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Journal articles on the topic 'Conditional discriminant analysis'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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1

Barrat, Sabine, and Salvatore Tabbone. "A Progressive Learning Method for Symbol Recognition." JUCS - Journal of Universal Computer Science 14, no. (2) (2008): 224–36. https://doi.org/10.3217/jucs-014-02-0224.

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This paper deals with a progressive learning method for symbol recognition which improves its own recognition rate when new symbols are recognized in graphic documents. We propose a discriminant analysis method which provides allocation rules from a training set of labelled data. However a discriminant analysis method is efficient only if the training set and the test data are defined in the same conditions but it is rare in real life. In order to overcome this problem, a conditional vector is added to each instance to take into account the parasitic effects between the test data and the train

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Rohmana, Shella Faiz, Agus Rusgiyono, and Sugito Sugito. "PENENTUAN FAKTOR-FAKTOR YANG MEMPENGARUHI INTENSITAS CURAH HUJAN DENGAN ANALISIS DISKRIMINAN GANDA DAN REGRESI LOGISTIK MULTINOMIAL (Studi Kasus: Data Curah Hujan Kota Semarang dari Stasiun Meteorologi Maritim Tanjung Emas Periode Oktober 2018 – Maret 2019)." Jurnal Gaussian 8, no. 3 (2019): 398–406. http://dx.doi.org/10.14710/j.gauss.v8i3.26684.

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Meteorologist develop rainfall forecasting methods to obtain better and more accurate rainfall information. One of them is the research of grid data and the method of grouping rainfall. According to BMKG, rainfall is classified into light, medium, and heavy rain. This study aims to determine the factors that influencing rainfall grouping using multiple discriminant analysis with a stepwise selection method. This study uses the daily climate data of Semarang City for period of October 2018 to March 2019. Based on its partial F value, the wind speed variable is eliminated so the significant vari

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Misra, R. K. "Quadratic Discriminant Analysis with Covariance for Stock Delineation and Population Differentiation: A Study of Beaked Redfishes (Sebastes mentella and S. fasciatus)." Canadian Journal of Fisheries and Aquatic Sciences 42, no. 10 (1985): 1672–76. http://dx.doi.org/10.1139/f85-209.

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Stock delineation is of vital importance in fisheries management programs. Linear discriminant function (LDF) has been employed extensively in population differentiation studies but is of severely restricted usefulness when populations differ in their dispersion matrices. Quadratic discriminant function (QDF) is the appropriate analysis to employ in these situations. Here, I analyzed morphometric data of beaked redfishes (Sebastes mentella and S. fasciatus) by a recently developed conditional QDF.

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Stabingis, Giedrius, Kęstutis Dučinskas, and Lijana Stabingienė. "Comparison of spatial classification rules with different conditional distributions of class label." Nonlinear Analysis: Modelling and Control 19, no. 1 (2014): 109–17. http://dx.doi.org/10.15388/na.2014.1.7.

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In this paper spatial classification rules based on Bayes discriminant functions are considered. The novelty of this work is that the statistical supervised classification method is improved by extending the influence of spatial correlation between observation to be classified and training sample. Such methods are used for data containing spatially correlated noise. Method accuracy is tested experimentally on artificially corrupted images. This classification rule with distance based conditional distribution for class label shows advantage against other classification rule ignoring such influe

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Xiao, Yutang, Hongbo Shi, Boyu Wang, Yang Tao, Shuai Tan, and Bing Song. "Weighted Conditional Discriminant Analysis for Unseen Operating Modes Fault Diagnosis in Chemical Processes." IEEE Transactions on Instrumentation and Measurement 71 (2022): 1–14. http://dx.doi.org/10.1109/tim.2022.3152235.

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Siddiqi, Muhammad Hameed, Rahman Ali, Adil Mehmood Khan, Young-Tack Park, and Sungyoung Lee. "Human Facial Expression Recognition Using Stepwise Linear Discriminant Analysis and Hidden Conditional Random Fields." IEEE Transactions on Image Processing 24, no. 4 (2015): 1386–98. http://dx.doi.org/10.1109/tip.2015.2405346.

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Hino, Hideitsu, and Noboru Murata. "A Conditional Entropy Minimization Criterion for Dimensionality Reduction and Multiple Kernel Learning." Neural Computation 22, no. 11 (2010): 2887–923. http://dx.doi.org/10.1162/neco_a_00027.

