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Parveen Rahamathulla M, Sam Emmanuel WR, Bindhu A, Mustaq Ahmed M. YOLOv8's advancements in tuberculosis identification from chest images. Front Big Data 2024; 7:1401981. [PMID: 38994120 PMCID: PMC11236731 DOI: 10.3389/fdata.2024.1401981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 05/29/2024] [Indexed: 07/13/2024] Open
Abstract
Tuberculosis (TB) is a chronic and pathogenic disease that leads to life-threatening situations like death. Many people have been affected by TB owing to inaccuracy, late diagnosis, and deficiency of treatment. The early detection of TB is important to protect people from the severity of the disease and its threatening consequences. Traditionally, different manual methods have been used for TB prediction, such as chest X-rays and CT scans. Nevertheless, these approaches are identified as time-consuming and ineffective for achieving optimal results. To resolve this problem, several researchers have focused on TB prediction. Conversely, it results in a lack of accuracy, overfitting of data, and speed. For improving TB prediction, the proposed research employs the Selection Focal Fusion (SFF) block in the You Look Only Once v8 (YOLOv8, Ultralytics software company, Los Angeles, United States) object detection model with attention mechanism through the Kaggle TBX-11k dataset. The YOLOv8 is used for its ability to detect multiple objects in a single pass. However, it struggles with small objects and finds it impossible to perform fine-grained classifications. To evade this problem, the proposed research incorporates the SFF technique to improve detection performance and decrease small object missed detection rates. Correspondingly, the efficacy of the projected mechanism is calculated utilizing various performance metrics such as recall, precision, F1Score, and mean Average Precision (mAP) to estimate the performance of the proposed framework. Furthermore, the comparison of existing models reveals the efficiency of the proposed research. The present research is envisioned to contribute to the medical world and assist radiologists in identifying tuberculosis using the YOLOv8 model to obtain an optimal outcome.
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Affiliation(s)
- Mohamudha Parveen Rahamathulla
- Department of Basic Medical Science, College of Medicine, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - W. R. Sam Emmanuel
- Department of Computer Science and Research Centre, Nesamony Memorial Christian College, Marthandam, Tamil Nadu, India
| | - A. Bindhu
- Department of Computer Science, Infant Jesus College of Arts and Science for Women, Mulagumoodu, Tamil Nadu, India
| | - Mohamed Mustaq Ahmed
- Department of Information Technology, The New College, Chennai, Tamil Nadu, India
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2
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Murphy EK, Bertsch SR, Klein SB, Rashedi N, Sun Y, Joyner MJ, Curry TB, Johnson CP, Regimbal RJ, Wiggins CC, Senefeld JW, Shepherd JRA, Elliott JT, Halter RJ, Vaze VS, Paradis NA. Non-invasive biomarkers for detecting progression toward hypovolemic cardiovascular instability in a lower body negative pressure model. Sci Rep 2024; 14:8719. [PMID: 38622207 PMCID: PMC11018605 DOI: 10.1038/s41598-024-59139-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 04/08/2024] [Indexed: 04/17/2024] Open
Abstract
Occult hemorrhages after trauma can be present insidiously, and if not detected early enough can result in patient death. This study evaluated a hemorrhage model on 18 human subjects, comparing the performance of traditional vital signs to multiple off-the-shelf non-invasive biomarkers. A validated lower body negative pressure (LBNP) model was used to induce progression towards hypovolemic cardiovascular instability. Traditional vital signs included mean arterial pressure (MAP), electrocardiography (ECG), plethysmography (Pleth), and the test systems utilized electrical impedance via commercial electrical impedance tomography (EIT) and multifrequency electrical impedance spectroscopy (EIS) devices. Absolute and relative metrics were used to evaluate the performance in addition to machine learning-based modeling. Relative EIT-based metrics measured on the thorax outperformed vital sign metrics (MAP, ECG, and Pleth) achieving an area-under-the-curve (AUC) of 0.99 (CI 0.95-1.00, 100% sensitivity, 87.5% specificity) at the smallest LBNP change (0-15 mmHg). The best vital sign metric (MAP) at this LBNP change yielded an AUC of 0.6 (CI 0.38-0.79, 100% sensitivity, 25% specificity). Out-of-sample predictive performance from machine learning models were strong, especially when combining signals from multiple technologies simultaneously. EIT, alone or in machine learning-based combination, appears promising as a technology for early detection of progression toward hemodynamic instability.
