Tuberculosis (TB) remains a major global health challenge, particularly in regions with limited access to rapid and reliable diagnostic facilities. Traditional diagnostic methods are often time-consuming, expensive, and require specialized infrastructure, which delays early detection. To address these limitations, this paper proposes a noise-robust hybrid framework for early tuberculosis detection using cough audio recordings. The system integrates preprocessing techniques for noise reduction, feature extraction using Mel-Frequency Cepstral Coefficients (MFCCs) and spectrograms, and classification using both machine learning models (Support Vector Machine, Random Forest) and deep learning architectures (Convolutional Neural Networks and CNN–RNN). An ensemble decision-making approach is employed to combine predictions from multiple models and improve reliability. Experimental results demonstrate that the proposed method achieves an accuracy of 94.8% and performs effectively even in noisy real-world conditions. The framework provides a scalable, cost-effective, and non-invasive solution for tuberculosis screening, making it suitable for deployment in resource-constrained environments.
The present study examines the impact of Artificial Intelligence (AI) on Human Resource (HR) decision-making and employee experience in modern organisations. The rapid integration of AI technologies into HR functions has significantly transformed traditional practices such as rec…
Chronic kidney disease impacts millions worldwide, and delayed diagnosis results in unfavorable outcomes and higher healthcare expenses. Recent advancements in machine learning present promising diagnostic features, but their “black box” nature restricts clinical uptake. This rev…
Parkinson's disease is a progressive neurodegenerative disorder that primarily affects movement, balance, and motor coordination due to the gradual loss of dopamine-producing neurons. Early identification of the disease is essential for timely medical intervention and improved pa…
In this study, we aimed to create a system that uses machine learning to detect and classify diabetes in an e-healthcare setting. We used Ensemble Decision Tree algorithms for selecting important features from a large set of data. Detecting diabetes accurately is a big challenge…
In recent years, the integration of machine learning and data mining techniques in sports analytics has significantly improved decision-making processes in team management. This project focuses on the application of machine learning algorithms to analyze football player performan…
Estimating the number of people present in a crowded scene from an image is a challenging computer vision problem, particularly under conditions of severe occlusion, scale variation, and non-uniform crowd distribution. This paper presents a deep learning framework for crowd densi…