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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Sudalaimuthu Thalavaipillai
Hindustan Institute of Technology and Science
Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Symptoms based endometriosis prediction using machine learning Visalaxi Sankaravadivel; Sudalaimuthu Thalavaipillai
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3254

Abstract

Endometriosis a painful disorder that stripes the uterus both inside and outside. Endometriosis can be diagnosed by the medical practitioners with the help of traditional scanning procedures. Laparoscopic surgery is the authentic method for identifying the advanced stages of endometriosis. The statistical approach is a state-of-art method for identifying the various stages of endometriosis using laparoscopic images. The paper focuses on a well-known statistical method known as chi-square and correlation coefficients are implemented for identifying the symptoms that are correlated with various stages of endometriosis. Chi-square analysis performs the association between symptoms and stages of endometriosis. With these analysis, an algorithm was proposed known as endometriosis prediction factor algorithm (EPF). The EPF algorithm predicts the presence of endometriosis if the derived value is greater than 1. From the chi-square analysis, it is identified that mild endometriosis is influenced 34% by menstrual flow, minimal endometriosis is influenced 40% by dysmenorrhea, where moderate endometriosis is influenced 31% by tenderness and deep infiltrating endometriosis is influenced 22% by adnexal mass.
AMIGOS: a robust emotion detection framework through Gaussian ResiNet Bakkialakshmi V. S.; Sudalaimuthu Thalavaipillai
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3783

Abstract

Affective computing is the study of the deep extraction of emotional impacts that triggers humans for various reasons. Emotions directly reflect on human behaviour. The proposed analysis is inclined towards deep emotion extraction through a novel concept with less computation time. Designing a robust analysis model is focused on here. AMIGOS dataset on affect, and personality modelling is considered here. A novel gaussian ResiNet (GRN) algorithm is evaluated here. Any changes in the emotions of humans are the brainy response given to the actions faced. The features of the given physiological factors are considered for analysis, further with GMM-ResiNet (GRN) a low computational structure is used for classification. The novel gaussian ResiNet (GRN) is created from the given dataset for similar feature validations. The system predicts the correlated relative data from the training set and testing set and achieved the performance metrics using error rate (ER), algorithm computation time (ACT), full computation time (FCT), and accuracy (AUC). novel gaussian ResiNet (GRN) is created and tested with processed data of the AMIGOS dataset. The model created is validated with state-of-art approaches and achieved an accuracy of 92.6%.
Co-Authors Bakkialakshmi V. S. Pon Ramlingam Surya Rajeswar Visalaxi Sankaravadivel