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Affiliation(s)
- Ethan K Murphy
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA.
| | - Spencer R Bertsch
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
| | - Samuel B Klein
- Geisel School of Medicine, Dartmouth College, Hanover, NH, 03755, USA
- Dartmouth-Hitchcock Medical Center, Lebanon, NH, 03756, USA
| | - Navid Rashedi
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
| | - Yifei Sun
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
| | - Michael J Joyner
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, 55902, USA
| | - Timothy B Curry
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, 55902, USA
| | - Christopher P Johnson
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, 55902, USA
| | - Riley J Regimbal
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, 55902, USA
| | - Chad C Wiggins
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, 55902, USA
| | - Jonathon W Senefeld
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, 55902, USA
| | - John R A Shepherd
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, 55902, USA
| | - Jonathan Thomas Elliott
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
- Geisel School of Medicine, Dartmouth College, Hanover, NH, 03755, USA
- Dartmouth-Hitchcock Medical Center, Lebanon, NH, 03756, USA
| | - Ryan J Halter
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
- Geisel School of Medicine, Dartmouth College, Hanover, NH, 03755, USA
| | - Vikrant S Vaze
- Thayer School of Engineering, Dartmouth College, Hanover, NH, 03755, USA
| | - Norman A Paradis
- Geisel School of Medicine, Dartmouth College, Hanover, NH, 03755, USA
- Dartmouth-Hitchcock Medical Center, Lebanon, NH, 03756, USA
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Zhou G, Lee MC, Wang X, Zhong D, Githeko AK, Yan G. Mapping Potential Malaria Vector Larval Habitats for Larval Source Management in Western Kenya: Introduction to Multimodel Ensembling Approaches. Am J Trop Med Hyg 2024; 110:421-430. [PMID: 38350135 PMCID: PMC10919169 DOI: 10.4269/ajtmh.23-0108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 11/03/2023] [Indexed: 02/15/2024] Open
Abstract
Identification and mapping of larval sources are a prerequisite for effective planning and implementing mosquito larval source management (LSM). Ensemble modeling is increasingly used for prediction modeling, but it lacks standard procedures. We proposed a detailed framework to predict potential malaria vector larval habitats by using multimodel ensemble modeling, which includes selection of models, ensembling method, and predictors, evaluation of variable importance, prediction of potential larval habitats, and assessment of prediction uncertainty. The models were built and validated based on multisite, multiyear field observations and climatic/environmental variables. Model performance was tested using independent field observations. Overall, we found that the ensembled model predicted larval habitats with about 20% more accuracy than the average of the individual models ensembled. Key larval habitat predictors in western Kenya were elevation, geomorphon class, and precipitation for the 2 months prior. Additional predictors may be required to increase the predictive accuracy of the larva-positive habitats. This is the first study to provide a detailed framework for the process of multimodel ensemble modeling for malaria vector habitats. Mapping of potential habitats will be helpful in LSM planning.
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Affiliation(s)
- Guofa Zhou
- Program in Public Health, University of California, Irvine, California
| | - Ming-Chieh Lee
- Program in Public Health, University of California, Irvine, California
| | - Xiaoming Wang
- Program in Public Health, University of California, Irvine, California
| | - Daibin Zhong
- Program in Public Health, University of California, Irvine, California
| | - Andrew K. Githeko
- Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya
| | - Guiyun Yan
- Program in Public Health, University of California, Irvine, California
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Sahoo M, Mitra M, Pal S. Improved Detection of Dry Age-Related Macular Degeneration from Optical Coherence Tomography Images using Adaptive Window Based Feature Extraction and Weighted Ensemble Based Classification Approach. Photodiagnosis Photodyn Ther 2023:103629. [PMID: 37244451 DOI: 10.1016/j.pdpdt.2023.103629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 05/20/2023] [Accepted: 05/22/2023] [Indexed: 05/29/2023]
Abstract
BACKGROUND Dry Age-related macular degeneration (AMD), which affects the older population, can lead to blindness when left untreated. Preventing vision loss in elderly needs early identification. Dry-AMD diagnosis is still time-consuming and very subjective, depending on the ophthalmologist. Setting up a thorough eye-screening system to find Dry-AMD is a very difficult task. METHODOLOGY This study aims to develop a weighted majority voting (WMV) ensemble-based prediction model to diagnose Dry-AMD. The WMV approach combines the predictions from base-classifiers and chooses the class with greatest vote based on assigned weights to each classifier. A novel feature extraction method is used along the retinal pigment epithelium (RPE) layer, with the number of windows calculated for each picture playing an important part in identifying Dry-AMD/normal images using the WMV methodology. Pre-processing using hybrid-median filter followed by scale-invariant feature transform based segmentation of RPE layer and curvature flattening of retina is employed to measure exact thickness of RPE layer. RESULT The proposed model is trained on 70% of the OCT image database (OCTID) and evaluated on remaining OCTID and SD-OCT Noor dataset. Model has achieved accuracy of 96.15% and 96.94%, respectively. The suggested algorithm's effectiveness in Dry-AMD identification is demonstrated by comparison with alternative approaches. Even though the suggested model is only trained on the OCTID, it has performed well when tested on additional dataset. CONCLUSION The suggested architecture can be used for quick eye-screening for early identification of Dry-AMD. The recommended method may be applied in real-time since it requires fewer complexity and learning-variables.