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Reducing the dimensionality of high-dimensional data without losing its essential information is an important task in information processing. When class labels of training data are available, Fisher discriminant analysis (FDA) has been widely used. However, the optimality of FDA is guaranteed only in a very restricted ideal circumstance, and it is often observed that FDA does not provide a good classification surface for many real problems. This letter treats the problem of supervised dimensionality reduction from the viewpoint of information theory and proposes a framework of dimensionality r

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Bi, Yingtao, and Daniel R. Jeske. "The efficiency of logistic regression compared to normal discriminant analysis under class-conditional classification noise." Journal of Multivariate Analysis 101, no. 7 (2010): 1622–37. http://dx.doi.org/10.1016/j.jmva.2010.03.001.

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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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Baliyan, Mohit, and Evgeny M. Mirkes. "Linear Dimensionality Reduction: What Is Better?" Data 10, no. 5 (2025): 70. https://doi.org/10.3390/data10050070.

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This research paper focuses on dimensionality reduction, which is a major subproblem in any data processing operation. Dimensionality reduction based on principal components is the most used methodology. Our paper examines three heuristics, namely Kaiser’s rule, the broken stick, and the conditional number rule, for selecting informative principal components when using principal component analysis to reduce high-dimensional data to lower dimensions. This study uses 22 classification datasets and three classifiers, namely Fisher’s discriminant classifier, logistic regression, and K nearest neig

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Boedeker, Peter, and Nathan T. Kearns. "Linear Discriminant Analysis for Prediction of Group Membership: A User-Friendly Primer." Advances in Methods and Practices in Psychological Science 2, no. 3 (2019): 250–63. http://dx.doi.org/10.1177/2515245919849378.

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In psychology, researchers are often interested in the predictive classification of individuals. Various models exist for such a purpose, but which model is considered a best practice is conditional on attributes of the data. Under certain conditions, linear discriminant analysis (LDA) has been shown to perform better than other predictive methods, such as logistic regression, multinomial logistic regression, random forests, support-vector machines, and the K-nearest neighbor algorithm. The purpose of this Tutorial is to provide researchers who already have a basic level of statistical trainin

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Okun, Morris A. "Predictors of Volunteer Status in a Retirement Community." International Journal of Aging and Human Development 36, no. 1 (1993): 57–74. http://dx.doi.org/10.2190/89u5-bbb5-la35-y435.

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In the present study, actual volunteers, latent volunteers, conditional volunteers, and definite nonvolunteers living in a retirement community are compared on social-structural, role, environmental, resource, lifestyle, and individual difference variables. Three functions emerged from the discriminant analysis. Actual volunteers attend church frequently, are free of activity limitations due to health, have volunteered previously, and belong to several clubs and organizations. Latent volunteers engage in informal religious behaviors, attend church about once a month, are about seventy years ol

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Zulnaidi, Hutkemri, Chan Choon Tak, and Leong Kwan Eu. "Measurement Model Testing: Adaption of Metacognitive Awareness Toward Mathematic Reasoning Among Undergraduate Education Students." Contemporary Mathematics and Science Education 3, no. 2 (2022): ep22021. http://dx.doi.org/10.30935/conmaths/12510.

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This quantitative research aimed to measure the metacognitive awareness model toward mathematics reasoning among 184 university students. Metacognitive awareness demonstrates convergent, and discriminant validity was performed, which includes six factors: conditional knowledge, declarative knowledge, procedural knowledge, monitoring, planning, and evaluation. Data analysis was using exploratory factor analysis. The results indicated that Cronbach’s alpha coefficients demonstrated that metacognitive awareness was a reliable instrument researcher could use to evaluate university students’ mathem

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Kumar, Shashvat *. Raman Chadha Arshpreet Kaur. "PERFORMANCE INVESTIGATION OF GENERATIVE MODELS FOR CLASSIFICATION OF ALCOHOLS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 6 (2016): 110–18. https://doi.org/10.5281/zenodo.54675.

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Classification is the process related to categorization, the process in which ideas and objects are understood. It helps in clear identification of species for classification of various chemical compounds like Alcohol, Wine various discriminative approaches have been used .Discriminative methods offer good predictive performance and have been widely used in many applications but are unable to make efficient use of the unlabelled information. In such scenarios generative approaches have better applicability, as they are able to knob problems, such as in scenarios where variability in the range

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Lazurenko, Dmitry, Igor Shepelev, Dmitry Shaposhnikov, Anton Saevskiy, and Valery Kiroy. "Discriminative Frequencies and Temporal EEG Segmentation in the Motor Imagery Classification Approach." Applied Sciences 12, no. 5 (2022): 2736. http://dx.doi.org/10.3390/app12052736.

