Rafia Nishat Toma
Rafia Nishat Toma
Faculty member, ECE Discipline, Khulna University; PhD Student, University of Ulsan, South Korea.
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Electricity theft detection in smart grid systems: A CNN-LSTM based approach
MN Hasan, RN Toma, AA Nahid, MMM Islam, JM Kim
Energies 12 (17), 3310, 2019
Bearing fault diagnosis of induction motors using a genetic algorithm and machine learning classifiers
RN Toma, AE Prosvirin, JM Kim
Sensors 20 (7), 1884, 2020
Bearing fault classification of induction motors using discrete wavelet transform and ensemble machine learning algorithms
R Nishat Toma, JM Kim
Applied Sciences 10 (15), 5251, 2020
Bearing fault classification using ensemble empirical mode decomposition and convolutional neural network
R Nishat Toma, CH Kim, JM Kim
Electronics 10 (11), 1248, 2021
Electricity theft detection to reduce non-technical loss using support vector machine in smart grid
RN Toma, MN Hasan, AA Nahid, B Li
2019 1st International Conference on Advances in Science, Engineering and …, 2019
Analysis the effect of changing height of the substrate of square shaped microstrip patch antenna on the performance for 5G application
RN Toma, IA Shohagh, MN Hasan
International Journal of Wireless and Microwave Technologies 3, 33-45, 2019
A deep autoencoder-based convolution neural network framework for bearing fault classification in induction motors
RN Toma, F Piltan, JM Kim
Sensors 21 (24), 8453, 2021
Strict-Feedback Backstepping Digital Twin and Machine Learning Solution in AE Signals for Bearing Crack Identification
F Piltan, RN Toma, D Shon, K Im, HK Choi, DS Yoo, JM Kim
Sensors 22 (2), 539, 2022
A Bayesian optimization framework for the prediction of diabetes mellitus
MA Rahman, SM Shoaib, M Al Amin, RN Toma, MA Moni, MA Awal
2019 5th International Conference on Advances in Electrical Engineering …, 2019
FPGA implementation of LBlock lightweight block cipher
MN Hasan, MT Hasan, RN Toma, M Maniruzzaman
2016 3rd International Conference on Electrical Engineering and Information …, 2016
A Bearing Fault Classification Framework Based on Image Encoding Techniques and a Convolutional Neural Network under Different Operating Conditions
RN Toma, F Piltan, K Im, D Shon, TH Yoon, DS Yoo, JM Kim
Sensors 22 (13), 4881, 2022
Induction Motor Bearing Fault Diagnosis Using Statistical Time Domain Features and Hypertuning of Classifiers
RN Toma, JM Kim
Advances in Computer Science and Ubiquitous Computing, 259-265, 2021
Bearing Fault Classification of Induction Motor Using Statistical Features and Machine Learning Algorithms
RN Toma, J Kim
International Conference on Intelligent Systems Design and Applications, 243-254, 2022
Comparative Analysis of Continuous Wavelet Transforms on Vibration signal in Bearing Fault Diagnosis of Induction Motor
RN Toma, FH Toma, JM Kim
2021 International Conference on Electronics, Communications and Information …, 2021
Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features
RN Toma, Y Gao, F Piltan, K Im, D Shon, TH Yoon, DS Yoo, JM Kim
Sensors 22 (22), 8958, 2022
Data-Driven Fault Classification of Induction Motor Based on Recurrence Plot and Deep Convolution Neural Network
RN Toma, Y Gao, JM Kim
Machine Learning and Artificial Intelligence, 64-71, 2022
A Novel Fault Diagnosis Method Based on MADCNN for Rolling Bearings
Y Gao, F Piltan, Z Ahmad, RN Toma, JM Kim
Machine Learning and Artificial Intelligence, 56-63, 2022
Home Occupancy Classification Using Machine Learning Techniques along with Feature Selection
AA Nahid, N Sikder, MH Abid, RN Toma, IA Talin, LE Ali
International Journal of Engineering and Manufacturing (IJEM)
A Baloch, TD Memon, F Memon, B Lal, V Viyas, T Jan
IJEM 11 (4), 2021
Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network. Electronics 2021, 10, 1248
R Nishat Toma, CH Kim, JM Kim
s Note: MDPI stays neutral with regard to jurisdictional claims in published …, 2021
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