P. M. Ameer

2.2k total citations · 1 hit paper
35 papers, 1.6k citations indexed

About

P. M. Ameer is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, P. M. Ameer has authored 35 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Vision and Pattern Recognition, 12 papers in Artificial Intelligence and 12 papers in Electrical and Electronic Engineering. Recurrent topics in P. M. Ameer's work include Underwater Vehicles and Communication Systems (11 papers), Indoor and Outdoor Localization Technologies (9 papers) and Brain Tumor Detection and Classification (7 papers). P. M. Ameer is often cited by papers focused on Underwater Vehicles and Communication Systems (11 papers), Indoor and Outdoor Localization Technologies (9 papers) and Brain Tumor Detection and Classification (7 papers). P. M. Ameer collaborates with scholars based in India, United States and Norway. P. M. Ameer's co-authors include S. Deepak, Lillykutty Jacob, K. A. Abdul Nazeer, Anurag Kumar, Chandramani Singh, D. Manjunath, R. David Koilpillai, Tapas Roy, Mohamed Abbas and Amal BaQais and has published in prestigious journals such as Neurocomputing, IEEE Internet of Things Journal and Electronics Letters.

In The Last Decade

P. M. Ameer

33 papers receiving 1.5k citations

Hit Papers

align trajectories

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.

Brain tumor classification using deep CNN features via transfer learning

2019 859 citations S. Deepak, P. M. Ameer Computers in Biology and Medicine profile →

Peers

P. M. Ameer

NEURO

CVPR

AI

RNMI

EEE

Nagwan Abdel Samee Saudi Arabia

Junding Sun China

Xieping Gao China

Zhenbing Liu China

Jun Jiang China

M.N. Ahmed United States

Cem Direkoğlu Türkiye

Keh-Shih Chuang Taiwan

Nebras Ahmed Mohamed United States

Subrahmanyam Murala India

Nagwan Abdel Samee Saudi Arabia

P. M. Ameer

232 ×0.3

NEURO

348 ×0.4

CVPR

617 ×1.0

AI

280 ×0.8

RNMI

108 ×0.8

EEE

Citations per year, relative to P. M. Ameer P. M. Ameer (= 1×) peers Nagwan Abdel Samee

Countries citing papers authored by P. M. Ameer

Since Specialization

Citations

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

Fields of papers citing papers by P. M. Ameer

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by P. M. Ameer. 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 P. M. Ameer. The network helps show where P. M. Ameer may publish in the future.

Co-authorship network of co-authors of P. M. Ameer

This figure shows the co-authorship network connecting the top 25 collaborators of P. M. Ameer. A scholar is included among the top collaborators of P. M. Ameer 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 P. M. Ameer. P. M. Ameer 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.

Sagheer, Sameera V. Mohd, et al.. (2025). A Deep Learning Approach to Classification of Diseases in Date Palm Leaves. Computers, materials & continua/Computers, materials & continua (Print). 84(1). 1329–1349.

2.

Ameer, P. M., et al.. (2024). Multi image super resolution of MRI images using generative adversarial network. Journal of Ambient Intelligence and Humanized Computing. 15(4). 2241–2253. 2 indexed citations

3.

George, Sudhish N., et al.. (2024). MedDefend: Securing Medical IoT With Adaptive Noise-Reduction-Based Adversarial Detection. IEEE Internet of Things Journal. 12(8). 10808–10817.

4.

Ameer, P. M., et al.. (2022). A content-based image retrieval system for the diagnosis of lymphoma using blood micrographs: An incorporation of deep learning with a traditional learning approach. Computers in Biology and Medicine. 145. 105463–105463. 13 indexed citations

5.

Ameer, P. M., et al.. (2022). Identification of white blood cells for the diagnosis of acute myeloid leukemia. International Journal of Imaging Systems and Technology. 32(4). 1307–1317. 3 indexed citations

6.

Deepak, S. & P. M. Ameer. (2020). Retrieval of brain MRI with tumor using contrastive loss based similarity on GoogLeNet encodings. Computers in Biology and Medicine. 125. 103993–103993. 40 indexed citations

7.

Ameer, P. M., et al.. (2020). Localization and recognition of leukocytes in peripheral blood: A deep learning approach. Computers in Biology and Medicine. 126. 104034–104034. 50 indexed citations

8.

Ameer, P. M., et al.. (2020). Graph‐based optimal asynchronous scheduling in large propagation delay underwater acoustic sensor networks. International Journal of Communication Systems. 33(11). 1 indexed citations

9.

Deepak, S. & P. M. Ameer. (2020). Automated Categorization of Brain Tumor from MRI Using CNN features and SVM. Journal of Ambient Intelligence and Humanized Computing. 12(8). 8357–8369. 178 indexed citations

10.

Deepak, S. & P. M. Ameer. (2019). Brain tumor classification using deep CNN features via transfer learning. Computers in Biology and Medicine. 111. 103345–103345. 859 indexed citations breakdown →

11.

Nazeer, K. A. Abdul, et al.. (2019). A Hierarchical Clustering algorithm based on Silhouette Index for cancer subtype discovery from genomic data. Neural Computing and Applications. 32(15). 11459–11476. 20 indexed citations

12.

Ameer, P. M., et al.. (2019). Edge Detection in Polarimetric SAR Image based on Bandelet Transform. International Journal of Computer Applications. 181(45). 33–38. 1 indexed citations

13.

Ameer, P. M., et al.. (2019). On the fundamental limit to the use of cognitive radio in underwater acoustic sensor networks. Telecommunication Systems. 71(2). 303–308. 3 indexed citations

14.

Ameer, P. M., et al.. (2018). Three‐dimensional sensor network connectivity considering border effects and channel randomness with application to underwater networks. IET Communications. 12(8). 994–1002. 7 indexed citations

15.

Ameer, P. M., et al.. (2017). Connectivity at wireless network borders under superimposed fading-shadowing effects. 1. 2041–2046. 2 indexed citations

16.

Nazeer, K. A. Abdul, et al.. (2017). An enhanced deterministic K-Means clustering algorithm for cancer subtype prediction from gene expression data. Computers in Biology and Medicine. 91. 213–221. 79 indexed citations

17.

Ameer, P. M., et al.. (2017). Energy efficient geographic clustered multi-hop routing for underwater sensor networks. 409–414. 8 indexed citations

18.

Ameer, P. M., et al.. (2017). Localization under anchor node uncertainty for underwater acoustic sensor networks. International Journal of Communication Systems. 31(2). 23 indexed citations

19.

Ameer, P. M. & Lillykutty Jacob. (2010). Localization Using Ray Tracing for Underwater Acoustic Sensor Networks. IEEE Communications Letters. 14(10). 930–932. 67 indexed citations

20.

Ameer, P. M., et al.. (2006). Analysis of Network Architectures for Zigbee Sensor Clusters. 1–6. 7 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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