2022
DOI: 10.3390/electronics11111750
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A Hybrid Ensemble Stacking Model for Gender Voice Recognition Approach

Abstract: Gender recognition by voice is a vital research subject in speech processing and acoustics, as human voices have many remarkable characteristics. Voice recognition is beneficial in a variety of applications, including mobile health care systems, interactive systems, crime analysis, and recognition systems. Several algorithms for voice recognition have been developed, but there is still potential for development in terms of the system’s accuracy and efficiency. Recent research has focused on combining ensemble … Show more

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Cited by 11 publications
(5 citation statements)
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“…The essence of stacking ensemble learning is to orchestrate a diverse collection of models, such as DNNs, to work in parallel and synergize their predictions through a meta-regression framework, such as RF, to refine the overall prediction accuracy [29,64]. However, because many of these weak learners are based on ML or DL methods that rely heavily on data characteristics, stacking ensemble learning may have limitations in improving solar PV power generation predictions.…”
Section: Stage 2: Ranger-based Online Learning Model For Multistep-ah...mentioning
confidence: 99%
“…The essence of stacking ensemble learning is to orchestrate a diverse collection of models, such as DNNs, to work in parallel and synergize their predictions through a meta-regression framework, such as RF, to refine the overall prediction accuracy [29,64]. However, because many of these weak learners are based on ML or DL methods that rely heavily on data characteristics, stacking ensemble learning may have limitations in improving solar PV power generation predictions.…”
Section: Stage 2: Ranger-based Online Learning Model For Multistep-ah...mentioning
confidence: 99%
“…The primary objective of data preprocessing is to enhance the quality of the data and tailor it to meet the requirements of the data mining task at hand. Data preprocessing, integral to the data preparation phase, encompasses various processing activities applied to raw data to ready it for subsequent processing steps (Alkhammash et al, 2022). Historically, it has served as a crucial initial stage in the data mining process.…”
Section: Data Preprocessingmentioning
confidence: 99%
“…K-fold cross-validation is a method in machine learning that is used to assess the performance of a model with a small amount of data [35]. It works by splitting the data into k subsets of equal size or "folds," using k-1 of them to train the model and holding out the remaining fold for validation.…”
Section: K-fold Cross-validationmentioning
confidence: 99%