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A linear discriminant analysis transformation-based approach to the classification of three different motor imagery types for brain–computer interfaces was considered. The study involved 16 conditionally healthy subjects (12 men, 4 women, mean age of 21.5 years). First, the search for subject-specific discriminative frequencies was conducted in the task of movement-related activity. This procedure was shown to increase the classification accuracy compared to the conditional common spatial pattern (CSP) algorithm, followed by a linear classifier considered as a baseline approach. In addition, a

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Chung, Jiwon, and Kyungho Lee. "Credit Card Fraud Detection: An Improved Strategy for High Recall Using KNN, LDA, and Linear Regression." Sensors 23, no. 18 (2023): 7788. http://dx.doi.org/10.3390/s23187788.

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Efficiently and accurately identifying fraudulent credit card transactions has emerged as a significant global concern along with the growth of electronic commerce and the proliferation of Internet of Things (IoT) devices. In this regard, this paper proposes an improved algorithm for highly sensitive credit card fraud detection. Our approach leverages three machine learning models: K-nearest neighbor, linear discriminant analysis, and linear regression. Subsequently, we apply additional conditional statements, such as “IF” and “THEN”, and operators, such as “>“ and “<“, to the results. T

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Tama, Bayu Adhi, and Sunghoon Lim. "A Comparative Performance Evaluation of Classification Algorithms for Clinical Decision Support Systems." Mathematics 8, no. 10 (2020): 1814. http://dx.doi.org/10.3390/math8101814.

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Classification algorithms are widely taken into account for clinical decision support systems. However, it is not always straightforward to understand the behavior of such algorithms on a multiple disease prediction task. When a new classifier is introduced, we, in most cases, will ask ourselves whether the classifier performs well on a particular clinical dataset or not. The decision to utilize classifiers mostly relies upon the type of data and classification task, thus making it often made arbitrarily. In this study, a comparative evaluation of a wide-array classifier pertaining to six diff

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Yan, Yifan, and Weiwei Yu. "Cross-age face synthesis based on conditional adversarial autoencoder." Frontiers in Computing and Intelligent Systems 3, no. 1 (2023): 65–71. http://dx.doi.org/10.54097/fcis.v3i1.6026.

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Face aging aims to render face images with desired age attribute. It has tremendous impact to a wide-range of applications, e.g., criminal investigation, entertainment. The rapid development of generative adversarial networks (GANs) has shown impressive results in face aging. Among them, the Conditional Adversarial Autoencoder (CAAE) proposed in 2017 has achieved good results in face aging. However, the generated faces still have the problems that the aging features are not obvious and the identity information are not well maintained. In addition, research have shown that the human aging proce

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Vergari, F., M. Della Seta, M. Del Monte, P. Fredi, and E. Lupia Palmieri. "Landslide susceptibility assessment in the Upper Orcia Valley (Southern Tuscany, Italy) through conditional analysis: a contribution to the unbiased selection of causal factors." Natural Hazards and Earth System Sciences 11, no. 5 (2011): 1475–97. http://dx.doi.org/10.5194/nhess-11-1475-2011.

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Abstract. In this work the conditional multivariate analysis was applied to evaluate landslide susceptibility in the Upper Orcia River Basin (Tuscany, Italy), where widespread denudation processes and agricultural practices have a mutual impact. We introduced an unbiased procedure for causal factor selection based on some intuitive statistical indices. This procedure is aimed at detecting among different potential factors the most discriminant ones in a given study area. Moreover, this step avoids generating too small and statistically insignificant spatial units by intersecting the factor map

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Karaliutė, Marta, and Kęstutis Dučinskas. "Performance of the supervised generative classifiers of spatio-temporal areal data using various spatial autocorrelation indexes." Nonlinear Analysis: Modelling and Control 28 (February 22, 2023): 1–14. http://dx.doi.org/10.15388/namc.2023.28.31434.

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This article is concerned with a generative approach to supervised classification of spatio-temporal data collected at fixed areal units and modeled by Gaussian Markov random field. We focused on the classifiers based on Bayes discriminant functions formed by the log-ratio of the class conditional likelihoods. As a novel modeling contribution, we propose to use decision threshold values induced by three popular spatial autocorrelation indexes, i.e., Moran’s I, Geary’s C and Getis–Ord G. The goal of this study is to extend the recent investigations in the context of geostatistical and hidden Ma

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Rasoarimalala, Nomena F., Tanjona Ramiadantsoa, Jean Claude Rakotonirina, and Brian L. Fisher. "Linear Morphometry of Male Genitalia Distinguishes the Ant Genera Monomorium and Syllophopsis (Hymenoptera: Formicidae) in Madagascar." Insects 15, no. 8 (2024): 605. http://dx.doi.org/10.3390/insects15080605.

