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Journal articles on the topic 'Kernelized multivariate fisher discriminant'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

Nakkiran, Arunadevi, and Vidyaa Thulasiraman. "Elastic net feature selected multivariate discriminant mapreduce classification." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (2022): 587. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp587-596.

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Analyzing the <span>big stream data and other valuable information is a significant task. Several conventional methods are designed to analyze the big stream data. But the scheduling accuracy and time complexity is a significant issue. To resolve, an elastic-net kernelized multivariate discriminant map reduce classification (EKMDMC) is introduced with the novelty of elastic-net regularization-based feature selection and kernelized multivariate fisher Discriminant MapReduce classifier. Initially, the EKMDMC technique executes the feature selection to improve the prediction accuracy using

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Nakkiran, Arunadevi, and Vidyaa Thulasiraman. "Elastic net feature selected multivariate discriminant mapreduce classification." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (2022): 587–96. https://doi.org/10.11591/ijeecs.v26.i1.pp587-596.

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Analyzing the big stream data and other valuable information is a significant task. Several conventional methods are designed to analyze the big stream data. But the scheduling accuracy and time complexity is a significant issue. To resolve, an elastic-net kernelized multivariate discriminant map reduce classification (EKMDMC) is introduced with the novelty of elastic-net regularization-based feature selection and kernelized multivariate fisher Discriminant MapReduce classifier. Initially, the EKMDMC technique executes the feature selection to improve the prediction accuracy using the Elastic-

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Saeroni, Amanah, Memi Nor Hayati, and Rito Goejantoro. "KLASIFIKASI TINGKAT KELANCARAN NASABAH DALAM MEMBAYAR PREMI DENGAN MENGGUNAKAN METODE K-NEAREST NEIGHBOR DAN ANALISIS DISKRIMINAN FISHER (Studi kasus: Data Nasabah PT. Prudential Life Samarinda Tahun 2019)." Jurnal Statistika Universitas Muhammadiyah Semarang 8, no. 2 (2020): 88. http://dx.doi.org/10.26714/jsunimus.8.2.2020.88-94.

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Classification is a technique to form a model of data that is already known to its classification group. The model that was formed will be used to classify new objects. The K-Nearest Neighbor (K-NN) algorithm is a method for classifying new objects based on their K nearest neighbor. Fisher discriminant analysis is a multivariate technique for separating objects in different groups to form a discriminant function for allocate new objects in groups. This research has a goal to determine the results of classifying customer premium payment status using the K-NN method and Fisher discriminant analy

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Liu, Xingye, Jingye Li, Xiaohong Chen, Lin Zhou, and Kangkang Guo. "Bayesian discriminant analysis of lithofacies integrate the Fisher transformation and the kernel function estimation." Interpretation 5, no. 2 (2017): SE1—SE10. http://dx.doi.org/10.1190/int-2016-0025.1.

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The accurate identification of lithofacies is indispensable for reservoir parameter prediction. In recent years, the application of multivariate statistical methods has gained more and more attention in petroleum geology. In terms of the identification for lithofacies, the commonly used multivariate statistical methods include discriminant analysis and cluster analysis. Fisher and Bayesian discriminant analyses are two different discriminant analysis methods, which include intrinsic advantages and disadvantages. Given the discriminant efficiency of different methods, calculation cost, difficul

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Imran, Sajida, and Young-Bae Ko. "A Novel Indoor Positioning System Using Kernel Local Discriminant Analysis in Internet-of-Things." Wireless Communications and Mobile Computing 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/2976751.

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WLAN based localization is a key technique of location-based services (LBS) indoors. However, the indoor environment is complex; received signal strength (RSS) is highly uncertain, multimodal, and nonlinear. The traditional location estimation methods fail to provide fair estimation accuracy under the said environment. We proposed a novel indoor positioning system that considers the nonlinear discriminative feature extraction of RSS using kernel local Fisher discriminant analysis (KLFDA). KLFDA extracts location features in a well-preserved kernelized space. In the new kernel featured space, n

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Fan, Bing Chen. "Application of Progressively Statistical Discriminant Models." Applied Mechanics and Materials 55-57 (May 2011): 1922–25. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.1922.

