Artificial neural network-based psychological assessment model for predicting the mental health problem in children facing psychological abuse and depression

2021 ◽  
pp. 101711
Author(s):  
Fang Rao ◽  
Wei Cao ◽  
Jianxue Huang ◽  
C. Sivapragash
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
Artificial Neural Network ◽  
Mental Health Problem ◽  
Health Problem ◽  
Psychological Assessment ◽  
Psychological Abuse ◽  
Assessment Model ◽  
Artificial Neural

scholarly journals The Analysis of Performace Model Tiered Artificial Neural Network for Assessment of Coronary Heart Disease

2017 ◽  
Vol 7 (4) ◽  
pp. 2183
Author(s):  
Wiharto Wiharto ◽  
Harianto Herianto ◽  
Hari Kusnanto
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Assessment Model ◽  
Model Assessment ◽  
Marquardt Algorithm ◽  
One Step ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>The assessment model of coronary heart disease is so much developed in line with the development of information technology, particularly the field of artificial intelligence. Unfortunately, the assessment models developed mostly do not use such an approach made by the clinician, the tiered approach. This study aims to analyze the performance of a tiered model assessment. The method used for each level is, preprocessing, building architecture artificial neural network (ANN), conduct training using the Levenberg-Marquardt algorithm and one step secant, as well as testing the system. The study is divided into the terms of the stages in the examination procedure. The test results showed the influence of each level, both when the output level of the previous positive or negative, were tested back at the next level. The performance evaluation may indicate that the top level provides performance improvement and or reinforce the previous level. </p>

Development of a Burst Capacity Model for Corroded Pipelines Under Internal Pressure and Axial Compression Using Artificial Neural Network

2020 ◽  
Author(s):  
Shulong Zhang ◽  
Wenxing Zhou ◽  
Shenwei Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Internal Pressure ◽  
Axial Compression ◽  
Full Scale ◽  
Assessment Model ◽  
Ground Movement ◽  
Ann Model ◽  
Capacity Model ◽  
Artificial Neural

Abstract In-service pipelines are often subjected to longitudinal forces and bending moments resulting from, for example, ground movement or formation of free spans in addition to internal pressures. In practice, there are some site-specific cases where corrosion anomalies interact with the external loads. A refined assessment model is required to understand the load carrying capacity of pipe. In this study, a burst capacity model for corroded pipelines under combined internal pressure and axial compression is developed based on extensive parametric three-dimensional (3D) elasto-plastic finite element analyses (FEA) and artificial neural network (ANN) technique. The parametric FEA employs the ultimate tensile strength (UTS)-based burst criterion and idealizes corrosion defects as semi-ellipsoidal shaped flaws. The FEA model is validated by full-scale burst tests of pipe specimens containing semi-ellipsoidal shaped flaws reported in the literature. Extensive parametric FEA are carried out to evaluate the burst capacity of corroded pipelines under combined internal pressure and axial compression by varying the pipe geometric and material properties, defect depth, length and width, and magnitude of axial compressive stress. Based on the FEA results, an ANN model is developed utilizing the open-source platform PYTHON to predict the burst capacity of corroded pipelines under combined internal pressure and axial compression. The well-trained ANN model is further validated by full-scale burst tests of corroded pipelines under combined internal pressure and axial compression carried out by Det Norske Veritas (DNV).

The Application of Fuzzy Analytic Hierarchy Process Method and Artificial Neural Network Model in the Elevator Risk Assessment

2014 ◽  
Vol 1014 ◽  
pp. 552-555
Author(s):  
Xin Shi Li ◽  
You Cai Xu ◽  
Ran Tao ◽  
Shu Guo ◽  
Kun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Artificial Neural Network ◽  
Analytic Hierarchy Process ◽  
Assessment Model ◽  
Fuzzy Analytic Hierarchy Process ◽  
Risk Assessment Model ◽  
Analytic Hierarchy ◽  
Artificial Neural ◽  
Hierarchy Process

