Diwakar Tripathi

885 total citations
31 papers, 582 citations indexed

About

Diwakar Tripathi is a scholar working on Artificial Intelligence, Accounting and Information Systems. According to data from OpenAlex, Diwakar Tripathi has authored 31 papers receiving a total of 582 indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Artificial Intelligence, 11 papers in Accounting and 6 papers in Information Systems. Recurrent topics in Diwakar Tripathi's work include Imbalanced Data Classification Techniques (11 papers), Financial Distress and Bankruptcy Prediction (11 papers) and Metaheuristic Optimization Algorithms Research (5 papers). Diwakar Tripathi is often cited by papers focused on Imbalanced Data Classification Techniques (11 papers), Financial Distress and Bankruptcy Prediction (11 papers) and Metaheuristic Optimization Algorithms Research (5 papers). Diwakar Tripathi collaborates with scholars based in India, Italy and United Kingdom. Diwakar Tripathi's co-authors include Damodar Reddy Edla, Venkatanareshbabu Kuppili, Alok Kumar Shukla, Ramalingaswamy Cheruku, Annushree Bablani, Chandramohan Dhasarathan, Dharavath Ramesh, Achyut Shankar, M. Shanmugam and Shailesh Khapre and has published in prestigious journals such as Scientific Reports, ACM Computing Surveys and IEEE Transactions on Information Forensics and Security.

In The Last Decade

Diwakar Tripathi

29 papers receiving 561 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Diwakar Tripathi India 16 375 207 86 73 59 31 582
Christoforos Anagnostopoulos United Kingdom 10 155 0.4× 35 0.2× 26 0.3× 144 2.0× 6 0.1× 22 448
K. R. Seeja India 12 271 0.7× 42 0.2× 16 0.2× 195 2.7× 20 0.3× 35 787
Minglong Lei China 11 205 0.5× 48 0.2× 20 0.2× 36 0.5× 3 0.1× 28 343
Pedro G. Espejo Spain 3 507 1.4× 16 0.1× 59 0.7× 9 0.1× 40 0.7× 6 855
Albert H.R. Ko Canada 6 272 0.7× 24 0.1× 12 0.1× 9 0.1× 12 0.2× 9 380
Baljeet Kaur India 11 204 0.5× 6 0.0× 42 0.5× 54 0.7× 6 0.1× 53 490
Mohammad Shafiul Alam Bangladesh 12 197 0.5× 8 0.0× 11 0.1× 26 0.4× 12 0.2× 44 421
Hakan Gündüz Türkiye 9 158 0.4× 10 0.0× 12 0.1× 19 0.3× 7 0.1× 19 638
R. Raja Subramanian India 11 133 0.4× 22 0.1× 8 0.1× 24 0.3× 11 0.2× 59 339

Countries citing papers authored by Diwakar Tripathi

Since Specialization
Citations

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

Fields of papers citing papers by Diwakar Tripathi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Diwakar Tripathi

