scholarly journals Recognition of Emotional State Based On Handwriting Analysis and Psychological Assessment

2019 ◽  
Vol 8 (6) ◽  
pp. 4395-4402
Keyword(s):  
Psychological Assessment ◽  
Signal Analysis ◽  
Cross Validation ◽  
Emotional State ◽  
Psychological Treatment ◽  
Negative Emotions ◽  
Sample Analysis ◽  
Handwriting Analysis ◽  
Test Dataset ◽  
The Individual

Emotions describe the physiological states of an individual and are generated subconsciously. They motivate, organize, and guide perception, thought, and action. Emotions can be positive or negative. Negative emotions manifest in the form of depression, anxiety and stress. It is necessary to identify negative emotions of an individual who might be in the need for counseling or psychological treatment. Body signal analysis, handwriting analysis, and psychological assessment are some mechanisms to measure them. In this paper, emotional state is being measured through the person’s handwriting sample analysis and psychological assessment. Psychological assessment is done by using the results of DASS questionnaire attempted by the individual. Convolutional Neural Network (CNN) algorithm is used to find the emotional state of an individual from his/her handwriting sample. Comparative analysis is performed to suggest counseling/medication if required. The final CNN model is formed by using the ensemble method over cross-validation models. The accuracy achieved by the CNN model over the test dataset is 91.25%.

scholarly journals FBSEM: A Novel Feature-Based Stacked Ensemble Method for Sentiment Analysis

2020 ◽  
Vol 12 (6) ◽  
pp. 11-22
Author(s):  
Yasin Görmez ◽  
◽  
Yunus E. Işık ◽  
Mustafa Temiz ◽  
Zafer Aydın
Keyword(s):  
Sentiment Analysis ◽  
Cross Validation ◽  
Emotional State ◽  
Ensemble Methods ◽  
Extraction Methods ◽  
Error Rates ◽  
Ensemble Method ◽  
Learning Tasks ◽  
Feature Based ◽  
The Individual

Sentiment analysis is the process of determining the attitude or the emotional state of a text automatically. Many algorithms are proposed for this task including ensemble methods, which have the potential to decrease error rates of the individual base learners considerably. In many machine learning tasks and especially in sentiment analysis, extracting informative features is as important as developing sophisticated classifiers. In this study, a stacked ensemble method is proposed for sentiment analysis, which systematically combines six feature extraction methods and three classifiers. The proposed method obtains cross-validation accuracies of 89.6%, 90.7% and 67.2% on large movie, Turkish movie and SemEval-2017 datasets, respectively, outperforming the other classifiers. The accuracy improvements are shown to be statistically significant at the 99% confidence level by performing a Z-test.

scholarly journals NIMG-08. PREDICTION OF LOWER-GRADE GLIOMA MOLECULAR SUBTYPES USING DEEP LEARNING

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii148-ii148
Author(s):  
Yoshihiro Muragaki ◽  
Yutaka Matsui ◽  
Takashi Maruyama ◽  
Masayuki Nitta ◽  
Taiichi Saito ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cross Validation ◽  
Molecular Subtype ◽  
Learning Model ◽  
Group Classification ◽  
Training Dataset ◽  
Lower Grade ◽  
Test Dataset ◽  
Ct Data ◽  
Deep Learning Model

Abstract INTRODUCTION It is useful to know the molecular subtype of lower-grade gliomas (LGG) when deciding on a treatment strategy. This study aims to diagnose this preoperatively. METHODS A deep learning model was developed to predict the 3-group molecular subtype using multimodal data including magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT). The performance was evaluated using leave-one-out cross validation with a dataset containing information from 217 LGG patients. RESULTS The model performed best when the dataset contained MRI, PET, and CT data. The model could predict the molecular subtype with an accuracy of 96.6% for the training dataset and 68.7% for the test dataset. The model achieved test accuracies of 58.5%, 60.4%, and 59.4% when the dataset contained only MRI, MRI and PET, and MRI and CT data, respectively. The conventional method used to predict mutations in the isocitrate dehydrogenase (IDH) gene and the codeletion of chromosome arms 1p and 19q (1p/19q) sequentially had an overall accuracy of 65.9%. This is 2.8 percent point lower than the proposed method, which predicts the 3-group molecular subtype directly. CONCLUSIONS AND FUTURE PERSPECTIVE A deep learning model was developed to diagnose the molecular subtype preoperatively based on multi-modality data in order to predict the 3-group classification directly. Cross-validation showed that the proposed model had an overall accuracy of 68.7% for the test dataset. This is the first model to double the expected value for a 3-group classification problem, when predicting the LGG molecular subtype. We plan to apply the techniques of heat map and/or segmentation for an increase in prediction accuracy.

