scholarly journals Management Competency Model: Predictive Neural Network Approach for Empirical Components of Construction Project Proficiency

2021 ◽  
Author(s):  
Dante L. Silva ◽  
Kevin Lawrence M. De Jesus ◽  
Bernard S. Villaverde ◽  
Edgar M. Adina
Keyword(s):  
Neural Network ◽  
Human Resource Development ◽  
Predictive Performance ◽  
Performance Model ◽  
Competency Model ◽  
Management Competencies ◽  
Neural Network Approach ◽  
Construction Companies ◽  
Effective Leader ◽  
Management Competency

Management competencies are skills that incorporate the understanding, proficiencies and qualities essential for successful performance. Individuals in the top of the organizational hierarchy presents himself being an effective leader by immersing to a readily difficult activity in the project. Molding the important management competencies was found to be hard since the efficacy of a competent construction manager is dependent on countless administrative aspects. The current study intended to offer a construction management competency for human resource development in construction companies. Utilizing this competency model, the company could increase its performance capacity and productivity. This study developed a competency theory and a hybrid predictive model with specific foci on construction managers. The Management Competency Framework Assessment Instrument was developed, following an approach through factor-item analytic mode. Furthermore, this research constructs a predictive performance model using Artificial Neural Network through the factors associated to successful management performance. A sensitivity analysis was also implemented to assess the relative importance of individual factors to the effective construction performance.

scholarly journals Predicting The Risk and Timing of Bipolar Disorder In Offspring of Bipolar Parents: Exploring The Utility of a Neural Network Approach

2020 ◽  
Author(s):  
Alysha Cooper ◽  
Julie Horrocks ◽  
Sarah Margaret Goodday ◽  
Charles Keown-Stoneman ◽  
Anne Duffy
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
At Risk ◽  
Bipolar Disorder ◽  
High Risk ◽  
Logistic Model ◽  
Predictive Performance ◽  
Future Research ◽  
Routine Practice ◽  
Neural Network Approach

Abstract BackgroundBipolar disorder onset peaks over early adulthood and confirmed family history is a robust risk factor. However, penetrance within families varies and most children of bipolar parents will not develop the illness. Individualized risk prediction would be helpful for identifying those young people most at risk and to inform targeted intervention. Using prospectively collected data from the Canadian Flourish High-Risk Offspring cohort study available in routine practice, we explored the use of a neural network, known as the Partial Logistic Artificial Neural Network (PLANN) to predict the time to diagnosis of bipolar spectrum disorders Results Overall, for predictive performance, PLANN outperformed the more traditional logistic model for one year, three year and five-year predictions. PLANN was better able to discriminate or rank individuals based on their risk of developing bipolar disorder, better able to predict the probability of developing bipolar disorder and had higher accuracy than the logistic model. ConclusionsThis evaluation of PLANN is a useful step in the investigation of using neural networks as tools in the prediction of diagnosis of mental health for at-risk individuals and demonstrated the potential that neural networks have in this field. Future research is needed to replicate these findings in a separate high-risk sample.

scholarly journals Predicting the risk and timing of major mood disorder in offspring of bipolar parents: exploring the utility of a neural network approach

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Alysha Cooper ◽  
Julie Horrocks ◽  
Sarah Goodday ◽  
Charles Keown-Stoneman ◽  
Anne Duffy
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
At Risk ◽  
High Risk ◽  
Mood Disorder ◽  
Mood Disorders ◽  
Predictive Performance ◽  
Future Research ◽  
Routine Practice ◽  
Neural Network Approach

Abstract Background Bipolar disorder onset peaks over early adulthood and confirmed family history is a robust risk factor. However, penetrance within families varies and most children of bipolar parents will not develop the illness. Individualized risk prediction would be helpful for identifying those young people most at risk and to inform targeted intervention. Using prospectively collected data from the Canadian Flourish High-risk Offspring cohort study available in routine practice, we explored the use of a neural network, known as the Partial Logistic Artificial Neural Network (PLANN) to predict the time to diagnosis of major mood disorders in 1, 3 and 5-year intervals. Results Overall, for predictive performance, PLANN outperformed the more traditional discrete survival model for 3-year and 5-year predictions. PLANN was better able to discriminate or rank individuals based on their risk of developing a major mood disorder, better able to predict the probability of developing a major mood disorder and better able to identify individuals who would be diagnosed in future time intervals. The average AUC achieved by PLANN for 5-year prediction was 0.74, which indicates good discrimination. Conclusions This evaluation of PLANN is a useful step in the investigation of using neural networks as tools in the prediction of mood disorders in at-risk individuals and the potential that neural networks have in this field. Future research is needed to replicate these findings in a separate high-risk offspring sample.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

Assessment of Self-Heating Susceptibility of Indian Coal Seams – A Neural Network Approach

2014 ◽  
Vol 59 (4) ◽  
pp. 1061-1076 ◽  
Author(s):  
D.C. Panigrahi ◽  
S.K. Ray
Keyword(s):  
Neural Network ◽  
Spontaneous Combustion ◽  
Potential Method ◽  
Oxidation Potential ◽  
Wet Oxidation ◽  
Experimental Conditions ◽  
Neural Network Approach ◽  
Indian Coal ◽  
Petrographic Analyses ◽  
Artificial Neural Network Ann

Abstract The paper addresses an electro-chemical method called wet oxidation potential technique for determining the susceptibility of coal to spontaneous combustion. Altogether 78 coal samples collected from thirteen different mining companies spreading over most of the Indian Coalfields have been used for this experimental investigation and 936 experiments have been carried out by varying different experimental conditions to standardize this method for wider application. Thus for a particular sample 12 experiments of wet oxidation potential method were carried out. The results of wet oxidation potential (WOP) method have been correlated with the intrinsic properties of coal by carrying out proximate, ultimate and petrographic analyses of the coal samples. Correlation studies have been carried out with Design Expert 7.0.0 software. Further, artificial neural network (ANN) analysis was performed to ensure best combination of experimental conditions to be used for obtaining optimum results in this method. All the above mentioned analysis clearly spelt out that the experimental conditions should be 0.2 N KMnO4 solution with 1 N KOH at 45°C to achieve optimum results for finding out the susceptibility of coal to spontaneous combustion. The results have been validated with Crossing Point Temperature (CPT) data which is widely used in Indian mining scenario.

A neural network approach to anomaly detection in spectra

1997 ◽  
Author(s):  
Daniel Benzing ◽  
Kevin Whitaker ◽  
Dedra Moore ◽  
Daniel Benzing ◽  
Kevin Whitaker ◽  
...  
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Network Approach ◽  
Neural Network Approach

A Neural Network Approach In A Backward Heat Conduction Problem

2016 ◽  
Author(s):  
Fabio Tokio Mikki ◽  
Edison Issamoto ◽  
Jefferson I. da Luz ◽  
Pedro Paulo Balbi de Oliveira ◽  
Haroldo F. Campos-Velho ◽  
...  
Keyword(s):  
Neural Network ◽  
Heat Conduction ◽  
Heat Conduction Problem ◽  
Network Approach ◽  
Neural Network Approach
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