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              <text>AN IOT-BASED COMPUTATIONAL INTELLIGENCE MODEL TO PERFORM GENE ANALYTICS IN PATERNITY TESTING AND COMPARISON FOR HEALTH 4.0</text>
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              <text>Correlation Clustering; Gene Analysis; Iot; Paternity Testing</text>
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              <text>Parental comparison and parenthood testing are essential in various legal and medical scenarios. The accuracy and reliability of these tests heavily rely on the gene analysis algorithms used. However, analyzing the quality of succession data are quite challenging due to the presence of detrimental characteristics. To address this issue, we propose using machine learning-based algorithms such as clustering (Correlation-based) and Classification (Modified Naive Bayesian) to separate these characteristics from the parent-child gene array. This progression helps to identify, validate, and select tools, techniques for scrutinizing indecent sequences, leading to accurate and reliable results. In this paper, we present an IoT-based intelligence tool for parental comparison that uses a secure gene analysis algorithm. The model employs multiple sensors and devices to collect genetic data, which is then securely processed and analyzed using contemporary algorithms. The suggested model uses advanced techniques such as encryption and decryption to ensure the privacy and confidentiality of the genetic information. Our experimental consequences reveal that the proposed model is reliable, secure, and provides accurate results. The model has the potential to be used in various legal and medical contexts where the security and reliability of genetic data are critical.  2023 Little Lion Scientific.</text>
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              <text>Arputharaj V.J.; Sankar K.; Kumar A.S.; Sridevi M.; Prasad D.D.</text>
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              <text>Journal of Theoretical and Applied Information Technology, Vol-101, No. 14, pp. 5781-5796.</text>
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              <text>Little Lion Scientific</text>
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              <text>2023-01-01</text>
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              <text>Restricted Access</text>
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              <text>ISSN: 19928645</text>
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              <text>Arputharaj V.J., Department of Computer Science, CHRIST (Deemed to Be University), India; Sankar K., Department of Computer Science and Engineering (Data Science), CVR College of Engineering, Telangana, Hyderabad, India; Kumar A.S., Department of Computer Science, Skyline University, Nigeria; Sridevi M., Department of Computer Science and Engineering, CVR College of Engineering, (V) Ibrahimpatnam (M), R R District, Telangana, Mangalpalli, 501 510, India; Prasad D.D., Department of ECE, Vishnu Institute of Technology, AP, Bhimavaram, India</text>
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