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Extracting relevant data from real-world experiments is often challenging with intrinsic materials and device property dispersion, such as in organic electronics. However, multivariate data analysis can often be a mean to circumvent this and to extract more information when larger datasets are used with learning algorithms instead of physical models. Here, we report on identifying relevant information descriptors for organic electrochemical transistors (OECTs) to classify aqueous electrolytes by ionic composition. Applying periodical gate pulses at different voltage magnitudes, we extracted a reduced number of nonredundant descriptors from the rich drain-current dynamics, which provide enough information to cluster electrochemical data by principal component analysis between Ca2+-, K+-, and Na+-rich electrolytes. With six current values obtained at the appropriate time domain of the device charge/discharge transient, one can identify the cationic identity of a locally probed transient current with only a single micrometric device. Applied to OECT-based neural sensors, this analysis demonstrates the capability for a single nonselective device to retrieve the rich ionic identity of neural activity at the scale of each neuron individually when learning algorithms are applied to the device physics.

Image copy detection is an important technology of copyright protection. This paper proposes an efficient hashing method for image copy detection using 2D-2D (two-directional two-dimensional) PCA (Principal Component Analysis). The key is the discovery of the translation invariance of 2D-2D PCA. With the property of translation invariance, a novel model of extracting rotation-invariant low-dimensional features is designed by combining PCT (Polar Coordinate Transformation) and 2D-2D PCA. The PCT can convert an input rotated image to a translation matrix. Since the 2D-2D PCA is invariant to translation, the low-dimensional features learned from the translation matrix are rotation-invariant. Moreover, vector distances of low-dimensional features are stable to common digital operations and thus hash construction with the vector distances is of robustness and compactness. Three open image datasets are exploited to conduct various experiments for validating efficiencies of the proposed method. The results demonstrate that the proposed method is much better than some representative hashing methods in the performances of classification and copy detection.

Residential spatial differentiation is an important social issue for every large city to explore. The “subcultural” clusters formed by the subjective choices of a city’s residents divide the living spaces of the city and create residential differentiation. A study of this phenomenon would help to facilitate the allocation of urban resources and the formulation of relevant development strategies. In recent years, Hangzhou, one of the major developed cities in China, has seen a gradual increase in the proportion of its non-local population. Employing principal component analysis and K-means clustering, this study investigated the city’s spatial differentiation of living spaces at the community level and identified the social clustering areas. The Shannon-Wiener index and the index of dissimilarity were used to examine the degree of residential segregation in each administrative district. The results showed that the functional distinctions among the districts of Hangzhou were pronounced and had certain degrees of class solidification. A certain spatial coupling was observed between residential differentiation and housing prices. More reasonable allocations of social resources would help to ameliorate the negative effects caused by the spatial differentiation of living spaces.

Reading difficulties are prevalent worldwide, including in economically developed countries, and are associated with low academic achievement and unemployment. Longitudinal studies have identified several early childhood predictors of reading ability, but studies frequently lack genotype data that would enable testing of predictors with heritable influences. The National Child Development Study (NCDS) is a UK birth cohort study containing direct reading skill variables at every data collection wave from age 7 years through to adulthood with a subsample (final n = 6431) for whom modern genotype data are available. It is one of the longest running UK cohort studies for which genotyped data are currently available and is a rich dataset with excellent potential for future phenotypic and gene-by-environment interaction studies in reading. Here, we carry out imputation of the genotype data to the Haplotype Reference Panel, an updated reference panel that offers greater imputation quality. Guiding phenotype choice, we report a principal components analysis of nine reading variables, yielding a composite measure of reading ability in the genotyped sample. We include recommendations for use of composite scores and the most reliable variables for use during childhood when conducting longitudinal, genetically sensitive analyses of reading ability.

In the vehicle-following scenario of autonomous driving, the change of driving style in the front vehicle will directly affect the decision on the rear vehicle. In this paper, a strategy based on a probabilistic neural network (PNN) for front vehicle intention recognition is proposed, which enables the rear vehicle to obtain the driving intention of the front vehicle without communication between the two vehicles. First, real vehicle data with different intents are collected and time—frequency domain variables are extracted. Secondly, Principal Component Analysis (PCA) is performed on the variables in order to obtain comprehensive features. Meanwhile, two cases are classified according to whether the front vehicle can transmit data to the rear vehicle. Finally, two recognition models are trained separately according to a PNN algorithm, and the two models obtained from the training are verified separately. When the front vehicle can communicate with the rear vehicle, the recognition accuracy of the corresponding PNN model reaches 96.39% (simulation validation) and 95.08% (real vehicle validation). If it cannot, the recognition accuracy of the corresponding PNN model reaches 78.18% (simulation validation) and 73.74% (real vehicle validation).

We propose a new test for structural changes in large dimensional factor models via a discrete Fourier transform (DFT) approach. If structural changes occur, the conventional principal component analysis fails to estimate common factors and factor loadings consistently. The estimated residuals will contain information about structural changes. Therefore, we can compare the DFT of the estimated residuals with the null (zero) spectrum implied by no structural change. The proposed test is powerful against both smooth structural changes and abrupt structural breaks with a possibly unknown number of breaks and unknown break dates in factor loadings. It can detect a class of local alternatives at the parametric rate. As a result, the test is asymptotically more efficient than the existing tests in the factor model literature. And our test is also robust to serial and cross-sectional dependence of unknown form without having to estimate any long-run variance-covariance matrix. Moreover, it is easy to implement and tuning parameter-free. Monte Carlo studies demonstrate its reasonable size and excellent power in detecting various forms of structural changes in factor loadings. In an application to the U.S. macroeconomic data, we find significant and robust evidence of time-varying factor loadings.

High-resolution functional MRI (fMRI) is largely hindered by random thermal noise. Random matrix theory (RMT)-based principal component analysis (PCA) is promising to reduce such noise in fMRI data. However, there is no consensus about the optimal strategy and practice in implementation. In this work, we propose a comprehensive RMT-based denoising method that consists of 1) rank and noise estimation based on a set of newly derived multiple criteria, and 2) optimal singular value shrinkage, with each module explained and implemented based on the RMT. By incorporating the variance stabilizing approach, the denoising method can deal with low signal-to-noise ratio (SNR) (such as5) magnitude fMRI data with favorable performance compared to other state-of-the-art methods. Results from both simulation and in-vivo high-resolution fMRI data show that the proposed denoising method dramatically improves image restoration quality, promoting functional sensitivity at the same level of functional mapping blurring compared to existing denoising methods. Moreover, the denoising method can serve as a drop-in step in data preprocessing pipelines along with other procedures aimed at removal of structured physiological noises. We expect that the proposed denoising method will play an important role in leveraging high-quality, high-resolution task fMRI, which is desirable in many neuroscience and clinical applications.

