Search Results - (( pattern visualization learning algorithm ) OR ( learning application optimisation algorithm ))
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An improvement of back propagation algorithm using halley third order optimisation method for classification problems
Published 2020“…The GD method not performed well in large scale applications and when higher learning performances are required. …”
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Survival versus non-survival prediction after acute coronary syndrome in Malaysian population using machine learning technique / Nanyonga Aziida
Published 2019“…Self-Organizing Feature (SOM) was used to visualize and identify the relationship and pattern between factors affecting mortality after ACS. …”
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Reinforcement Learning Algorithm for Optimising Durian Irrigation Systems: Maximising Growth and Water Efficiency
Published 2024“…This study presents a Reinforcement Learning-based algorithm designed to optimise irrigation for Durio Zibethinus (i.e., durian) trees, aiming to maximise tree growth and reduce water usage. …”
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Neural Network Based Pattern Recognition in Visual Inspection System for Intergrated Circuit Mark Inspection
Published 1998“…Industrial visual machine inspection system uses template or feature matching methods to locate or inspect parts or pattern on parts. …”
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Improving F-Score of the imbalance visualized pattern dataset for yield prediction robustness
Published 2008“…In a non closed loop manufacturing process, a prediction model of the yield outcome can be achieved by visualizing the temporal historical data pattern generated from the inspection machine, discretize to visualized data patterns, and map them into machine learning algorithm.Our previous study shows that combination of under-sampling and over sampling techniques unabel wider range of data sets where SMOTE+VDM and random under-sampling produced robust classifier performance of handling better with different batches of prediction test data.In this paper, the integration of K* entropy base similarity distance function with SMOTE, CNN+Tomek Links and the introduction of SMOTE and SMaRT (Synthetic Majority Replacement Technique)combination, has improved the classifiers F-Score robustness.…”
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Conference or Workshop Item -
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Evaluating JA-ABC5 hyperparameter optimisation with classifiers
Published 2024“…This study extends our knowledge of machine learning model optimisation, which has the potential to enhance the effectiveness of these models across a range of applications.…”
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An interactive analytics approach for sustainable and resilient case studies: a machine learning perspective
Published 2023“…To show the methods applicability, this paper uses the proposed algorithm in three sustainable and resilient case studies. …”
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Sustainable Management Of River Water Quality Using Artificial Intelligence Optimisation Algorithms
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Exploratory study of Kohonen network for human health state classification
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Handling imbalance visualized pattern dataset for yield prediction
Published 2008“…The prediction of the yield outcome in a non close loop manufacturing process can be achieved by visualizing the historical data pattern generated from the inspection machine, transform the data pattern and map it into machine learning algorithm for training, in order to automatically generate a prediction model without the visual interpretation needs to be done by human. …”
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Kernel and multi-class classifiers for multi-floor wlan localisation
Published 2016“…Unlike the classical kNN algorithm which is a regression type algorithm, the proposed localisation algorithms utilise machine learning classification for both linear and kernel types. …”
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Particle Swarm Optimisation with Improved Learning Strategy
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Classification of diabetic retinopathy clinical features using image enhancement technique and convolutional neural network / Abdul Hafiz Abu Samah
Published 2021“…In general, this thesis introduces an automated machine learning algorithm for detecting diabetic retinopathy (DR) in fundus images. …”
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Leveraging transfer learning and label optimization for enhanced traditional Chinese medicine ner performance
Published 2024“…To address these challenges, this research aims to optimise the application of deep learning models for NER and achieve enhanced results. …”
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