Search Results - (( variable learning based algorithm ) OR ( parameter extraction path algorithm ))
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Evaluation of robot path planning algorithms in global static environments: genetic algorithm vs ant colony optimization algorithm / Nohaidda Sariff and Norlida Buniyamin
Published 2010“…This paper presents the application of Genetic Algorithm and Ant Colony Optimization (ACO) Algorithm for robot path planning (RPP) in global static environment. …”
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Article -
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Online Mapping-Based Navigation System for Wheeled Mobile Robot in Road Following and Roundabout
Published 2018“…In this work, the online mapping of mobile robot employing the utilization of sensor fusion technique is used to extract the road characteristics that will be used with path planning algorithm to enable the robot to move from a certain start position to predetermined goal, such as road curbs, road borders, and roundabout. …”
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Book Chapter -
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Autonomous road roundabout detection and navigation system for smart vehicles and cities using laser simulator–fuzzy logic algorithms and sensor fusion
Published 2020“…The path generation algorithm was fully derived within the local maps and tested with a WMR platform in real-time.…”
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Ensemble-based machine learning algorithms for classifying breast tissue based on electrical impedance spectroscopy
Published 2020“…In addition, the ranked order of the variables based on their importance differed across the ML algorithms. …”
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Conference or Workshop Item -
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Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition
Published 2007“…The current dialogue act recognition models, namely cue-based models, are based on machine learning techniques, particularly statistical ones. …”
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Thesis -
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Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models
Published 2023“…Based on the identical dataset, the GA-BP and PSO-BP algorithms are also compared to the PCA-BAS-ENN algorithm. …”
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One day ahead daily peak hour load forecasting by using invasive weed optimization learning algorithm based Artificial Neural Network
Published 2012“…By using 'seen' and 'unseen' of electrical energy demand data were used to test the performance of the proposed algorithm. Based on result obtained, it shows that IWO learning algorithm is capable to produce accurate prediction load demand. …”
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Student Project -
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Enhanced Adaptive Confidence-Based Q Routing Algorithms For Network Traffic
Published 2004“…These two adaptive routing algorithms enhance the existing Confidence-based Q (CQ) and Confidence-based Dual Reinforcement Q (CDRQ) Routing Algorithms. …”
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Thesis -
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Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition
Published 2010“…The model is, essentially, a dynamic Bayesian network induced from manually annotated dialogue corpus via dynamic Bayesian machine learning algorithms. Furthermore, the dynamic Bayesian network's random variables are constituted from sets of lexical cues selected automatically by means of a variable length genetic algorithm, developed specifically for this purpose. …”
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Weather prediction in Kota Kinabalu using linear regressions with multiple variables
Published 2021“…Numerical weather prediction is the process of using existing numerical data on weather conditions to forecast the weather using machine learning algorithms. This study employs machine learning algorithms, a linear regression model using statistics, and two optimization approaches, the normal equation approach, and gradient descent approach to predict the weather based on a few variables. …”
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Proceedings -
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Assessment of forest aboveground biomass estimation from superview-1 satellite image using machine learning approaches / Azinuddin Mohd Asri
Published 2022“…In contrast, machine learning is used to calculate the accuracy assessment of dependent between independent variables. …”
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Thesis -
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Modeling time series data using Genetic Algorithm based on Backpropagation Neural network
Published 2018“…This study showed the task of optimizing the topology structure and the parameter values (e.g., weights) used in the BPNN learning algorithm by using the GA. Based on the results obtained, a better prediction result can be produced by the proposed GA-BPNN learning algorithm.…”
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A decomposed streamflow non-gradientbased artificial intelligence forecasting algorithm with factoring in aleatoric and epistemic variables / Wei Yaxing
Published 2024“…The dissertation aims to develop an effectively decomposed time-series nongradient- based artificial intelligence model for forecasting a time-series regression machine learning task. …”
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Thesis -
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Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms
Published 2020“…A new mutation vector inspired by the two-opposite path (2-Opt) algorithm with adaptive mutation scalar (F ) and crossover rate (CR) control parameters were employed to enhance the exploration and exploitation phases of the proposed algorithm. …”
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Depression prediction using machine learning: a review
Published 2022“…The aim of this study is to identify important variables used in depression prediction, recent depression screening tools adopted, and the latest machine learning algorithms used. …”
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Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization
Published 2024“…This paper presents an innovative approach that combines deep learning (DL) with Teaching-Learning-Based Optimization (TLBO) to predict wind power output accurately. …”
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Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things
Published 2022“…Here, the memory consumption can be reduced by enabling a feature selection algorithm that excludes nonrelevant features and preserves the relevant ones. the algorithm is developed based on the variable length of the PSO. …”
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Thesis -
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Green building valuation based on machine learning algorithms / Thuraiya Mohd ... [et al.]
Published 2021“…This experiment used five common machine learning algorithms namely 1) Linear Regressor, 2) Decision Tree Regressor, 3) Random Forest Regressor, 4) Ridge Regressor and 5) Lasso Regressor tested on a real estate data-set of covering Kuala Lumpur District, Malaysia. 3 set of experiments was conducted based on the different feature selections and purposes The results show that the implementation of 16 variables based on Experiment 2 has given a promising effect on the model compare the other experiment, and the Random Forest Regressor by using the Split approach for training and validating data-set outperformed other algorithms compared to Cross-Validation approach. …”
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Conference or Workshop Item -
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