Strain and strain rate are effective traumatic brain injury metrics. In finite element (FE) head model, thousands of elements were used to represent the spatial distribution of these metrics. Owing that these metrics are resulted from brain inertia, their spatial distribution can be represented in more concise pattern. Since head kinematic features and brain deformation vary largely across head impact types (Zhan et al., 2021), we applied principal component analysis (PCA) to find the spatial co-variation of injury metrics (maximum principal strain (MPS), MPS rate (MPSR) and MPS × MPSR) in four impact types: simulation, football, mixed martial arts and car crashes, and used the PCA to find patterns in these metrics and improve the machine learning head model (MLHM).We applied PCA to decompose the injury metrics for all impacts in each impact type, and investigate the spatial co-variation using the first principal component (PC1). Furthermore, we developed a MLHM to predict PC1 and then inverse-transform to predict for all brain elements. The accuracy, the model complexity and the size of training dataset of PCA-MLHM are compared with previous MLHM (Zhan et al., 2021).PC1 explained variance on the datasets. Based on PC1 coefficients, the corpus callosum and midbrain exhibit high variance on all datasets. Finally, the PCA-MLHM reduced model parameters by 74% with a similar MPS estimation accuracy.The brain injury metric in a dataset can be decomposed into mean components and PC1 with high explained variance.The spatial co-variation analysis enables better interpretation of the patterns in brain injury metrics. It also improves the efficiency of MLHM.

In the current context of population growth and climate change, it is essential to effectively manage groundwater resources, to improve their quality, and to determine the behaviour of certain contaminants. Groundwater quality can be worsened most often by anthropogenic factors but can also be altered by natural factors depending on the chemical signatures of water sources (i.e., hydrochemical reactions) as a result of mixing processes. In these cases, the use of mixing calculations and multivariate statistical analysis (MSA) methods is crucial for determining the reactions that occur, the origin and fate of the detected compounds, ions or parameters, and the behaviour of the system. Thus, these methods ascertain processes that affect the chemical composition (i.e., quality) of groundwater bodies, and this information is needed for designing groundwater management strategies that exploit aquifers in a sustainable way. However, these methods are rarely employed, as few investigations that consider them focus on urban aquifers. Here, mixing calculations and other MSA methods that consider major ions and environmental isotopes are utilized in an aquifer located in a rural area associated with the Niebla-Posadas aquifer, Spain, where groundwater quality has deteriorated due to geogenic factors. This study proves the usefulness of these methods for deriving essential information that is needed (1) to properly manage the exploitation of aquifers, (2) to avoid the deterioration of groundwater bodies, and (3) to identify the reasons behind poor groundwater quality., Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This study was supported by the “Agencia Estatal de Investigación” from the Spanish Ministry of Science and Innovation and the IDAEA-CSIC, a Centre of Excellence Severo Ochoa (CEX2018-000794-S). EP gratefully acknowledges the support received through the grant RYC2020-029225-I funded by MCIN/AEI/ 10.13039/501100011033 and by “ESF Investing in your future” and the Award for Scientific Research into Urban Challenges in the City of Barcelona 2020 (20S08708) from the Barcelona city council. RC gratefully acknowledges the financial support from the Balearic Island Government through the Margalida Comas postdoctoral fellowship programme (PD/036/2020). The authors would like to thank the European Commission, the Spanish Foundation for Science and Technology (FECYT) and Spanish State Research Agency (AEI) for funding in the frame of the collaborative international consortium (URBANWAT) financed under the 2018 Joint call of the WaterWorks2017 ERA-NET Cofund. This ERA-NET is an integral part of the activities developed by the Water JPI. Additionally, the authors would also like to thank the Ministry of Science, Innovation and Universities, for funding the project UNBIASED (Ref: RTI2018-097346-B-I00) under the 2018 call of the “Proyectos de I+D Retos Investigación.

The presence of a 22q11.2 microdeletion (22q11.2 deletion syndrome [22q11DS]) ranks among the greatest known genetic risk factors for the development of psychotic disorders. There is emerging evidence that the cerebellum is important in the pathophysiology of psychosis. However, there is currently limited information on cerebellar neuroanatomy in 22q11DS specifically.High-resolution 3T magnetic resonance imaging was acquired in 79 individuals with 22q11DS and 70 typically developing control subjects (N = 149). Lobar and lobule-level cerebellar volumes were estimated using validated automated segmentation algorithms, and subsequently group differences were compared. Hierarchical clustering, principal component analysis, and graph theoretical models were used to explore intercerebellar relationships. Cerebrocerebellar structural connectivity with cortical thickness was examined via linear regression models.Individuals with 22q11DS had, on average, 17.3% smaller total cerebellar volumes relative to typically developing subjects (p.0001). The lobules of the superior posterior cerebellum (e.g., VII and VIII) were particularly affected in 22q11DS. However, all cerebellar lobules were significantly smaller, even after adjusting for total brain volumes (all cerebellar lobules p.0002). The superior posterior lobule was disproportionately associated with cortical thickness in the frontal lobes and cingulate cortex, brain regions known be affected in 22q11DS. Exploratory analyses suggested that the superior posterior lobule, particularly Crus I, may be associated with psychotic symptoms in 22q11DS.The cerebellum is a critical but understudied component of the 22q11DS neuroendophenotype.

Agricultural productivity plays a key role in determining the economy of a country. Detection of plant leaf disease is a vital task as it greatly affects the agricultural productivity. If proper detection is not done, it may lead to serious damage in the quality and quantity of the agricultural yield. In this research we propose a novel scheme for the detection of plant leaf diseases using deep convolutional neural networks (DCNN). In the proposed framework, initially, the plant leaf images are preprocessed using filtering and enhancement techniques. In our work, image filtering is done using 2D Adaptive Anisotropic Diffusion Filter (2D AADF) for noise removal. Using these de-noised images, enhancement is done using Adaptive Mean Adjustment (AMA) technique. This step helps to intensify the region of interest in the image. Using the enhanced image, segmentation is performed by means of clustering and thresholding. Clustering is done using the Improved Fast Fuzzy C Means Clustering (IFFCMC) Algorithm and image thresholding is performed using the Adaptive Otsu (AO) thresholding algorithm. From the segmented images, features are extracted using grey level co-occurrence matrix (GLCM). Dimensionality reduction of features is performed using principle component analysis (PCA). Finally, classification is done using a novel DCNN architecture. The proposed architecture has four convolutional layers, two fully connected layers and one SoftMax layer. Experimental results show that the proposed framework is effective and achieves best classification results other classifiers.

[ES] Este trabajo es una propuesta metodológica de construcción de índices de sostenibilidad para las ganaderías de bovino de leche en Cantabria. Mediante análisis factoriales de componentes principales, se obtienen 12 indicadores, 4 por cada dimensión (económica, ambiental y social), que permiten crear tres índices sintéticos de sostenibilidad y uno global. Las dimensiones que alcanzan mayores puntuaciones son la ambiental y la social en detrimento de la económica. Además, se observa una relación directa entre la sostenibilidad global y el tamaño productivo que, no se detecta para la dimensión ambiental, pero si en la social y económica. [EN] This work is a methodological proposal for the construction of synthetic sustainability index for dairy cattle farms in Cantabria. By means of principal component factor analysis, we obtain 12 indicators, 4 for each dimension (economic, environmental and social) that allow to create three synthetic indexes and a global index. Dimensions that reach the highest scores are environmental and social. Furthermore, we observe a direct relationship between global sustainability and the productive size, which, however, we did not detect for the environmental dimension but we do have in the social and economic., https://doi.org/10.7201/earn.2022.02.06

In the European Union, the public sector buildings are considered significant energy consumers and are, thus, the subject of several directives that aim to ensure the renovation of existing and the construction of new buildings as nearly zero-energy buildings. Therefore, as part of the decision making, it is necessary to properly plan the renovation or construction. This research provides models for predicting the energy costs of the public sector buildings, which are dependent upon its characteristics (i. e., constructional, occupational, energy, etc.). For this purpose, a real data set of Croatian public buildings was used, which included 150 variables and 1724 observations. Since the data set consisted of a large number of variables, the motivation for the dimensionality reduction was addressed first. Then, the independent component analysis, the principal component analysis, and the factor analysis were performed as the dimensionality reduction methods for variable extraction. The results of these analyses were used as inputs for modelling the energy costs of the public sector buildings. The obtained models were compared to the model built on original variables. The obtained models show the application potential in decision making for building renovation and construction in the public sector of Croatia, whereas the best performance of prediction in terms of RMSE and SMAPE was achieved by the model that integrated the independent component analysis with the linear regression.

