Principal Component Analysis Dimensionality Reduction Research Articles

Enhancing NILM classification via robust principal component analysis dimension reduction

Non-intrusive load monitoring (NILM) techniques estimate the consumption of individual appliances in a household or facility, based on aggregated reading from a centralized meter. Usually, NILM techniques are shown to be improved when various power features and additional power quality parameters are included. However, adding power features leads to increased time complexity which is a disadvantage to real-time operation. Previous attempt to operate a principal component analysis (PCA) method to reduce the dimension of the problem managed to improve the run time but with considerably low accuracy. To this end, we utilize a robust PCA approach, to mitigate the influence of outliers in the data as a measure for improved performance. The proposed procedure achieves extraordinary results with accuracy over 96% for 600 hours long record of power quality measurements of the consumption of seven appliances from the standard AMPds dataset.

Content-based image retrieval of Indian traditional textile motifs using deep feature fusion

In the fast-paced fashion world, unique designs are like early birds, grabbing attention as online shopping surges. Fabric texture plays an immense role in selecting the perfect design. Indian Traditional textile motifs are pivotal, showing rich cultural origins and attracting worldwide art fanatics. Yet, technology-driven abstract forms are posing a challenge for them. The decline of handmade artistic ability due to computerization is concerning. Crafting new designs associated with the latest trends is time- consuming and requires diligence. In this work an interactive CBIR (content-based image retrieval) system is presented. It utilizes deep features from InceptionV3 and InceptionResNetV2 models to match query designs with a database of traditional Indian textiles. Its performance is tested with Caltech-101, Corel-1K state-of-the-art datasets, and Indian Textiles datasets and the results are shown to be finer than the existing approaches. The similarity-based fine-grained saliency maps (SBFGSM) approach is employed to visualize the importance of features. Our approach combines deep feature fusion with PCA dimensionality reduction and speeds up search using a clustering approach. Relevance feedback is employed to refine the retrievals. This tool is expected to benefit designers by accelerating the design cycles by bridging the gap between human creativity and A.I. assistance.

Antimicrobial Activity Classification of Imidazolium Derivatives Predicted by Artificial Neural Networks.

This study assesses the Multilayer Perceptron (MLP) neural network, complemented by other Machine Learning techniques (CART, PCA), in predicting the antimicrobial activity of 140 newly designed imidazolium chlorides against Klebsiella pneumoniae before synthesis. Emphasis is on leveraging molecular properties for predictive analysis. Classification and regression decision trees (CART) identified the top 200 predictive molecular descriptors. Principal Component Analysis (PCA) reduced these descriptors to 5 components, retaining 99.57% of raw data information. Antimicrobial activity, categorized as high or low, was based on experimentally proven minimal inhibitory concentration (MIC), with a cut-point at MIC = 0.856mol/L. A 12-fold cross-validation trained the MLP (architecture 5-12-2 with 5 Principal Components). The MLP exhibited commendable performance, achieving almost 90% correct classifications across learning, validation, and test sets, outperforming models without PCA dimension reduction. Key metrics, including accuracy (0.907), sensitivity (0.905), specificity (0.909), and precision (0.891), were notably high. These results highlight the MLP model's efficacy with PCA as a high-quality classifier for determining antimicrobial activity. The study concludes that the MLP neural network, along with CART and PCA, is a robust tool for predicting the antimicrobial activity class of imidazolium chlorides against Klebsiella pneumoniae. CART and PCA, used in this study, allowed input variable reduction without significant information loss. High classification accuracy and associated metrics affirm the method's potential utility in pre-synthesis assessments, offering valuable insights for antimicrobial compound design.

