Use Of Principal Component Analysis Research Articles

Biresponse nonparametric regression model in principal component analysis with truncated spline estimator

ObjectivesThis study aims to model data that contain two correlated responses, multicollinearity in predictors, and has a pattern that does not follow a parametric form. MethodsWe propose the use of principal component analysis of truncated splines in a biresponse model. The use of principal components to overcome correlations between predictors, and biresponse to overcome correlations between responses by involving weighted estimates from the covariance matrix. In the PCA spline contains the optimal knot points which control the accuracy of the regression curve. The knot point chosen is the point which has the smallest GCV value among all knot points. In addition, we also consider the value of MSE in showing the model's ability. ResultsWe demonstrated the ability of this method through simulation studies and obtained smaller GCV and MSE values compared to parametric regression and PCA. Furthermore, the data for type 2 diabetes mellitus, obtained two main components with different patterns of change. Based on the analysis, it was found that LDL cholesterol, total cholesterol, and triglycerides had a greater effect on changes in the pattern of fasting blood sugar and HbA1C. ConclusionsThe small errors of the simulation data indicate the accurate capabilities of the biresponse spline PCA model. The diabetes data analysis, it shows that patients need to pay attention to their cholesterol and triglyceride levels within normal limits.

Using machine learning algorithms to multidimensional analysis of subjective thermal comfort in a library

The thermal comfort in public buildings is affected by multiple psychological and physical factors. A deep understanding of these factors is necessary for intelligent ventilation control and architectural design. In this study, it was quantified the physiological and psychological parameters of the occupants in a library situated in Changsha using a questionnaire. Then, with the use of Principal Component Analysis (PCA), the dimensionality of the database was reduced. We found that the Zero-mean and unit variance projection is better than the Zero-mean projection, and 43-dimensional data can replace more than 90% of the original 61-dimensional data. Machine learning algorithms were used to analyze the results after PCA, which showed that the effect of thermal comfort on emotions is greater than that of emotions on thermal comfort. In addition, the performance of six machine learning algorithms (Linear Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Classification and Regression Trees (CART), Gaussian Naive Bayes (NB), and Support Vector Machine (SVM)) were compared. It is found that the predicted results of LDA were more accurate, and other algorithms showed different performances in different cases. These findings can contribute to the study of the subjective and objective feelings of indoor thermal comfort in public buildings, thereby guiding architectural design, intelligent control of ventilation systems, and realizing human-building interaction interfaces.

Data Complexity Based Evaluation of the Model Dependence of Brain MRI Images for Classification of Brain Tumor and Alzheimer’s Disease

The convolutional neural networks (CNN) have shown promising results for various classification problems over the past years. However, selecting various CNN architectures is still challenging as each architecture performs differently with the same dataset. This research aims to evaluate the dependence of brain MRI on various predictive models of CNN based on the complexity of the data for Brain Tumor and Alzheimer’s Disease. Our proposed approach has three parts. First part is the pre-processing of the data which mainly focuses on class balancing and the estimation of data complexity. The second part uses stratified k-fold cross-validation for more reliable results. The last part corresponds to the implementation of four CNN models applying described methods. This paper compares the classification performance of rigorous experimentation on four CNN variants namely S-CNN (CNN trained from scratch), ResNet50, InceptionV3, and Xception over two brain MRI image datasets evaluated with and without the use of Principal Component Analysis (PCA). The work benchmarks CNN models by comparing the average scores of Accuracy, Precision, Recall, F1 score, and AUC score from the stratified five-fold cross-validation.

Principal Component Analysis Based Hybrid Speckle Noise Reduction Technique for Medical Ultrasound Imaging

Ultrasound imaging is the safest and most widely used medical imaging technique available today. The main disadvantage of ultrasound imaging is the presence of speckle noise in its images that may obscure pathological changes in the body and makes diagnosis more challenging. Therefore, many techniques were proposed to reduce speckle and improve image quality. Unfortunately, variations of their performance with different scan parameters and due to their methodologies make it hard to choose which one to adopt in clinical practice. In this work, we consider the problem of combining the information from multiple speckle filters and propose the use of principal component analysis to find the optimal set of weights that would retain the most information and hence would better represent the data in the final image. The new technique is implemented to process ultrasound images collected from a research system and the outcomes are compared to the individual techniques and their average using quantitative image quality metrics. The proposed technique has potential for utilization in clinical settings to provide consistently better-quality combined images that may help improve diagnostic accuracy.