Çalışmada geometrik morfometri yöntemi ile Honamli, Kıl, Kilis ve Saanen keçisine ait kafataslarının basal yönden analizi amaçlandı. Çalışmada her ırk için 6’şar adet erkek ve dişi olmak üzere toplamda 48 adet ergin keçi kafatası kullanıldı. Kafatasları basal yönden fotoğraflandıktan sonra 11 adet homolog landmark işaretlendi. Çalışmada ırk baz alındığında dişi ve erkek Honamli, Kıl, Kilis ve Saanen keçi kafatasları birbirinden önemli düzeyde ayrıldı. En belirgin kümelenmeler dişi Kilis ve Saanen, erkek Honamli ve Kilis keçisine ait kafatasları arasında görüldü. Sonuç olarak elde edilen bilgilerin arkeolojik kazılardan elde edilen geviş getirenlere ait kafatası kalıntıları için referans oluşturabileceği düşünülmektedir., The study aimed to analyze the skulls of Honamli, Hair, Kilis, and Saanen goats from the basal aspect using the geometric morphometric method. In the study, 48 adult goat skulls were used for each breed in six males and females. After the skulls were photographed from the basal aspect, 11 homologous landmarks were marked. In the study, based on the breed, male and female Honamli, Hair, Kilis, and Saanen goat skulls were significantly separated from each other. The most prominent clusters were seen between female Kilis and Saanen’s skulls and male Honamli and Kilis goats. As a result, it is thought that the information obtained could create a reference for the skull remains of ruminants obtained from archaeological excavations.

The NoGo P3 event-related potential (ERP) component is often related to response inhibition, although its function in equiprobable Go/NoGo tasks is debated. Previous findings concerning the auditory equiprobable NoGo P3 (or P3a) could be distorted by averaging latency-variable ERP components. This study aimed to control NoGo P3 latency jitter to investigate the component's relationship with inhibitory demands and its neuronal sources across trials. P3 latency jitter was controlled using a novel procedure to enable single-trial P3 quantification across 126 healthy young adults (M

It is necessary to identify the appropriate traits that influence yield in a given environment as part of a breeding programme. The objective of this study was to identify the morphological traits that contribute to maize grain weight (GWt) under abiotic stress conditions. Three drought-tolerant maize varieties were grown under no-stress (NHWS), heat-stress (HS), and combined heat- and water-stress (CHWS) conditions. Data from 19 morphological traits were analysed. The correlation results revealed that eight traits consistently produced a significant positive relationship with GWt under the three growth conditions. The path coefficient analysis revealed that in the NHWS, HS, and CHWS conditions, five traits consistently had a positive direct effect on the GWt. Given the magnitude of the positive direct effects, increasing dry biomass yield, harvest index, and grain number in the NHWS; grain number, harvest index, and ear width in the HS; and harvest index, days till silk appearance, leaf chlorophyll content, and grain number in the CHWS will increase GWt. Under various abiotic stress conditions, maize phenotypic expression varied. Therefore, the identified traits that contributed positively to GWt under various stress conditions should be considered when developing a maize improvement programme in a stress-prone environment.

Honey is a natural product made by honeybees, its composition depends on factors such as climate, soil and plant source. In this study, the nutritional parameters, phenolic composition, antioxidant activity and antibacterial ability of 30 different types of honey of different botanical and geographical origins in Vietnam were investigated. The study focused on the characterization and evaluation of the influence of plant origin and geographical location on physical–chemical properties and biological activities (antioxidant and antibacterial). The obtained results show that all honey samples meet quality standards according to international standards and Vietnamese standards, except for some exceptions recorded in moisture, 5-hydroxymethylfurfural (HMF) value and ash. These samples were explored for the detection of 13 polyphenols by using high-performance liquid chromatography (HPLC). The classification of honey samples collected from different regions and botanical sources was performed by principal component analysis (PCA), and it was observed that certain phenolic compounds contributed to the identification of honey samples. In addition, the correlation between physicochemical properties, chemical composition and biological activity of most honeys was also first clarified in this study. Overall, our data provide an overview data set and essential results in creating a database on the world honey trait map.

The South Pacific region is an area affected by characteristic precipitation patterns undergoing extreme events such as tropical cyclones and droughts. First, a daily weather typing of precipitation is presented, based on principal component analysis and k-means clustering using precipitation and atmospheric circulation variables derived from sea-level pressure and wind reanalysis fields. As a result, five weather types (WTs) are presented, able to capture distinct precipitation spatiotemporal patterns, interpretable in terms of salient regional climate features. Second, we undertake the calibration of the TRMM precipitation product using a set of rain gauge stations as reference and scaling and empirical quantile mapping (eQM) as calibration techniques. Furthermore, we build upon the weather-type classification to compare the results with a WTconditioned calibration approach. Overall, our results underpin the need of adjusting the existing TRMM biases, mostly relevant for the upper tail of their distribution, and advocate the use of correction techniques able to deal with quantile-dependent biases-such as eQM-instead of a simple scaling, in order to obtain a more realistic representation of extreme precipitation events. The conditioning has shown only a marginal added value over the simple approach, although this minor improvement may prove relevant for applications focused on extreme event analysis. Furthermore, the weather types created can be applied to a wide variety of conditioned analyses in this region. AFRICULTURES, Grant/Award Number: 774652; Beach4Cast, Grant/Award Number: PID2019-107053RB-I00; CORDyS, Grant/Award Number: PID2020-116595RB-I00; INDECIS, Grant/Award Number: 690462

Introduction The type of body composition modulates the severity of some musculoskeletal conditions, in fibromyalgia syndrome (FMS), this type of association remains relatively unexplored. Objective To analyze the association between the type of body composition and FMS using Principal Component Analysis (PCA). The FMS clinical outcome measures were: Symptom Severity Scale (SSS), Widespread Pain Index (WPI; and Fibromyalgia Impact Questionnaire (FIQ). Methods Forty-three women with FMS (ACR 2010 criteria) were clinically and anthropometrically evaluated. The anthropometric data were integrated into two indicators using a PCA methodology (PCA-Fat and PCA-muscle). Additionally, the patients were classified into high and low categories for each clinical indicator, which were used as dependent variables in binomial logistic regression (BLR) models. Results We found a positive correlation between PCA-Fat with WPI (r = 0.326, P = .043) and FIQ (r = 0.325, P = .044), and negative correlation (r = −0.384, P = .013) between PCA-muscle and SSS. In the BLR analysis, PCA-Fat was a significant predictor for high WPI (OR = 2.477, P = .038); while for high SSS, PCA-muscle (OR = 0.303, P = .009) was an inversely significant predictor. Conclusions The results suggest that the volume of fat mass can negatively modulate the severity of FMS. We propose that the evaluation of body composition should be a basic element for the clinical approach of patients with FMS.

