<?xml version="1.0" encoding="UTF-8"?>
<doi_batch version="4.3.0" xmlns="http://www.crossref.org/doi_resources_schema/4.3.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/doi_resources_schema/4.3.0 http://www.crossref.org/schema/deposit/doi_resources4.3.0.xsd">
<head>
<doi_batch_id>b33611b1-c307-45c8-987d-2c9da4c18898</doi_batch_id>
<depositor>
<name>beie</name>
<email_address>director@blueeyesintelligence.org</email_address>
</depositor>
</head>
<body>
<doi_citations>
<doi>10.35940/ijitee.H9269.0710921</doi>
<citation_list><citation key="ref0"><unstructured_citation>Lucy Fricker, &quot;Causes and Challenges of Healthcare Fraud in the US&quot;, North Carolina State University Raleigh, NC, USA,2013</unstructured_citation></citation><citation key="ref1"><unstructured_citation>https://www.researchgate.net/publication/220924701</unstructured_citation></citation><citation key="ref2"><unstructured_citation>Qi Liu,&quot; Healthcare fraud detection: A survey and a clustering model incorporating Geo-location information&quot;, Rutgers University Newark, New Jersey, United States,2013.</unstructured_citation></citation><citation key="ref3"><doi>10.1109/ICSSSM.2006.320598</doi><unstructured_citation>Yi Peng,&quot; Application of Clustering Methods to Health Insurance Fraud Detection&quot;, Institute of Information Science, Technology &amp; Engineering, USA,2006.</unstructured_citation></citation><citation key="ref4"><unstructured_citation>Shivani S. Waghade,&quot; A Comprehensive Study of Healthcare Fraud Detection based on Machine Learning&quot;, Shri Ramdeobaba College of Engineering and Management, Nagpur,2018.</unstructured_citation></citation><citation key="ref5"><unstructured_citation>Ajith Abraham,&quot; Computational Intelligence Models for Insurance Fraud Detection&quot;, Machine Intelligence Research Lab, WA, USA,2013.</unstructured_citation></citation><citation key="ref6"><doi>10.1186/s40537-019-0181-8</doi><unstructured_citation>Matthew Herland, &quot;The effects of a class rarity on the evaluation of supervised healthcare fraud detection models&quot;, Florida Atlantic University,777 Glades Road, Boca Raton, FL, USA,2019.</unstructured_citation></citation><citation key="ref7"><unstructured_citation>UC San Diego,&quot; Combating Health Care Fraud and Abuse: Conceptualization and Prototyping Study of a Blockchain Antifraud Framework&quot;, School of Medicine, Department of Anesthesiology and Division of Infectious Diseases and Global Public Health, La Jolla, CA, United States,2020.</unstructured_citation></citation><citation key="ref8"><unstructured_citation>P. Naga Jyothi,&quot; Performance on Fraud Detection in Medical Claims of Healthcare Data&quot;,2019.</unstructured_citation></citation><citation key="ref9"><unstructured_citation>Ms. Meena Kumari,&quot; FICCI Working Paper on Health Insurance Fraud&quot;, Joint Director, IRDA,2019.</unstructured_citation></citation><citation key="ref10"><doi>10.1109/HIS.2007.13</doi><unstructured_citation>Renata M. C. R. de Souza, &quot;A Clustering Method for Mixed Feature-Type Symbolic Data using Adaptive Squared Euclidean Distances&quot;, Centro de Informatica - CIn / UFPEAv. Prof. Luiz Freire, s/n - Cidade Universitaria,2007.</unstructured_citation></citation><citation key="ref11"><doi>10.1109/3477.809041</doi><unstructured_citation>T. V. Ravi, &quot;Clustering of Symbolic Objects Using Gravitational Approach&quot;, IBM Solutions Research Center, Indian Institute of Technology, New Delhi, India, 1999.</unstructured_citation></citation><citation key="ref12"><unstructured_citation>Anderson F.B.F. Costa,&quot; A Kernel K-means Clustering Method for Symbolic Interval Data&quot;, Federal University,2010.</unstructured_citation></citation><citation key="ref13"><unstructured_citation>K. Chidananda Gowda,&quot; Symbolic Clustering Using a New ////99Similarity Measure&quot;, S.J. Coll. of Eng., Mysore, India,1992.</unstructured_citation></citation><citation key="ref14"><unstructured_citation>Miin-Shen Yang, &quot;Fuzzy clustering algorithms for mixed feature variables&quot;, Department of Applied Mathematics,2003.</unstructured_citation></citation><citation key="ref15"><unstructured_citation>Vipin Kumar &quot;Similarity Measures for Categorical Data: A Comparative Evaluation&quot;, Proceedings of the SIAM International Conference on Department of Computer Science and Engineering University of Minnesota,2008</unstructured_citation></citation><citation key="ref16"><unstructured_citation>J. D. Kittoe, &quot;Exploring fraud and abuse in National Health Insurance Scheme (NHIS) using data mining technique as a statistical model&quot;, Department of Computer Science and Engineering,2017.</unstructured_citation></citation><citation key="ref17"><doi>10.1142/S0218001419560081</doi><unstructured_citation>Shanmukhappa A Angadi, Sanjeevakumar M Hatture, Face Recognition Through Symbolic Modeling of Face Graphs and Texture, International Journal of Pattern Recognition and Artificial Intelligence, 33(12), 2019.</unstructured_citation></citation><citation key="ref18"><unstructured_citation>Rashmi. P.Karchi Nagarajan Munusamy, Exploration of Unmixing and Classification of Hyperspectral Imagery, International Journal of Innovative Technology and Exploring Engineering(IJITEE), 8(7), pp. 723-733, 2019.</unstructured_citation></citation><citation key="ref19"><doi>10.1016/B978-0-12-821633-0.00011-8</doi><unstructured_citation>Sanjeevakumar M Hatture, Nagaveni Kadakol, Clinical diagnostic systems based on machine learning and deep learning, Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics, pp. 159-183, 2021.</unstructured_citation></citation></citation_list>
</doi_citations>
</body>
</doi_batch>
