Advancements in Machine Learning for Tuberculosis: A Systematic Analysis of Clinical Symptom Severity Datasets and Multimodal Fusion

Explore the article titled Advancements in Machine Learning for Tuberculosis: A Systematic Analysis of Clinical Symptom Severity Datasets and Multimodal Fusion from IJIRT Volume 13, Issue 1. This study evaluates the effectiveness of teaching programs on waste management knowledge among women.

Authors

S Dhanalakshmi (ORCID: https://orcid.org/0000-0002-9610-6728)
Suresh S

Publication Details

Journal: International Journal of Innovative Research in Technology
Published: 2026-06-17
DOI: https://doi.org/10.64643/ijirtv13i1-205399-459
Primary Topic: Tuberculosis Research and Epidemiology
Type: article
Field-Weighted Citation Impact: 0.00

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article

Advancements in Machine Learning for Tuberculosis: A Systematic Analysis of Clinical Symptom Severity Datasets and Multimodal Fusion

S Dhanalakshmi, Suresh S

Jun 17, 2026

International Journal of Innovative Research in Technology

Tuberculosis Research and Epidemiology

article

Advancements in Machine Learning for Tuberculosis: A Systematic Analysis of Clinical Symptom Severity Datasets and Multimodal Fusion

S Dhanalakshmi, Suresh S

June 17, 2026article en

Abstract

International Journal of Innovative Research in TechnologyVol. 13(1)

Openalex Percentile: Top 8%

Tuberculosis Research and Epidemiology

DOI Open Access PDF OpenAlex

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