An integrated approach based on the use of data mining methods is proposed in order to improve the efficiency of the analysis of photon counting histograms during study of the molecular composition of a substance by fluorescence fluctuation spectroscopy. The principal component method is used to test the hypothesis about cluster separability of the multidimensional experimental data. The reason for compression of the data cloud to characteristic nonlinearity, or so-called arcuate (arc-shaped) cloud, in the space of the first two principal components is investigated. Examples of simulated data sets of molecular systems with various brightness and concentration characteristics are considered. Interpretation and subsequent quantitative analysis of the data are complicated by nonlinear effects. It was established that the arcuate nature of the data cloud is a consequence of the presence of significant variation in one or more physical parameters and results and, in particular, from significant increase in the spread of the brightness or concentration parameters of the molecules. If only one type of molecule is being studied these parameters can provide additional indicators for assessing the quality of the experiments and can also be used for characterizing the system in the case of a mixture of various types of molecules. It was proposed to use normalization of local weighted smoothing of the scattering diagram to eliminate nonlinear effects in the space of the principal components.

Many methods for monitoring multivariate processes are built on principal component analysis (PCA), which, however, simply tells whether the process is faulty or not. In fact, there is still room for the improvement of the early detection performance by exploiting fully the information given by fault directions. To this end, this article proposes a novel directional PCA (diPCA) approach. First, by narrowing down faults to a specified direction or composite mutually orthogonal directions, diPCA can speed fault detection and facilitate accurate fault diagnosis. It also has good theoretical properties that guarantee concise control limits. Second, with appropriate fault directions, diPCA provides a unified framework for process monitoring and includes existing monitoring indices, such as Hotelling's T² and the squared prediction error (SPE), as special cases. Third, diPCA also naturally results in a new combined monitoring statistic, which is composed of both T² and SPE, and provides an optimal ratio of their combination. The Monte Carlo simulation results have demonstrated the power of the proposed monitoring and diagnostic methods stemming from diPCA. The proposed methods have also been implemented into the Tennessee Eastman process.

The mismatch negativity (MMN) component of the human event-related potential (ERP) is frequently interpreted as a sensory prediction-error signal. However, there is ambiguity concerning the neurophysiology underlying hypothetical prediction and prediction-error signalling components, and whether these can be dissociated from overlapping obligatory components of the ERP that are sensitive to physical properties of sounds. In the present study, a hierarchical recurrent neural network (RNN) was fitted to ERP data from 38 subjects. After training the model to reproduce ERP waveforms evoked by 80 dB standard and 70 dB deviant stimuli, it was used to simulate a response to 90 dB deviant stimuli. Internal states of the RNN effectively combine to generate synthetic ERPs, where individual hidden units are loosely analogous to population-level sources. Model behaviour was characterised using principal component analysis of stimulus condition, layer, and individual unit responses. Hidden units were categorised according to their temporal response fields, and statistically significant differences among stimulus conditions were observed for amplitudes of units peaking in the 0 to 75 ms (P50), 75 to 125 ms (N1), and 250 to 400 ms (N3) latency ranges, surprisingly not including the measurement window of MMN. The model demonstrated opposite polarity changes in MMN amplitude produced by falling (70 dB) and rising (90 dB) intensity deviant stimuli, consistent with loudness dependence of sensory ERP components. Although perhaps less parsimoniously, these observations could be interpreted within the context of predictive coding theory, as examples of negative and positive prediction errors, respectively.

With the development of Earth observation technology, very-high-resolution (VHR) image has become an important data source of change detection. Nowadays, deep learning methods have achieved conspicuous performance in the change detection of VHR images. Nonetheless, most of the existing change detection models based on deep learning require annotated training samples. In this paper, a novel unsupervised model called kernel principal component analysis (KPCA) convolution is proposed for extracting representative features from multi-temporal VHR images. Based on the KPCA convolution, an unsupervised deep siamese KPCA convolutional mapping network (KPCA-MNet) is designed for binary and multi-class change detection. In the KPCA-MNet, the high-level spatial-spectral feature maps are extracted by a deep siamese network consisting of weight-shared PCA convolution layers. Then, the change information in the feature difference map is mapped into a 2-D polar domain. Finally, the change detection results are generated by threshold segmentation and clustering algorithms. All procedures of KPCA-MNet does not require labeled data. The theoretical analysis and experimental results demonstrate the validity, robustness, and potential of the proposed method in two binary change detection data sets and one multi-class change detection data set.

Purpose: To develop a denoising strategy leveraging redundancy in high-dimensional data. Theory and Methods: The SNR fundamentally limits the information accessible by MRI. This limitation has been addressed by a host of denoising techniques, recently including the so-called MPPCA: principal component analysis of the signal followed by automated rank estimation, exploiting the Marchenko-Pastur distribution of noise singular values. Operating on matrices comprised of data patches, this popular approach objectively identifies noise components and, ideally, allows noise to be removed without introducing artifacts such as image blurring, or nonlocal averaging. The MPPCA rank estimation, however, relies on a large number of noise singular values relative to the number of signal components to avoid such ill effects. This condition is unlikely to be met when data patches and therefore matrices are small, for example due to spatially varying noise. Here, we introduce tensor MPPCA (tMPPCA) for the purpose of denoising multidimensional data, such as from multicontrast acquisitions. Rather than combining dimensions in matrices, tMPPCA uses each dimension of the multidimensional data's inherent tensor-structure to better characterize noise, and to recursively estimate signal components. Results: Relative to matrix-based MPPCA, tMPPCA requires no additional assumptions, and comparing the two in a numerical phantom and a multi-TE diffusion MRI data set, tMPPCA dramatically improves denoising performance. This is particularly true for small data patches, suggesting that tMPPCA can be especially beneficial in such cases. Conclusions: The MPPCA denoising technique can be extended to high-dimensional data with improved performance for smaller patch sizes.

Laser tweezers Raman spectroscopy enables multiplexed, quantitative chemical and morphological analysis of individual bionanoparticles such as drug-loaded nanoliposomes, yet it requires minutes-scale acquisition times per particle, leading to a lack of statistical power in typical small-sized data sets. The long acquisition times present a bottleneck not only in measurement time but also in the analytical throughput, as particle concentration (and thus throughput) must be kept low enough to avoid swarm measurement. The only effective way to improve this situation is to reduce the exposure time, which comes at the expense of increased noise. Here, we present a hybrid principal component analysis (PCA) denoising method, where a small number (∼30 spectra) of high signal-to-noise ratio (SNR) training data construct an effective principal component subspace into which low SNR test data are projected. Simulations and experiments prove the method outperforms traditional denoising methods such as the wavelet transform or traditional PCA. On experimental liposome samples, denoising accelerated data acquisition from 90 to 3 s, with an overall 4.5-fold improvement in particle throughput. The denoised data retained the ability to accurately determine complex morphochemical parameters such as lamellarity of individual nanoliposomes, as confirmed by comparison with cryo-EM imaging. We therefore show that hybrid PCA denoising is an efficient and effective tool for denoising spectral data sets with limited chemical variability and that the RR-NTA technique offers an ideal path for studying the multidimensional heterogeneity of nanoliposomes and other micro/nanoscale bioparticles.