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Morphometric analyses of male genitalia are routinely used to distinguish genera and species in beetles, butterflies, and flies, but are rarely used in ants, where most morphometric analyses focus on the external morphology of the worker caste. In this work, we performed linear morphometric analysis of the male genitalia to distinguish Monomorium and Syllophopsis in Madagascar. For 80 specimens, we measured 10 morphometric characters, especially on the paramere, volsella, and penisvalvae. Three datasets were made from linear measurements: mean (raw data), the ratios of characters (ratio data),

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Yoon, Sung-Hyun, Jong-June Jeon, and Ha-Jin Yu. "Regularized Within-Class Precision Matrix Based PLDA in Text-Dependent Speaker Verification." Applied Sciences 10, no. 18 (2020): 6571. http://dx.doi.org/10.3390/app10186571.

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In the field of speaker verification, probabilistic linear discriminant analysis (PLDA) is the dominant method for back-end scoring. To estimate the PLDA model, the between-class covariance and within-class precision matrices must be estimated from samples. However, the empirical covariance/precision estimated from samples has estimation errors due to the limited number of samples available. In this paper, we propose a method to improve the conventional PLDA by estimating the PLDA model using the regularized within-class precision matrix. We use graphical least absolute shrinking and selection

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Abidine, M'Hamed Bilal, Lamya Fergani, Belkacem Fergani, and Anthony Fleury. "Improving Human Activity Recognition in Smart Homes." International Journal of E-Health and Medical Communications 6, no. 3 (2015): 19–37. http://dx.doi.org/10.4018/ijehmc.2015070102.

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Even if it is now simple and cheap to collect sensors information in a smart home environment, the main issue remains to infer high-level activities from these simple readings. The main contribution of this work is twofold. Firstly, the authors demonstrate the efficiency of a new procedure for learning Optimized Cost-Sensitive Support Vector Machines (OCS-SVM) classifier based on the user inputs to appropriately tackle the problem of class imbalanced data. It uses a new criterion for the selection of the cost parameter attached to the training errors. Secondly, this method is assessed and comp

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Moya, Luis, Yukio Endo, Genki Okada, Shunichi Koshimura, and Erick Mas. "Drawback in the Change Detection Approach: False Detection during the 2018 Western Japan Floods." Remote Sensing 11, no. 19 (2019): 2320. http://dx.doi.org/10.3390/rs11192320.

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Synthetic aperture radar (SAR) images have been used to map flooded areas with great success. Flooded areas are often identified by detecting changes between a pair of images recorded before and after a certain flood. During the 2018 Western Japan Floods, the change detection method generated significant misclassifications for agricultural targets. To evaluate whether such a situation could be repeated in future events, this paper examines and identifies the causes of the misclassifications. We concluded that the errors occurred because of the following. (i) The use of only a single pair of SA

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Khan, Zafar Ali, and Won Sohn. "A Model for Abnormal Activity Recognition and Alert Generation System for Elderly Care by Hidden Conditional Random Fields Using R-Transform and Generalized Discriminant Analysis Features." Telemedicine and e-Health 18, no. 8 (2012): 641–47. http://dx.doi.org/10.1089/tmj.2011.0268.

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Wang, Lei, Qian Li, and Jin Qin. "Rotating Machinery Fault Diagnosis Method Based on Improved Semisupervised Generative Confrontation Network." Scientific Programming 2021 (December 23, 2021): 1–14. http://dx.doi.org/10.1155/2021/1761446.

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Error diagnosis and detection have become important in modern production due to the importance of spinning equipment. Artificial neural network pattern recognition methods are widely utilized in rotating equipment fault detection. These methods often need a large quantity of sample data to train the model; however, sample data (especially fault samples) are uncommon in engineering. Preliminary work focuses on dimensionality reduction for big data sets using semisupervised methods. The rotary machine’s polar coordinate signal is used to build a GAN network structure. ANN and tiny samples are ut

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Meng, Yu, Jianping Xuan, Long Xu, and Jie Liu. "Dynamic Reweighted Domain Adaption for Cross-Domain Bearing Fault Diagnosis." Machines 10, no. 4 (2022): 245. http://dx.doi.org/10.3390/machines10040245.

