On overview of PCA application strategy in processing high dimensionality forensic data

Lee, Loong Chuen; Jemain, Abdul Aziz

doi:10.1016/j.microc.2021.106608

Cited by 52 publications

(17 citation statements)

References 112 publications

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“…For the recognition and evaluation of signals collected by sensors, machine learning algorithms, such as decision tree, SVM, and neural network, are important technologies [22], and PCA is often used to reduce the dimension of collected signal data [23].…”

Section: Research On Sensor Inmentioning

confidence: 99%

Application of Intelligent Sensor Network in the Assessment of Table Tennis Teaching and Training Intensity, Training Volume, and Physical Fitness

Zhang

2022

Journal of Sensors

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Sensor technology has been deeply into the sports industry, with the help of sensors to monitoring and collection data for the physical training in real-time. In table tennis, wearable sensors can record the amount of training, movement essentials, and the number of strokes of both hands and assist in the testing and evaluation of table tennis. This paper analyzes the application of wearable sensors in table tennis training activities and the details of signal collection and feature extraction. At the same time, machine learning technology often used to recognize and test table tennis training data, and a support vector machine (SVM) is one of the representative classifiers. Applying the processed signal data to the classification and testing of SVM can effectively identify the movement and evaluate the training effect and athletes’ physical fitness. The integration of intelligent sensors and table tennis can effectively improve the evaluation efficiency and quality in the process of teaching and training.

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Section: Research On Sensor Inmentioning

confidence: 99%

Application of Intelligent Sensor Network in the Assessment of Table Tennis Teaching and Training Intensity, Training Volume, and Physical Fitness

Zhang

2022

Journal of Sensors

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“…The first three principal components were selected for principal component analysis of spectral data. Its purpose is to retain the main information of spectral data to the maximum extent, prevent data missing, reduce the redundancy of spectral data, and meet the requirement that the cumulative contribution rate of principal component analysis is greater than 80% ( Lee and Jemain, 2021 ). Spectral data was visualized using PCA, presenting five peanut seed samples as “clusters”.…”

Section: Resultsmentioning

confidence: 99%

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

Zou

et al. 2022

Front. Plant Sci.

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Moldy peanut seeds are damaged by mold, which seriously affects the germination rate of peanut seeds. At the same time, the quality and variety purity of peanut seeds profoundly affect the final yield of peanuts and the economic benefits of farmers. In this study, hyperspectral imaging technology was used to achieve variety classification and mold detection of peanut seeds. In addition, this paper proposed to use median filtering (MF) to preprocess hyperspectral data, use four variable selection methods to obtain characteristic wavelengths, and ensemble learning models (SEL) as a stable classification model. This paper compared the model performance of SEL and extreme gradient boosting algorithm (XGBoost), light gradient boosting algorithm (LightGBM), and type boosting algorithm (CatBoost). The results showed that the MF-LightGBM-SEL model based on hyperspectral data achieves the best performance. Its prediction accuracy on the data training and data testing reach 98.63% and 98.03%, respectively, and the modeling time was only 0.37s, which proved that the potential of the model to be used in practice. The approach of SEL combined with hyperspectral imaging techniques facilitates the development of a real-time detection system. It could perform fast and non-destructive high-precision classification of peanut seed varieties and moldy peanuts, which was of great significance for improving crop yields.

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“…Because of its versatility, PCA has several applications in a variety of disciplines. 24,25 It computes the correlation between variables that contain redundant data. The correlation is expressed in terms of principal components, which are calculated by multiplying eigenvalues from covariance matrix C, by using the following equation:…”

Section: Principal Component Analysismentioning

confidence: 99%

“…Principal component analysis (PCA) 23 is one of the most widely used unsupervised feature reduction techniques. Because of its versatility, PCA has several applications in a variety of disciplines 24,25 . It computes the correlation between variables that contain redundant data.…”

Section: Preliminariesmentioning

confidence: 99%

NSGA‐II‐XGB: Meta‐heuristic feature selection with XGBoost framework for diabetes prediction

Gupta

Rajput

Gunjan

et al. 2022

Concurrency and Computation

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Diabetes is one of the most prevalent causes of casualties in the modern world. Early diagnosis of diabetes is the most promising way for increasing the chances of patients' survival. The ever-growing technology of the current era, machine learning-based algorithms pave the door in the healthcare industry by delivering efficient decision support services in real-time. However, high-dimensionality of the data obtained using multiple sources increases the computation time and significantly impacts the models' efficiency in classifying the results. Feature selection improves learning performance and reduces the computational cost by selecting subsets of features and eliminating unnecessary and irrelevant features. In this article, an attempt has been made to develop a hybrid machine learning model based on non-dominated sorting genetic algorithm (NSGA-II) and ensemble learning for the efficient categorization of diabetes. The proposed work uses various data preprocessing techniques, such as missing data handling and normalization, prior to model training. The most prominent and salient features are selected by exploiting the potential of the NSGA-II in the diabetes dataset. Finally, an ensemble learning-based extreme gradient boosting (XGBoost) model is modeled using features selected by NSGA-II to classify patients as diabetic or non-diabetic. Theproposed methodology is experimentally validated using a hybridized dataset comprising 23 features, with 1288 instances of both male and female patients between the ages of 21 and 65. In addition, for performance evaluation, the results of statistical parameters are compared with several state-of-the-art decision-making models in the current domain. Experiment findings exemplify that the proposed NSGA-II-XGB approach gives better classification results with an average accuracy of 98.86%. Furthermore, the statistical results of specificity (88.6%), sensitivity (96.36%), and F-score (97.84%) also support the utility of the proposed methodology in the early diagnosis of diabetes.

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On overview of PCA application strategy in processing high dimensionality forensic data

Cited by 52 publications

References 112 publications

Application of Intelligent Sensor Network in the Assessment of Table Tennis Teaching and Training Intensity, Training Volume, and Physical Fitness

Application of Intelligent Sensor Network in the Assessment of Table Tennis Teaching and Training Intensity, Training Volume, and Physical Fitness

Rapid nondestructive detection of peanut varieties and peanut mildew based on hyperspectral imaging and stacked machine learning models

NSGA‐II‐XGB: Meta‐heuristic feature selection with XGBoost framework for diabetes prediction

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