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Discriminant analysis is an important multivariate statistical analysis, and plays an important part in pattern classification, data mining, machine learning et al. In this paper, based on principle of progressively statistical discriminant analysis under Fisher rule, a progressively statistical discriminant model is set up. The authors analyzed the data about the occurrence of the second generation of the corn borer in 21 years from 1985 to 2006 (except 1990) at Linyi, Shandong Province, and then set up three graded recognition pattern. The results tested the pest data showed that the fitting

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Jing, CHEN, and GAO Caixia. "Sparse Linear Discriminant Analysis Based on lq Regularization." Frontiers of Chinese Pure Mathematics 1, no. 2 (2023): 31–38. http://dx.doi.org/10.48014/fcpm.20230529001.

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Linear discriminant analysis plays an important role in feature extraction, data dimensionality reduction, and classification. With the progress of science and technology, the data that need to be processed are becoming increasingly large. However, in high-dimensional situations, linear discriminant analysis faces two problems: the lack of interpretability of the projected data since they all involve all p features, which are linear combinations of all features, as well as the singularity problem of the within-class covariance matrix. There are three different arguments for linear discriminant

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Ле Ван Хыонг, Нгуен Нгок Киенг, Нгуен Данг Хой, and Данг Хунг Куонг. "Applying Multivariate Statistical Methods for Predicting Pinus Forest Fire Danger at Bidoup-Nui Ba National Park." Труды Карадагской научной станции им. Т.И. Вяземского - природного заповедника РАН, no. 1 (13) (April 21, 2021): 45–53. http://dx.doi.org/10.21072/eco.2021.13.05.

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The paper presents results of applying multivariate statistical methods (CCA: canonical correlation analysis and DFA: discriminant function analysis) for determining canonical correlation between a set of variables {T, H, m1, K} and a set of variables {Pc, Tc} (T: temperature, H: relative humidity, m1: mass of dry fuels, K: burning coefficient, K = m1/M, with M: total mass of fire fuels, Pc: % burned fuels and Tc: burningtime) as well as through results of discriminant function analysis DFA to set up models of predicting forest fire danger at Bidoup - Nui Ba National Park. From research data i

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Li, Hanqi, Mingxing Jia, and Zhizhong Mao. "Dynamic Feature Extraction-Based Quadratic Discriminant Analysis for Industrial Process Fault Classification and Diagnosis." Entropy 25, no. 12 (2023): 1664. http://dx.doi.org/10.3390/e25121664.

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This paper introduces a novel method for enhancing fault classification and diagnosis in dynamic nonlinear processes. The method focuses on dynamic feature extraction within multivariate time series data and utilizes dynamic reconstruction errors to augment the feature set. A fault classification procedure is then developed, using the weighted maximum scatter difference (WMSD) dimensionality reduction criterion and quadratic discriminant analysis (QDA) classifier. This method addresses the challenge of high-dimensional, sample-limited fault classification, offering early diagnosis capabilities

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Ainurrochmah, Alifta, Memi Nor Hayati, and Andi M. Ade Satriya. "Alifta Ainurrochmah, Perbandinga." Jurnal Aplikasi Statistika & Komputasi Statistik 11, no. 2 (2020): 37. http://dx.doi.org/10.34123/jurnalasks.v11i2.156.

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Classification is a technique to form a model of data that is already known to its classification group. The model was formed will be used to classify new objects. Fisher discriminant analysis is multivariate technique to separate objects in different groups. Naive Bayes is a classification technique based on probability and Bayes theorem with assumption of independence. This research has a goal to compare the level of classification accuracy between Fisher's discriminant analysis and Naive Bayes method on the insurance premium payment status customer. The data used four independent variables

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Hu, Fei, Fan Yang, Huiqun Xie, et al. "Predicting the Occurrence of Advanced Schistosomiasis Based on FISHER Discriminant Analysis of Hematological Biomarkers." Pathogens 11, no. 9 (2022): 1004. http://dx.doi.org/10.3390/pathogens11091004.

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We established a model that predicts the possibility of chronic schistosomiasis (CS) patients developing into advanced schistosomiasis (AS) patients using special biomarkers that were detected in human peripheral blood. Blood biomarkers from two cohorts (132 CS cases and 139 AS cases) were examined and data were collected and analyzed by univariate and multivariate logistic regression analysis. Fisher discriminant analysis (FDA) for advanced schistosomiasis was established based on specific predictive diagnostic indicators and its accuracy was assessed using data of 109 CS. The results showed

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Chen, Song, Xue Hai Fu, He Rong Gui, and Lin Hua Sun. "Multivariate statistical analysis of the hydro-geochemical characteristics for Mining groundwater: a case study from Baishan mining, northern Anhui Province, China." Water Practice and Technology 8, no. 1 (2013): 131–41. http://dx.doi.org/10.2166/wpt.2013.014.