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Affiliation(s)
- Moumita Sahoo
- Department of Applied Electronics and Instrumentation Engineering, Haldia Institute of Technology, Haldia, West Bengal, India.
| | - Madhuchhanda Mitra
- Department of Applied Physics, University of Calcutta, Kolkata, West Bengal, India
| | - Saurabh Pal
- Department of Applied Physics, University of Calcutta, Kolkata, West Bengal, India
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Kumar R, Maheshwari S, Sharma A, Linda S, Kumar S, Chatterjee I. Ensemble learning-based early detection of influenza disease. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-21. [PMID: 37362719 PMCID: PMC10199437 DOI: 10.1007/s11042-023-15848-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 08/16/2022] [Accepted: 05/15/2023] [Indexed: 06/28/2023]
Abstract
Across the world, the seasonal disease influenza is a respiratory illness that impacts all age groups in many ways. Its symptoms are fever, chills, aches, pains, headaches, fatigue, cough, and weakness. Seasonal influenza can cause mild to severe illness and lead to death at times. The task of early detection of influenza is an important research area these days. Various studies show that machine learning techniques have attracted many researchers' attention to the early detection of influenza disease. In this paper, early detection of Influenza disease among all age groups is done using various machine learning techniques. Influenza Research Database and the Human Surveillance Records data sets are used. Data analysis is undertaken, and ensemble-based stacked algorithms are implemented on the whole data set. The performance of different models has been evaluated using different performance metrics. Overall, the study proposes efficient machine learning models that can be implemented to provide a cheaper and quicker diagnostic tool for detecting influenza.
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Affiliation(s)
- Ranjan Kumar
- Department of Computer Science, Aryabhatta College, University of Delhi, Delhi, 110021 India
| | - Sajal Maheshwari
- Department of Computer Science, Aryabhatta College, University of Delhi, Delhi, 110021 India
| | - Anushka Sharma
- Department of Computer Science, Aryabhatta College, University of Delhi, Delhi, 110021 India
| | - Sonal Linda
- Department of Computer Science, Aryabhatta College, University of Delhi, Delhi, 110021 India
| | - Subhash Kumar
- Department of Physics, Acharya Narendra Dev College, University of Delhi, Delhi, 110019 India
| | - Indranath Chatterjee
- Department of Computer Engineering, Tongmyong University, Busan, 48520 South Korea
- School of Technology, Woxsen University, Hyderabad, Telangana 500033 India
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Moradi M, Chen Y, Du X, Seddon JM. Deep ensemble learning for automated non-advanced AMD classification using optimized retinal layer segmentation and SD-OCT scans. Comput Biol Med 2023; 154:106512. [PMID: 36701964 DOI: 10.1016/j.compbiomed.2022.106512] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 11/30/2022] [Accepted: 12/31/2022] [Indexed: 01/11/2023]
Abstract
BACKGROUND Accurate retinal layer segmentation in optical coherence tomography (OCT) images is crucial for quantitatively analyzing age-related macular degeneration (AMD) and monitoring its progression. However, previous retinal segmentation models depend on experienced experts and manually annotating retinal layers is time-consuming. On the other hand, accuracy of AMD diagnosis is directly related to the segmentation model's performance. To address these issues, we aimed to improve AMD detection using optimized retinal layer segmentation and deep ensemble learning. METHOD We integrated a graph-cut algorithm with a cubic spline to automatically annotate 11 retinal boundaries. The refined images were fed into a deep ensemble mechanism that combined a Bagged Tree and end-to-end deep learning classifiers. We tested the developed deep ensemble model on internal and external datasets. RESULTS The total error rates for our segmentation model using the boundary refinement approach was significantly lower than OCT Explorer segmentations (1.7% vs. 7.8%, p-value = 0.03). We utilized the refinement approach to quantify 169 imaging features using Zeiss SD-OCT volume scans. The presence of drusen and thickness of total retina, neurosensory retina, and ellipsoid zone to inner-outer segment (EZ-ISOS) thickness had higher contributions to AMD classification compared to other features. The developed ensemble learning model obtained a higher diagnostic accuracy in a shorter time compared with two human graders. The area under the curve (AUC) for normal vs. early AMD was 99.4%. CONCLUSION Testing results showed that the developed framework is repeatable and effective as a potentially valuable tool in retinal imaging research.
