skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

Results 1 - 20 of 1,486  for All Library Resources

Sorted by: Sorted by: RelevanceOr hit Enter to replace sort method
Results 1 2 3 4 5 next page
Show only
Refined by: subject: Life Sciences & Biomedicine remove subject: Machine Learning remove
Result Number Material Type Add to My Shelf Action Record Details and Options
1
Prediction of Skin Disease Using Ensemble Data Mining Techniques and Feature Selection Method—a Comparative Study
Prediction of Skin Disease Using Ensemble Data Mining Techniques and Feature Selection Method—a Comparative Study
Material Type:
Article 		Add to My Research

Prediction of Skin Disease Using Ensemble Data Mining Techniques and Feature Selection Method—a Comparative Study

Applied biochemistry and biotechnology, 2020-02, Vol.190 (2), p.341-359 [Peer Reviewed Journal]

Springer Science+Business Media, LLC, part of Springer Nature 2019 ;Applied Biochemistry and Biotechnology is a copyright of Springer, (2019). All Rights Reserved. ;ISSN: 0273-2289 ;EISSN: 1559-0291 ;DOI: 10.1007/s12010-019-03093-z ;PMID: 31350666

Full text available

Citations Cited by
2
Fusion and Classification of SAR and Optical Data Using Multi-Image Color Components with Differential Gradients
Fusion and Classification of SAR and Optical Data Using Multi-Image Color Components with Differential Gradients
Material Type:
Article 		Add to My Research

Fusion and Classification of SAR and Optical Data Using Multi-Image Color Components with Differential Gradients

Remote sensing (Basel, Switzerland), 2023-01, Vol.15 (1), p.274 [Peer Reviewed Journal]

2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs15010274

Full text available

Citations Cited by
3
A Random Forest classifier-based approach in the detection of abnormalities in the retina
A Random Forest classifier-based approach in the detection of abnormalities in the retina
Material Type:
Article 		Add to My Research

A Random Forest classifier-based approach in the detection of abnormalities in the retina

Medical & biological engineering & computing, 2019, Vol.57 (1), p.193-203 [Peer Reviewed Journal]

International Federation for Medical and Biological Engineering 2018 ;Medical & Biological Engineering & Computing is a copyright of Springer, (2018). All Rights Reserved. ;ISSN: 0140-0118 ;EISSN: 1741-0444 ;DOI: 10.1007/s11517-018-1878-0 ;PMID: 30076537

Full text available

Citations Cited by
4
Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier
Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier
Material Type:
Article 		Add to My Research

Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier

Computers in biology and medicine, 2020-08, Vol.123, p.103899, Article 103899 [Peer Reviewed Journal]

2020 Elsevier Ltd ;Copyright © 2020 Elsevier Ltd. All rights reserved. ;2020. Elsevier Ltd ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2020.103899 ;PMID: 32768046

Full text available

Citations Cited by
5
Machine learning reduced workload with minimal risk of missing studies: development and evaluation of a randomized controlled trial classifier for Cochrane Reviews
Machine learning reduced workload with minimal risk of missing studies: development and evaluation of a randomized controlled trial classifier for Cochrane Reviews
Material Type:
Article 		Add to My Research

Machine learning reduced workload with minimal risk of missing studies: development and evaluation of a randomized controlled trial classifier for Cochrane Reviews

Journal of clinical epidemiology, 2021-05, Vol.133, p.140-151 [Peer Reviewed Journal]

2020 The Authors ;Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved. ;2020. The Authors ;2020 The Authors 2020 ;ISSN: 0895-4356 ;EISSN: 1878-5921 ;DOI: 10.1016/j.jclinepi.2020.11.003 ;PMID: 33171275

Full text available

Citations Cited by
6
Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery
Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery
Material Type:
Article 		Add to My Research

Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery

Remote sensing (Basel, Switzerland), 2015-01, Vol.7 (1), p.153-168 [Peer Reviewed Journal]

Copyright MDPI AG 2015 ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs70100153