PRAGMATIC POTENTIAL OF AUTHOR’S AND TRANSFORMED PHRASEOLOGIES IN THE WORK OF CARLOS RUIZ ZAFÓN «THE PRISONER OF HEAVEN»

2021 ◽  
pp. 18-28
Author(s):  
A. Yu. Bovsunivska A. Yu.
Keyword(s):  
Emotional State ◽  
Spanish Language ◽  
The Novel ◽  
Unique Combination ◽  
Textual Space ◽  
Different Types ◽  
General Linguistic ◽  
The Individual ◽  
Pragmatic Aspects ◽  
Individual Style

The article is devoted to the study of pragmatic aspects of the use of phraseology in the textual space of Carlos Ruiz Safón’s novel «Prisoner of Heaven». One of the defining features of the individual style of this well-known modern Spanish writer is the metaphoricity and figuration of aristic expression, the saturation of the text with phraseological units that play a significant role in creating a pragmatic charge of the work of art. Along with general linguistic phraseological units, which include commonly-used vocabulary, the author uses dialectal and authorial phraseological units, which is a feature of his individual style. All three designated groups of phraseological units mostly reflect the negative psychophysical and emotional state of the characters. The author uses dialectal, individually-authorial and modified phraseological units, which is a feature of his individual style. It is determined that transformation is one of the most productive and most effective ways to update linguistic means in works of art. Author’s modification of FU leads to a change in the semantics and structure of expression, gives it a more expressive or emotional coloring. Transformed phraseology is limited to individual usage and is subject to the context of the work. Modified FUs in the Zafón’s artistic space acquire certain aesthetic and artistic qualities. Their modification is mainly to create the desired stylistic effect – to achieve emotional or expressive expression, which increases the reader’s interest, focuses on the content, issues of the work, as well as reveals the potential expressive potential of the Spanish language. In the transformed FUs, not just a new meaning is traced, but a combination of the well-known and the occasional. The unique combination of different types of phraseological units in the novel is considered a manifestation of individual style and makes a representation of the individually-authorial linguistic picture of the world more expressive.

An experimental study on the effect of emotion lines in comics

Semiotica ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Amitash Ojha ◽  
Charles Forceville ◽  
Bipin Indurkhya
Keyword(s):  
Experimental Study ◽  
Emotional State ◽  
Negative Emotions ◽  
Emotional States ◽  
One To One

Abstract Both mainstream and art comics often use various flourishes surrounding characters’ heads. These so-called “pictorial runes” (also called “emanata”) help convey the emotional states of the characters. In this paper, using (manipulated) panels from Western and Indian comic albums as well as neutral emoticons and basic shapes in different colors, we focus on the following two issues: (a) whether runes increase the awareness in comics readers about the emotional state of the character; and (b) whether a correspondence can be found between the types of runes (twirls, spirals, droplets, and spikes) and specific emotions. Our results show that runes help communicate emotion. Although no one-to-one correspondence was found between the tested runes and specific emotions, it was found that droplets and spikes indicate generic emotions, spirals indicate negative emotions, and twirls indicate confusion and dizziness.

scholarly journals Research on Feature Extracted Method for Flutter Test Based on EMD and CNN

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hua Zheng ◽  
Zhenglong Wu ◽  
Shiqiang Duan ◽  
Jiangtao Zhou
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Wind Tunnel ◽  
Signal Analysis ◽  
Learning Algorithm ◽  
Structural Information ◽  
Structural Response ◽  
Training Dataset ◽  
Measured Signal ◽  
Test Dataset

