scholarly journals Gender discrimination, age group classification and carried object recognition from gait energy image using fusion of parallel convolutional neural network

2020 ◽  
Author(s):  
Newlin Shebiah Russel ◽  
Arivazhagan Selvaraj
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Convolutional Neural Network ◽  
Gender Discrimination ◽  
Group Classification ◽  
Age Group ◽  
Gait Energy Image ◽  
Age Group Classification

scholarly journals Age Group Classification using Convolutional Neural Network (CNN)

2021 ◽  
Vol 2084 (1) ◽  
pp. 012028
Author(s):  
Muhammad Firdaus Mustapha ◽  
Nur Maisarah Mohamad ◽  
Ghazali Osman ◽  
Siti Haslini Ab Hamid
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Age Groups ◽  
Group Classification ◽  
Classification Model ◽  
Age Group ◽  
Health Security ◽  
The Face ◽  
Age Group Classification ◽  
Two Age Groups

Abstract Age group classification is a complex task that is used to classify facial images or videos into predetermined age categories. It is an important task due to its numerous applications such as health, security, authentication system, recruitment, and also in intelligent social robots. Convolutional Neural Network (CNN) has recently shown excellent performance in analysing human face images and videos. This paper proposed an age group classification task using CNN that trained and tested with an All-Age Face (AAF) dataset. FaceNet deep learning model that uses CNN was applied in this study to compute a 128-d embedding that quantifies the face of the age group. The experiment included two age groups: Adolescence and Mature Adulthood. The proposed age group classification model achieved 84.90% accuracy for the training images and 85.12% accuracy for the test images. The experimental results showed that CNN is capable of achieving competitive classification accuracy throughout two age groups in the AAF dataset with unbalanced data distribution.

scholarly journals An evolving spatio-temporal approach for gender and age group classification with Spiking Neural Networks

2017 ◽  
Vol 9 (2) ◽  
pp. 145-156 ◽  
Author(s):  
Fahad Bashir Alvi ◽  
Russel Pears ◽  
Nikola Kasabov
Keyword(s):  
Neural Networks ◽  
Group Classification ◽  
Spiking Neural Networks ◽  
Age Group ◽  
Gender And Age ◽  
Age Group Classification ◽  
Spatio Temporal

scholarly journals Establishment of age group classification for risk stratification in glioma patients

2020 ◽  
Author(s):  
Zhiying Lin ◽  
Runwei Yang ◽  
Yawei Liu ◽  
Kaishu Li ◽  
Guozhong Yi ◽  
...  
Keyword(s):  
Molecular Markers ◽  
Risk Stratification ◽  
Tumor Size ◽  
Age Groups ◽  
Tumor Location ◽  
Group Classification ◽  
Age Group ◽  
Ki 67 ◽  
Who Grade ◽  
Age Group Classification

Abstract Objective: Age is associated with the prognosis of glioma patients, but there is no uniform standard of age-group classification to evaluate the prognosis of glioma patients. In this study, we aimed to establish an age group classification for risk stratification in glioma patients. Methods: A total of 1502 patients diagnosed with gliomas at Nanfang Hospital between 2000 and 2018 were enrolled. The WHO grade of glioma was used as a dependent variable to evaluate the effect of age on risk stratification. The evaluation model was established by logistic regression, and the Akaike information criterion (AIC) value of the model was used to determine the optimal cutoff points for age-classification. The differences in gender, WHO grade, pathological subtype, tumor cell differentiation direction, tumor size, tumor location, and molecular markers between different age groups were analyzed. The molecular markers included GFAP, EMA, MGMT, p53, NeuN, Oligo2, EGFR, VEGF, IDH1, Ki-67, 1p/19q, PR, CD3, H3K27M, and TS. Results: The proportion of men with glioma was higher than that of women with glioma (58.3% vs 41.7%). Analysis of age showed that appropriate classifications of age group were 0-14 years old (pediatric group), 15-47 years old (youth group), 48-63 years old (middle-aged group), and ≥64 years old (elderly group).The proportions of glioblastoma and large tumor size (4-6 cm) increased with age (p = 0.000, p = 0.018, respectively ). Analysis of the pathological molecular markers across the four age groups showed that the proportion of patients with larger than 10% area of Ki-67 expression or positive PR expression increased with age (p = 0.000, p = 0.017, respectively). Conclusion: Age was effective evaluating the risk of glioblastoma in glioma patients. Appropriate classifications of age group for risk stratification were 0-14 years old (pediatric group), 15-47 years old (young group), 48-63 years old (middle age group) and ≥ 64 years old (elderly group). There was significant heterogeneity in WHO grade, tumor size, tumor location and some molecular markers among the four age groups.

scholarly journals Stereo Vision-Based Object Recognition and Manipulation by Regions with Convolutional Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 210 ◽  
Author(s):  
Yi-Chun Du ◽  
Muslikhin Muslikhin ◽  
Tsung-Han Hsieh ◽  
Ming-Shyan Wang
Keyword(s):  
Neural Network ◽  
Object Recognition ◽  
Convolutional Neural Network ◽  
Stereo Vision ◽  
Fuzzy Inference ◽  
Target Object ◽  
Robot Arm ◽  
Segmentation Method ◽  
Inference System ◽  
Six Degree Of Freedom

This paper develops a hybrid algorithm of adaptive network-based fuzzy inference system (ANFIS) and regions with convolutional neural network (R-CNN) for stereo vision-based object recognition and manipulation. The stereo camera at an eye-to-hand configuration firstly captures the image of the target object. Then, the shape, features, and centroid of the object are estimated. Similar pixels are segmented by the image segmentation method, and similar regions are merged through selective search. The eye-to-hand calibration is based on ANFIS to reduce computing burden. A six-degree-of-freedom (6-DOF) robot arm with a gripper will conduct experiments to demonstrate the effectiveness of the proposed system.

Sign in / Sign up

Export Citation Format

Share Document