Piano score recognition is one of the important research contents in the field of music information retrieval, and it plays an important role in information processing. In order to reduce the influence of vocals on the progress of piano notes and restore the harmonic information corresponding to piano notes, the article models the harmonic information and vocal information corresponding to piano notes in the frequency spectrum. We use the phase space reconstruction method to extract the nonlinear feature parameters in the note audio and use some of the parameters as the training set to construct the support vector machine (SVM) classifier and the other part as the test set to test the recognition effect. Therefore, the method of adaptive signal decomposition and SVM is introduced into the signal preprocessing link, and the corresponding recognition process is established. In order to improve the performance of the support vector machine, the article uses measurement learning method to obtain the measurement learning and uses the measurement learning to replace the Euclidean distance of the Gaussian kernel function of the support vector machine. The SVM method of adaptive signal decomposition and the SVM method of principal component analysis are introduced into the preprocessing process of the note signal, and then the preprocessed signal is reconstructed in phase space, and the corresponding recognition process is established. The method of directly reconstructing the phase space of the original signal has higher accuracy and can be applied to the note recognition of continuous music segments. The final experimental results show that, compared with the current popular piano score recognition algorithm, the recognition accuracy of the proposed piano score recognition algorithm is improved by 3.5% to 12.2%.

Facing with rapidly increasing demands for analyzing high-order data or multiway data, feature-extracting methods become imperative for analysis and processing. The traditional feature-extracting methods, however, either need to overly vectorize the data and smash the original structure hidden in data, such as PCA and PCA-like methods, which is unfavorable to the data recovery, or cannot eliminate the redundant information very well, such as tucker decomposition (TD) and TD-like methods. To overcome these limitations, we propose a more flexible and more powerful tool, called the multiview principal components analysis (Multiview-PCA) in this article. By segmenting a random tensor into equal-sized subarrays called sections and maximizing variations caused by orthogonal projections of these sections, the Multiview-PCA finds principal components in a parsimonious and flexible way. In so doing, two new operations on tensors, the S -direction inner/outer product, are introduced to formulate tensor projection and recovery. With different segmentation ways characterized by section depth and direction, the Multiview-PCA can be implemented many times in different ways, which defines the sequential and global Multiview-PCA, respectively. These multiple Multiview-PCA take the PCA and PCA-like, and TD and TD-like as the special cases, which correspond to the deepest section depth and the shallowest section depth, respectively. We propose an adaptive depth and direction selection algorithm for the implementation of Multiview-PCA. The Multiview-PCA is then tested in terms of subspace recovery ability, compression ability, and feature extraction performance when applied to a set of artificial data, surveillance videos, and hyperspectral imaging data. All numerical results support the flexibility, effectiveness, and usefulness of Multiview-PCA.

Inspired by the mean calculation of RPCA_OM and inductiveness of IRPCA, we first propose an inductive robust principal component analysis method with removing the optimal mean automatically, which is shorted as IRPCA_OM. Furthermore, IRPCA_OM is extended to Schatten-p norm and a more general framework (i.e., EIRPCA_OM) is presented. The objective function of EIRPCA_OM includes two terms, the first term is a robust reconstruction error term constrained by an l2,1-norm and the second term is a regularization term constrained by a Schatten-p norm. The proposed EIRPCA_OM method is robust, inductive and accurate. However, on the high-dimensional data, it would spend a large computation cost in training stage. To this end, a fast version of EIRPCA_OM called as FEIRPCA_OM is proposed, and its basic idea is to eliminate the zero eigenvalues of data matrix. More importantly, an effective theoretical proof is presented to ensure that FEIRPCA_OM has faster processing speed than EIRPCA_OM when processing high-dimensional data, but without any performance loss. Based on it, we also can exchange the less performance loss for the higher computation efficiency by removing the small eigenvalues of data matrix. Experimental results on the public datasets demonstrate that FEIRPCA_OM works efficiently on the high-dimensional data.

Tensor singular value decomposition (t-SVD) has recently become increasingly popular for tensor recovery under partial and/or corrupted observations. However, the existing t -SVD-based methods neither make use of a rank prior nor provide an accurate rank estimation (RE), which would limit their recovery performance. From the practical perspective, the tensor RE problem is nontrivial and difficult to solve. In this article, we, therefore, aim to determine the correct rank of an intrinsic low-rank tensor from corrupted observations based on t-SVD and further improve recovery results with the estimated rank. Specifically, we first induce the equivalence of the tensor nuclear norm (TNN) of a tensor and its f -diagonal tensor. We then simultaneously minimize the reconstruction error and TNN of the f -diagonal tensor, leading to RE. Subsequently, we relax our model by removing the TNN regularizer to improve the recovery performance. Furthermore, we consider more general cases in the presence of missing data and/or gross corruptions by proposing robust tensor principal component analysis and robust tensor completion with RE. The robust methods can achieve successful recovery by refining the models with correct estimated ranks. Experimental results show that the proposed methods outperform the state-of-the-art methods with significant improvements.

Mature soybean phenotyping is an important process in soybean breeding; however, the manual process is time-consuming and labor-intensive. Therefore, a novel approach that is rapid, accurate and highly precise is required to obtain the phenotypic data of soybean stems, pods and seeds. In this research, we propose a mature soybean phenotype measurement algorithm called Soybean Phenotype Measure-instance Segmentation (SPM-IS). SPM-IS is based on a feature pyramid network, Principal Component Analysis (PCA) and instance segmentation. We also propose a new method that uses PCA to locate and measure the length and width of a target object via image instance segmentation. After 60,000 iterations, the maximum mean Average Precision (mAP) of the mask and box was able to reach 95.7%. The correlation coefficients R 2 of the manual measurement and SPM-IS measurement of the pod length, pod width, stem length, complete main stem length, seed length and seed width were 0.9755, 0.9872, 0.9692, 0.9803, 0.9656, and 0.9716, respectively. The correlation coefficients R 2 of the manual counting and SPM-IS counting of pods, stems and seeds were 0.9733, 0.9872, and 0.9851, respectively. The above results show that SPM-IS is a robust measurement and counting algorithm that can reduce labor intensity, improve efficiency and speed up the soybean breeding process.