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Certain progress has been made in fault diagnosis under cross-domain scenarios recently. Most researchers have paid almost all their attention to promoting domain adaptation in a common space. However, several challenges that will cause negative transfer have been ignored. In this paper, a reweighting method is proposed to overcome this difficulty from two aspects. First, extracted features differ greatly from one another in promoting positive transfer, and measuring the difference is important. Measured by conditional entropy, the weight of adversarial losses for those well aligned features a

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Jobard, Elodie, Laure Dossus, Laura Baglietto, et al. "Investigation of circulating metabolites associated with breast cancer risk by untargeted metabolomics: a case–control study nested within the French E3N cohort." British Journal of Cancer 124, no. 10 (2021): 1734–43. http://dx.doi.org/10.1038/s41416-021-01304-1.

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Abstract Background Perturbations in circulating metabolites prior to a breast cancer diagnosis are not well characterised. We aimed to gain more detailed knowledge to help understand and prevent the disease. Methods Baseline plasma samples from 791 breast cancer cases and 791 matched controls from the E3N (EPIC-France) cohort were profiled by nuclear magnetic resonance (NMR)-based untargeted metabolomics. Partial least-squares discriminant analysis (PLS-DA) models were built from NMR profiles to predict disease outcome, and odds ratios and false discovery rate (FDR)-adjusted CIs were calculat

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ALCAN, Veysel. "YAŞA BAĞLI DUYUSAL DEĞİŞİKLİKLERİN POSTURAL SALINIM DÜZENSİZLİĞİ ÜZERİNDEKİ ETKİSİNİN DEĞERLENDİRİLMESİ." Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 26, Özel Sayı (2023): 1109–20. http://dx.doi.org/10.17780/ksujes.1338361.

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Age-related decline in sensory inputs in elderly people leads to postural instability that increases irregularity of postural sway. This study aimed to examine the effect of visual or somatosensory inputs on postural sway irregularity in the elderly by using machine learning (ML). The feature set was extracted from entropy measurements including sample, fuzzy, distribution, conditional, and permutation. Then, the variables were classified by ML including support vector machines (SVM), k-nearest neighbors (k-NN), and linear discriminant analysis (LDA) algorithms. Classification performances wer

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ANTYPENKO, Nаdiia, and Maksym PROKOPENKO. "Methodological and theoretical fundamentals of bankruptcy probability assessment of "UkSATSE" enterprise." Economics. Finances. Law 5/2, no. - (2022): 5–9. http://dx.doi.org/10.37634/efp.2022.5(2).1.

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In the economic sciences today there is a large number of developments in the field of analysis and forecasting of insolvent companies, which in turn helps to find and assess the criteria for the probability of bankruptcy of insolvent entities on the example of state enterprise “Ukrainian State Air Traffic Services Enterprise (UkSATSE)”. This paper is devoted to the reflection of the main methods and models for estimating the probability of bankruptcy of the enterprise, which were proposed by foreign and domestic scientists. In particular, it is noted that the assessment of the threat of bankr

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Divyanth, L. G., D. S. Guru, Peeyush Soni, Rajendra Machavaram, Mohammad Nadimi, and Jitendra Paliwal. "Image-to-Image Translation-Based Data Augmentation for Improving Crop/Weed Classification Models for Precision Agriculture Applications." Algorithms 15, no. 11 (2022): 401. http://dx.doi.org/10.3390/a15110401.

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Applications of deep-learning models in machine visions for crop/weed identification have remarkably upgraded the authenticity of precise weed management. However, compelling data are required to obtain the desired result from this highly data-driven operation. This study aims to curtail the effort needed to prepare very large image datasets by creating artificial images of maize (Zea mays) and four common weeds (i.e., Charlock, Fat Hen, Shepherd’s Purse, and small-flowered Cranesbill) through conditional Generative Adversarial Networks (cGANs). The fidelity of these synthetic images was teste

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Shaikh, Faraz Ahmed, Muhammad Zuhaib Kamboh, Bilal Ahmad Alvi, Sheroz Khan, and Farhat Muhammad Khan. "Condition-Based Health Monitoring of Electrical Machines Using DWT and LDA Classifier." Sir Syed University Research Journal of Engineering & Technology 12, no. 2 (2022): 95–100. http://dx.doi.org/10.33317/ssurj.513.

