HCMMA-Net: A Hybrid Convolutional Multi-Modal Attention Network for Human Activity Recognition in Smart Homes Using Wearable Sensor Data

Ashfaq, N., Aziz, Z., Khan, M.H., Nisar, M.A., Khalid, A.

IEEE Access, 2025

Novel hybrid architecture combining convolutional networks with multi-modal attention mechanisms for smart home activity recognition.

Identification of Optimal Data Augmentation Techniques for Multimodal Time-Series Sensory Data: A Framework

Ashfaq, N., Khan, M.H., Nisar, M.A.

Information, 2024 , Vol. 15(6) , 343

Framework for identifying optimal data augmentation techniques for multimodal time-series sensory data in activity recognition.

The Detection of Alcohol Intoxication Using Electrooculography Signals from Smart Glasses and Machine Learning Techniques

Doniec, R.J., Piaseczna, N., Duraj, K., Sieciński, S., Irshad, M.T., Karpiel, I.

Systems and Soft Computing, 2024 , Vol. 6 , 200078

Novel approach for detecting alcohol intoxication using EOG signals from smart glasses combined with machine learning techniques.

Comparison of Feature Learning Methods for Non-Invasive Interstitial Glucose Prediction Using Wearable Sensors in Healthy Cohorts: A Pilot Study

Huang, X., Schmelter, F., Uhlig, A., Irshad, M.T., Nisar, M.A., Piet, A., Jablonski, L.

Intelligent Medicine, 2024 , Vol. 4(04) , 226-238

Pilot study comparing feature learning methods for non-invasive glucose prediction using wearable sensors.

Optimizing Sleep Staging on Multimodal Time Series: Leveraging Borderline Synthetic Minority Oversampling Technique and Supervised Convolutional Contrastive Learning

Huang, X., Schmelter, F., Irshad, M.T., Piet, A., Nisar, M.A., Sina, C., Grzegorzek, M.

Computers in Biology and Medicine, 2023 , Vol. 166 , 107501

Advanced approach for sleep staging optimization using multimodal time series data with synthetic minority oversampling and contrastive learning.

Publication Metrics & Profiles

Dr. Muhammad Adeel Nisar's Research Impact:

  • • Citations: 784 total citations
  • • h-index: 12
  • • i10-index: 15

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