Due to the inevitable deviations between the results of theoretical calculations and physical experiments, flutter tests and flutter signal analysis often play significant roles in designing the aeroelasticity of a new aircraft. The measured structural response from aeroelastic models in both wind tunnel tests and real fight flutter tests contain an abundance of structural information, but traditional methods tend to have limited ability to extract features of concern. Inspired by deep learning concepts, a novel feature extraction method for flutter signal analysis was established in this study by combining the convolutional neural network (CNN) with empirical mode decomposition (EMD). It is widely hypothesized that when flutter occurs, the measured structural signals are harmonic or divergent in the time domain, and that the flutter modal (1) is singular and (2) its energy increases significantly in the frequency domain. A measured-signal feature extraction and flutter criterion framework was constructed accordingly. The measured signals from a wind tunnel test were manually labeled “flutter” and “no-flutter” as the foundational dataset for the deep learning algorithm. After the normalized preprocessing, the intrinsic mode functions (IMFs) of the flutter test signals are obtained by the EMD method. The IMFs are then reshaped to make them the suitable size to be input to the CNN. The CNN parameters are optimized though the training dataset, and the trained model is validated through the test dataset (i.e., cross-validation). The accuracy rate of the proposed method reached 100% on the test dataset. The training model appears to effectively distinguish whether or not the structural response signal contains flutter. The combination of EMD and CNN provides effective feature extraction of time series signals in flutter test data. This research explores the connection between structural response signals and flutter from the perspective of artificial intelligence. The method allows for real-time, online prediction with low computational complexity.

scholarly journals Web Application for Emotion-based Music Player

2021 ◽  
Vol 9 (12) ◽  
pp. 11-14
Author(s):  
Kamal Naina Soni
Keyword(s):  
Computer Vision ◽  
Human Brain ◽  
Facial Expressions ◽  
Web Application ◽  
Emotional State ◽  
Current State ◽  
The Face ◽  
Current Mood ◽  
The Individual ◽  
Music Player

Abstract: Human expressions play an important role in the extraction of an individual's emotional state. It helps in determining the current state and mood of an individual, extracting and understanding the emotion that an individual has based on various features of the face such as eyes, cheeks, forehead, or even through the curve of the smile. A survey confirmed that people use Music as a form of expression. They often relate to a particular piece of music according to their emotions. Considering these aspects of how music impacts a part of the human brain and body, our project will deal with extracting the user’s facial expressions and features to determine the current mood of the user. Once the emotion is detected, a playlist of songs suitable to the mood of the user will be presented to the user. This can be a big help to alleviate the mood or simply calm the individual and can also get quicker song according to the mood, saving time from looking up different songs and parallel developing a software that can be used anywhere with the help of providing the functionality of playing music according to the emotion detected. Keywords: Music, Emotion recognition, Categorization, Recommendations, Computer vision, Camera

Cost-effectiveness and cost-utility evaluation of the individual vs. group transdiagnostic psychological treatment for emotional disorders in Primary Care (PsicAP-Costs) v2

2021 ◽  
Author(s):  
Angel Aguilera-Martin ◽  
Mario Gálvez-Lara ◽  
Fátima Cuadrado ◽  
Eliana Moreno ◽  
Francisco García-Torres ◽  
...  
Keyword(s):  
Primary Care ◽  
Cost Effectiveness ◽  
Emotional Disorders ◽  
Cognitive Behavioural Therapy ◽  
Psychological Treatment ◽  
Behavioural Therapy ◽  
Cost Utility ◽  
Cognitive Behavioural ◽  
Utility Evaluation ◽  
The Individual

The aim of this study is to compare, in cost-effectiveness and cost-utility terms, a brief transdiagnostic cognitive-behavioural therapy in two different modes, individual and group, with the treatment usually administered in primary care (TAU). Participants between 18 and 65 years old and with, according to the pretreatment evaluation, mild to moderate emotional disorders will be randomly allocated to the three clusters. They will be assessed again immediately after treatment and 6 and 12 months later. ClinicalTrials.gov: NCT04847310

