Ramdas Vankdothu

767 total citations · 1 hit paper
17 papers, 446 citations indexed

About

Ramdas Vankdothu is a scholar working on Neurology, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Ramdas Vankdothu has authored 17 papers receiving a total of 446 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Neurology, 7 papers in Artificial Intelligence and 5 papers in Computer Networks and Communications. Recurrent topics in Ramdas Vankdothu's work include Brain Tumor Detection and Classification (8 papers), COVID-19 diagnosis using AI (4 papers) and Machine Learning and ELM (3 papers). Ramdas Vankdothu is often cited by papers focused on Brain Tumor Detection and Classification (8 papers), COVID-19 diagnosis using AI (4 papers) and Machine Learning and ELM (3 papers). Ramdas Vankdothu collaborates with scholars based in India, South Africa and Azerbaijan. Ramdas Vankdothu's co-authors include Mohd Abdul Hameed, M. Venu Gopalachari and Xiaochun Cheng and has published in prestigious journals such as SHILAP Revista de lepidopterología, Computers & Electrical Engineering and Wireless Personal Communications.

In The Last Decade

Ramdas Vankdothu

15 papers receiving 417 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.

A Brain Tumor Identification and Classification Using Deep Learning based on CNN-LSTM Method

2022 153 citations Ramdas Vankdothu, Mohd Abdul Hameed et al. Computers & Electrical Engineering profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Ramdas Vankdothu India	9	248	213	176	96	72	17	446
Mohd Abdul Hameed India	8	245 1.0×	220 1.0×	184 1.0×	97 1.0×	50 0.7×	27	456
Jagadeesh Kakarla India	12	270 1.1×	269 1.3×	154 0.9×	106 1.1×	97 1.3×	23	522
Asim Munir Pakistan	10	167 0.7×	245 1.2×	186 1.1×	134 1.4×	29 0.4×	21	486
Sonali Dash India	15	82 0.3×	206 1.0×	106 0.6×	139 1.4×	37 0.5×	37	446
Seyed Reza Kamel Iran	7	133 0.5×	119 0.6×	129 0.7×	47 0.5×	61 0.8×	14	344
Balajee Maram India	9	78 0.3×	108 0.5×	109 0.6×	78 0.8×	49 0.7×	65	334
Wajdi Elhamzi Tunisia	7	348 1.4×	328 1.5×	196 1.1×	83 0.9×	11 0.2×	21	475
Omar Hisham Alsadoon Iraq	11	67 0.3×	129 0.6×	149 0.8×	83 0.9×	39 0.5×	37	375
Tiejun Yang China	11	108 0.4×	223 1.0×	135 0.8×	111 1.2×	11 0.2×	47	432
Zaka Ur Rehman Malaysia	8	179 0.7×	265 1.2×	117 0.7×	197 2.1×	15 0.2×	22	488

Countries citing papers authored by Ramdas Vankdothu

Since Specialization

Citations

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

Fields of papers citing papers by Ramdas Vankdothu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ramdas Vankdothu

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

All Works

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17 of 17 papers shown

Vankdothu, Ramdas, et al.. (2025). Multicast Scaling in Heterogeneous Wireless Sensor Networks for Security and Time Efficiency. Wireless Personal Communications. 145(3-4). 699–715.

Vankdothu, Ramdas, et al.. (2024). A brain tumor identification using fully convolution neural networks in the deep learning. SHILAP Revista de lepidopterología. 392. 1129–1129. 1 indexed citations

Vankdothu, Ramdas, et al.. (2024). Hybrid TDR-MI Based Wireless Sensor Network for Underground Water Pipeline Leakage Detection and Localization Using Pressure Residuals and Classifiers. Wireless Personal Communications. 139(2). 803–823. 3 indexed citations

Vankdothu, Ramdas & Xiaochun Cheng. (2024). Energy Efficient TDMA and Secure Based MAC Protocol for WSN Using AQL Coding and ASGWI Clustering. Wireless Personal Communications. 136(4). 2125–2143. 2 indexed citations

Vankdothu, Ramdas, et al.. (2024). Resource management system database maintenance in cloud computing. SHILAP Revista de lepidopterología. 392. 1134–1134. 2 indexed citations

Vankdothu, Ramdas & Mohd Abdul Hameed. (2024). An Effective Congestion and Interference Secure Routing Protocol for Internet of Things Applications in Wireless Sensor Network. Wireless Personal Communications. 140(1-2). 143–161. 1 indexed citations

Vankdothu, Ramdas, et al.. (2024). The Contribution of Strategic Information Systems to Organizational Transformation and Change. 1–6.

Gopalachari, M. Venu, et al.. (2023). Moving object detection using modified GMM based background subtraction. Measurement Sensors. 30. 100898–100898. 15 indexed citations

Hameed, Mohd Abdul, et al.. (2022). COVID-19 detection and classification for machine learning methods using human genomic data. Measurement Sensors. 24. 100537–100537. 18 indexed citations

10.

Vankdothu, Ramdas & Mohd Abdul Hameed. (2022). Brain tumor MRI images identification and classification based on the recurrent convolutional neural network. Measurement Sensors. 24. 100412–100412. 93 indexed citations

11.

Vankdothu, Ramdas & Mohd Abdul Hameed. (2022). Brain tumor segmentation of MR images using SVM and fuzzy classifier in machine learning. Measurement Sensors. 24. 100440–100440. 51 indexed citations

12.

Gopalachari, M. Venu, et al.. (2022). Intrusion detection models for IOT networks via deep learning approaches. Measurement Sensors. 25. 100641–100641. 37 indexed citations

13.

Vankdothu, Ramdas & Mohd Abdul Hameed. (2022). Mining for Enthalpy-Based Average High-Utility Patterns with Tighter Upper Bounds. SN Computer Science. 4(1). 1 indexed citations

14.

Vankdothu, Ramdas, et al.. (2022). Brain image identification and classification on Internet of Medical Things in healthcare system using support value based deep neural network. Computers & Electrical Engineering. 102. 108196–108196. 34 indexed citations

15.

Vankdothu, Ramdas, et al.. (2022). A Brain Tumor Identification and Classification Using Deep Learning based on CNN-LSTM Method. Computers & Electrical Engineering. 101. 107960–107960. 153 indexed citations breakdown →

16.

Vankdothu, Ramdas & Mohd Abdul Hameed. (2022). Adaptive features selection and EDNN based brain image recognition on the internet of medical things. Computers & Electrical Engineering. 103. 108338–108338. 26 indexed citations

17.

Vankdothu, Ramdas. (2020). Efficient Detection of Brain Tumor Using Unsupervised Modified Deep Belief Network in Big Data. Journal of Advanced Research in Dynamical and Control Systems. 12(SP4). 338–347. 9 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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