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A Preliminary Study on Camera Auto Calibration Problem Using Bat Algorithm
Published 2013“…For each iteration, the bats will try to improve its fitness by following the echolocation behavior of the microbats. A case study taken from database, provided by Le2i Universite de Bourgoune is used to evaluate the performance of the Bat Algorithm. …”
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A study on the parameter selection of bat algorithm in in optimizing parameters in camera auto calibration problem
Published 2022“…The Bat Algorithm's performance is evaluated using a case study from a database from Le2i Universite de Bourgoune. …”
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A Comparative Study of the Application of Swarm Intelligence in Kruppa-Based Camera Auto-Calibration
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Hybridization of Ensemble Kalman Filter and Non-linear Auto-regressive Neural Network for Financial Forecasting
Published 2014“…Therefore, a successful forecasting model must be able to capture longterm dependencies from the past chaotic data. In this study, a novel hybrid model, called UKF-NARX, consists of unscented kalman filter and non-linear auto-regressive network with exogenous input trained with bayesian regulation algorithm is modelled for chaotic financial forecasting. …”
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Book Section -
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Hybridization on Ensemble Kalman Filter and Non-Linear Auto-Regressive Neural Network for Financial Forecasting
Published 2014“…Therefore, a successful forecasting model must be able to capture longterm dependencies from the past chaotic data. In this study, a novel hybrid model, called UKF-NARX, consists of unscented kalman filter and non-linear auto-regressive network with exogenous input trained with bayesian regulation algorithm is modelled for chaotic financial forecasting. …”
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Comparative study between ARX and ARMAX system identification
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Hybridization of Ensemble Kalman Filter and Non-linear Auto-regressive Neural Network for Financial Forecasting
Published 2014“…Therefore, a successful forecasting model must be able to capture longterm dependencies from the past chaotic data. In this study, a novel hybrid model, called UKF-NARX, consists of unscented kalman filter and non-linear auto-regressive network with exogenous input trained with bayesian regulation algorithm is modelled for chaotic financial forecasting. …”
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Book Section -
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Hybridization of Ensemble Kalman Filter and Non-linear Auto-regressive Neural Network for Financial Forecasting
Published 2014“…Therefore, a successful forecasting model must be able to capture longterm dependencies from the past chaotic data. In this study, a novel hybrid model, called UKF-NARX, consists of unscented kalman filter and non-linear auto-regressive network with exogenous input trained with bayesian regulation algorithm is modelled for chaotic financial forecasting. …”
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An artificial neural network hybrid with wavelet transform for short-term wind speed forecasting: A preliminary case study
Published 2023Conference Paper -
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Comparisons of automated machine learning (AutoML) in predicting whistleblowing of academic dishonesty with demographic and theory of planned behavior
Published 2023“…Generally, based on the validation results of the prediction models, demographic attributes presented more importance than the TBP attributes. The findings of this study will be a great interest of many research scholars to conduct a more in-depth analysis on AutoML for many domains mainly in education and academic misconduct fields. â�¢ AutoML is the first of its kind to be empirically compared between TPOT and AutoModel in an application to predict academic dishonesty whistleblowing. â�¢ Besides accuracy performances of the AutoML, the proportion of the variance of each attribute from demographic and Theory of Planned Behavior (TPB) is also presented in the prediction models of academic dishonesty whistleblowing. â�¢ AutoML is a convenient and reproducible rapid modeling method of machine learning to be used in many kinds of prediction problem. …”
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Earthquake prediction model based on geomagnetic field data using automated machine learning
Published 2024“…Several features were extracted from them through wavelet scattering transform (WST). The features were used as the input to model optimization, of which the strategy for automatic algorithm selection and hyperparameter tuning was performed based on the asynchronous successive halving algorithm (ASHA). …”
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Yaw rate and sideslip control using h-infinity-pid controller during automated lane change manoeuver
Published 2021“…Two tuning algorithms were proposed; First, using stabilised boundary locus of KpKi value and Ziegler-Nichols (Z-N) tuning rules; Second, the extended version of the first algorithm hybrid with H¥ and Chein rules. …”
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Auto-encoder variants for solving handwritten digits classification problem
Published 2020“…Third, the main contribution of this study is performing the comparative study of the aforementioned three AE variants on the basis of their mathematical modeling and experiments. …”
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Text Summarization System with Bayesian Theorem on Oil & Gas Drilling Topic
Published 2007“…In this project, Bayes theorem algorithm is studied and experimented by the implementation of a textual summarizer. …”
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Final Year Project -
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Hybridization of ensemble kalman filter and non-linear auto-regressive neural network for financial forecasting
Published 2014“…Therefore, a successful forecasting model must be able to capture longterm dependencies from the past chaotic data. In this study, a novel hybrid model, called UKF-NARX, consists of unscented kalman filter and non-linear auto-regressive network with exogenous input trained with bayesian regulation algorithm is modelled for chaotic financial forecasting. …”
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AUTO-MANAGE PARKING SYSTEM (AMPS)
Published 2019“…The purpose of this paper is to develop the Auto-manage Parking System (AMPS) that will enhance the convenience and ease of use by capturing number plate of car. …”
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Final Year Project -
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A student learning style auto-detection model in a learning management system
Published 2023“…The proposed model can assist educators to improve learning content according to the suitability of students and recommend appropriate learning materials to students based on their characteristics and preferences. Future studies include the use of machine learning algorithms such as decision trees to auto-detect student learning styles in learning management systems.…”
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