incorporated in the annotations of the images in the dataset, while ensuring an adequate distribution of subjects across the racial groups so as to achieve an accurate Automatic Facial Age Estimation (AFAE). The principle of transfer learning is applied to the ResNet50 Convolutional Neural Network (CNN) initially pretrained for the task of object classification and finetuning it’s hyperparameters to propose an AFAE system that can be used to automate ages of humans across multiple racial groups. The mean absolute error of 4.25 years is obtained at the end of the research which proved the effectiveness and superiority of the proposed method.

scholarly journals Deep Convolutional neural network (CNN) in tea leaf chlorophyll estimation: A new direction of modern tea farming in Assam, India

2021 ◽  
Vol 13 (3) ◽  
pp. 1059-1064
Author(s):  
Utpal Barman
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Nearest Neighbor ◽  
Light Condition ◽  
Absolute Error ◽  
Small Scale ◽  
K Nearest Neighbor ◽  
Leaf Chlorophyll ◽  
Colour Features ◽  
Tea Leaf

This study presents the uprising of leaf chlorophyll estimation from traditional mechanical method to machine learning-based method. Earlier chlorophyll estimation techniques such as Spectrophotometer and Soil Plant Analysis Development (SPAD) meter demand cost, time, labour, skill, and expertise. A small-scale tea farmer may not afford these devices. The present study reports a low-cost digital method to predict the tea leaf chlorophyll using 1-D Convolutional Neural Network (1-D CNN). After capturing the tea leaf images using a digital camera in a natural light condition, a total of 12 different colour features were extracted from tea leaf images. A SPAD was used to estimate the original chlorophyll value of the tea leaves. The paper shows the correlation of original tea leaf chlorophyll with the extracted colour features of the tea leaf images. Apart from 1-D CNN, the Multiple Linear Regression (MLR) and K-Nearest Neighbor (KNN) were also applied to predict the tea leaf chlorophyll and compared their results with the 1-D CNN. The 1-D CNN model outperformed with an accuracy of 81.1%, Mean Absolute Error (MAE) of 3.01, and Root Mean Square Error (RMSE) of 4.18. The investigation system is very simple and cost-effective. It can be used in tea farming as a digital SPAD for faster and accurate leaf chlorophyll estimation in an easy way.

Automatic Age Estimation Based on LBP and GLCM Features Using SVM

2017 ◽  
Vol 7 (11) ◽  
pp. 186
Author(s):  
VENKATARAO RAMPAY
Keyword(s):  
Age Estimation ◽  
Local Binary Pattern ◽  
State Of The Art ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Support Vector ◽  
Facial Features ◽  
State Of Mind ◽  
Occurrence Matrix ◽  
The Mind

Face is generally considered as the reference frame of mind. Therefore, to estimate the feeling of the mind, many authors have considered the emotions from the facial expressions into consideration to identify the state of mind of an individual. Hence in this article we proposed a methodology for automatic age estimation based on Local Binary Pattern (LBP) and Grey Level Co- Occurrence Matrix (GLCM). The facial features are extracted using LBP and GLCM and these features are given as input’s to the Support Vector Machine (SVM) for age estimation. The experimentation on proposed method is carried out using FG-NET database and Mean Absolute Error (MAE) is calculated to compare the proposed method with state-of-the-art algorithms. Finally, the proposed methodology demonstrates the classification accuracy above 88%. 

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