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Affiliation(s)
- Mousa Moradi
- Department of Biomedical Engineering, University of Massachusetts, Amherst, MA, United States
| | - Yu Chen
- Department of Biomedical Engineering, University of Massachusetts, Amherst, MA, United States.
| | - Xian Du
- Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, MA, United States.
| | - Johanna M Seddon
- Department of Ophthalmology & Visual Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States.
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Mohammadifar A, Gholami H, Golzari S. Stacking- and voting-based ensemble deep learning models (SEDL and VEDL) and active learning (AL) for mapping land subsidence. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:26580-26595. [PMID: 36369445 DOI: 10.1007/s11356-022-24065-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 11/03/2022] [Indexed: 06/16/2023]
Abstract
This contribution presents a novel methodology based on the feature selection, ensemble deep learning (EDL) models, and active learning (AL) approach for prediction of land subsidence (LS) hazard and rate, and its uncertainty in an area involving two important plains - the Minab and Shamil-Nian plains - in the Hormozgan province, southern Iran. The important features controlling LS hazard were identified by ridge regression. Then, two EDL models were constructed by stacking (SEDL) and voting (VEDL) five dense deep learning (DL) models (model 1 to model 5) for mapping LS hazard. Thereafter, the predictive model performance was assessed by a precision-recall curve and Kolmogorov-Smirnov (KS) plot. A partial dependence plot (PDP), individual conditional expectation plots (ICEP), game theory, and a sensitivity analysis were used for the interpretability of the predictive DL model. According to SEDL - a model with higher accuracy - 34% (1624 km2), 14.7% (698 km2), and 19.2% (912 km2) of the total area were classified as being of very low, low, and moderate hazards, whereas 17.7% (845 km2) and 14.4% (683 km2) of area were classified as being of high and very high hazards, respectively. Based on all interpretability techniques, aquifer loss or groundwater drawdown is the most important feature controlling LS hazard, and it having the greatest impact on the SEDL model output. Based on a Taylor diagram and R2 as model performance assessment indicators, SEDL-AL (with R2 > 95% for training and test datasets) performed better than SEDL for quantify LS rate, the rate of LS ranging between 0 and 48.1 cm. The highest rate of LS occurred in the Minab plain - an area located downstream of the Minab Esteghlal dam. SEDL-AL was used to quantify the uncertainty associated with the LS rate. The observed values fell within predictions provided by SEDL-AL, which indicates a high accuracy of our predictive model. Overall, our newly developed modeling techniques are helpful tools for the spatial mapping of LS susceptibility and rate, and its uncertainty.
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Affiliation(s)
- Aliakbar Mohammadifar
- Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran
| | - Hamid Gholami
- Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran.
| | - Shahram Golzari
- Department of Electrical and Computer Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran
- Deep Learning Research Group, University of Hormozgan, Bandar Abbas, Hormozgan, Iran
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Ensemble Learning of Multiple Models Using Deep Learning for Multiclass Classification of Ultrasound Images of Hepatic Masses. BIOENGINEERING (BASEL, SWITZERLAND) 2023; 10:bioengineering10010069. [PMID: 36671641 PMCID: PMC9854883 DOI: 10.3390/bioengineering10010069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/29/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023]
Abstract
Ultrasound (US) is often used to diagnose liver masses. Ensemble learning has recently been commonly used for image classification, but its detailed methods are not fully optimized. The purpose of this study is to investigate the usefulness and comparison of some ensemble learning and ensemble pruning techniques using multiple convolutional neural network (CNN) trained models for image classification of liver masses in US images. Dataset of the US images were classified into four categories: benign liver tumor (BLT) 6320 images, liver cyst (LCY) 2320 images, metastatic liver cancer (MLC) 9720 images, primary liver cancer (PLC) 7840 images. In this study, 250 test images were randomly selected for each class, for a total of 1000 images, and the remaining images were used as the training. 16 different CNNs were used for training and testing ultrasound images. The ensemble learning used soft voting (SV), weighted average voting (WAV), weighted hard voting (WHV) and stacking (ST). All four types of ensemble learning (SV, ST, WAV, and WHV) showed higher values of accuracy than the single CNN. All four types also showed significantly higher deep learning (DL) performance than ResNeXt101 alone. For image classification of liver masses using US images, ensemble learning improved the performance of DL over a single CNN.