This figure shows the co-authorship network connecting the top 25 collaborators of Diwakar Tripathi. A scholar is included among the top collaborators of Diwakar Tripathi 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 Diwakar Tripathi. Diwakar Tripathi 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.
Shukla, Alok Kumar, et al.. (2025). Optimized breast cancer diagnosis using self-adaptive quantum metaheuristic feature selection. Scientific Reports. 15(1). 19900–19900. 2 indexed citations
2.
Tripathi, Diwakar, et al.. (2024). Nature-inspired optimization algorithms based feature selection: Application in credit scoring. Journal of Intelligent & Fuzzy Systems. 2 indexed citations
3.
Shukla, Alok Kumar, et al.. (2024). Ensemble feature ranking approach for software fault prediction. Journal of Intelligent & Fuzzy Systems.
4.
Dhasarathan, Chandramohan, et al.. (2023). A nomadic multi-agent based privacy metrics for e-health care: a deep learning approach. Multimedia Tools and Applications. 83(3). 7249–7272. 25 indexed citations
5.
Dhasarathan, Chandramohan, et al.. (2022). COVID-19 identification in chest X-ray images using intelligent multi-level classification scenario. Computers & Electrical Engineering. 104. 108405–108405. 11 indexed citations
6.
Tripathi, Diwakar, et al.. (2021). Credit Scoring Models Using Ensemble Learning and Classification Approaches: A Comprehensive Survey. Wireless Personal Communications. 123(1). 785–812. 25 indexed citations
7.
Shukla, Alok Kumar & Diwakar Tripathi. (2020). Detecting biomarkers from microarray data using distributed correlation based gene selection. Genes & Genomics. 42(4). 449–465. 29 indexed citations
8.
Shukla, Alok Kumar, et al.. (2020). Knowledge discovery in medical and biological datasets by integration of Relief-F and correlation feature selection techniques. Journal of Intelligent & Fuzzy Systems. 38(5). 6637–6648. 13 indexed citations
9.
Kuppili, Venkatanareshbabu, Diwakar Tripathi, & Damodar Reddy Edla. (2019). Credit score classification using spiking extreme learning machine. Computational Intelligence. 36(2). 402–426. 27 indexed citations
10.
Tripathi, Diwakar, Damodar Reddy Edla, Ramalingaswamy Cheruku, & Venkatanareshbabu Kuppili. (2019). A novel hybrid credit scoring model based on ensemble feature selection and multilayer ensemble classification. Computational Intelligence. 35(2). 371–394. 53 indexed citations
11.
Tripathi, Diwakar, et al.. (2019). Fraud Detection using Data Mining Techniques. 2 indexed citations
12.
Shukla, Alok Kumar & Diwakar Tripathi. (2019). Identification of potential biomarkers on microarray data using distributed gene selection approach. Mathematical Biosciences. 315. 108230–108230. 25 indexed citations
13.
Bablani, Annushree, Damodar Reddy Edla, Diwakar Tripathi, & Ramalingaswamy Cheruku. (2019). Survey on Brain-Computer Interface. ACM Computing Surveys. 52(1). 1–32. 48 indexed citations
14.
Bablani, Annushree, et al.. (2019). A Synergistic Concealed Information Test With Novel Approach for EEG Channel Selection and SVM Parameter Optimization. IEEE Transactions on Information Forensics and Security. 14(11). 3057–3068. 17 indexed citations
15.
Tripathi, Diwakar, Damodar Reddy Edla, & Ramalingaswamy Cheruku. (2018). Hybrid credit scoring model using neighborhood rough set and multi-layer ensemble classification. Journal of Intelligent & Fuzzy Systems. 34(3). 1543–1549. 44 indexed citations
16.
Tripathi, Diwakar, Damodar Reddy Edla, Venkatanareshbabu Kuppili, Annushree Bablani, & Dharavath Ramesh. (2018). Credit Scoring Model based on Weighted Voting and Cluster based Feature Selection. Procedia Computer Science. 132. 22–31. 40 indexed citations
17.
Bablani, Annushree & Diwakar Tripathi. (2018). A Review on Methods Applied on P300-Based Lie Detectors. Advances in intelligent systems and computing. 251–257. 1 indexed citations
18.
Bablani, Annushree, et al.. (2018). Subject based Deceit Identification using Empirical Mode Decomposition. Procedia Computer Science. 132. 32–39. 5 indexed citations
19.
Edla, Damodar Reddy, Diwakar Tripathi, Ramalingaswamy Cheruku, & Venkatanareshbabu Kuppili. (2017). An Efficient Multi-layer Ensemble Framework with BPSOGSA-Based Feature Selection for Credit Scoring Data Analysis. Arabian Journal for Science and Engineering. 43(12). 6909–6928. 34 indexed citations
20.
Tripathi, Diwakar, et al.. (2017). A Novel Web Fraud Detection Technique using Association Rule Mining. Procedia Computer Science. 115. 274–281. 11 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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