M. M. Manjurul Islam

According to our database1, M. M. Manjurul Islam authored at least 12 papers between 2015 and 2019.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.



In proceedings 
PhD thesis 



On csauthors.net:


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

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

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

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