Principal Component Analysis Dimensionality Reduction Research Articles

Enhancing Skin Cancer Diagnosis through the Integration of Deep Learning and Machine Learning Approaches

Skin cancer is a disease characterized by the uncontrolled proliferation of skin cells, typically manifesting as lesions or abnormal growths. Early diagnosis is critical for improving treatment outcomes. This study proposes an innovative approach to skin cancer diagnosis by integrating modern deep learning models with traditional machine learning algorithms. A three-phase methodology was developed. In the first phase, meaningful features were extracted from skin lesion images using various transfer learning models, including Xception, VGG16, ResNet152V2, InceptionV3, InceptionResNetV2, MobileNetV2, EfficientNetB2, and DenseNet201. In the second phase, dimensionality reduction was performed using Principal Component Analysis (PCA). In the final phase, the reduced feature sets were classified using K-Nearest Neighbors (KNN) and Random Forest (RF) algorithms. Experimental results demonstrated that the highest accuracy of 91.28% was achieved through the combination of DenseNet201 for feature extraction, PCA for dimensionality reduction, and Random Forest for classification. These findings highlight the effectiveness of integrating transfer learning models, dimensionality reduction techniques, and machine learning algorithms in enhancing the accuracy of skin cancer diagnosis.

Research on flood warning model based on dimensionality reduction and integrated neural network

Flood disasters have long-term impacts on human societies and ecosystems, and population growth and urbanization lead to changes in surface conditions that increase the frequency and magnitude of floods. Therefore, understanding and predicting flood probability and constructing a flood warning mechanism are urgent problems. In this paper, a flood warning mechanism based on the probability of flood occurrence is proposed to construct a high-performance and highly interpretable model in a step-by-step manner using machine learning and deep learning methods. First, the correlation level is elucidated by the PCA dimensionality reduction method. Secondly, the K-means clustering method is used to replace the qcut binning method, and the classification model is built by combining the multidimensional data point distribution and classified by polynomial logistic regression. Finally, the integrated neural network model is constructed, the visual model performance is used for full feature optimization, and the performance of the classification model is optimized to achieve the flood prediction effect through the mean square error as the combination of the weights of the integrated model.

A cardiac audio classification method based on image expression of multidimensional features.

Heart sound auscultation plays a crucial role in the early diagnosis of cardiovascular diseases. In recent years, great achievements have been made in the automatic classification of heart sounds, but most methods are based on segmentation features and traditional classifiers and do not fully exploit existing deep networks. This paper proposes a cardiac audio classification method based on image expression of multidimensional features (CACIEMDF). First, a 102-dimensional feature vector is designed by combining the characteristics of heart sound data in the time domain, frequency domain and statistical domain. Based on the feature vector, a two-dimensional feature projection space is constructed by PCA dimensionality reduction and the convex hull algorithm, and 102 pairs of coordinate representations of the feature vector in the two-dimensional space are calculated. Each one-dimensional component of the feature vector corresponds to a pair of 2D coordinate representations. Finally, the one-dimensional feature component value and its divergence into categories are used to fill the three channels of a color image, and a Gaussian model is used to dye the image to enrich its content. The color image is sent to a deep network such as ResNet50 for classification. In this paper, three public heart sound datasets are fused, and experiments are conducted using the above methods. The results show that for the two-classification/five-classification task of heart sounds, the method in this paper can achieve a classification accuracy of 95.68%/94.53% when combined with the current deep network.

Research on wind turbine icing prediction data processing and accuracy of machine learning algorithm

Studying the icing problem of wind turbine blades is crucial for optimizing wind farm operation and maintenance. Traditional machine learning algorithms like Random Forest and Support Vector Machines have limitations in handling complex time series data and capturing long-term dependencies, leading to insufficient accuracy and generalization. To address these gaps, this paper proposes a comprehensive approach involving advanced machine learning frameworks and feature engineering techniques. This paper based on the original SACDA dataset, four distinct types of processing are performed on the prediction data via PCA dimensionality reduction and the introduction of new features; meanwhile, the four types mentioned above of datasets are trained and predicted using the Gated Recycling Unit model (GRU), Random Forest (RF), GA-BP Neural Network (BP), and Extreme Learning Machine (ELM) models. The results indicate that the prediction accuracies of all four types of models are more satisfactory, with the RF model having the highest prediction accuracy and the overall accuracy remaining above 99 percent, the dataset + RF model after dimensionality reduction of the original sensitive features having the highest prediction accuracy and speed.