On the Use of Principal Components Analysis in Index Construction

On the Use of Principal Components Analysis in Index Construction

Застосування методу головних компонент в задачі аналізу спектрів вільних коливань

This article addresses the main methods used for analysis, distribution, and classification of input data space. The main aspects of applying these methods are analyzed and determined. In this study it is found that the method of the principal components analysis (PCA) is the most suitable. Possible principal components analysis algorithms are described. A combination of these algorithms is used to distribute the input data in the analysis of signals and their spectra obtained by non-destructive testing after applying a free oscillations method. The article aims to study the possibility of reducing the vector of informative features after applying principal components analysis without losing the quality of recognition of the state of objects. The objects of study may be components of electric motors (shunt magnetic conductor), parts of aircraft made of composite materials and other structures that require analysis by non-destructive testing methods. Spectra taken during non-destructive testing by the method of free oscillations of samples of carbon fiber panels from defective and defect-free zones were studied. The analysis of three, five and ten harmonics was conducted and the maximum number of the principal components and the values of maximum dispersions of these components for the input values set of amplitudes were determined. The Mahalanobis distance was used to analyze the quality of the distribution of the input data space into classes (defect-free and defect zones of the sample). The quality of the separation of values from the damaged and undamaged zones of the sample into two classes was improved. Therefore, the use of principal component analysis, in this study, can increase the reliability of the recognition of the state of objects.

Differentially private facial obfuscation via generative adversarial networks

From smartphones owned by the majority of teenage and adult populations to omnipresent closed-circuit television systems, the ubiquity of image-capturing devices in our everyday lives ensures that digital images of individuals are taken in the hundreds of millions on a daily basis. Many of these images capture individuals’ faces which, through facial recognition techniques, identify the individuals and thus represent a major privacy concern. Many countries and companies require facial obfuscation to conform to privacy laws or policies. Since images should be useful and look realistic, a trade-off arises between privacy and utility. The task is therefore to find a method of obfuscation that offers a formal privacy guarantee while preserving visual quality and maintaining facial attributes deemed acceptable for release (e.g., the pose of the head, gender, etc.). We address this task by proposing facial identity obfuscation through the application of differential privacy to image encodings in a generative adversarial network. We provide details on the design of the model architecture and training process that allow for the generation of photo-realistic obfuscated images. Through the use of principal component analysis, we control the application of noise to the model encodings in order to achieve a favourable trade-off between privacy and utility. We demonstrate the effectiveness of our approach through an experimental comparison against other methods of obfuscation which also offer a formal guarantee of privacy.

Optimized Tree Strategy with Principal Component Analysis Using Feature Selection-Based Classification for Newborn Infant's Jaundice Symptoms.

One of the most important and difficult research fields is newborn jaundice grading. The mitotic count is an important component in determining the severity of newborn jaundice. The use of principal component analysis (PCA) feature selection and an optimal tree strategy classifier to produce automatic mitotic detection in histopathology images and grading is given. This study makes use of real-time and benchmark datasets, as well as specific approaches for detecting jaundice in newborn newborns. According to research, the quality of the feature may have a negative impact on categorization performance. Additionally, compressing the classification method for exclusive main properties can result in a classification performance bottleneck. As a result, identifying appropriate characteristics for training the classifier is required. By combining a feature selection method with a classification model, this is possible. The major outcomes of this study revealed that image processing techniques are critical for predicting neonatal hyperbilirubinemia. Image processing is a method of translating analogue images to digital formats and manipulating them. The primary goal of medical image processing is to collect information useful for disease detection, diagnosis, monitoring, and therapy. Image datasets can be used to validate the performance of newborn jaundice detection. When compared to conventional approaches, it offers results that are accurate, quick, and time efficient. Accuracy, sensitivity, and specificity, which are common performance indicators, were also predictive.

