Search Results - (( parallel estimation learning algorithm ) OR ( variable detection path algorithm ))
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IMPLEMENTATION OF BEHAVIOUR BASED NAVIGATION IN A PHYSICALLY CONFINED SITE
Published 2017“…Selected basic behaviour-based algorithms such as wallfollower, obstacle avoidance, escape route and target detection are to be combined and its efficiency is measured. …”
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A real-time integrated fire detection and alarm (FDA) system for network based building automation
Published 2017“…Methods/Analysis: In this work, the shortest path algorithm was chosen for series of buildings connected by a fiber-optic network. …”
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LED based NIR spectroscopy for detection of lard adulteration in palm oil via chemometrics / Katrul Nadia Basri
Published 2017“…In order to remove the uninformative variables, cumulative adaptive reweighted sampling (CARS) has been performed. …”
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Classification and quantification of palm oil adulteration via portable NIR spectroscopy
Published 2017“…In order to remove the uninformative variables, variable selection using cumulative adaptive reweighted sampling (CARS) has been performed. …”
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Classification and quantification of palm oil adulteration via portable NIR spectroscopy
Published 2024“…In order to remove the uninformative variables, variable selection using cumulative adaptive reweighted sampling (CARS) has been performed. …”
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Super resolution imaging using modified lanr based on separable filtering
Published 2019“…The underlying idea is to process and reconstruct information in low and high frequency sub-bands based on separable property of neighbourhood filtering to achieve fast parallel and vectorized operation, while enhancing algorithmic performance by reducing computational burden resulting from computing the weighted function of every pixel for each pixel in an image. …”
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Thesis -
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Short-term Gini coefficient estimation using nonlinear autoregressive multilayer perceptron model
Published 2024“…System Identification (SI), a methodology utilized in domains like engineering and mathematical modeling to construct or refine dynamic system models from captured data, relies significantly on the Nonlinear Auto-Regressive (NAR) model due to its reliability and capability of integrating nonlinear functions, complemented by contemporary machine learning strategies and computational algorithms to approximate complex system dynamics to address these limitations. …”
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