Prediction of rock mass classification in tunnel boring machine tunneling using the principal component analysis (PCA)–gated recurrent unit (GRU) neural network

Prediction of rock mass classification in tunnel boring machine tunneling using the principal component analysis (PCA)–gated recurrent unit (GRU) neural network

Geometric space construction method combined of a spline-skinning based geometric variation method and PCA dimensionality reduction for ship hull form optimization

In the construction stage of ship hull geometric space, the representation and variation of hull forms pose challenges due to the complex nature of the geometry and the necessity for enhanced flexibility in space. In this study, we utilize T-spline technology for the representation of ship hulls, enabling a unified parameter domain with a reduced number of control points to effectively depict the entire hull surface. Furthermore, we introduce a method for geometric variation based on the T-spline parameter domain, which permits both local and global alterations, thus offering greater flexibility in modifying the geometry. By leveraging the T-spline representation and the geometric variation method, we developed a hybrid geometric approach for constructing the geometric space, which significantly increased the geometric flexibility in ship hull optimization. A geometric space characterized by a high degree of geometric freedom typically requires the use of numerous optimization variables and incurs considerable computational resource consumption. In this research, we apply a linear dimensionality reduction method, principal component analysis, to decrease the dimensionality of the geometric space. This method effectively preserves the original geometric design space, thereby eliminating the need for performance analyses. The numerical optimization results clearly indicate that the proposed method significantly enhances optimization efficiency.

Advancing mortality rate prediction in European population clusters: integrating deep learning and multiscale analysis

Accurately predicting population mortality rates is crucial for effective retirement insurance and economic policy formulation. Recent advancements in deep learning time series forecasting (DLTSF) have led to improved mortality rate predictions compared to traditional models like Lee-Carter (LC). This study focuses on mortality rate prediction in large clusters across Europe. By utilizing PCA dimensionality reduction and statistical clustering techniques, we integrate age features from high-dimensional mortality data of multiple countries, analyzing their similarities and differences. To capture the heterogeneous characteristics, an adaptive adjustment matrix is generated, incorporating sequential variation and spatial geographical information. Additionally, a combination of graph neural networks and a transformer network with an adaptive adjustment matrix is employed to capture the spatiotemporal features between different clusters. Extensive numerical experiments using data from the Human Mortality Database validate the superiority of the proposed GT-A model over traditional LC models and other classic neural networks in terms of prediction accuracy. Consequently, the GT-A model serves as a powerful forecasting tool for global population studies and the international life insurance field.

A method for predicting photovoltaic output power based on PCC-GRA-PCA meteorological elements dimensionality reduction method

ABSTRACT Photovoltaic (PV) power generation forecasting models require a large amount of meteorological data, which may include irrelevant and redundant information. As the volume of data increases, the dataset is likely to contain a significant amount of irrelevant and redundant information. This paper proposes a method for reducing dimensionality based on PCC-GRA-PCA method, which aims to simplify the model and reduce computational complexity. Firstly, the dimension reduction method analyzes the feature importance of various meteorological elements by using Pearson Correlation Coefficient (PCC) and Grey Relation Analysis (GRA), which can achieve the preliminary dimension reduction of data by selecting the most relevant features. Next, the data is processed using Principal Component Analysis (PCA) to achieve a secondary dimension reduction of meteorological data through feature transformation. Finally, a photovoltaic power prediction model has been established using the OVMD-tSSA-LSSVM algorithm. After analysis, it was found that the prediction model showed improvements in R2, MAE, RMSE, and MAPE after PCC-GRA-PCA dimensionality reduction compared to the prediction model before dimensionality reduction, as well as the prediction model after LDA and PCA dimensionality reduction. This demonstrates the effectiveness of reducing data dimensionality.

Research on Demagnetization Fault Diagnosis Method of Mine Cutting Permanent Magnet Synchronous Motor

To give timely and accurate diagnosis in the early stage of demagnetization failure for effective control and treatment, based on wavelet packet analysis, principal component analysis (PCA) dimensionality reduction, and least squares support vector machine(LSSVM), the extraction of features and the classification of demagnetization faults are completed. Since it is difficult to collect real data sets of demagnetization faults in practice, a two-dimensional finite element simulation model of permanent magnet synchronous motor (PMSM) under uniform demagnetization and partial demagnetization faults is established based on the Maxwell simulation platform. The wavelet packet analysis is used to extract the demagnetization feature of the A-phase current of the PMSM. Based on PCA dimensionality reduction, the dimensionality reduction of fault features is realized. The LSSVM is used to identify the fault and complete the fault classification. The simulation results show that the method has a high classification accuracy rate for demagnetization faults.