Comparisons of Countermovement Jump Force-Time Characteristics Among National Collegiate Athletic Association Division I American Football Athletes: Use of Principal Component Analysis.

Merrigan, JJ, Rentz, LE, Hornsby, WG, Wagle, JP, Stone, JD, Smith, HT, Galster, SM, Joseph, M, and Hagen, JA. Comparisons of countermovement jump force-time characteristics among NCAA Division I American football athletes: use of principal component analysis. J Strength Cond Res 36(2): 411-419, 2022-This study aimed to reduce the dimensionality of countermovement jump (CMJ) force-time characteristics and evaluate differences among positional groups (skills, hybrid, linemen, and specialists) within National Collegiate Athletic Association (NCAA) division I American football. Eighty-two football athletes performed 2 maximal effort, no arm-swing, CMJs on force plates. The average absolute and relative (e.g., power/body mass) metrics were analyzed using analysis of variance and principal component analysis procedures (p < 0.05). Linemen had the heaviest body mass and produced greater absolute forces than hybrid and skills but had lower propulsive abilities demonstrated by longer propulsive phase durations and greater eccentric to concentric mean force ratios. Skills and hybrid produced the most relative concentric and eccentric forces and power, as well as modified reactive strength indexes (RSIMOD). Skills (46.7 ± 4.6 cm) achieved the highest jump height compared with hybrid (42.8 ± 5.5 cm), specialists (38.7 ± 4.0 cm), and linemen (34.1 ± 5.3 cm). Four principal components explained 89.5% of the variance in force-time metrics. Dimensions were described as the (a) explosive transferability to concentric power (RSIMOD, concentric power, and eccentric to concentric forces) (b) powerful eccentric loading (eccentric power and velocity), (c) countermovement strategy (depth and duration), and (d) jump height and power. The many positional differences in CMJ force-time characteristics may inform strength and conditioning program designs tailored to each position and identify important explanatory metrics to routinely monitor by position. The overwhelming number of force-time metrics to select from may be reduced using principal component analysis methods, although practitioners should still consider the various metric's applicability and reliability.

Probabilistic Assessment of Power Systems with Renewable Energy Sources based on an Improved Analytical Approach

The increasing penetration of renewable energy sources has introduced great uncertainties and challenges into computation and analysis of electric power systems. To deal with uncertainties, probabilistic approaches need to be used. In this paper, we propose a new framework for probabilistic assessment of power systems taking into account uncertainties from input random variables such as load demands and renewable energy sources. It is based on the cumulant-based Probabilistic Power Flow (PPF) in combination with an improved clustering technique. The improved clustering technique is also developed in this study by making use of Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO) to reduce the range of variation in the input data, thus increasing the accuracy of the traditional cumulant-based PPF (TCPPF) method. In addition, thanks to adopting PCA for dimensionality reduction, the improved clustering technique can be effectively dealt with a large number of input random variables so that the proposed framework for probabilistic assessment can be applied for large power systems. The IEEE-118 bus test system is modified by adding five wind and eight solar photovoltaic power plants to examine the proposed method. Uncertainties from input random variables are represented by appropriate probabilistic models. Extensive testing on the test system indicates good performance of the proposed approach in comparison to the traditional cumulant-based PPF and Monte Carlo simulation. The IEEE-118 bus test system is modified by adding five wind and eight solar photovoltaic power plants to examine the proposed method. Extensive testing on the test system, using Matlab (R2015a) on an Intel Core i5 CPU 2.53 GHz/4.00 GB RAM PC, indicates good performance of the proposed approach (PPPF) in comparison to the TCPPF and Monte Carlo simulation (MCS): In terms of computation time, PPPF needs 4.54 seconds, while TCPPF and MCS require 2.63 and 251 seconds, respectively; ARMS errors are calculated for methods using benchmark MCS and their values clearly demonstrate the higher accuracy of PPPF in estimating probability distributions compared to TCPPF, i.e., the maximum (Max) and mean (Mean) values of ARMS errors of all output random variables are: ARMSPPPFmax = 0.11%, ARMSTCPPFmax = 0.55%, and ARMSPPPFmean = 0.06%, ARMSTCPPFmean = 0.35%.