Full text available

Citations Cited by
7
Comparing the performance of meta-classifiers—a case study on selected imbalanced data sets relevant for prediction of liver toxicity
Comparing the performance of meta-classifiers—a case study on selected imbalanced data sets relevant for prediction of liver toxicity
Material Type:
Article 		Add to My Research

Comparing the performance of meta-classifiers—a case study on selected imbalanced data sets relevant for prediction of liver toxicity

Journal of computer-aided molecular design, 2018-05, Vol.32 (5), p.583-590 [Peer Reviewed Journal]

The Author(s) 2018 ;Journal of Computer-Aided Molecular Design is a copyright of Springer, (2018). All Rights Reserved. ;ISSN: 0920-654X ;EISSN: 1573-4951 ;DOI: 10.1007/s10822-018-0116-z ;PMID: 29626291

Full text available

Citations Cited by
8
ilastik: interactive machine learning for (bio)image analysis
ilastik: interactive machine learning for (bio)image analysis
Material Type:
Article 		Add to My Research

ilastik: interactive machine learning for (bio)image analysis

Nature methods, 2019-12, Vol.16 (12), p.1226-1232 [Peer Reviewed Journal]

COPYRIGHT 2019 Nature Publishing Group ;Copyright Nature Publishing Group Dec 2019 ;ISSN: 1548-7091 ;EISSN: 1548-7105 ;DOI: 10.1038/s41592-019-0582-9 ;PMID: 31570887

Full text available

Citations Cited by
9
PlasForest: a homology-based random forest classifier for plasmid detection in genomic datasets
PlasForest: a homology-based random forest classifier for plasmid detection in genomic datasets
Material Type:
Article 		Add to My Research

PlasForest: a homology-based random forest classifier for plasmid detection in genomic datasets

BMC bioinformatics, 2021-06, Vol.22 (1), p.1-349, Article 349 [Peer Reviewed Journal]

COPYRIGHT 2021 BioMed Central Ltd. ;2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;Distributed under a Creative Commons Attribution 4.0 International License ;The Author(s) 2021 ;ISSN: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/s12859-021-04270-w ;PMID: 34174810

Full text available

Citations Cited by
10
Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation
Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation
Material Type:
Article 		Add to My Research

Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation

Remote sensing (Basel, Switzerland), 2021-11, Vol.13 (21), p.4405 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs13214405

Full text available

Citations Cited by
11
Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0
Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0
Material Type:
Article 		Add to My Research

Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0

Molecular plant pathology, 2018-09, Vol.19 (9), p.2094-2110 [Peer Reviewed Journal]

2018 BSPP and John Wiley & Sons Ltd ;2018 BSPP AND JOHN WILEY & SONS LTD ;ISSN: 1464-6722 ;EISSN: 1364-3703 ;DOI: 10.1111/mpp.12682 ;PMID: 29569316

Full text available

Citations Cited by
12
Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans
Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans
Material Type:
Article 		Add to My Research

Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans

Computers in biology and medicine, 2022-02, Vol.141, p.105127-105127, Article 105127 [Peer Reviewed Journal]

2021 Elsevier Ltd ;Copyright © 2021 Elsevier Ltd. All rights reserved. ;2021. Elsevier Ltd ;2021 Elsevier Ltd. All rights reserved. 2021 Elsevier Ltd ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2021.105127 ;PMID: 34915332

Full text available

Citations Cited by
13
Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion
Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion
Material Type:
Article 		Add to My Research

Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion

Plants (Basel), 2020-10, Vol.9 (10), p.1302 [Peer Reviewed Journal]

2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2020 by the authors. 2020 ;ISSN: 2223-7747 ;EISSN: 2223-7747 ;DOI: 10.3390/plants9101302 ;PMID: 33019765

Full text available

Citations Cited by
14
Examining evolutionary scale modeling‐derived different‐dimensional embeddings in the antimicrobial peptide classification through a KNIME workflow
Examining evolutionary scale modeling‐derived different‐dimensional embeddings in the antimicrobial peptide classification through a KNIME workflow
Material Type:
Article 		Add to My Research