Seismic fault identification in coal mines based on the self-organizing map-gray wolf optimizer-support vector machine algorithm

Accurate fault identification in coal mines is important to improve mine safety and economic benefits. We compare various intelligent algorithms for data preprocessing and optimization and analyze the construction methods of seismic attribute data sets and the performance of intelligent optimization algorithms using fault identification accuracy as the discrimination index to find a better combined model for seismic fault identification. First, the training data set is constructed by mining the fault and nonfault information revealed by the roadway. The distribution characteristics of the seismic attribute data indicate similarities among them, and they are nonlinearly separable. Directly using the attributes to construct the data set, the accuracy of fault identification using the support vector machine (SVM) model is 78.41%. Principal component analysis (PCA) and self-organizing mapping (SOM) neural networks are used to extract effective information and then combined with the SVM classification model, and the accuracy of fault identification is 83.82% and 87.47%, respectively. Compared with the original data and PCA dimensionality reduction data, the accuracy of fault detection is improved by 9.06% and 3.66%, respectively, indicating that SOM can effectively improve the accuracy of fault detection by eliminating similar attributes and reducing the weight of redundant information. Then, through a fixed attribute data set, genetic algorithm (GA), particle swarm optimization (PSO), and gray wolf optimizer (GWO) intelligent optimization algorithms are used to find the optimal kernel function parameter and penalty parameter of the SVM classifier. The accuracy rate of the SOM-GWO-SVM model reaches 91.12%, compared with the SOM-PSO-SVM and SOM-GA-SVM, and the model accuracy is increased by 5.2% and 5.61%, respectively. Compared with PSO and GA, the GWO algorithm has a better global search ability. The identification result of the SOM-GWO-SVM model is closest to the actual fault exposure, especially for the identification of “short” faults and associated faults, which has obvious advantages over the traditional manual interpretation in terms of efficiency and accuracy.

Implement an Intrusion Detection System Utilizing Machine Learning and Principal Component Analysis

The ever-evolving cybersecurity sector requires robust intrusion detection systems (IDS). Traditional rules-based measures are no longer sufficient due to the complexity of cyber threats, requiring new approaches. This study presents the architecture of an intrusion detection system combining machine learning and principal component analysis (PCA) to increase network security. A network traffic classification system was built and tested on the NSL-KDD dataset and used PCA for dimensionality reduction. The results were cross-validated to reduce overfitting and ensure generalizability of the model. Low-variance precision refers to the consistency of the cross-validation fold. The combination of PCA and machine learning models exceeds previous studies with an F1 score for the random forest model of over 99%. The study improves intrusion detection and network protection against cyber-attacks.

Coronary Heart Disease Prediction: A Novel Deep Learning-based Approach

Coronary Heart Disease (CHD) is reported to be one of the world’s deadliest diseases. Early prediction or detection and diagnosis of the disease can help prevent, cure, and reduce the damage it could cause. Artificial intelligent techniques such as Machine Learning and Deep Learning have proven useful for the early detection and prediction of disease. However, issues of irrelevant and redundant features in open datasets have contributed to low classification accuracy rates and high misclassification rates. This leaves a gap for continuous approaches for smart feature selection and high-performing models in the field for better results. This study investigates the impacts of cardiologist-inspired datasets and PCA dimensionality reduction techniques on the performance of CHD prediction. The compared results show that while PCA improves the CHD prediction accuracy for datasets obtained from cardiologists, there exist no statistically significant differences in the efficacy of the PCA method for CHD classification when applied to open datasets, however, MLP and LSTM offer promising results. The results further indicated that the effectiveness of expert-based feature selection techniques on CHD classification is relatively stable when compared with open-source datasets.