On the use of principal component analysis in the entropy based detection of denial‐of‐service attacks

AbstractThis paper investigates some possibilities for the use of the principal component analysis (PCA) algorithm in the detection of denial‐of‐service (DoS) attacks. Two simple traffic features that are widely used for the detection of DoS attacks are source and destination ports of packets. In this paper, we show that by using only the principal component of these two features as the input for detection a powerful detector can be built. Shannon entropy formula and two generalized entropy formulas have been used in combination with PCA. The input dataset is generated in an emulation testbed. The type of attack that is investigated is SYN flood.

Data‐driven virtual sensing and dynamic strain estimation for fatigue analysis of offshore wind turbine using principal component analysis

AbstractVirtual sensing enables estimation of stress in unmeasured locations of a system using a system model, physical sensors and a process model. The system model holds the relationship between the physical sensors and the desired stress response. A process model processes both the physical sensors and the system model to synthesise virtual sensors that ‘measure’ the desired stress response. Thus, virtual sensing enables mapping between the physical sensors (input) and the desired stress response (output). The system model is a mathematical model of the system based on knowledge or data of the system. Here, the data‐driven system model is constructed directly on data analyses for the specific system. In this paper, supervised learning and data‐driven system models are applied to strain estimation of an offshore wind turbine in the dynamic range through a novel use of principal component analysis (PCA); 40 min of training data is used to establish the data‐driven system model that can estimate the dynamic strain response with high precision for 2 months, while the estimated fatigue damage averaged out to −1.76% of the measured strain response.

Principal component analysis of groundwater sources pollution in Omu-Aran Community, Nigeria

Most developing countries rely on water sources that are usually not protected making them unsafe for drinking. It is imperative to ensure regular assessment and proper monitoring to evaluate their quality and ensure they meet standards before use. This study was aimed at identifying pollution sources of groundwater in the study area of Omu-Aran and assessing the wáter quality under varying temporal conditions. Ninety-six groundwater samples were collected from eight locations during the dry and wet seasons of 2019–2020. These samples were examined for water quality parameters (n = 10) using standard methods. The study adopted the use of principal component analysis (PCA), water quality index (WQI) and independent sample t test to analyze water pollution sources, fully assess water quality and examine temporal variations in the sampling stations respectively. The mean values for measured parameters all fall within the Nigerian Standard Drinking Water Quality guideline values with the exception of pH, nitrite, dissolved oxygen and T. coliform. This pollution was attributed to sewage pollution arising from anthropogenic sources. Water quality decreased during rainy season as compared to the dry season with significant differences (P < 0.05) between these periods except for pH, total hardness and fluoride. WQI ranged from 28.17 to 108.15 which lies on the “good” to “unsuitable for drinking” spectrum. Three latent factors were extracted for both the wet and dry seasons from measured parameters by means of PCA. They explain natural pollution and soil erosion phenomenom due to seasonal changes while organic matter oxidation and mineral dissolution are also identified as factors that affect the water quality in the study area. In conclusion, the study has been able to integrate the use of PCA and WQI to analyse recorded data for pollution source identification and water quality interpretation in the study area. Regular assessment and proper monitoring to evaluate the quality of these sources should be done in order to ensure they meet standards before use. Users should be encouraged to carry out disinfection and ensure their water sources are protected and not left exposed.