The tradition elevator risk assessment model depends on the expert experience, which causes that the assessment process takes a long time. To deal with this problem, this paper proposes a new risk assessment model which is based on fuzzy analytic hierarchy process (F-AHP) and artificial neural network (ANN). This model is applied to the risk-assessment of elevators. The results show that the assessment time is shorter and the accuracy is not lower, in comparison with the traditional model.

scholarly journals Associations Between Mental Health, Interoception, Psychological Flexibility, and Self-as-Context, as Predictors for Alexithymia: A Deep Artificial Neural Network Approach

2021 ◽  
Vol 12 ◽  
Author(s):  
Darren J. Edwards ◽  
Rob Lowe
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
Artificial Neural Network ◽  
Emotional Regulation ◽  
Online Survey ◽  
Psychological Flexibility ◽  
Psychological Inflexibility ◽  
Neural Network Approach ◽  
Non Linear ◽  
Artificial Neural

Background: Alexithymia is a personality trait which is characterized by an inability to identify and describe conscious emotions of oneself and others.Aim: The present study aimed to determine whether various measures of mental health, interoception, psychological flexibility, and self-as-context, predicted through linear associations alexithymia as an outcome. This also included relevant mediators and non-linear predictors identified for particular sub-groups of participants through cluster analyses of an Artificial Neural Network (ANN) output.Methodology: Two hundred and thirty participants completed an online survey which included the following questionnaires: Toronto alexithymia scale; Acceptance and Action Questionnaire 2 (AQQII); Positive and Negative Affect Scale (PANAS-SF), Depression, Anxiety, and Stress Scale 21 (DAS21); Multidimensional Assessment of Interoceptive Awareness (MAIA); and the Self-as-Context (SAC) scale. A stepwise backwards linear regression and mediation analysis were performed, as well as a cluster analysis of the non-linear ANN upper hidden layer output.Results: Higher levels of alexithymia were associated with increased psychological inflexibility, lower positive affect scores, and lower interoception for the subscales of “not distracting” and “attention regulation.” SAC mediated the relation between emotional regulation and total alexithymia. The ANNs accounted for more of the variance than the linear regressions, and were able to identify complex and varied patterns within the participant subgroupings.Conclusion: The findings were discussed within the context of developing a SAC processed-based therapeutic model for alexithymia, where it is suggested that alexithymia is a complex and multi-faceted condition, which requires a similarly complex, and process-based approach to accurately diagnose and treat this condition.

scholarly journals ASSESSING COLLUSION RISKS IN MANAGING CONSTRUCTION PROJECTS USING ARTIFICIAL NEURAL NETWORK

2018 ◽  
Vol 24 (5) ◽  
pp. 2003-2025 ◽  
Author(s):  
Ming Shan ◽  
Yun Le ◽  
Kenneth T. W. Yiu ◽  
Albert P. C. Chan ◽  
Yi Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Construction Projects ◽  
Real Life ◽  
Assessment Model ◽  
Developing Strategy ◽  
Neural Network Approach ◽  
Body Of Knowledge ◽  
Artificial Neural ◽  
Artificial Neural Network Approach

Being an insidious risk to construction projects, collusion has attracted extensive attention from numerous researchers around the world. However, little effort has ever been made to assess collusion, which is important and necessary for curbing collusion in construction projects. Specific to the context of China, this paper developed an artificial neural network model to assess collusion risk in construction projects. Based on a comprehensive literature review, a total of 22 specific collusive practices were identified first, and then refined by a two-round Delphi interview with 15 experienced experts. Subsequently, using the consolidated framework of collusive practices, a questionnaire was further developed and disseminated, which received 97 valid replies. The questionnaire data were then utilized to develop and validate the collusion risk assessment model with the facilitation of artificial neural network approach. The developed model was finally applied in a real-life metro project in which its reliability and applicability were both verified. Although the model was developed under the context of Chinese construction projects, its developing strategy can be applied in other countries, especially for those emerging economies that have a significant concern of collusion in their construction sectors, and thus contributing to the global body of knowledge of collusion.

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