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In the industry, continuous health monitoring of electric motors is considered as an essential requirement. The continuous operation of the electric motor may cause malfunctions and addressing them timely is a critical challenge. The development of an efficient health monitoring system based on the identification of electrical motor faults is on great demand. This paper addresses the fault detection technique using discrete wavelet transform (DWT) algorithm for continuous health monitoring of electric motor-based systems. The faults have been detected through Motor Current Signature Analysis (

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Gao, Lei, Yin Huang, Yonghong Liu, et al. "Bacterial Community Structure and Potential Microbial Coexistence Mechanism Associated with Three Halophytes Adapting to the Extremely Hypersaline Environment." Microorganisms 10, no. 6 (2022): 1124. http://dx.doi.org/10.3390/microorganisms10061124.

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Halophytes play a crucial ecological role in drought and saline–alkali environments. However, there is limited knowledge about the structure of bacterial communities and the potential microbial coexistence mechanism associated with halophytes. This study investigated the diversity and community structure of endophytic and rhizospheric bacteria associated with three halophytes by applying high-throughput sequencing and geochemistry analyses on the studied soils. We collected 18 plant and 21 soil samples, and sequenced the V3 and V4 hypervariable regions of the 16S rRNA gene using next-generatio

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Rokonuzzaman, MD, and Mohammad Akram Hossen. "Volatility Analysis of Stock Returns for Fifteen Listed Banks in Chittagong Stock Exchange." International Business Research 11, no. 9 (2018): 129. http://dx.doi.org/10.5539/ibr.v11n9p129.

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The aim of the study is to analyze and prediction of return for 15 popular banks in Chittagong Stock Exchange. The economic development of a country depends largely on the effective performance of stock market. In this study, secondary data from the CSE, Bangladesh with a sample period 1st January 2009 to 27th December 2015 for selected 15 banks, listed in Chittagong Stock Exchange. Descriptive statistics, important graphs, statistical tests, fitted dynamic regression models with ARCH effect are used to complete the analysis. It is found that for all banks, the return occurs high with a high r

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Kovacova, Maria, and Tomas Kliestik. "Logit and Probit application for the prediction of bankruptcy in Slovak companies." Equilibrium 12, no. 4 (2017): 775–91. http://dx.doi.org/10.24136/eq.v12i4.40.

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Research background: Prediction of bankruptcy is an issue of interest of various researchers and practitioners since the first study dedicated to this topic was published in 1932. Finding the suitable bankruptcy prediction model is the task for economists and analysts from all over the world. forecasting model using. Despite a large number of various models, which have been created by using different methods with the aim to achieve the best results, it is still challenging to predict bankruptcy risk, as corporations have become more global and more complex. Purpose of the article: The ai

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Nusinov, Volodymyr, Liudmyla Burkova, and Natalia Shura. "Estimating the Ukrainian companies’ financial potential and the probability of forced liquidation." Investment Management and Financial Innovations 17, no. 2 (2020): 26–39. http://dx.doi.org/10.21511/imfi.17(2).2020.03.

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The development of a global economy is impossible without economic ups and downs, which disrupt economic stability. The growth of the crisis in Ukrainian companies is no exception. In world practice, there are many methods for estimating the possibility of companies’ bankruptcy. At the same time, there are no methodological approaches to setting up the possible commencement of company’s liquidation during its bankruptcy. The article aims to develop a methodology for estimating the possibility of company’s liquidation due to the introduction of its bankruptcy procedure and to determine the fina

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Lev, Raskin, and Sira Oksana. "Development of methods for extension of the conceptual and analytical framework of the fuzzy set theory." Eastern-European Journal of Enterprise Technologies 6, no. 4(108) (2020): 14–21. https://doi.org/10.15587/1729-4061.2020.217630.

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Fuzzy set theory is an effective alternative to probability theory in solving many problems of studying processes and systems under conditions of uncertainty. The application of this theory is especially in demand in situations where the system under study operates under conditions of rapidly changing influencing parameters or characteristics of the environment. In these cases, the use of solutions obtained by standard methods of the probability theory is not quite correct. At the same time, the conceptual, methodological and hardware base of the alternative fuzzy set theory is not sufficientl

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Barvinok, Alexander. "Concentration of the mixed discriminant of well-conditioned matrices." Linear Algebra and its Applications 493 (March 2016): 120–33. http://dx.doi.org/10.1016/j.laa.2015.11.040.