Analisis DistilBERT dengan Support Vector Machine (SVM) untuk Klasifikasi Ujaran Kebencian pada Sosial Media Twitter

Hate speech is a significant issue in content management on social media platforms. Effective classification of hate speech plays a crucial role in maintaining a safe social media environment, combating discrimination, and protecting users. This study evaluates a hate speech classification model using SVM with linear and polynomial kernels. The dataset used consists of labeled Indonesian-language tweets. The importance of developing an effective classification model to address hate speech has led to the utilization of DistilBERT as a feature extraction method. However, DistilBERT has high-dimensional features, necessitating dimensionality reduction to reduce model complexity. Therefore, in this study, the PCA dimensionality reduction method is implemented with various scenarios of dimensionality, namely 10, 20, 30, 40, and 50. Evaluation is performed using F1-Score, and the entire study is evaluated using 10-fold cross-validation. The evaluation results indicate that in the scenario with a linear kernel, the model achieves the highest F1-Score of 0.75 in the 50-dimensional scenario. Meanwhile, in the scenario with a polynomial kernel, the model achieves the highest F1-Score of 0.7857 in the 50-dimensional scenario. These findings demonstrate that the use of a polynomial kernel with 50 dimensions yields the best performance in classifying hate speech.

Research on the Impact of Elective Subjects of College Entrance Examination on Talent Cultivation of Higher Vocational Colleges Based on Students’ Cognitive Behavioral Characteristics

Abstract The reform of the optional subjects of the college entrance examination under the new situation puts forward higher challenges to the talent cultivation of higher vocational colleges. In this paper, for students entering higher vocational colleges and universities after the new college entrance examination, the random forest framework is improved by mining students’ homework and quiz data with PCA dimensionality reduction. Behavioral feature extraction using Random Forest and coding definitions are proposed after feature screening. Based on general feature coding, the behavioral group differentiation extraction of higher education students was constructed, which finally provided label classification of students’ cognitive-behavioral features. After analyzing the impact of the optional subjects on the quality of higher vocational students, the learning behaviors of higher vocational students were dissected. The probability density distribution plots of students’ entry rates show that the overall probability density distribution curves of entry for different students are significantly different. The wave heights of the first, second, third and fourth academic years were 10.25, 16.48, 13.24 and 12.61, respectively, and the study behavior of senior students after the selection of subjects in the college entrance examination showed a unique pattern. In response to the behavioral performance of higher vocational students, institutions need to scientifically use assessment reports to launch talent development work.

Vegetable and fruit freshness detection based on deep features and principal component analysis

Vegetable and fruit freshness detecting can ensure that consumers get vegetables and fruits with good taste and rich nutrition, improve the health level of diet, and ensure that the agricultural and food industries provide high-quality products to meet consumer needs and increase sales and market share. At present, the freshness detection of vegetables and fruits mainly relies on manual observation and judgment, which has the problems of subjectivity and low accuracy, and it is difficult to meet the needs of large-scale, high-efficiency, and rapid detection. Although some studies have shown that large-scale detection of vegetable and fruit freshness can be carried out based on artificially extracted features, there is still the problem of poor adaptability of artificially extracted features, which leads to low efficiency of freshness detection. To solve this problem, this paper proposes a novel method for detecting the freshness of vegetables and fruits more objectively, accurately and efficiently using deep features extracted by pre-trained deep learning models of different architectures. First, resized images of vegetables and fruits are fed into a pre-trained deep learning model for deep feature extraction. Then, the deep features are fused and the fused deep features are dimensionally reduced to a representative low-dimensional feature space by principal component analysis. Finally, vegetable and fruit freshness are detected by three machine learning methods. The experimental results show that combining the deep features extracted by the three architecture pre-trained deep learning models GoogLeNet, DenseNet-201 and ResNeXt-101 combined with PCA dimensionality reduction processing has achieved the highest accuracy rate of 96.98% for vegetable and fruit freshness detection. This research concluded that the proposed method is promising to improve the efficiency of freshness detection of vegetables and fruits.