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Major ions were analyzed for twenty five groundwater samples collected from diverse aquifer in Baishan mining, northern Anhui province, China. Conventional graphical and multivariate statistical approach were completed to identify the hydro-geochemical process and water-rock interaction, that be combined with the Cluster Analysis (CA) and Fisher discriminant analysis to recognize the sealed samples, the result showed: the diverse samples have vary ions inheriting from aquifer, samples collected from Sandstone aquifer (SA) is characterized by the high concentration of Na+ + K+, for the feldspar

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Jiang, Jian-Hui, Roumiana Tsenkova, Yuqing Wu, Ru-Qin Yu, and Yukihiro Ozaki. "Principal Discriminant Variate Method for Classification of Multicollinear Data: Applications to Near-Infrared Spectra of Cow Blood Samples." Applied Spectroscopy 56, no. 4 (2002): 488–501. http://dx.doi.org/10.1366/0003702021954944.

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A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for effectively handling multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. This motivation furnishes the PDV method with improved stability in prediction without significant loss of separability. Different formulations for th

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Han, F., X. Huang, E. Teye, H. Gu, H. Dai, and L. Yao. "A nondestructive method for fish freshness determination with electronic tongue combined with linear and non-linear multivariate algorithms." Czech Journal of Food Sciences 32, No. 6 (2014): 532–37. http://dx.doi.org/10.17221/88/2014-cjfs.

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Electronic tongue coupled with linear and non-linear multivariate algorithms was attempted to address the drawbacks of fish freshness detection. Parabramis pekinensis fish samples stored at 4&deg;C were used. Total volatile basic nitrogen (TVB-N) and total viable count (TVC) of the samples were measured. Fisher liner discriminant analysis (Fisher LDA) and support vector machine (SVM) were applied comparatively to classify the samples stored at different days. The results revealed that SVM model was better than Fisher LDA model with a higher identification rate of 97.22% in the prediction s

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Zhang, Jie, Wenna Guo, Qiao Li, Faxin Sun, Xiaomeng Xu, and Hui Xu. "Discriminant Analysis of Traditional Chinese Medicinal Properties Based on Holistic Chemical Profiling by 1H-NMR Spectrometry." Evidence-Based Complementary and Alternative Medicine 2020 (March 9, 2020): 1–7. http://dx.doi.org/10.1155/2020/3141340.

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Medicinal property, which is closely related to drug chemical profiling, is the essence of traditional Chinese medicine (TCM) theory and has always been the focus of modern Chinese medicine. Based on dozens of classic and commonly used TCM herbs with recognized medicinal properties, the present study just aimed to investigate the feasibility and reliability of medicinal property discriminant by using 1H-NMR spectrometry, which provided a mass of spectral data showing holistic chemical profile for multivariate analysis and data mining, including principal component analysis (PCA), Fisher linear

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Lim, Jong Gwan, Mi-hye Kim, and Sahngwoon Lee. "Empirical Validation of Objective Functions in Feature Selection Based on Acceleration Motion Segmentation Data." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/280140.

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Recent change in evaluation criteria from accuracy alone to trade-off with time delay has inspired multivariate energy-based approaches in motion segmentation using acceleration. The essence of multivariate approaches lies in the construction of highly dimensional energy and requires feature subset selection in machine learning. Due to fast process, filter methods are preferred; however, their poorer estimate is of the main concerns. This paper aims at empirical validation of three objective functions for filter approaches, Fisher discriminant ratio, multiple correlation (MC), and mutual infor

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Hamidu, Aliyu Chamalwa, Rann Bakari Harun, and Sambo Uba Emmanuel. "Multivariate Modeling of Student Placement in Nigeria Higher Institutions in University of Maiduguri: An Application of Fisher's Linear Discriminant Analysis." Continental J. Applied Sciences 14, no. 1 (2019): 8–24. https://doi.org/10.5281/zenodo.2612919.