Optimization of multidimensional feature engineering and data partitioning strategies in heart disease prediction models

The relentless rise in heart disease incidence, a leading global cause of death, presents a significant public health challenge. Precise prediction of heart disease risk and early interventions are crucial. This study investigates the performance improvement of heart disease prediction models using machine learning and deep learning algorithms. Initially, we utilized the Heart Failure Prediction Dataset from Kaggle. After preprocessing to ensure data quality, three distinct feature engineering techniques were applied: PCA for dimensionality reduction, ET for feature selection, and Pearson's correlation coefficient for feature selection. We assessed their impact on model performance. The dataset was then partitioned into three different data split ratios—1:9, 2:8, and 3:7—to determine their specific effects on model performance. Twelve machine learning classifiers—LGBM, Adaboost, XGB, RF, DT, KNN, LR, GNB, ET, SVC, GB, and Bagging—were trained and evaluated based on five key metrics: accuracy, recall, precision, F1 score, and training time. The influence of different feature engineering methods and data partitioning ratios on model performance were systematically analyzed using paired-sample t-tests. Among the feature engineering methods compared, the Bagging classifier, when combined with feature selection via ET, exhibited superior performance. It achieved an accuracy of 97.48 % and an F1-Score of 97.48 % with a data split ratio of 1:9 between the test and training sets. With a 2:8 split, the accuracy was 94.96 % and the F1-Score was 94.95 %. For a 3:7 split, the accuracy was 94.12 % and the F1-Score was 94.11 %. Paired sample T-test results indicate that feature selection using Pearson correlation coefficient can shorten training duration, but this also leads to a decline in classifier performance. After applying PCA dimensionality reduction, compared with the control group, there was no significant difference in the training efficiency and efficacy of the classifier. However, feature selection through ET significantly reduced the training time for various classifiers while ensuring their performance.

Identification of psoriasis-associated immune marker G3BP2 through single-cell RNA sequencing and meta analysis.

Psoriasis is a chronic skin disease with an increasing prevalence each year. However, the mechanisms underlying its onset and progression remain unclear, and effective therapeutic targets are lacking. Therefore, we employs an innovative approach by combining single-cell RNA sequencing (scRNA-seq) with meta-analysis. This not only elucidates the potential mechanisms of psoriasis at the cellular level but also identifies immunoregulatory marker genes that play a statistically significant role in driving psoriasis progression through comprehensive analysis of multiple datasets. Skin tissue samples from 12 psoriasis patients underwent scRNA-seq, followed by quality control, filtering, PCA dimensionality reduction, and tSNE clustering analysis to identify T cell subtypes and differentially expressed genes (DEGs) in psoriatic skin tissue. Next, three psoriasis datasets were standardised and merged to identify differentially expressed genes (DEGs). Subsequently, weighted gene co-expression network analysis (WGCNA) was applied for clustering analysis of gene co-expression network modules and to assess the correlation between these modules and DEGs. Least absolute shrinkage and selection operator (LASSO) regression and receiver operating characteristic (ROC) curve analyses were conducted to select disease-specific genes and evaluate their diagnostic value. Single-cell data revealed nine cell types in psoriatic skin tissue, with seven T cell subtypes identified. Intersection analysis identified ADAM8 and G3BP2 as key genes. Through the integration of scRNA-seq and Meta analysis, we identified the immunoregulatory marker gene G3BP2, which is associated with the onset and progression of psoriasis and holds clinical significance. G3BP2 is speculated to promote the development of psoriasis by increasing the proportion of CD8+ T cells.

Chemometric Classification of Motor Oils Using 1H NMR Spectroscopy With Simultaneous Phase and Baseline Optimization

