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This paper gives a performance analysis of multiple vote classifiers based on metaclassification methods for estimating the risk of diabetes. The study's dataset includes a number of biological and clinical risk variables that can result in the development of diabetes. In the analysis, classifiers like Random Forest, Logistic Regression, Gradient Boosting, Support Vector Machines, and Artificial Neural Networks were used. In the study, each classifier was trained and evaluated separately, and the outcomes were compared to those attained using meta-classification methods. Some of the meta-classifiers used in the analysis included Majority Voting, Weighted Majority Voting, and Stacking. The effectiveness of each classifier was evaluated using a number of measures, including accuracy, precision, recall, F1-score, and Area under the Curve (AUC). The results show that meta-classification techniques often outperform solo classifiers in terms of prediction precision. Random Forest and Gradient Boosting, two different classifiers, had the highest accuracy, while Logistic Regression performed the worst. The best performing meta-classifier was stacking, which achieved an accuracy of 84.25%. Weighted Majority Voting came in second (83.86%) and Majority Voting came in third (82.95%). [ABSTRACT FROM AUTHOR]

The design and optimization of nanostructure-based surface acoustic wave (SAW) gas sensor is analyzed based on TiO2 sensing layer and modified electrode dimensions. The sensitivity of the gas sensor depends upon the type of sensing layer used and active surface area obtained by varying the aspect ratio. The performance of the sensor is observed from 0.1ppm to 100ppm concentration of hydrogen gas with respect to output displacement. The displacement of the sensor increases with the increase in concentration. The characteristic of the sensor is also studied by varying input and output inters digited transducers’ (IDT) height from 0.05μm to 0.5μm. The nanostructured TiO2 based sensor has shown increased total displacement and frequency shift of the device resulting enhanced sensitivity. At 0.05 μm IDT height the displacement is found to be a maximum. The operating frequency is considered to be 44 Mhz. Finite Element Modeling (FEM) is used for analysis of the sensor. Simulation is done in software named COMSOL Multiphysics to ensure the enhanced performance of the mechanically engineered surface-based (nanorod) SAW gas sensor. [ABSTRACT FROM AUTHOR]