Domain-Specific Datasets for Applied Machine Learning: A Structured Review Across Agriculture, Smart Cities, Public Health, and Finance

DOI

10.2991/978-94-6463-948-3_33How to use a DOI?

Keywords

Datasets; Machine Learning; Agriculture; Smart Cities; Public Health; Currency Recognition; Data Quality; Benchmarking

Abstract

Datasets will determine both the performance of models and the reproducibility, fairness and transferability of their results from laboratory to other applications, and are the foundation of machine learning today and the future of machine learning; this paper offers an exhaustive review of over 30 datasets (for which we have been involved) in agriculture; smart cities; public health; currency forensics; well being; and financial systems, including but not limited to, FruitNet, HelmetML, Indian Coins, and an assortment of financial datasets related to Insolvency. The collection of the different data sets demonstrates how domain specific data can establish a standard of utility, facilitate comparisons across domains and be replicable when using scientific methods. This paper creates a framework for classifying datasets by their application area, summarises the fundamental design principles of the datasets, highlights the challenges of benchmarking and annotating datasets as well as ethical issues. The increasing demand for structured financial datasets for insolvency predictions in India, and the linkages between the technical design of datasets and the economic and policy implications are also considered. In addition to providing a structure for dataset creators, machine learning practitioners and policymakers to evaluate the quality of datasets and their societal impact, this work provides opportunities for multimodal integration, collaboration and new opportunities in unexplored domains.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Vijaykant J. Kulkarni
AU  - Pallavi Rege
AU  - Radhakrishna Batule
AU  - Vidula Meshram
PY  - 2026
DA  - 2026/01/06
TI  - Domain-Specific Datasets for Applied Machine Learning: A Structured Review Across Agriculture, Smart Cities, Public Health, and Finance
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 457
EP  - 475
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_33
DO  - 10.2991/978-94-6463-948-3_33
ID  - Kulkarni2026
ER  -