The recent trends in collecting huge datasets have posed a great challenge that is brought by the high dimensionality and aggravated by the presence of irrelevant dimensions. Machine learning models for regression is recognized as a convenient way of improving the estimation for empirical models. Popular machine learning models is support vector regression (SVR). However, the usage of principal component analysis (PCA) as a variable reduction method along with SVR is suggested. The principal component analysis helps in building a predictive model that is simple as it contains the smallest number of variables and efficient. In this paper, we investigate the competence of SVR with PCA to explore its performance for a more accurate estimation. Simulation study and Renal Failure (RF) data of SVR optimized by four different kernel functions; linear, polynomial, radial basis, and sigmoid functions using R software, version (R x64 3.2.5) to compare the behavior of ε SVR and v-SVR models for different sample sizes ranges from small, moderate to large such as; 50, 100, and 150. The performance criteria are root mean squared error (RMSE) and coefficient of determination R2 showed the superiority of ε-SVR over v- SVR. Furthermore, the implementation of SVR after employing PCA improves the results. Also, the simulation results showed that the best performing kernel function is the linear kernel. For real data the results showed that the best kernels are linear and radial basis function. It is also clear that, with ε-SVR and v-SVR, the RMSE values for almost kernel functions decreased with increasing sample size. Therefore, the performance of ε-SVR improved after applying PCA. In addition sample size n=50 gave good results for linear and radial kernel

Patients afflicted by multiple sclerosis experience a relapsing-remitting course in about 85% of the cases. Furthermore, after a 10/15-year period their situation tends to worse, resulting in what is considered the second phase of multiple sclerosis. While treatments are now available to reduce the symptoms and slow down the progression of the disease, the administration of drugs must be adapted to the course of the disease, and predicting relapsing periods and the worsening of the symptoms can greatly improve the outcome of the treatment. For this reason, indicators such as the patient-reported outcome measures (PROMs) have been largely used to support early diagnosis and prediction of future relapsing periods in patients affected by multiple sclerosis. However, such indicators are insufficient, as the prediction they provide is often not accurate enough. In this paper, machine learning techniques have been applied to data obtained from clinical trial, in order to improve the prediction capabilities and provide doctors with an additional instrument to evaluate the clinical situation of patients. After the application of correlation indicators and the use of principal component analysis for the reduction of the dimensionality of the feature space, classification algorithms have been applied and compared, in order to identify the best suiting one for our purposes. After the application of re-balance algorithms, the accuracy of the machine learning-based prediction system reaches 79%, demonstrating the capability of the framework to correctly predict future progression of disability.

The high price of marketing of extra virgin olive oil (EVOO) requires the introduction of cost-effective and sustainable procedures that facilitate its authentication, avoiding fraud in the sector. Contrary to classical techniques (such as chromatography), near-infrared (NIR) spectroscopy does not need derivatization of the sample with proper integration of separated peaks and is more reliable, rapid, and cost-effective. In this work, principal component analysis (PCA) and then redundancy analysis (RDA)─which can be seen as a constrained version of PCA─are used to summarize the high-dimensional NIR spectral information. Then PCA and RDA factors are contemplated as explanatory variables in models to authenticate oils from qualitative or quantitative analysis, in particular, in the prediction of the percentage of EVOO in blended oils or in the classification of EVOO or other vegetable oils (sunflower, hazelnut, corn, or linseed oil) by the use of some machine learning algorithms. As a conclusion, the results highlight the potential of RDA factors in prediction and classification because they appreciably improve the results obtained from PCA factors in calibration and validation.

Polymorphic inversions are ubiquitous in humans and they have been linked to both adaptation and disease. Following their discovery in Drosophila more than a century ago, inversions have proved to be more elusive than other structural variants. A wide variety of methods for the detection and genotyping of inversions have recently been developed: multiple techniques based on selective amplification by PCR, short- and long-read sequencing approaches, principal component analysis of small variant haplotypes, template strand sequencing, optical mapping, and various genome assembly methods. Many methods apply complex wet lab protocols or increasingly refined bioinformatic analyses. This review is an attempt to provide a practical summary and comparison of the methods that are in current use, with a focus on metrics such as the maximum size of segmental duplications at inversion breakpoints that each method can tolerate, the size range of inversions that they recover, their throughput, and whether the locations of putative inversions must be known beforehand.

Non-destructive testing & evaluation techniques play an essential role in ensuring safety of materials in operation at various industry sectors. Pulse compressed favourable thermal wave imaging is one of the widely used non-destructive testing techniques due to its excellent noise rejection capabilities. However, the high dimensional thermal imaging data needs to be encoded into lossless compressed form to highlight the hidden defects inside the materials. This paper proposes a novel constrained and regularized autoencoder based thermography approach for sub-surface defect detection in a mild steel specimen. Certain properties such as non-correlation of encoded data, weight orthogonality, and weights with unit norm length have been highlighted which are non-existent in linear autoencoders but are responsible for better defect detection inside the materials inspected by frequency modulated thermal wave imaging. Novel constraints are formulated for autoencoder cost function to incorporate these significant properties. The proposed approach is able to provide better defect detection, in terms of signal to noise ratio of defects, than linear autoencoder as well as traditional principal component thermography approach. Also, non-correlation of encoded data is found to be the most significant factor in achieving better defect detection followed by properties ensuring weight orthogonality and weights with unit norm length.

The morphology of the mammalian middle ear-including the size, shape, and stiffness of individual ossicles-controls their vibrational response to sound and, is closely related to an animal's auditory capabilities. While the relationship between middle ear morphology and hearing frequency has been explored in living carnivorans, the size and shape of ossicles in fossil carnivorans have been sparsely documented. In this study, we present the first morphological data on four iconic carnivoran taxa from the Rancho La Brea Tar Pits: Smilodon fatalis, Panthera atrox, Canis dirus, and Arctodus simus. These data are contextualized with samples of extant felids, canids, and ursids to determine the extent to which the ossicles of these iconic fossil taxa resemble their living relatives. Six, five, and seven linear measurements were taken from the malleus, incus, and stapes, respectively. Comparisons of geometric means reveal that the ossicles of fossil canids and felids are similar in size to living analogs, but those of A. simus are significantly larger than those of any living ursid. Further, principal components analyses demonstrate close morphological affinities between fossil and extant taxa within canids and felids, and again, a greater disparity between fossil and extant ursids. Canids and ursids occupy distinct regions of the morphospace, yet both overlap the morphological range spanned by felids. While some elements-for example, the stapes-require further specimens to facilitate more nuanced interpretations of variation, our findings underscore the need for concerted efforts towards identifying and preserving these bones within fossil assemblages.

A simple, rapid, accurate, and non-destructive method was developed for the determination of cannabinoids, combining principal component analysis and multi-layer perceptron neural network to classify indole and indazole amide synthetic cannabinoids. Under the experimental conditions of this study, 25 experimental samples were successfully classified into two categories as the final classification, which guaranteed 96% correct rate. First, the samples were manually classified and divided into two categories according to the difference in peak position and peak intensity of the differential Raman characteristic peaks at 650-540 cm

This study used principal component analysis (PCA) of waveforms to extract movement patterns from hip and knee angle time-series data; and determined if the extracted movement patterns were predictors of ball velocity during a soccer kick. Twenty-three female and nineteen male professional soccer players performed maximal effort instep kicks while motion capture and post-impact ball velocities data were recorded. Three-dimensional hip and knee joint angle time-series data were calculated from the beginning of the kicking leg's backswing phase until the end of the follow-through phase and entered into separate PCAs for females and males. Three principal components (PC) (i.e., movement patterns) were extracted and PC scores were calculated. Pearson correlation coefficients were calculated to establish correlations between hip and knee PC scores and kicking velocity. Results showed better kicking performance in male players was associated with a greater difference between the hip extension at the end of the backswing/beginning of the leg cocking phases and hip flexion at the end of the follow-through phase (r = -0.519, p = 0.023) and a delayed internal rotation of the hip (r = 0.475, p = 0.040). No significant correlations between ball velocity and hip and knee kinematics were found for female players.