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Students’ placement into courses of study in most (if not all) tertiary institutions in Nigeria had long been a problematic exercise. Most students get placed into courses of study they are ill suited to and then for which they are ill prepared. This research work titled “Multivariate Modeling of Student Placement in Nigeria Higher Institutions in University of Maiduguri: An Application of Fisher’s Linear Discriminant Analysis”. Focused on the Statistical Analysis of classifying students into various department based on their scores on Unified Tertiary Matriculation Exa

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Pei, Mochao, Hongru Li, and He Yu. "A Novel Three-stage Feature Fusion Methodology and its Application in Degradation State Identification for Hydraulic Pumps." Measurement Science Review 21, no. 5 (2021): 123–35. http://dx.doi.org/10.2478/msr-2021-0018.

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Abstract The performance of feature is essential to the degradation state identification for hydraulic pumps. The initial feature set extracted from the vibration signal of the hydraulic pump is often high-dimensional and contains redundant information, which undermines the effectiveness of the feature set. The novel three-stage feature fusion scheme proposed in this paper aims to enhance the performance of the original features extracted from the vibration signal. First, sparse local Fisher discriminant analysis (SLFDA) performs intra-set fusion within the two original feature sets, respectiv

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ZHU, YE, TIANZI JIANG, YUAN ZHOU, and LISHA ZHAO. "DISCRIMINATIVE ANALYSIS OF FUNCTIONAL NEAR-INFRARED SPECTROSCOPY SIGNALS FOR DEVELOPMENT OF NEUROIMAGING BIOMARKERS OF ELDERLY DEPRESSION." Journal of Innovative Optical Health Sciences 03, no. 01 (2010): 69–74. http://dx.doi.org/10.1142/s1793545810000848.

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Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technology which is suitable for psychiatric patients. Several fNIRS studies have found abnormal brain activations during cognitive tasks in elderly depression. In this paper, we proposed a discriminative model of multivariate pattern classification based on fNIRS signals to distinguish elderly depressed patients from healthy controls. This model used the brain activation patterns during a verbal fluency task as features of classification. Then Pseudo-Fisher Linear Discriminant Analysis was performed on the feature space to genera

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Yang, Bo Ming, Zong Han Yang, Jong Kang Liu, Hui Yu Lee, and Chih Ming Kao. "Evaluation of Groundwater Quality at an Industrial Park Site Zone Using Statistical Analyses: A Case Study in Taiwan." Applied Mechanics and Materials 457-458 (October 2013): 1581–84. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.1581.

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Multivariate statistical analysis explains the huge and complicated current situation of the original data efficiently, concisely, and explicitly. It simplifies the original data into representative factors, or bases on the similarity between data to cluster and identify clustering outcome. In this study, the statistical software SPSS 12.0 was used to perform the multivariate statistical analysis to evaluate characteristics of groundwater quality at an industrial park site located in Kaohsiung, Taiwan. Results from the principal component analysis (PCA) and factor analyses (FA) show that seven

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Hino, Hideitsu, Nima Reyhani, and Noboru Murata. "Multiple Kernel Learning with Gaussianity Measures." Neural Computation 24, no. 7 (2012): 1853–81. http://dx.doi.org/10.1162/neco_a_00299.

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Kernel methods are known to be effective for nonlinear multivariate analysis. One of the main issues in the practical use of kernel methods is the selection of kernel. There have been a lot of studies on kernel selection and kernel learning. Multiple kernel learning (MKL) is one of the promising kernel optimization approaches. Kernel methods are applied to various classifiers including Fisher discriminant analysis (FDA). FDA gives the Bayes optimal classification axis if the data distribution of each class in the feature space is a gaussian with a shared covariance structure. Based on this fac

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Qureshi, Fatir, James Adams, Kathryn Hanagan, Dae-Wook Kang, Rosa Krajmalnik-Brown, and Juergen Hahn. "Multivariate Analysis of Fecal Metabolites from Children with Autism Spectrum Disorder and Gastrointestinal Symptoms before and after Microbiota Transfer Therapy." Journal of Personalized Medicine 10, no. 4 (2020): 152. http://dx.doi.org/10.3390/jpm10040152.