Classification and Identification of Contaminants in Recyclable Containers Based on a Recursive Feature Elimination-Light Gradient Boosting Machine Algorithm Using an Electronic Nose.

Establishing an excellent recycling mechanism for containers is of great importance for environmental protection, so many technical approaches applied during the whole recycling stage have become popular research issues. Among them, classification is considered a key step, but this work is mostly achieved manually in practical applications. Due to the influence of human subjectivity, the classification accuracy often varies significantly. In order to overcome this shortcoming, this paper proposes an identification method based on a Recursive Feature Elimination-Light Gradient Boosting Machine (RFE-LightGBM) algorithm using electronic nose. Firstly, odor features were extracted, and feature datasets were then constructed based on the response data of the electronic nose to the detected gases. Afterwards, a principal component analysis (PCA) and the RFE-LightGBM algorithm were applied to reduce the dimensionality of the feature datasets, and the differences between these two methods were analyzed, respectively. Finally, the differences in the classification accuracies on the three datasets (the original feature dataset, PCA dimensionality reduction dataset, and RFE-LightGBM dimensionality reduction dataset) were discussed. The results showed that the highest classification accuracy of 95% could be obtained by using the RFE-LightGBM algorithm in the classification stage of recyclable containers, compared to the original feature dataset (88.38%) and PCA dimensionality reduction dataset (92.02%).

Covert fault detection with imbalanced data using an improved autoencoder for optical networks

Covert faults are characterized by the performance parameters falling within the normal range, without any observable abnormalities. These types of faults pose a significant risk as they present no apparent warning signs of potential danger. Therefore, it is crucial to establish an efficient covert fault detection method to ensure the reliable and stable operation of optical networks. Data-driven technology, which reveals the internal relations and data patterns between the historical data by mining and analyzing the historical data, offers a new perspective for covert fault detection. However, equipment failures are extremely rare in real optical network systems, and the data imbalance of covert fault samples poses a challenge for standard machine learning classifiers in learning precise decision boundaries. To address this challenge, we propose a fault detection scheme based on an improved autoencoder for covert fault detection under data imbalance. The designed covert fault detection model exclusively utilizes normal samples during training and remains unaffected by data imbalance. Specifically, the model is specifically designed according to a number of encoder and decoder components to learn the normal sample data patterns in the latent space and detect covert faults based on the reconstruction errors in that space. To validate the proposed scheme, we conducted experiments using actual backbone data. According to the results, the detection accuracy and F1 score of the designed model on the test set were 0.9811 and 0.9527, and the false negative and false positive rates were 0.0026 and 0.0227, respectively. Furthermore, the visualization of the latent space reconstruction error principle for detecting covert faults was implemented using the principal component analysis dimension reduction and scatter plots.

Individual Tree Species Identification and Crown Parameters Extraction Based on Mask R-CNN: Assessing the Applicability of Unmanned Aerial Vehicle Optical Images

Automatic, efficient, and accurate individual tree species identification and crown parameters extraction is of great significance for biodiversity conservation and ecosystem function assessment. UAV multispectral data have the advantage of low cost and easy access, and hyperspectral data can finely characterize spatial and spectral features. As such, they have attracted extensive attention in the field of forest resource investigation, but their applicability for end-to-end individual tree species identification is unclear. Based on the Mask R-CNN instance segmentation model, this study utilized UAV hyperspectral images to generate spectral thinning data, spectral dimensionality reduction data, and simulated multispectral data, thereby evaluating the importance of high-resolution spectral information, the effectiveness of PCA dimensionality reduction processing of hyperspectral data, and the feasibility of multispectral data for individual tree identification. The results showed that the individual tree species identification accuracy of spectral thinning data was positively correlated with the number of bands, and full-band hyperspectral data were better than other hyperspectral thinning data and PCA dimensionality reduction data, with Precision, Recall, and F1-score of 0.785, 0.825, and 0.802, respectively. The simulated multispectral data are also effective in identifying individual tree species, among which the best result is realized through the combination of Green, Red, and NIR bands, with Precision, Recall, and F1-score of 0.797, 0.836, and 0.814, respectively. Furthermore, by using Green–Red–NIR data as input, the tree crown area and width are predicted with an RMSE of 3.16m2 and 0.51m, respectively, along with an rRMSE of 0.26 and 0.12. This study indicates that the Mask R-CNN model with UAV optical images is a novel solution for identifying individual tree species and extracting crown parameters, which can provide practical technical support for sustainable forest management and ecological diversity monitoring.

