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All Journal Jambura Journal of Biomathematics (JJBM)
Dhandapani, Prasantha Bharathi
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Identifying the Fetal Heart Rate and gender with Intuitionistic Fuzzy Total edge Magic Labelling Aruchamy, Pradeepa; Mahagaonkar, Pralahad; Ganesan, Gomathi; Dhandapani, Prasantha Bharathi; Yahya, Nisky Imansyah
Jambura Journal of Biomathematics (JJBM) Volume 6, Issue 2: June 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v6i2.30951

Abstract

The application of Intuitionistic fuzzy total edge magic labelling to a graphical image at the 20th week of gestation provides insights into facilitates gender prediction as well as assessment of fetal blood flow on a second-trimester Doppler ultrasound screen. Ultrasounds screen the fetal heart rate during the 20th week of gestation using Doppler ultrasound for blood flow. A fetal heart rate above 2.5 beats per second suggests a female baby, while a rate less than 2.5 beats per second indicates a male baby. We convert the fetal heart blood flow into a graphical image and label it using intuitionistic fuzzy total edge magic labelling.
Energy and Laplacian Energy of Pythagorean Intuitionistic Fuzzy Graphs with Applications in Medical Diagnosis Networks Saravanakumar, Anitha; Periyannan, Jayalakshmi; Dhandapani, Prasantha Bharathi; Yahya, Nisky Imansyah
Jambura Journal of Biomathematics (JJBM) Volume 6, Issue 4: December 2025
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjbm.v6i4.33977

Abstract

This study extends fuzzy graph energy analysis by introducing energy and Laplacian energy for Pythagorean Intuitionistic Fuzzy Graphs (PIFGs), a powerful generalization of intuitionistic fuzzy graphs capable of representing higher degrees of uncertainty. A novel connection matrix for PIFGs is defined, and new formulations for energy and Laplacian energy are established, along with sharp lower and upper bounds. Beyond theoretical contributions, the approach is applied to medical diagnosis networks, where vertices represent symptoms,  diagnostic tests,  and diseases,  and edges encode Pythagorean intuitionistic fuzzy relationships. These measures quantify both the overall strength of associations (energy) and their structural irregularity (Laplacian energy), offering interpretable indicators for diagnostic certainty or ambiguity.  The framework provides a robust mathematical basis for decision-making in biomedical contexts where data are uncertain, imprecise, or conflicting.
Co-Authors Aruchamy, Pradeepa Ganesan, Gomathi Mahagaonkar, Pralahad NISKY IMANSYAH YAHYA Periyannan, Jayalakshmi Saravanakumar, Anitha