M. M. Manjurul Islam
Orcid: 0000-0002-1823-1304
According to our database1,
M. M. Manjurul Islam
authored at least 16 papers
between 2015 and 2024.
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Bibliography
2024
Time-frequency envelope analysis-based sub-band selection and probabilistic support vector machines for multi-fault diagnosis of low-speed bearings.
J. Ambient Intell. Humaniz. Comput., February, 2024
2023
Cross-Version Software Defect Prediction Considering Concept Drift and Chronological Splitting.
Symmetry, October, 2023
2022
Bearing Fault Diagnosis Using Multidomain Fusion-Based Vibration Imaging and Multitask Learning.
Sensors, 2022
Spectrum Impact Analysis of Fault Proneness Statement for Improved Fault Localization.
Proceedings of the ICCA 2022: 2nd International Conference on Computing Advancements, Dhaka, Bangladesh, March 10, 2022
2019
Vision-Based Autonomous Crack Detection of Concrete Structures Using a Fully Convolutional Encoder-Decoder Network.
Sensors, 2019
Reliable multiple combined fault diagnosis of bearings using heterogeneous feature models and multiclass support vector Machines.
Reliab. Eng. Syst. Saf., 2019
A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models.
Reliab. Eng. Syst. Saf., 2019
Automated bearing fault diagnosis scheme using 2D representation of wavelet packet transform and deep convolutional neural network.
Comput. Ind., 2019
An Improved Algorithm for Selecting IMF Components in Ensemble Empirical Mode Decomposition for Domain of Rub-Impact Fault Diagnosis.
IEEE Access, 2019
2018
Rub-Impact Fault Diagnosis Using an Effective IMF Selection Technique in Ensemble Empirical Mode Decomposition and Hybrid Feature Models.
Sensors, 2018
Crack Classification of a Pressure Vessel Using Feature Selection and Deep Learning Methods.
Sensors, 2018
An Improved Gas Classification Technique Using New Features and Support Vector Machines.
Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition, 2018
Intelligent Rub-Impact Fault Diagnosis Based on Genetic Algorithm-Based IMF Selection in Ensemble Empirical Mode Decomposition and Diverse Features Models.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2018, 2018
Motor Bearing Fault Diagnosis Using Deep Convolutional Neural Networks with 2D Analysis of Vibration Signal.
Proceedings of the Advances in Artificial Intelligence, 2018
2017
A Hybrid Feature Selection Scheme Based on Local Compactness and Global Separability for Improving Roller Bearing Diagnostic Performance.
Proceedings of the Artificial Life and Computational Intelligence, 2017
2015
Multi-fault Diagnosis of Roller Bearings Using Support Vector Machines with an Improved Decision Strategy.
Proceedings of the Advanced Intelligent Computing Theories and Applications, 2015