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Board-Level Functional Fault Diagnosis Using Artificial Neural Networks, Support-Vector Machines, and Weighted-Majority Voting

IEEE transactions on computer-aided design of integrated circuits and systems, 2013-05, Vol.32 (5), p.723-736

2013

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Board-Level Functional Fault Diagnosis Using Multikernel Support Vector Machines and Incremental Learning

IEEE transactions on computer-aided design of integrated circuits and systems, 2014-02, Vol.33 (2), p.279-290

2014

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Board-Level Functional Fault Identification Using Streaming Data

IEEE transactions on computer-aided design of integrated circuits and systems, 2021-09, Vol.40 (9), p.1920-1933

2021

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Adaptive Board-Level Functional Fault Diagnosis Using Incremental Decision Trees

IEEE transactions on computer-aided design of integrated circuits and systems, 2016-02, Vol.35 (2), p.323-336

2016

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Age and flexors as risk factors for cervical radiculopathy: A new machine learning method

Medicine (Baltimore), 2024-01, Vol.103 (4), p.e36939

2024

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Information-Theoretic Syndrome Evaluation, Statistical Root-Cause Analysis, and Correlation-Based Feature Selection for Guiding Board-Level Fault Diagnosis

IEEE transactions on computer-aided design of integrated circuits and systems, 2015-06, Vol.34 (6), p.1014-1026

2015

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Proteomic analysis to identification of hypoxia related markers in spinal tuberculosis: a study based on weighted gene co-expression network analysis and machine learning

BMC medical genomics, 2023-06, Vol.16 (1), p.142-142, Article 142

2023

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Identification of clinical heterogeneity and construction of a novel subtype predictive model in patients with ankylosing spondylitis: An unsupervised machine learning study

International immunopharmacology, 2023-04, Vol.117, p.109879-109879, Article 109879

2023

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Application of machine learning in prediction of bone cement leakage during single-level thoracolumbar percutaneous vertebroplasty

BMC surgery, 2023-03, Vol.23 (1), p.63-63, Article 63

2023

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Machine learning-based clustering in cervical spondylotic myelopathy patients to identify heterogeneous clinical characteristics

Frontiers in surgery, 2022-07, Vol.9, p.935656-935656

2022

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Comprehensive AI-assisted tool for ankylosing spondylitis based on multicenter research outperforms human experts

Frontiers in public health, 2023-02, Vol.11, p.1063633-1063633

2023

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Development and Validation of a Machine Learning-Based Nomogram for Prediction of Ankylosing Spondylitis

Rheumatology and therapy., 2022-10, Vol.9 (5), p.1377-1397

2022

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Adaptive Board-Level Functional Fault Diagnosis Using Decision Trees

2012 IEEE 21st Asian Test Symposium, 2012, p.202-207

2012

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Board-Level Functional Fault Diagnosis Using Learning Based on Incremental Support-Vector Machines

2012 IEEE 21st Asian Test Symposium, 2012, p.208-213

2012

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Handling Missing Syndromes in Board-Level Functional-Fault Diagnosis

2013 22nd Asian Test Symposium, 2013, p.73-78

2013

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Handling Missing Syndromes

Knowledge-Driven Board-Level Functional Fault Diagnosis, 2016, p.95-119

2016

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Introduction

Knowledge-Driven Board-Level Functional Fault Diagnosis, 2016, p.1-21

2016

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Information-Theoretic Syndrome and Root-Cause Evaluation

Knowledge-Driven Board-Level Functional Fault Diagnosis, 2016, p.79-93

2016

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Diagnosis Using Support Vector Machines (SVM)

Knowledge-Driven Board-Level Functional Fault Diagnosis, 2016, p.23-42

2016

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Conclusions

Knowledge-Driven Board-Level Functional Fault Diagnosis, 2016, p.143-146

2016

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Diagnosis Using Multiple Classifiers and Majority-Weighted Voting (WMV)

Knowledge-Driven Board-Level Functional Fault Diagnosis, 2016, p.43-59

2016

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Knowledge Discovery and Knowledge Transfer

Knowledge-Driven Board-Level Functional Fault Diagnosis, 2016, p.121-142

2016

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Adaptive Diagnosis Using Decision Trees (DT)

Knowledge-Driven Board-Level Functional Fault Diagnosis, 2016, p.61-78

2016

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Board-Level Functional Fault Diagnosis Using Artificial Neural Networks, Support-Vector Machines, and Weighted-Majority Voting

Board-Level Functional Fault Diagnosis Using Multikernel Support Vector Machines and Incremental Learning

Board-Level Functional Fault Identification Using Streaming Data

Adaptive Board-Level Functional Fault Diagnosis Using Incremental Decision Trees

Age and flexors as risk factors for cervical radiculopathy: A new machine learning method

Information-Theoretic Syndrome Evaluation, Statistical Root-Cause Analysis, and Correlation-Based Feature Selection for Guiding Board-Level Fault Diagnosis

Proteomic analysis to identification of hypoxia related markers in spinal tuberculosis: a study based on weighted gene co-expression network analysis and machine learning

Identification of clinical heterogeneity and construction of a novel subtype predictive model in patients with ankylosing spondylitis: An unsupervised machine learning study

Application of machine learning in prediction of bone cement leakage during single-level thoracolumbar percutaneous vertebroplasty

Machine learning-based clustering in cervical spondylotic myelopathy patients to identify heterogeneous clinical characteristics

Comprehensive AI-assisted tool for ankylosing spondylitis based on multicenter research outperforms human experts

Development and Validation of a Machine Learning-Based Nomogram for Prediction of Ankylosing Spondylitis

Adaptive Board-Level Functional Fault Diagnosis Using Decision Trees

Board-Level Functional Fault Diagnosis Using Learning Based on Incremental Support-Vector Machines

Handling Missing Syndromes in Board-Level Functional-Fault Diagnosis

Handling Missing Syndromes

Introduction

Information-Theoretic Syndrome and Root-Cause Evaluation

Diagnosis Using Support Vector Machines (SVM)

Conclusions

Diagnosis Using Multiple Classifiers and Majority-Weighted Voting (WMV)

Knowledge Discovery and Knowledge Transfer

Adaptive Diagnosis Using Decision Trees (DT)

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