https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 Iteratively refining breast cancer intrinsic subtypes in the METABRIC dataset https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:23687 Wed 19 Apr 2023 16:42:57 AEST ]]> Predicting the printed circuit board cycle time of surface-mount-technology production lines using a symbiotic organism search-based support vector regression ensemble https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:42053 Wed 17 Aug 2022 14:12:24 AEST ]]> Heterogeneous defect prediction through multiple kernel learning and ensemble learning https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:33722 Wed 12 Dec 2018 15:55:02 AEDT ]]> Black lung detection on chest X-ray radiographs using deep learning https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:50291 Wed 06 Mar 2024 15:30:05 AEDT ]]> From ensemble learning to meta-analytics: a review on trends in business applications https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:45579 Wed 02 Nov 2022 08:23:58 AEDT ]]> Detection and Visualisation of Pneumoconiosis Using an Ensemble of Multi-Dimensional Deep Features Learned from Chest X-rays https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:51993 Tue 26 Sep 2023 11:01:04 AEST ]]> Deep Ensemble Learning for the Automatic Detection of Pneumoconiosis in Coal Worker’s Chest X-ray Radiography https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:51987 Globally, coal remains one of the natural resources that provide power to the world. Thousands of people are involved in coal collection, processing, and transportation. Particulate coal dust is produced during these processes, which can crush the lung structure of workers and cause pneumoconiosis. There is no automated system for detecting and monitoring diseases in coal miners, except for specialist radiologists. This paper proposes ensemble learning techniques for detecting pneumoconiosis disease in chest X-ray radiographs (CXRs) using multiple deep learning models. Three ensemble learning techniques (simple averaging, multi-weighted averaging, and majority voting (MVOT)) were proposed to investigate performances using randomised cross-folds and leave-one-out cross-validations datasets. Five statistical measurements were used to compare the outcomes of the three investigations on the proposed integrated approach with state-of-the-art approaches from the literature for the same dataset. In the second investigation, the statistical combination was marginally enhanced in the ensemble of multi-weighted averaging on a robust model, CheXNet. However, in the third investigation, the same model elevated accuracies from 87.80 to 90.2%. The investigated results helped us identify a robust deep learning model and ensemble framework that outperformed others, achieving an accuracy of 91.50% in the automated detection of pneumoconiosis.]]> Tue 26 Sep 2023 10:47:09 AEST ]]> A Stacked Meta-Ensemble for Protein Inter-Residue Distance Prediction https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:50432 Tue 25 Jul 2023 19:01:32 AEST ]]> Assessing short-term voltage stability of electric power systems by a hierarchical intelligent system https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:29584 Sat 24 Mar 2018 07:32:08 AEDT ]]> Voltage stability margin prediction by ensemble based extreme learning machine https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:26911 Sat 24 Mar 2018 07:23:36 AEDT ]]> Ranking of high-value social audiences on Twitter https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:24701 Sat 24 Mar 2018 07:10:53 AEDT ]]> A Novel Ensemble Learning Approach for Stock Market Prediction Based on Sentiment Analysis and the Sliding Window Method https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:44832 Mon 24 Oct 2022 09:52:57 AEDT ]]> Breast cancer intrinsic subtypes: a critical conception in bioinformatics https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:30639 Mon 23 Sep 2019 11:53:06 AEST ]]> Hierarchical ensemble learning for Alzheimer's disease classification https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:35263 Mon 08 Jul 2019 16:52:24 AEST ]]> A Multi-objective Meta-Analytic Method for Customer Churn Prediction https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:42849 Mon 05 Sep 2022 15:26:27 AEST ]]> Automatic classification of distal radius fracture using a two-stage ensemble deep learning framework https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:51530 Fri 08 Sep 2023 12:10:04 AEST ]]>