Milind Shah

526 total citations
16 papers, 378 citations indexed

About

Milind Shah is a scholar working on Electrical and Electronic Engineering, Mechanical Engineering and Automotive Engineering. According to data from OpenAlex, Milind Shah has authored 16 papers receiving a total of 378 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Electrical and Electronic Engineering, 4 papers in Mechanical Engineering and 3 papers in Automotive Engineering. Recurrent topics in Milind Shah's work include Advanced Battery Technologies Research (3 papers), Natural Language Processing Techniques (3 papers) and Electric Vehicles and Infrastructure (2 papers). Milind Shah is often cited by papers focused on Advanced Battery Technologies Research (3 papers), Natural Language Processing Techniques (3 papers) and Electric Vehicles and Infrastructure (2 papers). Milind Shah collaborates with scholars based in India, Nepal and United Kingdom. Milind Shah's co-authors include Vinay Vakharia, Pranav Nair, Vishal Ashok Wankhede, Vivek Patel, Khaled Giasin, Danil Yurievich Pimenov, Jay Vora, Rakesh Chaudhari, Pankaj Sahlot and Muhammad Fazal Ijaz and has published in prestigious journals such as Sensors, Energies and The International Journal of Advanced Manufacturing Technology.

In The Last Decade

Milind Shah

14 papers receiving 366 citations

Peers

Milind Shah
Sujit S. Pardeshi India
Sheng Huang China
Amit Kumar Das India
David B. Durocher United States
Xiaobang Wang China
Cheng Qian China
Abhishek Agarwal South Africa
Y.P. Nikitin Russia
Minglun Fang China
Sujit S. Pardeshi India
Milind Shah
Citations per year, relative to Milind Shah Milind Shah (= 1×) peers Sujit S. Pardeshi

Countries citing papers authored by Milind Shah

Since Specialization
Citations

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

Fields of papers citing papers by Milind Shah

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Milind Shah

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

All Works

16 of 16 papers shown
1.
Nair, Pranav, Vinay Vakharia, Milind Shah, et al.. (2024). AI-Driven Digital Twin Model for Reliable Lithium-Ion Battery Discharge Capacity Predictions. International Journal of Intelligent Systems. 2024. 1–18. 47 indexed citations
2.
Shah, Milind, et al.. (2024). Utilizing TGAN and ConSinGAN for Improved Tool Wear Prediction: A Comparative Study with ED-LSTM, GRU, and CNN Models. Electronics. 13(17). 3484–3484. 18 indexed citations
3.
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Vakharia, Vinay, et al.. (2023). Estimation of Lithium-ion Battery Discharge Capacity by Integrating Optimized Explainable-AI and Stacked LSTM Model. Batteries. 9(2). 125–125. 63 indexed citations
5.
Nair, Pranav, et al.. (2023). Predicting Li-Ion Battery Remaining Useful Life: An XDFM-Driven Approach with Explainable AI. Energies. 16(15). 5725–5725. 25 indexed citations
6.
Vakharia, Vinay, Jay Vora, Sakshum Khanna, et al.. (2022). Experimental investigations and prediction of WEDMed surface of nitinol SMA using SinGAN and DenseNet deep learning model. Journal of Materials Research and Technology. 18. 325–337. 44 indexed citations
7.
Shah, Milind, Vinay Vakharia, Rakesh Chaudhari, et al.. (2022). Tool wear prediction in face milling of stainless steel using singular generative adversarial network and LSTM deep learning models. The International Journal of Advanced Manufacturing Technology. 121(1-2). 723–736. 63 indexed citations
8.
9.
Shah, Milind, et al.. (2022). Non-negative Matrix Factorization on a Multi-lingual Overlapped Speech Signal: A Signal and Perception Level Analysis. International Journal of Computing and Digital Systems. 11(1). 39–52. 1 indexed citations
10.
11.
Shah, Milind, et al.. (2021). Linear Mixed Effect Modelling for Analyzing Prosodic Parameters for Marathi Language Emotions. International Journal of Advanced Computer Science and Applications. 12(12).
12.
Shah, Milind, et al.. (2021). Investigating batch normalization in spoken language understanding. Journal of Physics Conference Series. 1812(1). 12022–12022. 1 indexed citations
13.
Shah, Milind, et al.. (2021). ENHANCING LEARNING IN SPOKEN LANGUAGE UNDERSTANDING BY MITIGATING INTERNAL COVARIANT SHIFT IN LEARNING MODELS. Indian Journal of Computer Science and Engineering. 12(1). 297–305. 1 indexed citations
14.
Shah, Milind, et al.. (2020). Extending the Classifier Algorithms in Machine Learning to Improve the Performance in Spoken Language Understanding Systems Under Deficient Training Data. Advances in Science Technology and Engineering Systems Journal. 5(6). 464–471. 2 indexed citations
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16.
Shah, Milind, et al.. (2017). Using Non-Linear Support Vector Machines for Detection of Activities of Daily Living. Indian Journal of Science and Technology. 10(36). 1–8. 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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