Gyroscope explorer terrain angles classification

Yalung, Christofer N.; Majeed, Salah Al; Adolfo, C. M.; Karam, Jalal; Mirtskhulava, Lela

doi:10.1109/ewdts.2016.7807679

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Another suggestion is to utilise knowledge-based techniques of PCA-SRP in different neural network architectures such as CNNs, RNNs, LSTMs, and GNNs. Furthermore, an investigation into various field applications to explore the incorporation of the investigated cleaning techniques, such as real-time biomedical automation, image-based medical diagnosis classification, and human thoughts processes [48][49][50][51] could be a desirable research avenue.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Sample Reduction for Physiological Data Analysis Using Principal Component Analysis in Artificial Neural Network

et al. 2021

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With its potential, extensive data analysis is a vital part of biomedical applications and of medical practitioner interpretations, as data analysis ensures the integrity of multidimensional datasets and improves classification accuracy; however, with machine learning, the integrity of the sources is compromised when the acquired data pose a significant threat in diagnosing and analysing such information, such as by including noisy and biased samples in the multidimensional datasets. Removing noisy samples in dirty datasets is integral to and crucial in biomedical applications, such as the classification and prediction problems using artificial neural networks (ANNs) in the body’s physiological signal analysis. In this study, we developed a methodology to identify and remove noisy data from a dataset before addressing the classification problem of an artificial neural network (ANN) by proposing the use of the principal component analysis–sample reduction process (PCA–SRP) to improve its performance as a data-cleaning agent. We first discuss the theoretical background to this data-cleansing methodology in the classification problem of an artificial neural network (ANN). Then, we discuss how the PCA is used in data-cleansing techniques through a sample reduction process (SRP) using various publicly available biomedical datasets with different samples and feature sizes. Lastly, the cleaned datasets were tested through the following: PCA–SRP in ANN accuracy comparison testing, sensitivity vs. specificity testing, receiver operating characteristic (ROC) curve testing, and accuracy vs. additional random sample testing. The results show a significant improvement in the classification of ANNs using the developed methodology and suggested a recommended range of selectivity (Sc) factors for typical cleaning and ANN applications. Our approach successfully cleaned the noisy biomedical multidimensional datasets and yielded up to an 8% increase in accuracy with the aid of the Python language.

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Section: Conclusion and Future Researchmentioning

confidence: 99%