Estimating water quality has been one of the significant challenges faced by the world in recent decades. This paper presents a water quality prediction model utilizing the principal component regression technique. Firstly, the water quality index (WQI) is calculated using the weighted arithmetic index method. Secondly, the principal component analysis (PCA) is applied to the dataset, and the most dominant WQI parameters have been extracted. Thirdly, to predict the WQI, different regression algorithms are used to the PCA output. Finally, the Gradient Boosting Classifier is utilized to classify the water quality status. The proposed system is experimentally evaluated on a Gulshan Lake-related dataset. The results demonstrate 95% prediction accuracy for the principal component regression method and 100% classification accuracy for the Gradient Boosting Classifier method, which show credible performance compared with the state-of-art models.

This study examines determinant factors for the expansion of informal settlements in Gondar, a historical city in Ethiopia. In order to address this study objective, quantitative data were employed from 196 randomly selected households in four sites. The study indicated that a single factor is not determinant for the expansion of informal settlements in the city. The results of the study revealed that socio-economic, political and legislative, administrative, and demographic factors are statistically significant. These four dimensions together accounts 61.4 percent of all factors that facilitate the formation of informal settlement in Gondar city of Ethiopia. More specifically, socio-economic dimension is the first influential factor with 30.9%; political and legislative dimension is the second dominant factor with 15.7% followed by administrative dimension (10.2%); and demographic factors are contributed the least(4.6%) for the expansion of informal settlements. The study emphasized the need to implement appropriate urban planning, land and housing policy aimed at improving the efficient use of urban land. Improving the efficient use of urban land is imperative to address land prices and housing shortages, protecting farmland and ecosystems, and mainly to tackle land hoarding, urban sprawl and informal settlements.

Background: Understanding the perspective of health care professionals (HCPs) is significant to the imple-mentation of deprescribing in older adults with limited life expectancy (LLE) but a tool to assess this is lacking. Objective: This study aimed to develop and validate a survey tool for assessing HCPs attitudes towards depres-cribing (HATD) in older adults with LLE. Methods: An online survey was used to collect data to determine the psychometric properties of a 49-item questionnaire generated from literature review, expert opinion and pretesting. 108 HCPs (doctors, nurses and pharmacists) with experience or interest in palliative care or a member of a palliative care team/organisation completed the survey. Results: Principal component analysis of the participants' data resulted in a 23-item questionnaire structured in five factors, named HATD tool. The factors were related to concerns about deprescribing (7 items), perceived burden of medications on patients (7 items), organisational support for deprescribing (4 items), assurance to deprescribing (2 items) and perceived involvement of patients in medication management (3 items). The HATD tool had valid descriptive statistics (Kaiser-Meyer-Olkin measure: 0.708; Bartlett's test of sphericity: p < 0.001, determinant: 1.35E-5; variance explained: 60.4%; nonredundant residuals with absolute values > 0.05: 39%). The reliability statistics of all the factors were >= 0.750 for both Cronbach's alpha (alpha) and composite reliability (CR) except for the patient-involvement factor (alpha = 0.644 but CR = 0.787). Conclusions: The 23-itemed HATD tool is a valid and reliable tool to assess the attitudes and beliefs of HCPs towards deprescribing in older adults with LLE in the Australian setting. Refereed/Peer-reviewed

There is a need for facial biometric based authentication system that responds quickly despite large number of registered users. The main challenge in these authentication systems is accuracy along with quick response time. The response time can be lowered by having lesser number of comparisons of the probe image with gallery images. To enhance the response time we propose gender classification as a pre-processing stage so that the validation of the user continues by taking only those facial images which are in accordance with the gender of the user. We have used Principal Component Analysis to create male and female Eigen spaces during the training phase of the classifier.To validate our classifier we have used ORL, Indian and FG-NET databases. Our proposed method gives improved recognition accuracy for the images having ageing variations.The proposed Gender classification model gives an improved accuracy of 57.44%, 59.13% and 61.13 % for the probe images taken from the FG-NET database with training images from ORL, Indian and FG-NET databases respectively as compared to PCA.Also the number of comparisons are reduced by an average of 38% by incorporating the gender classification as the pre-processing stage in the age invariant face recognition system.

The Covid-19 virus emerged in 2019 and spread all over the world in a short time. It caused millions of people to be infected and hundreds of thousands to die. The number of cases is increasing day by day and new variants of the virus are emerging. Polymerase Chain Reaction (PCR) tests are used to detect people with this disease. It is very important to examine the conditions of the people with the disease and to determine the intensive care and mortality rates in advance. In this study, Principal Component Analysis (PCA) was used as a feature extraction method to determine mortality rates from Covid-19 patients, and the successful results of the method were demonstrated with the most popular machine learning techniques. Machine learning techniques used in the study are K-Nearest Neighbor (KNN), Linear Discrimination Analysis (LDA), Extra Trees, Random Tree, Rep Tree and Naive Bayes algorithms. In the performance evaluation of these techniques, Accuracy, Precision, Sensitivity, Rms, F-score values were calculated. In addition, ROC Curves and Confusion matrices were examined and the results were compared. As a result, it was seen that the best performance was obtained with the use of Linear Discrimination Analysis (PCA+LDA) after applying Principal component analysis. With the PCA+LDA application, an accuracy rate of 96.39% was obtained. In the article, it has also been revealed that Pneumonia, Diabetes, COPD and Asthma patients, Pregnant, Elderly and Intubated people are more affected and the risk of death is higher from the Covid- 19 virus by using feature extraction. This study is important in terms of examining the lethality of virus variants, taking the necessary precautions for the treatment of risky patients isolation of patients at risk of death, and improving hospital capacity planning., Covid-19 virüsü 2019 yılında ortaya çıktı ve kısa bir sürede tüm dünyaya yayıldı. Milyonlarca insanın enfekte olmasına ve yüz binlerce insanın ölümüne neden oldu. Her geçen gün vaka sayısı artmakta ve virüsün yeni varyantlar meydana gelmektedir. Bu hastalığa sahip kişileri tespit etmek için Polimeraz Zincir Reaksiyonu (PCR) testleri uygulanmaktadır. Hastalığı tespit edilen kişilerin durumlarının incelenmesi yoğun bakım ve ölüm oranlarının önceden tespiti oldukça önemlidir. Bu çalışmada Covid-19 hastalarından ölüm oranlarının tespitinde özellik çıkarımı yöntemi olarak Temel Bileşen Analizi (PCA) kullanılmış ve yöntemin başarılı sonuçları en popüler makine öğrenmesi teknikleri ile gösterilmiştir. Çalışmada kullanılan makine öğrenmesi teknikleri K-En Yakın Komşu (KNN), Doğrusal Ayrımcılık Analizi (LDA), Extra Ağaçlar, Random Tree, Rep Tree ve Naive Bayes algoritmalarıdır. Bu tekniklerin performans değerlendirmesinde Doğruluk, Kesinlik, Duyarlılık, Rms, F-skoru değerleri hesaplanmıştır. Ayrıca ROC Eğrileri ve Karışıklık matrisleri incelenerek sonuçlar karşılaştırılmıştır. Sonuç olarak, en iyi performansın Temel bileşenler analizi uygulandıktan sonra Doğrusal Ayrım Analizi (PCA+LDA) kullanımı ile elde edildiği görülmüştür. PCA+LDA uygulaması ile %96,39 Doğruluk oranı elde edilmiştir. Makalede ayrıca özellik çıkarımının kullanılmasıyla Covid-19 virüsünden Zatürre, Şeker, KOAH ve Astım hastalarının, hamile, yaşlı ve entrube insanların daha çok etkilendiği ve ölüm riskinin daha yüksek olduğu ortaya çıkmıştır. Virüsün varyantlarının ölümcüllüğünün incelenmesi, riskli hastaların tedavisi, ölüm riski bulunan hastaların izolasyonu için gereken önlemlerin alınması ve hastane kapasite planlamasının iyileştirilmesi açısından bu çalışma önem arz etmektedir.

