Mukta Paliwal

963 total citations · 1 hit paper
9 papers, 739 citations indexed

About

Mukta Paliwal is a scholar working on Artificial Intelligence, Global and Planetary Change and Management Science and Operations Research. According to data from OpenAlex, Mukta Paliwal has authored 9 papers receiving a total of 739 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 4 papers in Global and Planetary Change and 3 papers in Management Science and Operations Research. Recurrent topics in Mukta Paliwal's work include Neural Networks and Applications (3 papers), Stock Market Forecasting Methods (3 papers) and Flood Risk Assessment and Management (2 papers). Mukta Paliwal is often cited by papers focused on Neural Networks and Applications (3 papers), Stock Market Forecasting Methods (3 papers) and Flood Risk Assessment and Management (2 papers). Mukta Paliwal collaborates with scholars based in India. Mukta Paliwal's co-authors include Usha A. Kumar, Anand Patwardhan, Arun B. Inamdar, A. R. Abhyankar and N. L. Sarda and has published in prestigious journals such as Expert Systems with Applications, Applied Soft Computing and Natural Hazards.

In The Last Decade

Mukta Paliwal

8 papers receiving 699 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.

Neural networks and statistical techniques: A review of applications

2007 601 citations Mukta Paliwal, Usha A. Kumar Expert Systems with Applications profile →

Peers

Mukta Paliwal

AI

MSOR

EEE

ME

IS

Usha A. Kumar India

Roselina Sallehuddin Malaysia

Maher Maalouf United Arab Emirates

İnci Batmaz Türkiye

Amalia Luque Sendra Spain

Leorey Marquez Australia

Amit Kumar Goel India

Alejandro Manuel Martín-Gómez Spain

Anh‐Duc Pham Vietnam

Jesús M. Pérez Spain

Usha A. Kumar India

Mukta Paliwal

236 ×1.1

AI

112 ×1.2

MSOR

93 ×1.0

EEE

69 ×1.0

ME

63 ×1.1

IS

Citations per year, relative to Mukta Paliwal Mukta Paliwal (= 1×) peers Usha A. Kumar

Countries citing papers authored by Mukta Paliwal

Since Specialization

Citations

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

Fields of papers citing papers by Mukta Paliwal

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mukta Paliwal

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

All Works

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9 of 9 papers shown

1.

Abhyankar, A. R., Anand Patwardhan, Mukta Paliwal, & Arun B. Inamdar. (2019). IDENTIFICATION OF FLOODED AREAS DUE TO SEVERE STORM USING ENVISAT ASAR DATA AND NEURAL NETWORKS. 10(2). 113–120. 13 indexed citations

2.

Paliwal, Mukta, et al.. (2013). Identification of vulnerable areas in municipal corporation of Greater Mumbai due to extreme events based on socio economic indicators. DSpace (IIT Bombay). 5 indexed citations

3.

Paliwal, Mukta & Anand Patwardhan. (2013). Identification of clusters in tropical cyclone tracks of North Indian Ocean. Natural Hazards. 68(2). 645–656. 16 indexed citations

4.

Paliwal, Mukta & Usha A. Kumar. (2011). The predictive accuracy of feed forward neural networks and multiple regression in the case of heteroscedastic data. Applied Soft Computing. 11(4). 3859–3869. 7 indexed citations

5.

Paliwal, Mukta, Anand Patwardhan, & N. L. Sarda. (2011). Analyzing tropical cyclone tracks of North Indian Ocean. 1–6. 2 indexed citations

6.

Paliwal, Mukta & Usha A. Kumar. (2011). Assessing the contribution of variables in feed forward neural network. Applied Soft Computing. 11(4). 3690–3696. 53 indexed citations

7.

Paliwal, Mukta & Usha A. Kumar. (2008). A study of academic performance of business school graduates using neural network and statistical techniques. Expert Systems with Applications. 36(4). 7865–7872. 42 indexed citations

8.

Paliwal, Mukta & Usha A. Kumar. (2008). The predictive accuracy of feed forward neural networks and multiple regression in the case of heteroscedastic data. 2. 430–434.

9.

Paliwal, Mukta & Usha A. Kumar. (2007). Neural networks and statistical techniques: A review of applications. Expert Systems with Applications. 36(1). 2–17. 601 indexed citations breakdown →

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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