The Architecture and Early Findings of a Working SMS-Based System for Individuals with Mild to Moderate Depression

2013 ◽  
pp. 20-32
Author(s):  
Elizabeth M. LaRue ◽  
Hassan A. Karimi ◽  
Ann M. Mitchell ◽  
Joy Y. Zang
Keyword(s):  
Health Care Professionals ◽  
Emotional State ◽  
Mental Health Disorders ◽  
Emotional Reaction ◽  
Text Messages ◽  
Mobile Technologies ◽  
The World ◽  
Health Disorders ◽  
The Individual

Depression is one of the leading mental health disorders in the world. With an exponential rate of growth, the disease will soon surpass the ability of health care professionals to monitor and treat individuals. The use of mobile technologies offers new insights into disease progression, real-time emotional reaction data collection, and care in vivo. This chapter describes the architecture of a software system that continuously monitors an individual’s emotional state through SMS and responds to the individual with supportive text messages. Along with early findings from the working system, the development of the emotional state queries and responses is described.

scholarly journals Heartbeat Detection by Laser Doppler Vibrometry and Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5362 ◽  
Author(s):  
Luca Antognoli ◽  
Sara Moccia ◽  
Lucia Migliorelli ◽  
Sara Casaccia ◽  
Lorenzo Scalise ◽  
...  
Keyword(s):  
Machine Learning ◽  
Signal Analysis ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Laser Doppler Vibrometer ◽  
Laser Doppler ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Real World Application ◽  
Testing Protocol

Background: Heartbeat detection is a crucial step in several clinical fields. Laser Doppler Vibrometer (LDV) is a promising non-contact measurement for heartbeat detection. The aim of this work is to assess whether machine learning can be used for detecting heartbeat from the carotid LDV signal. Methods: The performances of Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbor (KNN) were compared using the leave-one-subject-out cross-validation as the testing protocol in an LDV dataset collected from 28 subjects. The classification was conducted on LDV signal windows, which were labeled as beat, if containing a beat, or no-beat, otherwise. The labeling procedure was performed using electrocardiography as the gold standard. Results: For the beat class, the f1-score (f1) values were 0.93, 0.93, 0.95, 0.96 for RF, DT, KNN and SVM, respectively. No statistical differences were found between the classifiers. When testing the SVM on the full-length (10 min long) LDV signals, to simulate a real-world application, we achieved a median macro-f1 of 0.76. Conclusions: Using machine learning for heartbeat detection from carotid LDV signals showed encouraging results, representing a promising step in the field of contactless cardiovascular signal analysis.

scholarly journals Children in Need—Diagnostics, Epidemiology, Treatment and Outcome of Early Onset Anorexia Nervosa

Nutrients ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1932 ◽  
Author(s):  
Herpertz-Dahlmann ◽  
Dahmen
Keyword(s):  
Anorexia Nervosa ◽  
Eating Disorder ◽  
Psychological Assessment ◽  
Psychiatric Comorbidity ◽  
State Of The Art ◽  
Psychological Treatment ◽  
Young Patients ◽  
Treatment Strategies ◽  
Specific Treatment ◽  
Restrictive Eating

Knowledge of anorexia nervosa (AN) in childhood is scarce. This review gives a state-of-the-art overview on the definition, classification, epidemiology and etiology of this serious disorder. The typical features of childhood AN in comparison to adolescent AN and avoidant restrictive eating disorder (ARFID) are described. Other important issues discussed in this article are somatic and psychiatric comorbidity, differential diagnoses and medical and psychological assessment of young patients with AN. Special problems in the medical and psychological treatment of AN in children are listed, although very few studies have investigated age-specific treatment strategies. The physical and mental outcomes of childhood AN appear to be worse than those of adolescent AN, although the causes for these outcomes are unclear. There is an urgent need for ongoing intensive research to reduce the consequences of this debilitating disorder of childhood and to help patients recover.

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