Effectiveness of principal component analysis in breast cancer diagnosis: An empirical and visualization analysis

With the development of computer science technology, it is increasingly common to apply computer science technology to the medical field, and one of the typical examples is to predict the probability of cancer cells recurrence in cancer patients. However, there is still a lack of unified explanation for the impact of the principal component analysis (PCA) dimensionality reduction method used in prediction on the final experimental results. Therefore, the research topic of this paper is the effect of PCA dimensionality reduction method on the accuracy of cancer prediction. The methodology in this paper is as follows: First, patient data were collected, based on a usable machine learning repositor. It has 569 instances, which contains both malignant and benign tumors. Second, exploratory data analysis is performed on the data, detecting outliers and discovering existing correlated data. Then, the comparative method is used to explore the effectiveness of PCA dimensionality reduction method on the prediction accuracy by observing and analyzing tables and images. It is found that the PCA dimensionality reduction method plays a positive role for boosting the prediction accuracy, but the PCA method does not significantly improve the prediction accuracy for the data that has been processed and the dimension is not high.

Evaluating Autoencoders as a Dimensionality Reduction Mechanism to Support Clustering Brazilian Agricultural Diversity

Brazilian agricultural production presents high spatial diversity, challenging the conception of public policies. This article proposes an approach for grouping Brazilian municipalities according to their agricultural production. We combine a feature extraction using autoencoders and clustering based on k-means and Self-Organizing Maps. We used panel data from IBGE’s annual estimates of the production value of permanent and temporary crops, animal products, aquaculture, plant extractivism, forestry, planted areas, and herd population between 1999 and 2018. We analyzed different structures of simple stacked and incomplete autoencoders, varying the number of layers and neurons in each, and evaluated the asymmetric exponential linear loss function to handle the sparse data. We applied the Isomap, Kernel PCA, Truncated SVD, and MDS dimensionality reduction methods for comparative purposes. Results showed that the autoencoders could extract characteristics from the transformed raw data to allow the clustering of municipalities to reveal regional and evenintra-regional patterns. The autoencoders improved comparative performance as the intrinsic dimensionality increased.

Detection of Fraudulent Health Insurance Claims Based on Decision Tree with Principal Component Analysis

Fraudulent health insurance claims pose a significant challenge to insurance companies and healthcare providers, leading to substantial financial losses and compromised service quality. In this study, we focused on detecting fraudulent health insurance claims using the decision tree algorithm and principal component analysis (PCA). The objective was to gain valuable insights and extract meaningful patterns from the dataset to enhance fraud detection capabilities. We developed a comprehensive method that employed the decision tree algorithm to build a decision tree-based model and the PCA for dimensionality reduction. By analyzing the data using these algorithms, we were able to capture important patterns and relationships within the dataset. The decision tree algorithm demonstrated reasonable performance, while the PCA exhibited even better results, leveraging the advantage of dimensionality reduction. The findings of this study have significant implications for fraud detection in the healthcare industry. The insights gained from applying the decision tree algorithms and PCA can aid in making informed decisions, identifying trends, and uncovering hidden patterns within the data. Our study recommends implementing advanced fraud detection systems that incorporate these algorithms, continuous monitoring and evaluation, collaboration and data sharing, further research and development, and adherence to regulatory compliance. By following these recommendations, stakeholders in the insurance and healthcare industries can strengthen their fraud detection capabilities, protect their organizations from financial losses, and maintain the integrity of their services. The use of decision tree algorithms and PCA, combined with effective strategies, can significantly contribute to the detection of fraudulent health insurance claims and the overall security and sustainability of the healthcare system.