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Fecal microbiota transplant (FMT) holds significant promise for patients with Autism Spectrum Disorder (ASD) and gastrointestinal (GI) symptoms. Prior work has demonstrated that plasma metabolite profiles of children with ASD become more similar to those of their typically developing (TD) peers following this treatment. This work measures the concentration of 669 biochemical compounds in feces of a cohort of 18 ASD and 20 TD children using ultrahigh performance liquid chromatography-tandem mass spectroscopy. Subsequent measurements were taken from the ASD cohort over the course of 10-week Micr

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Merhof, Dorit, Pawel J. Markiewicz, Günther Platsch, et al. "Optimized Data Preprocessing for Multivariate Analysis Applied to 99mTc-ECD SPECT Data Sets of Alzheimer's Patients and Asymptomatic Controls." Journal of Cerebral Blood Flow & Metabolism 31, no. 1 (2010): 371–83. http://dx.doi.org/10.1038/jcbfm.2010.112.

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Multivariate image analysis has shown potential for classification between Alzheimer's disease (AD) patients and healthy controls with a high-diagnostic performance. As image analysis of positron emission tomography (PET) and single photon emission computed tomography (SPECT) data critically depends on appropriate data preprocessing, the focus of this work is to investigate the impact of data preprocessing on the outcome of the analysis, and to identify an optimal data preprocessing method. In this work, technetium-99methylcysteinatedimer (99mTc-ECD) SPECT data sets of 28 AD patients and 28 as

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Oliveira, Leonardo Bernardes Taverny de, Antonio Clementino dos Santos, Hugo Mariano Rodrigues de Oliveira, Tiago Barbalho André, Durval Nolasco das Neves Neto, and Otacílio Silveira Júnior. "Characteristics and classification of the quality and productive standards of the mombaça grass under a livestock-forest system or full sun." Semina: Ciências Agrárias 39, no. 4 (2018): 1447. http://dx.doi.org/10.5433/1679-0359.2018v39n4p1447.

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The objective of this study is to identify qualitative and productive changes in Mombasa grass under a livestock-forest system or full sun and to classify the variables that are most relevant for evaluating the qualitative performance of these systems. The experiments were conducted in shade areas, with four replications for each of the 12 treatments, including four growth cycles and three levels of shading in Mombasa grass, totaling 48 experimental units. Calcium (Ca), magnesium (Mg), potassium (K), phosphorus (P), nitrogen (N) and dry mass production (DMP) (kg ha-1) were used as discriminato

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Adams, James B., Troy Vargason, Dae-Wook Kang, Rosa Krajmalnik-Brown, and Juergen Hahn. "Multivariate Analysis of Plasma Metabolites in Children with Autism Spectrum Disorder and Gastrointestinal Symptoms Before and After Microbiota Transfer Therapy." Processes 7, no. 11 (2019): 806. http://dx.doi.org/10.3390/pr7110806.

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Current diagnosis of autism spectrum disorder (ASD) is based on assessment of behavioral symptoms, although there is strong evidence that ASD affects multiple organ systems including the gastrointestinal (GI) tract. This study used Fisher discriminant analysis (FDA) to evaluate plasma metabolites from 18 children with ASD and chronic GI problems (ASD + GI cohort) and 20 typically developing (TD) children without GI problems (TD − GI cohort). Using three plasma metabolites that may represent three general groups of metabolic abnormalities, it was possible to distinguish the ASD + GI cohort from

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Cárdenas Parra, Leidy Yurany, Ana Elisa Rojas Rodríguez, Jorge Enrique Pérez Cárdenas, and Juan Manuel Pérez-Agudelo. "Molecular Evaluation of the mRNA Expression of the ERG11, ERG3, CgCDR1, and CgSNQ2 Genes Linked to Fluconazole Resistance in Candida glabrata in a Colombian Population." Journal of Fungi 10, no. 7 (2024): 509. http://dx.doi.org/10.3390/jof10070509.

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Introduction: The study of Candida glabrata genes associated with fluconazole resistance, from a molecular perspective, increases the understanding of the phenomenon with a view to its clinical applicability. Objective: We sought to establish the predictive molecular profile of fluconazole resistance in Candida glabrata by analyzing the ERG11, ERG3, CgCDR1, and CgSNQ2 genes. Method: Expression was quantified using RT-qPCR. Metrics were obtained through molecular docking and Fisher discriminant functions. Additionally, a predictive classification was made against the susceptibility of C. glabra

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Md Nor, Norazwan, Che Rosmani Che Hassan, and Mohd Azlan Hussain. "A review of data-driven fault detection and diagnosis methods: applications in chemical process systems." Reviews in Chemical Engineering 36, no. 4 (2020): 513–53. http://dx.doi.org/10.1515/revce-2017-0069.

