Mubarak G. Abdu-Aguye

Orcid: 0000-0001-7010-8103

According to our database1, Mubarak G. Abdu-Aguye authored at least 12 papers between 2018 and 2023.

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

Timeline

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Bibliography

2023
On the Effects of Filtering Methods on Adversarial Timeseries Data.
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geo-Privacy and Data Utility for Smart Societies, 2023

Recurrence-Based Disentanglement for Detecting Adversarial Attacks on Timeseries Classifiers.
Proceedings of the 31st European Signal Processing Conference, 2023

2022
Investigating strategies towards adversarially robust time series classification.
Pattern Recognit. Lett., 2022

2020
Massive MIMO CSI Feedback Based on Generative Adversarial Network.
IEEE Commun. Lett., 2020

Adaptive Pooling Is All You Need: An Empirical Study on Hyperparameter-insensitive Human Action Recognition Using Wearable Sensors.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Improved IMU-based Human Activity Recognition using Hierarchical HMM Dissimilarity.
Proceedings of the 17th International Conference on Informatics in Control, 2020

Detecting Adversarial Attacks In Time-Series Data.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

2019
Competitive Feature Extraction for Activity Recognition based on Wavelet Transforms and Adaptive Pooling.
Proceedings of the International Joint Conference on Neural Networks, 2019

On the Feasibility of On-body Roaming Models in Human Activity Recognition.
Proceedings of the 16th International Conference on Informatics in Control, 2019

Robust Human Activity Recognition based on Deep Metric Learning.
Proceedings of the 16th International Conference on Informatics in Control, 2019

VersaTL: Versatile Transfer Learning for IMU-based Activity Recognition using Convolutional Neural Networks.
Proceedings of the 16th International Conference on Informatics in Control, 2019

2018
Novel Approaches to Activity Recognition Based on Vector Autoregression and Wavelet Transforms.
Proceedings of the 17th IEEE International Conference on Machine Learning and Applications, 2018


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