Mamta Bansal

448 total citations · 1 hit paper
32 papers, 250 citations indexed

About

Mamta Bansal is a scholar working on Artificial Intelligence, Information Systems and Signal Processing. According to data from OpenAlex, Mamta Bansal has authored 32 papers receiving a total of 250 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 5 papers in Information Systems and 5 papers in Signal Processing. Recurrent topics in Mamta Bansal's work include Sentiment Analysis and Opinion Mining (6 papers), Biometric Identification and Security (4 papers) and Advanced Text Analysis Techniques (4 papers). Mamta Bansal is often cited by papers focused on Sentiment Analysis and Opinion Mining (6 papers), Biometric Identification and Security (4 papers) and Advanced Text Analysis Techniques (4 papers). Mamta Bansal collaborates with scholars based in India, United States and Sweden. Mamta Bansal's co-authors include M. Hanmandlu, Sushma Malik, Rakhi Singh, Garima Bhardwaj, Shubhi Gupta, Pankaj Kumar, Shantaram Vasikarla, Sanjay Sharma, Mohammad Shamim and B. Gangwar and has published in prestigious journals such as SHILAP Revista de lepidopterología, Soft Computing and Optik.

In The Last Decade

Mamta Bansal

24 papers receiving 235 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.

Metaheuristic Algorithms for Optimization: A Brief Review

2024 59 citations Mamta Bansal et al. SHILAP Revista de lepidopterología profile →

Peers

Mamta Bansal

AI

HIM

CVPR

IS

PPCH

Kwang Ho Park South Korea

Umair Muneer Butt Pakistan

Krishnamoorthi Makkithaya India

Saman Iftikhar Pakistan

Ramin Ghorbani Netherlands

German Prado-Arechiga Mexico

K. Kousalya India

K. Meena India

Akshay Jain United States

Jerónimo Hernández-González Spain

Kwang Ho Park South Korea

Mamta Bansal

72 ×0.8

AI

42 ×0.8

HIM

24 ×0.6

CVPR

24 ×0.6

IS

3 ×0.1

PPCH

Citations per year, relative to Mamta Bansal Mamta Bansal (= 1×) peers Kwang Ho Park

Countries citing papers authored by Mamta Bansal

Since Specialization

Citations

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

Fields of papers citing papers by Mamta Bansal

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mamta Bansal

This figure shows the co-authorship network connecting the top 25 collaborators of Mamta Bansal. A scholar is included among the top collaborators of Mamta Bansal 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 Mamta Bansal. Mamta Bansal 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.

Kaur, Simranjeet, et al.. (2025). Mitigating dye and organic pollutant-driven surface water pollution using ZnO nanoparticles: a sustainable strategy for climate resilience. Frontiers in Environmental Science. 13.

2.

Bansal, Mamta, et al.. (2025). Effective Diabetes Prediction using an IoT-based Integrated Ensemble Machine Learning Framework. Engineering Technology & Applied Science Research. 15(1). 20064–20070. 1 indexed citations

3.

Chhabra, Shibba Takkar, Gagandeep Kaur, Pankaj Kumar, et al.. (2024). Drug abuse and ACS in the very young (less than 30 years): Demographic, clinical and angiographic profile. Indian Heart Journal. 76(5). 355–357.

4.

Bansal, Mamta, et al.. (2024). Design Of A Block Chain Based Model For Secure And Effective Pharmaceutical Supply Chain. Nanotechnology Perceptions. 382–391.

5.

Bansal, Cherry, et al.. (2023). Technical expertise and behavior of the doctor- a major determinant of waiting time of OPD patients. International Journal of Community Medicine and Public Health. 10(2). 677–681.

6.

Bansal, Mamta, et al.. (2022). Patent Classification with Intelligent Keyword Extraction. 1–7. 2 indexed citations

7.

Bansal, Mamta, et al.. (2022). Covid 19 Pandemic Impact on Social Relations. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations

8.

Bansal, Mamta, et al.. (2022). Analysis of various Smart Healthcare Technologies. 629–634. 1 indexed citations

9.

Bansal, Mamta, et al.. (2021). Ear-based authentication using information sets and information modelling. Soft Computing. 25(16). 11123–11138. 1 indexed citations

10.

Bansal, Mamta, et al.. (2021). Text Summarization Approaches Using Machine Learning & LSTM. Revista Gestão Inovação e Tecnologias. 11(4). 5010–5026. 4 indexed citations

11.

Bansal, Mamta, et al.. (2020). Feature Extraction through Sentiment Analysis of Tourist Sentiments using Deep Learning Techniques like CNN, RNN and LSTM. International Journal of Recent Technology and Engineering (IJRTE). 9(1). 2254–2261.

12.

Bansal, Mamta, et al.. (2020). Study of Intrauterine Foetal Death in Government Medical College in Tribal Area of Bastar. Journal of Evolution of Medical and Dental Sciences. 9(32). 2257–2260.

13.

Bansal, Mamta, et al.. (2020). Developing a Model for Sentiment Analysis Technique in the field of Tourism using Deep Learning. International Journal of Recent Technology and Engineering (IJRTE). 8(6). 456–462. 1 indexed citations

14.

Bansal, Mamta, et al.. (2020). Real-Time Sentiment Analysis Towards Machine Learning. International journal of scientific and technology research. 9(2). 987–989. 1 indexed citations

15.

Malik, Sushma & Mamta Bansal. (2019). Recommendation System: Techniques and Issues. International Journal of Recent Technology and Engineering (IJRTE). 8(3). 2821–2824. 2 indexed citations

16.

Bansal, Mamta, et al.. (2019). Stemming and Lemmatization of Tweets for Sentiment Analysis using R. International Journal of Recent Technology and Engineering (IJRTE). 8(2). 2038–2040. 2 indexed citations

17.

Chhabra, Shibba Takkar, Gurleen Kaur, Mamta Bansal, et al.. (2019). Cardiac Health Awareness Among Women Presenting for Routine Health Checkup at Tertiary Care Center: Time Trends Over 15 Years. SHILAP Revista de lepidopterología. 4(2). 79–84. 2 indexed citations

18.

Bansal, Mamta, et al.. (2019). Sublingual versus vaginal use of Misoprostol for induction of labor. International Journal of Reproduction Contraception Obstetrics and Gynecology. 8(12). 4960–4960. 1 indexed citations

19.

Bansal, Mamta, et al.. (2016). Study of near miss cases in obstetrics and maternal mortality in Bastar, Chhattisgarh, India. International Journal of Reproduction Contraception Obstetrics and Gynecology. 620–623. 20 indexed citations

20.

Kumar, Sunil, et al.. (2015). Computational modeling and emerging trend in agriculture. International Conference on Computing for Sustainable Global Development. 1156–1160. 3 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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