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An interpretable machine learning prognostic system for risk stratification in oropharyngeal cancer. Int J Med Inform 2022; 168:104896. [DOI: 10.1016/j.ijmedinf.2022.104896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 09/27/2022] [Accepted: 10/07/2022] [Indexed: 11/07/2022]
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Damaneh JM, Ahmadi J, Rahmanian S, Sadeghi SMM, Nasiri V, Borz SA. Prediction of wild pistachio ecological niche using machine learning models. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Xu Z, Lee CKM, Lv Y, Chan J. Ensemble Capsule Network with an Attention Mechanism for the Fault Diagnosis of Bearings from Imbalanced Data Samples. SENSORS 2022; 22:s22155543. [PMID: 35898042 PMCID: PMC9332463 DOI: 10.3390/s22155543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 07/19/2022] [Accepted: 07/21/2022] [Indexed: 11/16/2022]
Abstract
In order to solve the problem of imbalanced and noisy data samples for the fault diagnosis of rolling bearings, a novel ensemble capsule network (Capsnet) with a convolutional block attention module (CBAM) that is based on a weighted majority voting method is proposed in this study. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method was used to decompose the raw vibration signal into different IMF signals, which are noise reduction signals. Secondly, the IMF signals were input into the Capsnet with CBAM in order to diagnose the fault category preliminarily. Finally, the weighted majority voting method was utilized so as to fuse all of the preliminary diagnosis results in order to obtain the final diagnostic decision. In order to verify the effectiveness of the proposed ensemble of Capsnet with CBAM, this method was applied to the fault diagnosis of rolling bearings with imbalanced and different SNR data samples. The diagnostic results show that the proposed diagnostic method can achieve higher levels of accuracy than other methods, such as single CNN, single Capsnet, ensemble CNN and an ensemble capsule network without CBAM and that it has stronger immunity to noise than an ensemble capsule network without CBAM.
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Affiliation(s)
- Zengbing Xu
- Centre for Advances in Reliability and Safety, Hong Kong; (Z.X.); (J.C.)
- School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China
| | - Carman Ka Man Lee
- Centre for Advances in Reliability and Safety, Hong Kong; (Z.X.); (J.C.)
- Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong
- Correspondence:
| | - Yaqiong Lv
- School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430062, China;
| | - Jeffery Chan
- Centre for Advances in Reliability and Safety, Hong Kong; (Z.X.); (J.C.)
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Abiodun TN, Okunbor D, Chukwudi Osamor V. Remote Health Monitoring in Clinical Trial using Machine Learning Techniques: A Conceptual Framework. HEALTH AND TECHNOLOGY 2022; 12:359-364. [PMID: 35308032 PMCID: PMC8916791 DOI: 10.1007/s12553-022-00652-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 02/12/2022] [Accepted: 02/23/2022] [Indexed: 11/05/2022]
Abstract
Monitoring any process is crucial and very necessary, this is to ensure that standard protocols and procedures are strictly adhered to, monitoring clinical trials is not an exception. It is one of the most crucial processes that should be monitored because human subjects are involved. In trying to monitor clinical trial, information and communication technology techniques can be deployed to facilitate the process and hence improve accuracy. This research formulates a new conceptual framework for monitoring clinical trial using Support Vector Machine and Artificial Neural Network classifiers with physiological datasets from a wearable device. The proposed framework prototype consists of data collection module, data transmission module, and data analysis and prediction module. The data analytic and prediction module is the core section of the proposed framework tailored with data analysis. These datasets are preprocessed and transformed and then used to train and test the system, through different experimental analysis including bagging Support Vector Machine (SVM) and Artificial Neural Network (ANN). The outcome of the analysis presents classification into three different categories, such as fit, unfit, and undecided participants. These various classifications are used to determine if a participant should be allowed to continue in the trial or not. This research provides a framework that is useful in monitoring clinical trial remotely, thereby informing the decision-making process of the research team.
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