Fully automated age-weighted expression classification using real and apparent age

2022 ◽  
Author(s):  
Nora Al-Garaawi ◽  
Tim Morris ◽  
Timothy F. Cootes
Keyword(s):  
Expression Classification ◽  
Apparent Age

scholarly journals Combined method of 3H/3He apparent age and on-site helium analysis to identify groundwater flow processes and transport of perchloroethylene (PCE) in an urban area

2021 ◽  
Vol 238 ◽  
pp. 103773
Author(s):  
Christian Moeck ◽  
Andrea L. Popp ◽  
Matthias S. Brennwald ◽  
Rolf Kipfer ◽  
Mario Schirmer
Keyword(s):  
Groundwater Flow ◽  
Urban Area ◽  
Combined Method ◽  
Flow Processes ◽  
Apparent Age

Concise CNN model for face expression recognition

2021 ◽  
pp. 1-9
Author(s):  
Harshadkumar B. Prajapati ◽  
Ankit S. Vyas ◽  
Vipul K. Dabhi
Keyword(s):  
Network Architecture ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Face Expression ◽  
Computational Overhead ◽  
Average Accuracy ◽  
Proposed Model ◽  
Face Expression Recognition ◽  
Expression Classification ◽  
Machine Interaction

Face expression recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.

scholarly journals MicroRNA expression classification for pediatric multiple sclerosis identification

2021 ◽  
Author(s):  
Gabriella Casalino ◽  
Giovanna Castellano ◽  
Arianna Consiglio ◽  
Nicoletta Nuzziello ◽  
Gennaro Vessio
Keyword(s):  
Machine Learning ◽  
Multiple Sclerosis ◽  
Expression Profiles ◽  
Healthy Children ◽  
Multifactorial Diseases ◽  
Hyperactivity Disorder ◽  
Pediatric Multiple Sclerosis ◽  
Mirna Expression Profiles ◽  
Selection Algorithms ◽  
Expression Classification

Abstract MicroRNAs (miRNAs) are a set of short non-coding RNAs that play significant regulatory roles in cells. The study of miRNA data produced by Next-Generation Sequencing techniques can be of valid help for the analysis of multifactorial diseases, such as Multiple Sclerosis (MS). Although extensive studies have been conducted on young adults affected by MS, very little work has been done to investigate the pathogenic mechanisms in pediatric patients, and none from a machine learning perspective. In this work, we report the experimental results of a classification study aimed at evaluating the effectiveness of machine learning methods in automatically distinguishing pediatric MS from healthy children, based on their miRNA expression profiles. Additionally, since Attention Deficit Hyperactivity Disorder (ADHD) shares some cognitive impairments with pediatric MS, we also included patients affected by ADHD in our study. Encouraging results were obtained with an artificial neural network model based on a set of features automatically selected by feature selection algorithms. The results obtained show that models developed on automatically selected features overcome models based on a set of features selected by human experts. Developing an automatic predictive model can support clinicians in early MS diagnosis and provide new insights that can help find novel molecular pathways involved in MS disease.

scholarly journals Enhanced Expectation of External Sensations of the Chest Regulates the Emotional Perception of Fearful Faces

2021 ◽  
Vol 11 (7) ◽  
pp. 946
Author(s):  
Won-Mo Jung ◽  
In-Seon Lee ◽  
Ye-Seul Lee ◽  
Yeonhee Ryu ◽  
Hi-Joon Park ◽  
...  
Keyword(s):  
Visual Cues ◽  
Brain Activity ◽  
Body Parts ◽  
Emotional Perception ◽  
Fearful Faces ◽  
Bodily Sensations ◽  
Facial Expression Classification ◽  
Bodily States ◽  
Expression Classification ◽  
Stimulation Of

Emotional perception can be shaped by inferences about bodily states. Here, we investigated whether exteroceptive inferences about bodily sensations in the chest area influence the perception of fearful faces. Twenty-two participants received pseudo-electrical acupuncture stimulation at three different acupoints: CV17 (chest), CV23 (chin), and PC6 (left forearm). All stimuli were delivered with corresponding visual cues, and the control condition included visual cues that did not match the stimulated body sites. After the stimulation, the participants were shown images with one of five morphed facial expressions, ranging from 100% fear to 100% disgust, and asked to classify them as fearful or disgusted. Brain activity was measured using functional magnetic resonance imaging during the facial expression classification task. When the participants expected that they would receive stimulation of the chest (CV17), the ratio of fearful to non-fearful classifications decreased compared to the control condition, and brain activities within the periaqueductal gray and the default mode network decreased when they viewed fearful faces. Our findings suggest that bodily sensations around the chest, but not the other tested body parts, were selectively associated with fear perception and that altering external inferences inhibited the perception of fearful faces.

scholarly journals Influence of a physical education teacher’s perceived age on high school pupils’ perceptions of effectiveness and learning

2018 ◽  
Vol 26 (1) ◽  
pp. 22-35
Author(s):  
Colin G Pennington ◽  
Matthew D Curtner-Smith ◽  
Stefanie A Wind
Keyword(s):  
High School ◽  
Physical Education ◽  
Statistical Tests ◽  
Middle Aged ◽  
Different Ages ◽  
Twelfth Grade ◽  
Lesson Content ◽  
Perceived Age ◽  
Apparent Age

Few studies have focused on the issues older physical education (PE) teachers encounter which may limit their effectiveness. The purpose of this study was to examine the influence of a PE teacher’s apparent age on high school pupils’ learning and perceptions of the teacher. Participants were 114 ninth, tenth, eleventh, and twelfth grade pupils. They were randomly assigned to watch one of two virtually identical filmed swimming lessons taught by the same teacher. In the young-appearance lesson, the teacher taught as his normal and relatively youthful self. In the middle-aged lesson (MAL), his appearance had been aged by a theatrical makeup artist. Following the viewing of their assigned lesson, pupils completed an examination over lesson content and a questionnaire asking them about their perceptions of the teacher. Inferential statistical tests indicated that the pupils who watched the MAL perceived the teacher more favorably. Performance on the content examination was similar for pupils who watched either film. These findings provided support for a psychological/developmental explanation of how and why pupils respond to and learn from PE teachers of different ages.

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