ABSTRACTHere, we demonstrate mid‐field 1H NMR spectroscopy combined with chemometrics to be powerful in the classification and authentication of motor oils (MOs). The 1H NMR data were processed with a new algorithm for simultaneous phase and baseline correction, which, for crowded spectra such as those of the refinery products, allowed for more accurate estimation of phase parameters than other literature approaches tested. A principal component analysis (PCA) model based on the unbinned CH3 fingerprint region (0.6–1.0 ppm) enabled the differentiation of hydrocracked and poly‐α‐olefin‐based MOs and was effective in resolving mixtures of these base stocks with conventional base oils. PCA analysis of the 1.0‐ to 1.14‐ppm region enabled the detection of poly (isobutylene) additive and was useful for differentiating between single‐grade and multigrade MOs. Non‐equidistantly binned 1H NMR data were used to detect the addition of esters and to establish discriminant models for classifying MOs by viscosity grade and by major categories of synthetic, semisynthetic, and mineral oils. The performances of four classifiers (linear discriminant analysis [LDA], quadratic discriminant analysis [QDA], naïve Bayes classifier [NBC], and support vector machine [SVM]) with and without PCA dimensionality reduction were compared. In both tasks, SVM showed the best efficiency, with average error rates of ~2.3% and 8.15% for predicting major MO categories and viscosity grades, respectively. The potential to merge spectra collected from different NMR instruments is discussed for models based on spectral binning. It is also shown that small errors in phase parameters are not detrimental to binning‐based PCA models.

A Comprehensive Study on the Role of Momentum in the Sports Games

This study aims to explore the effects of momentum and swing on tennis match results. The paper believes that momentum may be a key factor affecting player performance and match results. The research methods include data preprocessing, PCA dimensionality reduction, Pearson correlation coefficient analysis, logistic regression model and random forest model. Data preprocessing includes outlier treatment, one-hot encoding and normalization. PCA is used to reduce the dimension of match indicators and extract the main information. Pearson correlation coefficient analysis is used to test the correlation between match results and momentum. Logistic regression model is used to predict the probability of a player winning a point, and random forest model is used to predict match fluctuations. The study uses data from the 2023 US Open to validate the model. Sensitivity analysis is performed by changing model parameters to ensure model accuracy. The conclusion shows that momentum does affect match results, and our model can predict match performance and fluctuations with high accuracy. This is of great significance for coaches and athletes to formulate strategies and improve match performance. This study is of great value in understanding the momentum effect in tennis and how to use this knowledge to improve athlete performance.

Identification of Escherichia coli strains using MALDI-TOF MS combined with long short-term memory neural networks.

The current study aims to develop a new technique for the precise identification of Escherichia coli strains, utilizing matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) combined with a long short-term memory (LSTM) neural network. A total of 48 Escherichia coli strains were isolated and cultured on tryptic soy agar medium for 24 hours for the generation of MALDI-TOF MS spectra. Eight hundred MALDI-TOF MS spectra were obtained per strain, resulting in a database of 38,400 spectra. Fifty percent of the data was utilized for LSTM neural network training, with fine-tuned parameters for strain-level identification. The other half served as the test set to assess model performance. Traditional PCA dimension reduction of MALDI-TOF MS spectra indicated 47 out of 48 strains to be unclassifiable. In contrast, the LSTM neural network demonstrated remarkable efficacy. After 20 training epochs, the model achieved a loss value of 0.0524, an accuracy of 0.999, a precision of 0.985, and a recall of 0.982. When tested on the unseen data, the model attained an overall accuracy of 92.24%. The integration of MALDI-TOF MS and LSTM neural network markedly enhances the identification of Escherichia coli strains. This innovative approach offers an effective and accurate tool for MALDI-TOF MS-based strain-level identification, thus expanding the analytical capabilities of microbial diagnostics.

Logistic regression for cardiovascular diseases prediction by integrating PCA and K-means ++

This research introduces a novel method for forecasting cardiovascular diseases using an advanced combination of K-means++ clustering, Principal Component Analysis (PCA), and Logistic Regression techniques. Given the global impact of cardiovascular diseases as a primary cause of death, this research utilizes a comprehensive dataset to tackle the prediction challenges associated with CVDs. Initially employing K-means++ for enhanced data quality, followed by PCA for dimensionality reduction, the study applies Logistic Regression for outcome prediction, achieving remarkable accuracy, specificity, and sensitivity. This methodological rigor offers a promising avenue for early and accurate CVDs detection, significantly outperforming traditional predictive models. By refining data through these steps, the study ensures the predictive model is built on a solid foundation, enhancing the reliability and generalizability of the predictions. The integration of these advanced analytical techniques marks a step forward in the pursuit of effective cardiovascular disease management, highlighting the importance of data preprocessing in predictive modeling.