Examining evolutionary scale modeling‐derived different‐dimensional embeddings in the antimicrobial peptide classification through a KNIME workflow

Protein science, 2024-04, Vol.33 (4), p.e4928-n/a [Peer Reviewed Journal]

2024 The Protein Society. ;2024 The Protein Society ;ISSN: 0961-8368 ;EISSN: 1469-896X ;DOI: 10.1002/pro.4928 ;PMID: 38501511

Full text available

Citations Cited by
15
Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning
Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning
Material Type:
Article 		Add to My Research

Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning

Remote sensing (Basel, Switzerland), 2018-02, Vol.10 (2), p.276 [Peer Reviewed Journal]

Copyright MDPI AG 2018 ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs10020276

Full text available

Citations Cited by
16
Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images
Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images
Material Type:
Article 		Add to My Research

Social Group Optimization–Assisted Kapur’s Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images

Cognitive computation, 2020-09, Vol.12 (5), p.1011-1023 [Peer Reviewed Journal]

The Author(s) 2020 ;ISSN: 1866-9956 ;EISSN: 1866-9964 ;DOI: 10.1007/s12559-020-09751-3

Digital Resources/Online E-Resources

Citations Cited by
17
Multiclass wound image classification using an ensemble deep CNN-based classifier
Multiclass wound image classification using an ensemble deep CNN-based classifier
Material Type:
Article 		Add to My Research

Multiclass wound image classification using an ensemble deep CNN-based classifier

Computers in biology and medicine, 2021-07, Vol.134, p.104536-104536, Article 104536 [Peer Reviewed Journal]

2021 ;Copyright Elsevier Limited Jul 2021 ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2021.104536

Full text available

Citations Cited by
18
Diabetic retinopathy classification based on multipath CNN and machine learning classifiers
Diabetic retinopathy classification based on multipath CNN and machine learning classifiers
Material Type:
Article 		Add to My Research

Diabetic retinopathy classification based on multipath CNN and machine learning classifiers

Australasian physical & engineering sciences in medicine, 2021-09, Vol.44 (3), p.639-653 [Peer Reviewed Journal]

Australasian College of Physical Scientists and Engineers in Medicine 2021. ;ISSN: 0158-9938 ;EISSN: 1879-5447 ;DOI: 10.1007/s13246-021-01012-3

Full text available

Citations Cited by
19
The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation
The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation
Material Type:
Article 		Add to My Research

The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation

BioData mining, 2021-02, Vol.14 (1), p.13-13, Article 13 [Peer Reviewed Journal]

COPYRIGHT 2021 BioMed Central Ltd. ;2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2021 ;ISSN: 1756-0381 ;EISSN: 1756-0381 ;DOI: 10.1186/s13040-021-00244-z ;PMID: 33541410

Full text available

Citations Cited by
20
Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals
Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals
Material Type:
Article 		Add to My Research

Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals

Computers in biology and medicine, 2022-01, Vol.140, p.105120-105120, Article 105120 [Peer Reviewed Journal]

2021 Elsevier Ltd ;Copyright © 2021 Elsevier Ltd. All rights reserved. ;2021. Elsevier Ltd ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2021.105120 ;PMID: 34896884

Full text available

Citations Cited by

Results 1 - 20 of 1,486  for All Library Resources

Sorted by: Sorted by: RelevanceOr hit Enter to replace sort method
Results 1 2 3 4 5 next page

Personalize your results

  1. Edit

Refine Search Results

Expand My Results

  1.   

Show only

  1. Peer-reviewed Journals (1,471)

Refine My Results

Creation Date 

From To
Refine
  1. Before 2008  (8)
  2. 2008 To 2011  (11)
  3. 2012 To 2015  (42)
  4. 2016 To 2020  (475)
  5. After 2020  (951)
  6. More options open sub menu

Resource Type 

  1. Articles  (1,482)
  2. Conference Proceedings  (2)
  3. Books  (2)
  4. More options open sub menu

Language 

  1. Japanese  (98)
  2. Portuguese  (1)
  3. German  (1)
  4. Norwegian  (1)
  5. More options open sub menu
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait