Arpit Bhardwaj

1.6k total citations
45 papers, 901 citations indexed

About

Arpit Bhardwaj is a scholar working on Artificial Intelligence, Cognitive Neuroscience and Experimental and Cognitive Psychology. According to data from OpenAlex, Arpit Bhardwaj has authored 45 papers receiving a total of 901 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 17 papers in Cognitive Neuroscience and 8 papers in Experimental and Cognitive Psychology. Recurrent topics in Arpit Bhardwaj's work include EEG and Brain-Computer Interfaces (17 papers), Evolutionary Algorithms and Applications (9 papers) and Emotion and Mood Recognition (8 papers). Arpit Bhardwaj is often cited by papers focused on EEG and Brain-Computer Interfaces (17 papers), Evolutionary Algorithms and Applications (9 papers) and Emotion and Mood Recognition (8 papers). Arpit Bhardwaj collaborates with scholars based in India, Ethiopia and Malaysia. Arpit Bhardwaj's co-authors include Aruna Tiwari, Aditi Sakalle, Shivani Goel, Harshit Bhardwaj, Pradeep Tomar, Arvind Kumar, Wubshet Ibrahim, Michael Brünig, Steffen Gerke and Anurag Singh Baghel and has published in prestigious journals such as Expert Systems with Applications, Nanotechnology and International Journal of Solids and Structures.

In The Last Decade

Arpit Bhardwaj

43 papers receiving 856 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Arpit Bhardwaj India 16 353 241 121 104 90 45 901
Roberto Prevete Italy 16 403 1.1× 236 1.0× 46 0.4× 44 0.4× 38 0.4× 63 1.1k
Baha Şen Türkiye 14 220 0.6× 366 1.5× 61 0.5× 51 0.5× 55 0.6× 62 1.0k
Andrea Apicella Italy 14 196 0.6× 206 0.9× 78 0.6× 36 0.3× 35 0.4× 52 805
Galip Aydın Türkiye 17 365 1.0× 191 0.8× 64 0.5× 37 0.4× 21 0.2× 45 1.2k
Hyun‐Chul Kim South Korea 18 345 1.0× 192 0.8× 66 0.5× 15 0.1× 63 0.7× 58 1.5k
Anthony S. Maida United States 14 712 2.0× 608 2.5× 39 0.3× 48 0.5× 29 0.3× 76 1.7k
Ninad Mehendale India 17 332 0.9× 100 0.4× 169 1.4× 46 0.4× 29 0.3× 90 1.2k
Eman M. G. Younis Egypt 14 326 0.9× 102 0.4× 171 1.4× 111 1.1× 17 0.2× 35 819
Türker Tuncer Türkiye 18 336 1.0× 230 1.0× 58 0.5× 34 0.3× 35 0.4× 89 1.2k
Weifeng Xu United States 19 236 0.7× 522 2.2× 30 0.2× 50 0.5× 57 0.6× 86 1.4k

Countries citing papers authored by Arpit Bhardwaj

Since Specialization
Citations

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

Fields of papers citing papers by Arpit Bhardwaj

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Arpit Bhardwaj

This figure shows the co-authorship network connecting the top 25 collaborators of Arpit Bhardwaj. A scholar is included among the top collaborators of Arpit Bhardwaj 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 Arpit Bhardwaj. Arpit Bhardwaj 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.
Nigam, Aditya, et al.. (2025). M2SM: multi modal signature matching network utilizing spatio-temporal features extracted from online signature. International Journal on Document Analysis and Recognition (IJDAR). 28(4). 701–715.
2.
Goel, Shivani, et al.. (2024). Classification of imagined speech of vowels from EEG signals using multi-headed CNNs feature fusion network. Digital Signal Processing. 148. 104447–104447. 5 indexed citations
3.
Bhardwaj, Arpit & Phanish Suryanarayana. (2024). Strain engineering of Zeeman and Rashba effects in transition metal dichalcogenide nanotubes and their Janus variants: an ab initio study. Nanotechnology. 35(18). 185701–185701. 4 indexed citations
4.
5.
Uddin, Ziya, et al.. (2023). Modelling of Viscosity and Thermal Conductivity of Water-Based Nanofluids using Machine-Learning Techniques. International Journal of Mathematical Engineering and Management Sciences. 8(5). 817–840. 8 indexed citations
6.
Baghel, Anurag Singh, et al.. (2022). Optimization of Pesticides Spray on Crops in Agriculture using Machine Learning. Computational Intelligence and Neuroscience. 2022. 1–10. 30 indexed citations
7.
Bhardwaj, Arpit, et al.. (2022). Tree-Based and Machine Learning Algorithm Analysis for Breast Cancer Classification. Computational Intelligence and Neuroscience. 2022. 1–6. 14 indexed citations
8.
Bhardwaj, Harshit, Pradeep Tomar, Aditi Sakalle, et al.. (2022). Personality Prediction with Hybrid Genetic Programming using Portable EEG Device. Computational Intelligence and Neuroscience. 2022. 1–8. 2 indexed citations
9.
Goel, Shivani, et al.. (2022). EEG Signals to Digit Classification Using Deep Learning-Based One-Dimensional Convolutional Neural Network. Arabian Journal for Science and Engineering. 48(8). 9675–9691. 7 indexed citations
10.
Gupta, Akash, et al.. (2021). An Analysis on Traffic Signs Identification Model. 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N). 527–530. 1 indexed citations
11.
Kumar, Arvind, et al.. (2020). A novel fitness function in genetic programming for medical data classification. Journal of Biomedical Informatics. 112. 103623–103623. 28 indexed citations
12.
Goel, Shivani, et al.. (2020). A novel fitness function in genetic programming to handle unbalanced emotion recognition data. Pattern Recognition Letters. 133. 272–279. 20 indexed citations
13.
Gerke, Steffen, et al.. (2019). Experiments with the X0-specimen on the effect of non-proportional loading paths on damage and fracture mechanisms in aluminum alloys. International Journal of Solids and Structures. 163. 157–169. 39 indexed citations
14.
Bhardwaj, Harshit, Aditi Sakalle, Aruna Tiwari, Madhushi Verma, & Arpit Bhardwaj. (2018). Breast Cancer Diagnosis using Simultaneous Feature Selection and Classification: A Genetic Programming Approach. 2186–2192. 7 indexed citations
15.
Bhardwaj, Harshit, Aditi Sakalle, Arpit Bhardwaj, & Aruna Tiwari. (2018). Classification of electroencephalogram signal for the detection of epilepsy using Innovative Genetic Programming. Expert Systems. 36(1). 28 indexed citations
16.
Bhardwaj, Arpit, et al.. (2016). A genetically optimized neural network model for multi-class classification. Expert Systems with Applications. 60. 211–221. 24 indexed citations
17.
Bhardwaj, Arpit, et al.. (2015). A novel genetic programming approach for epileptic seizure detection. Computer Methods and Programs in Biomedicine. 124. 2–18. 50 indexed citations
18.
19.
Bhardwaj, Arpit, et al.. (2014). A genetically optimized neural network for classification of breast cancer disease. 693–698. 9 indexed citations
20.
Purohit, Anuradha, Arpit Bhardwaj, Aruna Tiwari, & Narendra S. Chaudhari. (2011). Handling the Problem of Code Bloating to Enhance the Performance of Classifier Designed Using Genetic Programming.. Indian International Conference on Artificial Intelligence. 333–342. 2 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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