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AbstractFault detection and diagnosis (FDD) systems are developed to characterize normal variations and detect abnormal changes in a process plant. It is always important for early detection and diagnosis, especially in chemical process systems to prevent process disruptions, shutdowns, or even process failures. However, there have been only limited reviews of data-driven FDD methods published in the literature. Therefore, the aim of this review is to provide the state-of-the-art reference for chemical engineers and to promote the application of data-driven FDD methods in chemical process syst

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Mugo, Robinson, and Sei-Ichi Saitoh. "Ensemble Modelling of Skipjack Tuna (Katsuwonus pelamis) Habitats in the Western North Pacific Using Satellite Remotely Sensed Data; a Comparative Analysis Using Machine-Learning Models." Remote Sensing 12, no. 16 (2020): 2591. http://dx.doi.org/10.3390/rs12162591.

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To examine skipjack tuna’s habitat utilization in the western North Pacific (WNP) we used an ensemble modelling approach, which applied a fisher- derived presence-only dataset and three satellite remote-sensing predictor variables. The skipjack tuna data were compiled from daily point fishing data into monthly composites and re-gridded into a quarter degree resolution to match the environmental predictor variables, the sea surface temperature (SST), sea surface chlorophyll-a (SSC) and sea surface height anomalies (SSHA), which were also processed at quarter degree spatial resolution. Using the

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Guo, Meijun, Jie Shen, Xi-e. Song, et al. "Comprehensive evaluation of fluroxypyr herbicide on physiological parameters of spring hybrid millet." PeerJ 7 (September 26, 2019): e7794. http://dx.doi.org/10.7717/peerj.7794.

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Foxtail millet (Setaria italic L.) is an important food and fodder crop that is cultivated worldwide. Quantifying the effects of herbicides on foxtail millet is critical for safe herbicide application. In this study, we analyzed the effects of different fluroxypyr dosages on the growth parameters and physiological parametric of foxtail millet, that is, peroxidation characteristics, photosynthetic characteristics, and endogenous hormone production, by using multivariate statistical analysis. Indicators were screened via Fisher discriminant analysis, and the growth parameters, peroxidation chara

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Pan, Shixin, Chong Liu, Jiarui Chen, et al. "Age and flexors as risk factors for cervical radiculopathy: A new machine learning method." Medicine 103, no. 4 (2024): e36939. http://dx.doi.org/10.1097/md.0000000000036939.

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This study aimed to investigate the risk factors for cervical radiculopathy (CR) along with identifying the relationships between age, cervical flexors, and CR. This was a retrospective cohort study, including 60 patients with CR enrolled between December 2018 and June 2020. In this study, we measured C2 to C7 Cobb angle, disc degeneration, endplate degeneration, and morphology of paraspinal muscles and evaluated the value of predictive methods using receiver operating characteristic curves. Next, we established a diagnostic model for CR using Fisher discriminant model and compared different m

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Efimenko, O. O., I. M. Retunska, and T. O. Marturosova. "Forecasting early climax on taking into account of reproductive and biological age indicators." Biomedical and Biosocial Anthropology, no. 35 (May 5, 2019): 23–28. http://dx.doi.org/10.31393/bba35-2019-04.

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A great asset of our time is a significant increase in life expectancy. This is especially true for women’s health issues, as women live longer than men and are more stubbornly opposed to age-related changes and aging, trying to preserve not only beauty and youth, but also reproductive function. The use of algorithms and mathematical models for predicting the occurrence of pathology in medical practice makes it possible to predict in advance not only the fact of the occurrence of this complication, but also to determine the likelihood of its occurrence, which is very important for the subseque

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Lunghi, Monia, Laura Vanelli, Paolo Bernasconi, et al. "The Relationship between the Immunophenotypic Profile and Cytogenetic Abnormalities in Acute Myeloid Leukemia." Blood 106, no. 11 (2005): 4495. http://dx.doi.org/10.1182/blood.v106.11.4495.4495.