Random Forest is an ensemble of decision trees based on the bagging and random subspace concepts. As suggested by Breiman, the strength of unstable learners and the diversity among them are the ensemble models' core strength. In this paper, we propose two approaches known as oblique and rotation double random forests. In the first approach, we propose rotation based double random forest. In rotation based double random forests, transformation or rotation of the feature space is generated at each node. At each node different random feature subspace is chosen for evaluation, hence the transformation at each node is different. Different transformations result in better diversity among the base learners and hence, better generalization performance. With the double random forest as base learner, the data at each node is transformed via two different transformations namely, principal component analysis and linear discriminant analysis. In the second approach, we propose oblique double random forest. Decision trees in random forest and double random forest are univariate, and this results in the generation of axis parallel split which fails to capture the geometric structure of the data. Also, the standard random forest may not grow sufficiently large decision trees resulting in suboptimal performance. To capture the geometric properties and to grow the decision trees of sufficient depth, we propose oblique double random forest. The oblique double random forest models are multivariate decision trees. At each non-leaf node, multisurface proximal support vector machine generates the optimal plane for better generalization performance. Also, different regularization techniques (Tikhonov regularization, axis-parallel split regularization, Null space regularization) are employed for tackling the small sample size problems in the decision trees of oblique double random forest. The proposed ensembles of decision trees produce trees with bigger size compared to the standard ensembles of decision trees as bagging is used at each non-leaf node which results in improved performance. The evaluation of the baseline models and the proposed oblique and rotation double random forest models is performed on benchmark 121 UCI datasets and real-world fisheries datasets. Both statistical analysis and the experimental results demonstrate the efficacy of the proposed oblique and rotation double random forest models compared to the baseline models on the benchmark datasets.

Missing data is a significant issue in metabolomics that is often neglected when conducting data preprocessing, particularly when it comes to imputation. This can have serious implications for downstream statistical analyses and lead to misleading or uninterpretable inferences. In this study, we aim to identify the primary types of missingness that affect untargeted metabolomics data and compare strategies for imputation using two real-world comprehensive two-dimensional gas chromatography (GC × GC) data sets. We also present these goals in the context of experimental replication whereby imputation is conducted in a within-replicate-based fashion─the first description and evaluation of this strategy─and introduce an R package

This paper aims to develop a generic face detection and recognition system that will automate the process of collecting school attendance by recognizing students' frontal faces from classroom photographs. The reliability of the data collected is the biggest problem with the traditional attendance management systems. Many automated methods, such as biometric attendance, are being used. However, technical difficulties with scanning devices always affect the efficiency of such techniques. This paper employs principal component analysis approaches for face detection and OpenCV for face recognition to improve data quality and information accessibility for legitimate parties. The Python programming language was used for the development of the proposed system, while SQL was used for the development of the database that houses the information of users in the system. The new system was tested and shown to be not only safe but also protects students' identities by offering an anonymous attendance environment.

Colon cancer (CC) is one of the most common gastrointestinal malignant tumors with a high mortality rate. Glycolysis is an important pathway for tumors to obtain energy. However, its role in CC remains largely unknown. In present study, we analyzed glycolysis-related gene expression to depict clinical characteristics and its relationship with tumor immunity in CC to find potential target treatments. A prognostic model based on 13 glycolysis-related genes was established by univariate and multivariate Cox regression analyses. The efficacy of the gene model was tested via survival analysis, receiver operating characteristic analysis, and principal component analysis. Furthermore, our findings revealed and validated 13 glycolysis-related genes (

The clinical scores are applied to determine the stage of cognitive function in patients with end-stage renal disease (ESRD). However, accurate clinical scores are hard to come by. This paper proposed an integrated prediction framework with GPLWLSV to predict clinical scores of cognitive functions in ESRD patients. GPLWLSV incorporated three parts, graph theoretic algorithm (GTA) and principal component analysis (PCA), whale optimization algorithm with Levy flight (LWOA), and least squares support vector regression machine (LSSVRM). GTA was adopted to extract features from the brain functional networks in ESRD patients, while PCA was used to select features. LSSVRM was built to explore the relationship between the selected features and the clinical scores of ESRD patients. Whale optimization algorithm (WOA) was introduced to select better parameters of the kernel function in LSSVRM; it aims to improve the exploration competence of LSSVRM. Levy flight was used to optimize the ability to jump out of local optima in WOA and improve the convergence of coefficient vectors in WOA, which lead to an increase in the generalization ability and convergence speed of WOA. The results validated that the prediction accuracy of GPLWLSV was higher than that of several comparable frameworks, such as GPSV, GPLSV, and GPWLSV. In particular, the average of root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of ESRD patients was 2.40, 2.06, and 9.83%, respectively. The proposed framework not only can predict the clinical scores more accurately but also can capture imaging markers associated with decline of cognitive function. It helps to understand the potential relationship between structural changes in the brain and cognitive function of ESRD patients.

Automotive paint is one of the most important evidence in solving vehicle-related criminal cases. It contains the critical information about the suspected vehicle, providing essential clues for the investigation. In this study, a novel approach based on optical coherence tomography combined with multivariate statistical methods was proposed to facilitate rapid, accurate and nondestructive identification of different brands of automotive paints. 164 automotive paint samples from 8 different manufacturers were analyzed by a spectral-domain optical coherence tomography system (SD-OCT). Two-dimensional cross-sectional OCT images and three-dimensional OCT reconstruction of vehicle paints of different paints were obtained to show the internal structural differences. Visual discrimination of A-scan data after registration and averaging processing was first used to distinguish different samples. An scanning electron microscope was utilized to obtain the cross-sectional image of the sample to evaluate the effectiveness of OCT technique. Then the original A-scan data, first derivative data and second derivative data of 136 paints with four layers from 7 different manufacturers were collected. Multivariate statistical methods, including principal component analysis (PCA), multi-layer perceptron (MLP), k-nearest neighbor (KNN) algorithm and Bayes discriminant analysis (BDA), were used to analyze different datasets. The results show the hybrid PCA and BDA model based on the first derivative OCT data achieved the best result of 100% accuracy on the testing dataset for identifying automotive paints. It is demonstrated that the OCT technique combined with multivariate statistics could be a promising method for identifying the automotive paints rapidly and accurately.