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Banta, Jim E., Ivie C. Egiebor, Chanell Grismore, Macy Westbrook, and James M. Banta. "Interaction of asthma, co-occurring mental illness, and geography on California emergency department visits, 2005–2014." Exploration of Asthma & Allergy 2, no. 6 (2024): 551–71. http://dx.doi.org/10.37349/eaa.2024.00064.

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Aim: To determine temporal changes in the frequency of asthma and mental illness in California emergency department (ED) visits and examine predictors of both asthma diagnosis and non-routine discharge from asthma visits. Methods: Public-use, all-payer ED data from non-federal, acute-care hospitals (2005–2014) were obtained for cross-sectional analysis. Due to substantial missing data, we used fully conditional specification multiple imputation with discriminant functions for age group, sex, race, and ethnicity. Multivariable logistic regression was used to examine asthma diagnosis (yes/no) am

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Lee, Sang Heon, Arun Kharghoria, and Akhil Datta-Gupta. "Electrofacies Characterization and Permeability Predictions in Complex Reservoirs." SPE Reservoir Evaluation & Engineering 5, no. 03 (2002): 237–48. http://dx.doi.org/10.2118/78662-pa.

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Summary We propose a two-step approach to permeability prediction from well logs that uses nonparametric regression in conjunction with multivariate statistical analysis. First, we classify the well-log data into electrofacies types. This classification does not require any artificial subdivision of the data population; it follows naturally based on the unique characteristics of well-log measurements reflecting minerals and lithofacies within the logged interval. A combination of principal components analysis (PCA), model-based cluster analysis (MCA), and discriminant analysis is used to chara

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Bruwer, Johan, and Elton Li. "Domain-specific market segmentation using a latent class mixture modelling approach and wine-related lifestyle (WRL) algorithm." European Journal of Marketing 51, no. 9/10 (2017): 1552–76. http://dx.doi.org/10.1108/ejm-10-2016-0593.

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Purpose Since the publication of Van Raaij and Verhallen’s seminal work in European Journal of Marketing in 1994, identifying the domain-specific market segmentation approach as one of the most feasible for segmenting markets, there has been surprisingly limited development in this field, with the food domain as the only exception. This study aims to develop a methodological approach using latent class mixture modelling as contribution in the domain-specific market segmentation field. Design/methodology/approach This study captures the AIO lifestyle perspective using a domain-specific 80-item

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Devaux, M. F., D. Bertrand, P. Robert, and M. Qannari. "Application of Multidimensional Analyses to the Extraction of Discriminant Spectral Patterns from NIR Spectra." Applied Spectroscopy 42, no. 6 (1988): 1015–19. http://dx.doi.org/10.1366/0003702884430227.

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A method for extracting NIR discriminant spectral patterns without any reference to chemical values is suggested. First, groups of samples have to be defined a priori. Second, this method involves two procedures: the application of Principal Component Analysis (PCA) on spectral data and Factorial Discriminant Analysis (FDA) on the PC scores. Discriminant spectral patterns are assessed as linear combinations of PCA eigenvectors with weights determined by FDA. This method was applied on an illustrative collection of wheat semolina conditioned at 3 levels of water concentration. Three groups were

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Phillips, Roger T. J., and Joseph R. Desloges. "Alluvial floodplain classification by multivariate clustering and discriminant analysis for low-relief glacially conditioned river catchments." Earth Surface Processes and Landforms 40, no. 6 (2014): 756–70. http://dx.doi.org/10.1002/esp.3681.

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Khokh, A., and V. Shalaboda. "Forensic comparative analysis of soils by the method of sporo-pollen analysis using projection to latent structures discriminant analysis." Theory and Practice of Forensic Science and Criminalistics 23, no. 1 (2021): 212–24. http://dx.doi.org/10.32353/khrife.1.2021.16.

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Extensive use of sporo-pollen analysis is largely conditioned by such specific features of pollen and spores as their very-large-scale production in plants, the ability to preserve in soils, presence of characteristic morphological features enabling to distinguish and identify individual taxa, etc. In Belarus, the method of sporo-pollen analysis has also been implemented while forensic soil examination: it is increasingly applied to solve identification tasks in comparative analysis to establish the belonging of soil layers on physical evidence to the searched area. Its main advantage is

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Aggarwala, Jyotsna, Rinku Garg, and Subhra Chatterjee. "Linear Discriminant Analysis of Various Physiological and Psychological Parameters among Indian Elite Male Athletes of Different Types of Sports." Sport Mont 20, no. 3 (2022): 53–60. http://dx.doi.org/10.26773/smj.221009.

