Mahbubul Alam

504 total citations
28 papers, 311 citations indexed

About

Mahbubul Alam is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Neurology. According to data from OpenAlex, Mahbubul Alam has authored 28 papers receiving a total of 311 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 9 papers in Artificial Intelligence and 5 papers in Neurology. Recurrent topics in Mahbubul Alam's work include Brain Tumor Detection and Classification (5 papers), Advanced Memory and Neural Computing (4 papers) and Advanced Neural Network Applications (4 papers). Mahbubul Alam is often cited by papers focused on Brain Tumor Detection and Classification (5 papers), Advanced Memory and Neural Computing (4 papers) and Advanced Neural Network Applications (4 papers). Mahbubul Alam collaborates with scholars based in United States, United Kingdom and Japan. Mahbubul Alam's co-authors include Khan M. Iftekharuddin, Lasitha Vidyaratne, Chester Dolph, Manar D. Samad, Linmin Pei, Md Zahangir Alom, Tarek M. Taha, Ahmed Farahat, Dipanjan Ghosh and Fahim Ahmed and has published in prestigious journals such as Nanoscale, IEEE Transactions on Neural Networks and Learning Systems and Neural Networks.

In The Last Decade

Mahbubul Alam

28 papers receiving 299 citations

Peers

Mahbubul Alam

AI

CVPR

CN

RNMI

NEURO

Lasitha Vidyaratne United States

T. Rajendran India

Yiding Lv China

Rashima Mahajan India

Антон Попов Ukraine

C. Jiménez-Mesa Spain

Samed Jukić Bosnia and Herzegovina

Xufeng Yao China

Liyan Chen China

Gökhan Altan Türkiye

Lasitha Vidyaratne United States

Mahbubul Alam

121 ×1.4

AI

134 ×1.7

CVPR

223 ×3.2

CN

112 ×1.8

RNMI

126 ×2.0

NEURO

Citations per year, relative to Mahbubul Alam Mahbubul Alam (= 1×) peers Lasitha Vidyaratne

Countries citing papers authored by Mahbubul Alam

Since Specialization

Citations

This map shows the geographic impact of Mahbubul Alam'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 Mahbubul Alam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mahbubul Alam more than expected).

Fields of papers citing papers by Mahbubul Alam

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mahbubul Alam. 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 Mahbubul Alam. The network helps show where Mahbubul Alam may publish in the future.

Co-authorship network of co-authors of Mahbubul Alam

This figure shows the co-authorship network connecting the top 25 collaborators of Mahbubul Alam. A scholar is included among the top collaborators of Mahbubul Alam 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 Mahbubul Alam. Mahbubul Alam is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Alam, Mahbubul, Vaibhav Tripathi, Chintan Bhatt, & Mohit Prakash Mohanty. (2025). A novel framework embedding Bayesian-optimized ensemble machine learning and explainable artificial intelligence (XAI) to improve flood prediction in complex watersheds. Environmental and Sustainability Indicators. 27. 100760–100760. 1 indexed citations

2.

Khan, Rajwali, Naveed ur Rehman, Sujith Kalluri, et al.. (2025). 2D MoTe₂ memristors for energy-efficient artificial synapses and neuromorphic applications. Nanoscale. 17(21). 13174–13206. 1 indexed citations

3.

Kumar, Aman, et al.. (2024). Diagnostics-LLaVA. Annual Conference of the PHM Society. 16(1). 2 indexed citations

4.

Alam, Mahbubul, et al.. (2024). Exploring Graph-Based Algorithms for Accurate Brain Tumor Segmentation. 1956–1961. 1 indexed citations

5.

Dolph, Chester, Corey A. Ippolito, Louis J. Glaab, et al.. (2023). Adversarial Learning Improves Vision-Based Perception from Drones with Imbalanced Datasets. Journal of Aerospace Information Systems. 20(8). 489–507. 5 indexed citations

6.

Lee, Xian Yeow, Lasitha Vidyaratne, Mahbubul Alam, et al.. (2023). XDNet: A Few-Shot Meta-Learning Approach for Cross-Domain Visual Inspection. 4375–4384. 3 indexed citations

7.

Vidyaratne, Lasitha, et al.. (2021). Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data With Spatial Information. ePubs (Science and Technology Facilities Council, Research Councils UK). 11 indexed citations

8.

Alam, Mahbubul, et al.. (2021). Remaining Useful Life Estimation from Event Data. Annual Conference of the PHM Society. 13(1). 1 indexed citations

9.

Alam, Mahbubul, et al.. (2020). End-to-end Multimodel Deep Learning for Malware Classification. 1–7. 15 indexed citations

10.

Alam, Mahbubul, et al.. (2019). Feature-Guided Deep Radiomics for Glioblastoma Patient Survival Prediction. Frontiers in Neuroscience. 13. 966–966. 42 indexed citations

11.

Vidyaratne, Lasitha, et al.. (2018). Glioblastoma and Survival Prediction. Lecture notes in computer science. 10670. 358–368. 24 indexed citations

12.

Alam, Mahbubul, Lasitha Vidyaratne, & Khan M. Iftekharuddin. (2018). Novel deep generative simultaneous recurrent model for efficient representation learning. Neural Networks. 107. 12–22. 6 indexed citations

13.

Alam, Mahbubul, Lasitha Vidyaratne, & Khan M. Iftekharuddin. (2018). Sparse Simultaneous Recurrent Deep Learning for Robust Facial Expression Recognition. IEEE Transactions on Neural Networks and Learning Systems. 29(10). 4905–4916. 39 indexed citations

14.

Alom, Md Zahangir, Mahbubul Alam, Tarek M. Taha, & Khan M. Iftekharuddin. (2017). Object recognition using cellular simultaneous recurrent networks and convolutional neural network. 2873–2880. 15 indexed citations

15.

Dolph, Chester, et al.. (2017). Deep learning of texture and structural features for multiclass Alzheimer's disease classification. 2259–2266. 47 indexed citations

16.

Alam, Mahbubul, Lasitha Vidyaratne, & Khan M. Iftekharuddin. (2016). Efficient feature extraction with simultaneous recurrent network for metric learning. 2. 1195–1201. 2 indexed citations

17.

Alam, Mahbubul, et al.. (2016). Deep SRN for robust object recognition: A case study with NAO humanoid robot. 1. 1–7. 6 indexed citations

18.

Vidyaratne, Lasitha, et al.. (2016). Deep recurrent neural network for seizure detection. 1202–1207. 49 indexed citations

19.

Alam, Mahbubul, Lasitha Vidyaratne, & Khan M. Iftekharuddin. (2015). Novel hierarchical Cellular Simultaneous Recurrent neural Network for object detection. 1–7. 5 indexed citations

20.

Alam, Mahbubul, et al.. (2013). Exploring Image-based Classification to Detect Vehicle Make and Model. 2 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.

Explore authors with similar magnitude of impact