Aminu Da’u

903 total citations · 1 hit paper
15 papers, 584 citations indexed

About

Aminu Da’u is a scholar working on Information Systems, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Aminu Da’u has authored 15 papers receiving a total of 584 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Information Systems, 10 papers in Artificial Intelligence and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Aminu Da’u's work include Recommender Systems and Techniques (9 papers), Sentiment Analysis and Opinion Mining (8 papers) and Spam and Phishing Detection (3 papers). Aminu Da’u is often cited by papers focused on Recommender Systems and Techniques (9 papers), Sentiment Analysis and Opinion Mining (8 papers) and Spam and Phishing Detection (3 papers). Aminu Da’u collaborates with scholars based in Malaysia, Nigeria and Saudi Arabia. Aminu Da’u's co-authors include Naomie Salim, Idris Rabiu, Maged Nasser, Noreen Izza Arshad, Abdulalem Ali, Faisal Saeed, Ibtehal Nafea, Layla Hasan, Nor Azman Ismail and Alhuseen Omar Alsayed and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Information Sciences.

In The Last Decade

Aminu Da’u

14 papers receiving 561 citations

Hit Papers

Recommendation system based on deep learning methods: a s... 2019 2026 2021 2023 2019 50 100 150 200
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Recommendation system based on deep learning methods: a systematic review and new directions
    2019 231 citations Aminu Da’u, Naomie Salim Artificial Intelligence Review profile →

Peers

Aminu Da’u
Fernando Mourão Brazil
Zeynep Batmaz Türkiye
Benedikt Loepp Germany
Ali Yürekli Türkiye
Sanjay Kumar Jain India
Vibhor Kant India
Fernando Díez Spain
Ali Elkahky United States
Jiangzhou Deng China
Zuohui Fu United States
Fernando Mourão Brazil
Aminu Da’u
Citations per year, relative to Aminu Da’u Aminu Da’u (= 1×) peers Fernando Mourão

Countries citing papers authored by Aminu Da’u

Since Specialization
Citations

This map shows the geographic impact of Aminu Da’u's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Aminu Da’u with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aminu Da’u more than expected).

Fields of papers citing papers by Aminu Da’u

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aminu Da’u. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Aminu Da’u. The network helps show where Aminu Da’u may publish in the future.

Co-authorship network of co-authors of Aminu Da’u

This figure shows the co-authorship network connecting the top 25 collaborators of Aminu Da’u. A scholar is included among the top collaborators of Aminu Da’u based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Aminu Da’u. Aminu Da’u is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

15 of 15 papers shown
1.
Abdullah, Siti Norul Huda Sheikh, et al.. (2025). Deep Learning in Biometric Authentication: Challenges, Recent Advancements, and Future Trends. Journal of Advances in Information Technology. 16(4). 458–477.
2.
Nasser, Maged, Noreen Izza Arshad, Abdulalem Ali, et al.. (2025). A systematic review of multimodal fake news detection on social media using deep learning models. Results in Engineering. 26. 104752–104752. 9 indexed citations
3.
Nasser, Maged, et al.. (2024). Topic-aware neural attention network for malicious social media spam detection. Alexandria Engineering Journal. 111. 540–554. 1 indexed citations
4.
Alsayed, Alhuseen Omar, et al.. (2023). A systematic literature review for understanding the effectiveness of advanced techniques in diabetes self-care management. Alexandria Engineering Journal. 79. 274–295. 7 indexed citations
5.
Rabiu, Idris, et al.. (2022). Drift Detection Method Using Distance Measures and Windowing Schemes for Sentiment Classification. Computers, materials & continua/Computers, materials & continua (Print). 74(3). 6001–6017. 1 indexed citations
6.
Rabiu, Idris, Naomie Salim, Aminu Da’u, & Maged Nasser. (2021). Modeling sentimental bias and temporal dynamics for adaptive deep recommendation system. Expert Systems with Applications. 191. 116262–116262. 8 indexed citations
7.
Da’u, Aminu, Naomie Salim, & Idris Rabiu. (2021). Multi-level attentive deep user-item representation learning for recommendation system. Neurocomputing. 433. 119–130. 15 indexed citations
8.
Rabiu, Idris, et al.. (2020). Exploiting dynamic changes from latent features to improve recommendation using temporal matrix factorization. Egyptian Informatics Journal. 22(3). 285–294. 5 indexed citations
9.
Da’u, Aminu, Naomie Salim, & Idris Rabiu. (2020). An adaptive deep learning method for item recommendation system. Knowledge-Based Systems. 213. 106681–106681. 30 indexed citations
10.
Rabiu, Idris, et al.. (2020). Recommender System Based on Temporal Models: A Systematic Review. Applied Sciences. 10(7). 2204–2204. 33 indexed citations
11.
Da’u, Aminu & Naomie Salim. (2019). Recommendation system based on deep learning methods: a systematic review and new directions. Artificial Intelligence Review. 53(4). 2709–2748. 231 indexed citations breakdown →
12.
Da’u, Aminu, et al.. (2019). Weighted aspect-based opinion mining using deep learning for recommender system. Expert Systems with Applications. 140. 112871–112871. 72 indexed citations
13.
Da’u, Aminu & Naomie Salim. (2019). Aspect extraction on user textual reviews using multi-channel convolutional neural network. PeerJ Computer Science. 5. e191–e191. 21 indexed citations
14.
Da’u, Aminu, et al.. (2019). Recommendation system exploiting aspect-based opinion mining with deep learning method. Information Sciences. 512. 1279–1292. 105 indexed citations
15.
Da’u, Aminu & Naomie Salim. (2019). Sentiment-Aware Deep Recommender System With Neural Attention Networks. IEEE Access. 7. 45472–45484. 46 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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