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The aim of this study was to categorize various physical, physiological, and psychological variables that discriminated among Indian elite male athletes participating in endurance sports (Group A, n=34, age=16.88±2.44 years), combat sports (Group B, n=35, age=17.40±1.37 years) and skill sports (Group C, n=34, age= 8.91±2.24 years) and non-athletic control group (Group D, N=33, years=18.48±2.03 years). Physical parameters included height, body weight, body fat percentage, lean body mass, muscle content, and total water content. Physiological parameters included heart rate variability and maxima

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Tian, Qiwen, and Sumiko Miyata. "A DDoS Attack Detection Method Using Conditional Entropy Based on SDN Traffic." IoT 4, no. 2 (2023): 95–111. http://dx.doi.org/10.3390/iot4020006.

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To detect each network attack in an SDN environment, an attack detection method is proposed based on an analysis of the features of the attack and the change in entropy of each parameter. Entropy is a parameter used in information theory to express a certain degree of order. However, with the increasing complexity of networks and the diversity of attack types, existing studies use a single entropy, which does not discriminate correctly between attacks and normal traffic and may lead to false positives. In this paper, we propose new state determination standards that use the normal distribution

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McIntosh, A. R., and F. Gonzalez-Lima. "Network interactions among limbic cortices, basal forebrain, and cerebellum differentiate a tone conditioned as a Pavlovian excitor or inhibitor: fluorodeoxyglucose mapping and covariance structural modeling." Journal of Neurophysiology 72, no. 4 (1994): 1717–33. http://dx.doi.org/10.1152/jn.1994.72.4.1717.

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1. The objective was to examine how opposite learned behavioral responses to the same physical tone were differentiated by the pattern of interactions between extraauditory neural regions. This was pursued using a new approach combining behavior, neuroimaging, and network analysis to integrate information about differences in regional activity with differences in the covariance relationships between brain areas. 2. A tone was used as either a Pavlovian conditioned excitor or inhibitor. Rats were conditioned with reinforced trials of a conditioned excitor (A+) intermixed with nonreinforced tria

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Palomino-Vasco, Mónica, María Isabel Rodríguez-Cáceres, Nielene Mora-Diez, Rosario Pardo-Botello, and María Isabel Acedo-Valenzuela. "Biogenic amines profile in red wines regarding aging and storage conditions." Journal of Food Composition and Analysis 83, no. 103295 (2019): 1–9. https://doi.org/10.1016/j.jfca.2019.103295.

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Abstract: The determination and quantification of eight biogenic amines in red wines without sample clean-up has been carried out by an ultra-high performance chromatographic method with fluorescent detection based on the o-phthaldialdehyde derivatization reaction. In these conditions, several monovarietal ‘Tempranillo’ wines (young, oak and aged ones) have been analyzed and the total concentrations of biogenic amines ranged between 22.2 and 73.4 mg L-1, which is in concordance with other Spanish red wines. No significant differences of total biogenic amine

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Hou, Ting, Xue Qiang Lv, and Li Ping Xu. "Chinese Patent Efficacy Phrase Recognition." Applied Mechanics and Materials 743 (March 2015): 510–14. http://dx.doi.org/10.4028/www.scientific.net/amm.743.510.

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Effect information, as foundation of the patent efficacy analysis, the completeness and accuracy has the decisive significance to analysis result. In order to improve the efficiency of extraction of effect information, the paper proposed one kind method based on conditional random field model (CRFs) to recognize effect phrases. Efficacy phrases are highly generalization, and divided into effect theme, attribute and the efficiency value of three parts. Phrase recognition is recognition of the three parts. We make use of the sentence features in which the phrases exit, the lexical features and c

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Golden, Glen J., Maryanne Opiekun, Talia Martin-Taylor, and Bruce A. Kimball. "Training the domestic ferret to discriminate odors associated with wildlife disease." PLOS ONE 16, no. 11 (2021): e0259415. http://dx.doi.org/10.1371/journal.pone.0259415.

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Recent avian influenza infection outbreaks have resulted in global biosecurity and economic concerns. Mallards are asymptomatic for the disease and can potentially spread AI along migratory bird flyways. In a previous study, trained mice correctly discriminated the health status of individual ducks on the basis of fecal odors when feces from post-infection periods were paired with feces from pre-infection periods. Chemical analyses indicated that avian influenza infection was associated with a marked increase of acetoin (3-hydroxy-2-butanone) in feces. In the current study, domesticated male f

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