Browsing by Author "Linul, E."

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Development of a Convolutional Neural Network Model to Predict Coronary Artery Disease Based on Single-Lead and Twelve-Lead ECG Signals
(MDPI, 2022) Vasudeva, S.T.; Rao, S.S.; Karanth P, N.; Shettigar, A.; Mahabala, C.; Kamath, P.; Gowdru Chandrashekarappa, M.; Linul, E.
Coronary artery disease (CAD) is one of the most common causes of heart ailments; many patients with CAD do not exhibit initial symptoms. An electrocardiogram (ECG) is a diagnostic tool widely used to capture the abnormal activity of the heart and help with diagnoses. Assessing ECG signals may be challenging and time-consuming. Identifying abnormal ECG morphologies, especially in low amplitude curves, may be prone to error. Hence, a system that can automatically detect and assess the ECG and treadmill test ECG (TMT-ECG) signals will be helpful to the medical industry in detecting CAD. In the present work, we developed an intelligent system that can predict CAD, based on ECG and TMT signals more accurately than any other system developed thus far. The distinct convolutional neural network (CNN) architecture deals with single-lead and multi-lead (12-lead) ECG and TMT-ECG data effectively. While most artificial intelligence-based systems rely on the universal dataset, the current work used clinical lab data collected from a renowned hospital in the neighborhood. ECG and TMT-ECG graphs of normal and CAD patients were collected in the form of scanned reports. One-dimensional ECG data with all possible features were extracted from the scanned report with the help of a modified image processing method. This feature extraction procedure was integrated with the optimized architecture of the CNN model leading to a novel prediction system for CAD. The automated computer-assisted system helps in the detection and medication of CAD with a high prediction accuracy of 99%. © 2022 by the authors.
Fretting wear behavior on LPBF processed AlSi10Mg alloy for different heat treatment conditions
(Elsevier Editora Ltda, 2024) Nanjundaiah, R.S.; Rao, S.S.; Praveenkumar, K.; Prabhu, T.R.; Shettigar, A.K.; Gowdru Chandrashekarappa, M.; Linul, E.
To widen the industrial application of additively manufactured (AM) parts, the study of fretting wear behavior is essential, as it ensures the safety and reliability that drive innovation in design and materials. This study explores the fretting wear behavior of the as-built and heat-treated state of AlSi10Mg alloy fabricated, viz., laser powder bed fusion (LPBF). Initially, the as-built and T5, T6, and stress-relieved (SR) heat-treated samples were examined using scanning electron microscopy (SEM) to gain insights into the microstructural changes. The as-built samples exhibited a higher hardness level (135 HV) primarily due to the presence of very fine microstructure of the α-Al cellular matrix with embedded Si. The α-Al cellular structure dissolved with various heat treatments, and Si particles coarsened. The hardness decreased to 85, 79, and 67 HV for the T5, T6, and SR conditions, respectively. Subsequently, fretting tests were conducted on the samples, applying various normal loads of 10, 50, and 100 N. Further, the samples were characterized by the coefficient of friction (COF), worn surface morphology, and wear volume loss. The investigation showed that the as-built material showed less wear volume loss under all loading conditions than the heat-treated conditions. Furthermore, the T5 heat treated sample had a lower wear volume when compared to the T6 and SR heat-treated samples. The heat-treated sample exhibits compressive stress, whereas the LPBF processed, the as-built sample shows tensile stress. © 2024 The Authors
Quantitative Analysis of Solar Photovoltaic Panel Performance with Size-Varied Dust Pollutants Deposition Using Different Machine Learning Approaches
(MDPI, 2022) Tripathi, A.K.; Mangalpady, M.; Elumalai, E.P.; Abbas, M.; Afzal, A.; Saboor, S.; Linul, E.
In this paper, the impact of dust deposition on solar photovoltaic (PV) panels was examined, using experimental and machine learning (ML) approaches for different sizes of dust pollutants. The experimental investigation was performed using five different sizes of dust pollutants with a deposition density of 33.48 g/m2 on the panel surface. It has been noted that the zero-resistance current of the PV panel is reduced by up to 49.01% due to the presence of small-size particles and 15.68% for large-size (ranging from 600 µ to 850 µ). In addition, a significant reduction of nearly 40% in sunlight penetration into the PV panel surface was observed due to the deposition of a smaller size of dust pollutants compared to the larger size. Subsequently, different ML regression models, namely support vector machine (SVMR), multiple linear (MLR) and Gaussian (GR), were considered and compared to predict the output power of solar PV panels under the varied size of dust deposition. The outcomes of the ML approach showed that the SVMR algorithms provide optimal performance with MAE, MSE and R2 values of 0.1589, 0.0328 and 0.9919, respectively; while GR had the worst performance. The predicted output power values are in good agreement with the experimental values, showing that the proposed ML approaches are suitable for predicting the output power in any harsh and dusty environment. © 2022 by the authors.