Abdullahi Uwaisu Muhammad

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 8

Top Topics

Top Venues

Earth Sci. Informatics

IEEE Trans. Netw. Serv. Manag.

Comput. Commun.

Publications

Andri Pranolo, Xiaofeng Zhou, Yingchi Mao, Bambang Widi Pratolo, Aji Prasetya Wibawa, Agung Bella Putra Utama, Abdoul Fatakhou Ba, Abdullahi Uwaisu Muhammad
Exploring LSTM-based Attention Mechanisms with PSO and Grid Search under Different Normalization Techniques for Energy demands Time Series Forecasting. Knowl. Eng. Data Sci. 7 (1) (2024)
Tasiu Muazu, Yingchi Mao, Abdullahi Uwaisu Muhammad, Muhammad Ibrahim, Umar Muhammad Mustapha Kumshe, Omaji Samuel
A federated learning system with data fusion for healthcare using multi-party computation and additive secret sharing. Comput. Commun. 216 (2024)
Tasiu Muazu, Yingchi Mao, Abdullahi Uwaisu Muhammad, Muhammad Ibrahim, Omaji Samuel, Prayag Tiwari
IoMT: A Medical Resource Management System Using Edge Empowered Blockchain Federated Learning. IEEE Trans. Netw. Serv. Manag. 21 (1) (2024)
Jibril Muhammad Adam, Weiquan Liu, Yu Zang, Muhammad Kamran Afzal, Saifullahi Aminu Bello, Abdullahi Uwaisu Muhammad, Cheng Wang, Jonathan Li
Deep learning-based semantic segmentation of urban-scale 3D meshes in remote sensing: A survey. Int. J. Appl. Earth Obs. Geoinformation 121 (2023)
Abdullahi Uwaisu Muhammad, S. I. Abba
Transfer learning for streamflow forecasting using unguaged MOPEX basins data set. Earth Sci. Informatics 16 (2) (2023)
Abdullahi Uwaisu Muhammad, Hamza Djigal, Tasiu Muazu, Jibril Muhammad Adam, Abdoul Fatakhou Ba, Umar Sani Dabai, Sani Tijjani, Muhammad Sabo Yahaya, Aliyu Ashiru, Umar Muhammad Mustapha Kumshe, Saddam Aliyu, Faruwa Ajibola Richard
An autoencoder-based stacked LSTM transfer learning model for EC forecasting. Earth Sci. Informatics 16 (4) (2023)
Abdullahi Uwaisu Muhammad, Xiaodong Li, Jun Feng
Using LSTM GRU and Hybrid Models for Streamflow Forecasting. MLICOM (2019)
Abdullahi Uwaisu Muhammad, Xiaodong Li, Jun Feng
Artificial Intelligence Approaches for Urban Water Demand Forecasting: A Review. MLICOM (2019)