Ashima Kukkar

647 total citations
26 papers, 317 citations indexed

About

Ashima Kukkar is a scholar working on Information Systems, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Ashima Kukkar has authored 26 papers receiving a total of 317 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Information Systems, 9 papers in Artificial Intelligence and 8 papers in Computer Networks and Communications. Recurrent topics in Ashima Kukkar's work include Software Engineering Research (9 papers), Software System Performance and Reliability (7 papers) and Online Learning and Analytics (5 papers). Ashima Kukkar is often cited by papers focused on Software Engineering Research (9 papers), Software System Performance and Reliability (7 papers) and Online Learning and Analytics (5 papers). Ashima Kukkar collaborates with scholars based in India, Vietnam and China. Ashima Kukkar's co-authors include Rajni Mohana, Anand Nayyar, Aman Sharma, Yugal Kumar, Naveen Chilamkurti, Byeong-Gwon Kang, Amit Sharma, K. R. Ramkumar, Muhammad Bilal and Kyung Sup Kwak and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Sensors.

In The Last Decade

Ashima Kukkar

23 papers receiving 303 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ashima Kukkar India 10 125 106 70 64 63 26 317
Muhammad Arif Shah Pakistan 12 189 1.5× 93 0.9× 67 1.0× 18 0.3× 91 1.4× 28 356
Wahiba Ben Abdessalem Karâa Tunisia 12 96 0.8× 119 1.1× 17 0.2× 9 0.1× 20 0.3× 38 330
Ahmed Tamrawi United States 10 546 4.4× 178 1.7× 191 2.7× 64 1.0× 251 4.0× 21 632
Ahmad Al‐Ahmad Kuwait 11 225 1.8× 115 1.1× 210 3.0× 5 0.1× 20 0.3× 23 437
Babak Bashari Rad Malaysia 8 128 1.0× 90 0.8× 151 2.2× 3 0.0× 31 0.5× 12 280
Christopher A. Choquette-Choo United States 6 67 0.5× 302 2.8× 47 0.7× 14 0.2× 9 0.1× 10 406
James T. Rayfield United States 7 184 1.5× 91 0.9× 153 2.2× 14 0.2× 24 0.4× 24 382
Shaochen Zhong United States 3 42 0.3× 140 1.3× 28 0.4× 8 0.1× 9 0.1× 6 278
P.S. Yu United States 10 176 1.4× 256 2.4× 115 1.6× 15 0.2× 85 1.3× 14 436
Jennifer Brings Germany 8 91 0.7× 102 1.0× 32 0.5× 45 0.7× 50 0.8× 31 227

Countries citing papers authored by Ashima Kukkar

Since Specialization
Citations

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

Fields of papers citing papers by Ashima Kukkar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ashima Kukkar

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

All Works

20 of 20 papers shown
1.
Kukkar, Ashima & Gagandeep Kaur. (2025). AEC: A novel adaptive ensemble classifier with LIME and SHAP-Based interpretability for fake news detection. Expert Systems with Applications. 281. 127751–127751.
2.
Kukkar, Ashima, et al.. (2025). LSTM-SHAP based academic performance prediction for disabled learners in virtual learning environments: a statistical analysis approach. Social Network Analysis and Mining. 15(1). 1 indexed citations
3.
Kukkar, Ashima, et al.. (2024). A Review on Facial Anti-spoofing Techniques. Lecture notes in networks and systems. 323–335.
4.
5.
Kukkar, Ashima, et al.. (2024). DengueFog: A Fog Computing-Enabled Weighted Random Forest-Based Smart Health Monitoring System for Automatic Dengue Prediction. Diagnostics. 14(6). 624–624. 2 indexed citations
6.
Kukkar, Ashima, et al.. (2024). A novel methodology using RNN + LSTM + ML for predicting student’s academic performance. Education and Information Technologies. 29(11). 14365–14401. 20 indexed citations
7.
Ramkumar, K. R., et al.. (2023). Deep adaptive CHIONet: designing novel herd immunity prediction of COVID-19 pandemic using hybrid RNN with LSTM. Multimedia Tools and Applications. 83(10). 29583–29615. 1 indexed citations
8.
Kukkar, Ashima, Yugal Kumar, Ashutosh Sharma, & Jasminder Kaur Sandhu. (2023). Bug severity classification in software using ant colony optimization based feature weighting technique. Expert Systems with Applications. 230. 120573–120573. 2 indexed citations
9.
Kukkar, Ashima, Rajni Mohana, Aman Sharma, & Anand Nayyar. (2023). Prediction of student academic performance based on their emotional wellbeing and interaction on various e-learning platforms. Education and Information Technologies. 28(8). 9655–9684. 41 indexed citations
10.
Sharma, Amit, et al.. (2022). Data mining applications in university information management system development. Journal of Intelligent Systems. 31(1). 207–220. 19 indexed citations
11.
Wang, Hong, et al.. (2022). Automatic control of computer application data processing system based on artificial intelligence. Journal of Intelligent Systems. 31(1). 177–192. 15 indexed citations
12.
Malik, Varun, Ruchi Mittal, Amit Mittal, et al.. (2022). Applying Data Mining for Clustering Shoppers Based on Store Loyalty. 4. 370–373. 1 indexed citations
13.
Liu, Ying, Ashima Kukkar, & Mohd Asif Shah. (2022). Study of industrial interactive design system based on virtual reality teaching technology in industrial robot. Paladyn Journal of Behavioral Robotics. 13(1). 45–55. 3 indexed citations
14.
Kukkar, Ashima, Umesh Kumar Lilhore, Jaroslav Frnda, et al.. (2022). ProRE: An ACO- based programmer recommendation model to precisely manage software bugs. Journal of King Saud University - Computer and Information Sciences. 35(1). 483–498. 10 indexed citations
15.
Kukkar, Ashima, Dinesh Gupta, Mukesh Soni, et al.. (2022). Optimizing Deep Learning Model Parameters Using Socially Implemented IoMT Systems for Diabetic Retinopathy Classification Problem. IEEE Transactions on Computational Social Systems. 10(4). 1654–1665. 35 indexed citations
16.
Ramkumar, K. R., et al.. (2021). Machine Learning Techniques and Implementation of Different ML Algorithms. 2021 2nd Global Conference for Advancement in Technology (GCAT). 1–6. 19 indexed citations
17.
Kukkar, Ashima, Rajni Mohana, & Yugal Kumar. (2020). Does bug report summarization help in enhancing the accuracy of bug severity classification?. Procedia Computer Science. 167. 1345–1353. 9 indexed citations
18.
19.
Kukkar, Ashima & Rajni Mohana. (2018). A Supervised Bug Report Classification with Incorporate and Textual field Knowledge. Procedia Computer Science. 132. 352–361. 24 indexed citations
20.
Kukkar, Ashima & Rajni Mohana. (2018). Bug Report Summarization by Using Swarm Intelligence Approaches. Recent Advances in Computer Science and Communications. 13(1). 53–67. 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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