Analysis of Some Factors Affecting the Growth of Castor Shrub and Suitability of its Seed Oil in Industrial Application

Some concern had been shown regarding the limited availability of castor seed to satisfy the rising yearning for its seed oil for use in industrial and domestic applications. This growing demand calls for refocus on backward integration in order to ensure sustained supply chain. This study adopts a factorial analysis that involves the use of Principal Component Analysis (PCA) and Kendall’s Coefficient of Concordance (KCC) as statistical procedures to analyze some critical factors affecting the growth of castor shrub and its seed. KCC analyzed the degree of agreement among the fifteen Judges who ranked the thirty-two identified variables affecting the growth of castor shrub and the suitability of its seed oil in industrial application in descending order of importance. The result of the KCC showed an index of concordance in ranking as indicating 61% agreement among the 15 judges. The PCA helped to analyze the Judges responses arranged in form of data matrix that was facilitated by the use of statistiXL software. The PCA result revealed significant parsimony in data reduction from thirty-two to four principal factors creatively labeled: Seed oil particularities, Resource Conversion Efficiency, Plant-cooperation-oriented yield and Soil Condition respectively. The implication of this is that the principal factors that influence the growth of castor shrub and the suitability of its seed oil in industrial application has been identified.

Geographically weighted principal component analysis for characterising the spatial heterogeneity and connectivity of soil heavy metals in Kumasi, Ghana

The use of principal component analysis (PCA) for soil heavy metals characterization provides useful information for decision making and policies regarding the potential sources of soil contamination. However, the concentration of heavy metal pollutants is spatially heterogeneous. Accounting for such spatial heterogeneity in soil heavy metal pollutants will improve our understanding with respect to the distribution of the most influential soil heavy metal pollutants. In this study, geographically weighted principal component analysis (GWPCA) was used to describe the spatial heterogeneity and connectivity of soil heavy metals in Kumasi, Ghana. The results from the conventional PCA revealed that three principal components cumulatively accounted for 86% of the total variation in the soil heavy metals in the study area. These components were largely dominated by Fe and Zn. The results from the GWPCA showed that the soil heavy metals are spatially heterogeneous and that the use of PCA disregards this considerable variation. This spatial heterogeneity was confirmed by the spatial maps constructed from the geographically weighted correlations among the variables. After accounting for the spatial heterogeneity, the proportion of variance explained by the three geographically weighted principal components ranged between 85% and 89%. The first three identified GWPC were largely dominated by Fe, Zn and As, respectively. The location of the study area where these variables are dominated provides information for remediation.

An analysis of electricity congestion price patterns in North America

The present paper illustrates the use of principal component analysis (PCA) on the congestion component of local (i.e. zonal) electricity price data to detect the most salient congestion patterns in electricity transmission grids managed by either a Regional Transmission Organization (RTO) or an Independent System Operator (ISO). Outputs from the PCA along with some data visualization tools are shown to make the identification of such patterns seamless and straightforward. An empirical analysis is conducted for three North American power systems, namely NYISO, ISO New England and PJM. Finally, a simple time series model representing the evolution of PCA scores is proposed.

Monetary policy and market interest rates: literature review using text analysis

PurposeThis paper aims to examine the relationship between monetary policy and market interest rates. This paper examines the efficiency of interest rate channel used in monetary regulation as well as implementation of monetary policy under low interest rates. This paper examines and reviews the scientific literature published over the past 30 years to determine primary research areas, to summarize their results and to identify appropriate measures of monetary policy to be used in practice in changing economic environment.Design/methodology/approachThis paper reviews 94 studies focused on the relationship between monetary policy and market interest rates in terms of meeting the goals of macroeconomic regulation. The articles are selected on the basis of Scopus citation and bibliometric analysis. A major feature of this paper is the use of text analysis (data preparation, frequency of terms and collocations use, examination of relationships between terms, use of principal component analysis to determine research thematic areas). Using the method of principal component analysis while studying abstracts this paper reveals thematic areas of the research. Thus, the conducted text analysis provides unbiased results.FindingsFirst, this paper examines the whole complex of relationships between monetary policy of central banks and market interest rates. Second, this research reviews a wide range of literature including recent studies focused on specific features of monetary policy under low and negative rates. Third, this study identifies and summarizes the thematic areas of all the researches using text analysis (transmission mechanism of monetary policy, efficiency of zero interest rate policy, monetary policy and term structure of interest rates, monetary policy and interest rate risk of banks, monetary policy of central banks and financial stability). Finally, this paper presents the most important findings of the studied articles related to the current situation and trends on the financial market as well as further research opportunities. This paper finds the principal results of studies on significant issues of monetary policy in terms of its efficiency under low interest rates, influence of its instruments on term structure of interest rates and role of banking sector in implementation of transmission mechanism of monetary policy.Research limitations/implicationsThe limitation of the review is examining articles for the study period of 30 years.Practical implicationsCentral banks of emerging economies should apply the instruments and results of the countries' monetary policies reviewed in this paper. Using text analysis this paper reveals the main thematic areas and summarizes findings of the articles under study. The analysis allows presenting the main ideas related to current economic situation.Social implicationsThe findings are of great value for adjusting the monetary policy of central banks. Also, these are important for people because these show the significant role of monetary policy for the economic growth.Originality/valueUsing text analysis this paper reveals the main thematic areas (transmission mechanism of monetary policy, efficiency of zero interest rate policy, monetary policy and term structure of interest rates, monetary policy and interest rate risk of banks, monetary policy of central banks and financial stability) and summarizes findings of the articles under study. The analysis allows defining the current ideas relevant to the monetary policy of developing countries. It is important for central banks because it examines the monetary policy problems and proposes optimal solutions.