Enhancing solar radiation predictions through COA optimized neural networks and PCA dimensionality reduction

The inherent variability and uncertainty of solar radiation, influenced by factors such as seasons, weather, and cloud cover, pose significant challenges in accurately forecasting energy output from solar power systems. Robust forecasting models that can capture the dynamic behavior of solar radiation are crucial for optimizing energy generation, distribution, and storage processes. This study proposes an integrated approach that combines neural networks, metaheuristic optimization algorithms, and principal component analysis (PCA) for precise solar radiation forecasting. The methodology uses one year of solar radiation data from November 2022 to October 2023 from Vellore district, India. PCA is employed for dimensionality reduction, mitigating the curse of dimensionality. The reduced dataset is segregated into winter, summer, and monsoon subsets to capture distinct seasonal patterns. Neural networks are trained on these seasonal datasets, with their weights optimized by metaheuristic algorithms, including particle swarm optimization (PSO), gray wolf optimization (GWO), whale optimization algorithm (WOA), and coati optimization algorithm (COA). A comprehensive comparative study evaluated the performance of ANN-PSO, ANFIS-GWO, ANN-WOA, ANN-COA, and traditional ANN models. The ANN-COA model, integrating ANN with COA for weight optimization and PCA for dimensionality reduction, achieved the most accurate forecasts, outperforming other approaches. It attained optimal RMSE of 12.06 for winter, 6.72 for summer, and 9.49 for monsoon season, MAE of 6.66, 5.50, and 6.25, MAPE of 3.58 %, 1.68 %, and 2.31 %, and R2 of 0.973, 0.982, and 0.985 for winter, summer, and monsoon seasons respectively, demonstrating its robustness and suitability for time-series solar radiation forecasting.

Construction and application of a novel WGAN-CNN-based predicting approach for dust concentration at underground coal mine working faces.

To enhance the real-time monitoring and early-warning capabilities for dust disasters in underground coal mine, this paper presents a novel WGAN-CNN-based prediction approach to predict the dust concentration at underground coal mine working faces. Dust concentration, wind speed, temperature, and methane concentration were collected as the original data due to their nonlinear relationship. The consistency between the generated and original data distributions was verified through PCA dimensionality reduction analysis. The predictive performance of this approach was assessed using five metrics (R2, EVS, MSE, RMSE, and MAE) and compared with three other algorithms (Random Forest Regressor, MLP Regressor, and LinearSVR). The findings indicate that a majority of the generated data falls within the distribution range of the real dataset, exhibiting reduced levels of volatility and dispersion. The R2 values of prediction results are all above 98%, and the MSE values are between 0.0007 and 0.0106. The proposed approach exhibits superior predictive accuracy and robust model generalization capabilities compared to alternative algorithms, thereby enhancing the real-time monitoring and early-warning level of dust disasters in underground coal mine. This will facilitate the realization of advanced prevention and control measures for dust disasters, showcasing a wide range of potential applications.

Enhancing Speaker Recognition Models with Noise-Resilient Feature Optimization Strategies

This paper delves into an in-depth exploration of speaker recognition methodologies, with a primary focus on three pivotal approaches: feature-level fusion, dimension reduction employing principal component analysis (PCA) and independent component analysis (ICA), and feature optimization through a genetic algorithm (GA) and the marine predator algorithm (MPA). This study conducts comprehensive experiments across diverse speech datasets characterized by varying noise levels and speaker counts. Impressively, the research yields exceptional results across different datasets and classifiers. For instance, on the TIMIT babble noise dataset (120 speakers), feature fusion achieves a remarkable speaker identification accuracy of 92.7%, while various feature optimization techniques combined with K nearest neighbor (KNN) and linear discriminant (LD) classifiers result in a speaker verification equal error rate (SV EER) of 0.7%. Notably, this study achieves a speaker identification accuracy of 93.5% and SV EER of 0.13% on the TIMIT babble noise dataset (630 speakers) using a KNN classifier with feature optimization. On the TIMIT white noise dataset (120 and 630 speakers), speaker identification accuracies of 93.3% and 83.5%, along with SV EER values of 0.58% and 0.13%, respectively, were attained utilizing PCA dimension reduction and feature optimization techniques (PCA-MPA) with KNN classifiers. Furthermore, on the voxceleb1 dataset, PCA-MPA feature optimization with KNN classifiers achieves a speaker identification accuracy of 95.2% and an SV EER of 1.8%. These findings underscore the significant enhancement in computational speed and speaker recognition performance facilitated by feature optimization strategies.

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

PurposeThis 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.MethodsClassification 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 = 0.856 mol/L. A 12-fold cross-validation trained the MLP (architecture 5-12-2 with 5 Principal Components).ResultsThe 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.ConclusionsThe 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.