Chintan Bhatt

Work place: U & P U. Patel Department of Computer Engineering, Chandubhai S. Patel Institute of Technology, Charotar University of Science And Technology (CHARUSAT), India



Research Interests: Computer Vision, Software Engineering


Chintan Bhatt is currently working as an Assistant Professor in Computer Engineering department, Chandubhai S. Patel Institute of Technology, Charotar University of Science And Technology (CHARUSAT). He is a member of IEEE, EAI, ACM, CSI, AIRCC and IAENG (International Association of Engineers). His areas of interest include Internet of Things, Data Mining, Networking, Mobile Computing, Big Data and Software Engineering. He has more than 10 years of teaching experience and research experience, having good teaching and research interests. He has more than 70 publications in Internet of Things, Computer Vision and Software Engineering, among which many publications are Scopus indexed. He has been awarded many CSI National Awards and a few CHARUSAT Research Paper Awards.

Author Articles
Data Quality for AI Tool: Exploratory Data Analysis on IBM API

By Ankur Jariwala Aayushi Chaudhari Chintan Bhatt Dac-Nhuong Le

DOI:, Pub. Date: 8 Feb. 2022

A huge amount of data is produced in every domain these days. Thus for applying automation on any dataset, the appropriately trained data plays an important role in achieving efficient and accurate results. According to data researchers, data scientists spare 80% of their time in preparing and organizing the data. To overcome this tedious task, IBM Research has developed a Data Quality for AI tool, which has varieties of metrics that can be applied to different datasets (in .csv format) to identify the quality of data. In this paper, we will be representing how the IBM API toolkit will be useful for different variants of datasets and showcase the results for each metrics in graphical form. This paper might be found useful for the readers to understand the working flow of the IBM data purifier tool, thus we have represented the entire flow of how to use IBM data quality for the AI toolkit in the form of architecture.

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