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Abstract We studied the immunophenotypic characteristics of 459 consecutive adult AML patients and their correlation with karyotype. Immunophenotype was performed using a panel of 26 directly conjugated monoclonal antibodies. Cytogenetic analysis was performed using a standard G-banding technique. Karyotype was available in 394 patients (not done in 15, failed in 50): 1) 45 (11.4%) were t(15;17) APL patients with a mature myeloid phenotype (HLA-DR-/CD13+ and/or CD33+). CD2 and CD56 were expressed in 20% and 13.3% of cases, respectively. CD11b-positivity was less frequent than in the other cyto

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Rakotonirina, Jean Claude, and Brian L. Fisher. "Revision of the Malagasy Camponotus subgenus Myrmosaga (Hymenoptera, Formicidae) using qualitative and quantitative morphology." ZooKeys 1098 (May 3, 2022): 1–180. http://dx.doi.org/10.3897/zookeys.1098.73223.

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The Camponotus subgenus Myrmosagasubgen. rev. from the Malagasy region is revised based on analysis of both qualitative morphological characters and morphometric traits. The multivariate analysis used the Nest Centroid (NC)-clustering method to generate species hypotheses based on 19 continuous morphological traits of minor workers. The proposed species hypotheses were confirmed by cumulative Linear Discriminant Analysis (LDA). Morphometric ratios for the subsets of minor and major workers were used in species descriptions and redefinitions. The present study places the subgenus Myrmopytiasyn.

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Volkova, N. V., Ju V. Malysheva, T. N. Iureva, and S. I. Kolesnikov. "The Role of Biologically Active Aqueous Humor Molecules of the Anterior Chamber and Lacrimal Fluid in the Implementation of the Hypotensive Effect of Non-Penetrating Deep Sclerectomy." Acta Biomedica Scientifica 6, no. 2 (2021): 126–32. http://dx.doi.org/10.29413/abs.2021-6.2.14.

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To date, the factors affecting the course of the reparative process after non-penetrating deep sclerectomy (NPDS) have not been fully determined. There is no systematic information about the regulatory role of the cytokines TGF-β, IL-6, IL-8 and MMP-9, VEGF A 121 and 165 in the formation mechanisms of the newly created pathways consistency of intraocular aqueous humor outflow.Purpose. To determine possible ways of impact of biologically active aqueous humor molecules of the anterior chamber and lacrimal fluid on the hypotensive effect of non-penetrating deep sclerectomy.Methods. A prospective

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Rakotonirina, Jean Claude, and Brian L. Fisher. "Revision of the Malagasy Camponotus subgenus Myrmosaga (Hymenoptera, Formicidae) using qualitative and quantitative morphology." ZooKeys 1098 (May 3, 2022): 1–180. https://doi.org/10.3897/zookeys.1098.73223.

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The Camponotus subgenus Myrmosaga subgen. rev. from the Malagasy region is revised based on analysis of both qualitative morphological characters and morphometric traits. The multivariate analysis used the Nest Centroid (NC)-clustering method to generate species hypotheses based on 19 continuous morphological traits of minor workers. The proposed species hypotheses were confirmed by cumulative Linear Discriminant Analysis (LDA). Morphometric ratios for the subsets of minor and major workers were used in species descriptions and redefinitions. The present study places the subgenus Myrmopytia sy

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Jin, Yisen, Xin Zhang, and Aaron J. Molstad. "Kernelized discriminant analysis for joint modeling of multivariate categorical responses." Journal of Computational and Graphical Statistics, July 9, 2025, 1–22. https://doi.org/10.1080/10618600.2025.2526412.

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Doherty, Conor T., and Meagan S. Mauter. "Fisher Discriminant Analysis for Extracting Interpretable Phenological Information from Multivariate Time Series Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 1–14. https://doi.org/10.1109/jstars.2024.3517415.

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Lee, Yeon-Hee, Jong Hyun Won, Q. Schick Auh, and Yung-Kyun Noh. "Age group prediction with panoramic radiomorphometric parameters using machine learning algorithms." Scientific Reports 12, no. 1 (2022). http://dx.doi.org/10.1038/s41598-022-15691-9.

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AbstractThe aim of this study is to investigate the relationship of 18 radiomorphometric parameters of panoramic radiographs based on age, and to estimate the age group of people with permanent dentition in a non-invasive, comprehensive, and accurate manner using five machine learning algorithms. For the study population (209 men and 262 women; mean age, 32.12 ± 18.71 years), 471 digital panoramic radiographs of Korean individuals were applied. The participants were divided into three groups (with a 20-year age gap) and six groups (with a 10-year age gap), and each age group was estimated usin

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Chen, Zailin, Xianfeng Cheng, Xingyu Wang, Shijun Ni, Qiulian Yu, and Junchun Hu. "Identification of core carcinogenic elements based on the age-standardized mortality rate of lung cancer in Xuanwei Formation coal in China." Scientific Reports 14, no. 1 (2024). http://dx.doi.org/10.1038/s41598-023-49975-5.