Qualitative and Quantitative Assessment of the SET HASDM Database

AbstractThe High Accuracy Satellite Drag Model (HASDM) is the operational thermospheric density model used by the US Space Force Combined Space Operations Center. By using real‐time data assimilation, HASDM can provide density estimates with increased accuracy over other empirical models. With historical HASDM density data being released publicly for the first time, we can analyze the data to compare dominant modes of variability in the upper atmosphere as modeled by HASDM and the Jacchia‐Bowman 2008 Empirical Thermospheric Density Model (JB2008), a Jacchia family model upon which density corrections are made as a part of the HASDM framework. This model comparison is conducted through the use of principal component analysis (PCA) which shows the increased variability of the HASDM dataset. We highlight HASDM's ability to capture the movement of lighter species during solar minimum conditions, unlike many empirical models. We then compare density from both models to the CHAllenging Minisatellite Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE) accelerometer‐derived density estimates. This comparison shows that HASDM more closely matches the accelerometer‐derived densities with mean absolute differences of compared to CHAMP and GRACE‐A, respectively. The comparison also reveals improved representation of cooling mechanisms due to NO and CO2 by the HASDM database.

Impact of Macroeconomic Factors on the Market Performance of Indonesia Sharia Stocks

This paper presents a study on the impact of macroeconomic factors on the market performance of Indonesia sharia stocks that is shown by the JII. The studied macroeconomic factors are the regional and global market indexes, oil price, the FED rate, and the US$ exchange rate. Regional markets considered in this paper are Singapore and Malaysia. On the other hand, the global markets considered in this paper are Hong Kong, Japan, and the United States of America. For this purpose, we have used global data from the period of 2009-2019. This paper's main contribution is the use of principal component analysis (PCA) to reduce the independent variables. Based on the PCA, it is found that the macroeconomic factors can be simplified into two factors, namely regional and global factors. Moreover, it is found that the oil price, the FED rate, and the US$ exchange rate have no effect on the sharia stock performance. Based on the regression analysis, it is found that these two factors have a positive impact with a significant level of 24.2%. It is also found that the global factor has a greater effect than the regional factor.

Economic and Financial Sustainability Dependency on Subsidies: The Case of Goat Farms in Greece

Goat farming is an important production sector not only for Greece, but also for other Mediterranean countries, as it contributes to the family economy in rural areas. Despite the importance of goat farming, this sector has experienced economic difficulties due to poor management and increased production costs. The aim of our research is to determine goat farm profitability by surveying goat farmers for revenues, variable costs, and fixed costs of their farms. With the use of Principal Component Analysis, all economic factors contributing to overall production costs are examined, as well as their specific impacts on cost formulation. According to our results, goat farms in Greece are not profitable and they cannot survive without government subsidies. Farm economics and agricultural policies could be leveraged to improve community and environmental outcomes in order for farms to be economically and financially sustainable.