Search Results - (( based competency model algorithm ) OR ( java implication based algorithm ))
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Academic leadership bio-inspired classification model using negative selection algorithm
Published 2015“…Managing employee’s competency is considered as the top challenge for human resource professional especially in the process to determine the right person for the right job that is based on their competency.As an alternative approach, this article attempts to propose academic leadership bio-inspired classification model using negative selection algorithm to handle this issue.This study consists of three phases; data preparation, model development and model analysis. …”
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2
Competing risks for reliability analysis using Cox’s model
Published 2007“…Design/methodology/approach – The parameters of the models have been estimated by method of maximum likelihood based on EM algorithm. …”
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Statistical process control for failure crushing time data using competing risks model
Published 2011“…This paper describes a Statistical Process Control (SPC) for failure crushing time data using competing risks model. The model is based on the widely known proportional hazard regression model for a variety of censoring. …”
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Statistical process control for failure crushing time data using competing risks model.
Published 2011“…This paper describes a Statistical Process Control (SPC) for failure crushing time data using competing risks model. The model is based on the widely known proportional hazard regression model for a variety of censoring. …”
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Furniture form innovation and human–machine comfort evaluation model based on genetic algorithm
Published 2024“…This research explores the Pareto-primarily based Genetic Algorithm (PGA) inside this framework, with the goal of fostering innovation in product layout and the generation of novel ideas. …”
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Cutpoint determination methods in competing risks subdistribution model
Published 2009“…Five cutpoint determination methods are developed based on regression of competing risks subdistribution. …”
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Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications
Published 2018“…This paper proposes a new modified methodology presented as a parametric technique to determine the system’s modeling equations based on the Bode plot equations and the vector fitting (VF) algorithm by fitting the experimental data points. …”
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Cutpoint determination methods in competing risks subdistribution model
Published 2009“…Five cutpoint determination methods are developed based on regression of competing risks subdistribution. …”
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Permodelan Rangkaian Neural Buatan Untuk Penilaian Kendiri Teknologi Maklumat Guru Pelatih
Published 2001“…The system functions as a research instrument generating questionnaires as well as performing rubric assessment in information technology competency online. The data entered online using web as a medium and then executing back propagation simulation to obtain the desired neural network model, and to predict information technology competency based on the model obtained. …”
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Spiral-sooty tern optimization algorithm for dynamic modelling of a twin rotor system
Published 2022“…A spiral model is incorporated into the Sooty-Tern Optimization Algorithm (STOA) structure. …”
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Multiple equations model selection algorithm with iterative estimation method
Published 2016“…A good model is a model that encapsulates the initial process and therefore represents a close estimate to the true model that generated the data.However, whenever there is more than one model to be considered, selection decision needs to be based on its competence to generalize, which is defined as a model’s ability to fit not only current data but also to forecast future data. …”
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Em Approach on Influence Measures in Competing Risks Via Proportional Hazard Regression Model
Published 2000“…In this thesis the analysis of this particular model was based on the cause-specific hazard of Cox model. …”
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A novel hybrid of nonlinear sine cosine algorithm and safe experimentation dynamics for model order reduction
Published 2023“…The current study introduces the hybridization of the Nonlinear Sine Cosine Algorithm (NSCA) and Safe Experimentation Dynamics (SED) as a novel optimization method for model order reduction of high-order single-input single-output (SISO) systems. …”
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A novel hybrid of nonlinear sine cosine algorithm and safe experimentation dynamics for model order reduction
Published 2023“…The current study introduces the hybridization of the Nonlinear Sine Cosine Algorithm (NSCA) and Safe Experimentation Dynamics (SED) as a novel optimization method for model order reduction of high-order single-input single-output (SISO) systems. …”
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Hybrid-discrete multi-objective particle swarm optimization for multi-objective job-shop scheduling
Published 2022“…It is also discovered that the hybrid-discrete MOPSO (HD-MOPSO) algorithm manages to obtain higher values in the performance metrics consisting of non-dominance ratio and hypervolume compared to the competing algorithms. …”
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Segmentation of MRI brain images using statistical approaches
Published 2011“…Therefore, these algorithms can be improved upon. A neighbourhood-based noise-reduction algorithm which uses the edges of an image is proposed. …”
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Parametric and Semiparametric Competing Risks Models for Statistical Process Control with Reliability Analysis
Published 2004“…The Expectation Maximization (EM) algorithm is utilized to obtain the estimate of the parameters in the models. …”
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Parameter identification of thermoelectric modules using enhanced slime mould algorithm (ESMA)
Published 2024“…Subsequent modification then involves substitution of an exponential function to the existing tangent hyperbolic function within formula p of the standard SMA in enabling improved probability variants via the selection of updated equations. Competency of the proposed algorithm in generating the optimal parameters for TEMs was appraised based on 21 benchmarked design parameters, following the objective of root mean square error (RMSE) minimization between the temperature of both actual and estimated models. …”
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