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AbstractIn this study, the core carcinogenic elements in Xuanwei Formation coal were identified. Thirty-one samples were collected based on the age-standardized mortality rate (ASMR) of lung cancer; Si, V, Cr, Co, Ni, As, Mo, Cd, Sb, Pb, and rare earth elements and yttrium (REYs) were analyzed and compared; multivariate statistical analyses (CA, PCA, and FDA) were performed; and comprehensive identification was carried out by combining multivariate statistical analyses with toxicology and mineralogy. The final results indicated that (1) the high-concentration Si, Ni, V, Cr, Co, and Cd in coal

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Csősz, Sándor, Ana C. Loss, and Brian L. Fisher. "Exploring the diversity of the Malagasy Ponera (Hymenoptera: Formicidae) fauna via integrative taxonomy." Organisms Diversity & Evolution, June 28, 2023. http://dx.doi.org/10.1007/s13127-023-00610-1.

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AbstractThe genus Ponera includes over 60 extant species worldwide. These tiny, endogeic predator ants are predominantly distributed in the Indomalaya and Australasia regions, with a few additional Holarctic species. Herein, we explore and describe the diversity of the Malagasy Ponera fauna through an integrative taxonomic approach. We obtained our morphological species hypotheses from multivariate analyses of ten continuous morphometric characters. Species boundaries and reliability of morphological clusters were tested via confirmatory Linear Discriminant Analysis (LDA), cross-validation (LO

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Marmarelis, Vasilis Z., Dae C. Shin, and Rong Zhang. "The Dynamic Relationship Between Cortical Oxygenation and End-Tidal CO2 Transient Changes Is Impaired in Mild Cognitive Impairment Patients." Frontiers in Physiology 12 (December 9, 2021). http://dx.doi.org/10.3389/fphys.2021.772456.

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Background: Recent studies have utilized data-based dynamic modeling to establish strong association between dysregulation of cerebral perfusion and Mild Cognitive Impairment (MCI), expressed in terms of impaired CO2 dynamic vasomotor reactivity in the cerebral vasculature. This raises the question of whether this is due to dysregulation of central mechanisms (baroreflex and chemoreflex) or mechanisms of cortical tissue oxygenation (CTO) in MCI patients. We seek to answer this question using data-based input-output predictive dynamic models.Objective: To use subject-specific data-based multiva

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Kerr, Hamish A., Eric H. Ledet, Juergen Hahn, and Kathryn Hollowood-Jones. "Quantitative Assessment of Balance for Accurate Prediction of Return to Sport From Sport-Related Concussion." Sports Health: A Multidisciplinary Approach, February 4, 2022, 194173812110688. http://dx.doi.org/10.1177/19417381211068817.

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Background: Determining when athletes are able to return to sport after sports-related concussion (SRC) can be difficult. Hypothesis: A multimodal algorithm using cognitive testing, postural stability, and clinical assessment can predict return to sports after SRC. Study Design: Prospective cohort. Level of Evidence: Level 2b. Methods: Athletes were evaluated within 2 to 3 weeks of SRC. Clinical assessment, Immediate Post Concussion and Cognitive Testing (ImPACT), and postural stability (Equilibrate) were conducted. Resulting data and machine learning techniques were used to optimize an algori

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Song, Peng, Jian Shi, Xinju Liu, et al. "Comprehensive Evaluation Method of the Tight Oil Reservoir Quality in the Ordos Basin." Geofluids 2025, no. 1 (2025). https://doi.org/10.1155/gfl/7745871.

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The Wuqi area of the Ordos Basin boasts significant resource potential in the Chang 8 member of the Yanchang Formation. However, under the dual control of lithology and physical properties, reservoirs are generally dense and heterogeneous, and the quality of the oil layer changes rapidly, which brings difficulties to the optimization of favorable areas. To evaluate the reservoir quality more accurately, based on core observations, and logging and dynamic data analysis, combined with casting thin sections, scanning electron microscopy, high‐pressure mercury injection, nuclear magnetic resonance

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