Partial Classification Research Articles

Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) Spectroscopy Analysis of Saliva as a Diagnostic Specimen for Rapid Classification of Oral Squamous Cell Carcinoma Using Chemometrics Methods

Background & Aim Recent advancements in analytical techniques have highlighted the potential of Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy as a quick, cost-effective, non-invasive, and efficient tool for cancer diagnosis. This study aims to evaluate the effectiveness of ATR-FTIR spectroscopy in combination with supervised machine learning classification models for diagnosing OSCC using saliva samples. Methods & Materials Eighty unstimulated whole saliva samples from OSCC patients and healthy controls were collected. The ATR-FTIR spectroscopy was performed and spectral data were used to classify healthy and OSCC groups. The data were analyzed using machine learning classification methods such as Partial Least Squares-Discriminant Analysis (PLS-DA) and Support Vector Machine Classification (SVM-C). The classification performance of the models was evaluated by computing sensitivity, specificity, precision, and accuracy. Results The samples were classified into two classes based on their spectral data. The obtained results demonstrate a high level of accuracy in the prediction sets of the PLS-DA and SVM-C models, with accuracy values of 0.960 and 0.962, respectively. The OSCC group sensitivity values for both PLS-DA and SVM-C models was 1.00, respectively. Conclusion The study indicates that ATR-FTIR spectroscopy, combined with chemometrics, is a potential method for the non-invasive diagnosis of OSCC using saliva samples. This method achieved high accuracy and the findings of this study suggest that ATR-FTIR spectroscopy could be further developed for clinical applications in OSCC diagnosis.

Multi-scale locality preserving projection for partial multi-view incomplete multi-label learning

Amidst advancements in feature extraction techniques, research on multi-view multi-label classifications has attracted widespread interest in recent years. However, real-world scenarios often pose a challenge where the completeness of multiple views and labels cannot be ensured. At present, only a handful of techniques have attempted to address the complex issue of partial multi-view incomplete multi-label classification, and the majority of these approaches overlook the significance of manifold structures between instances. To tackle these challenges, we propose a novel partial multi-view incomplete multi-label learning model, termed MSLPP. Differing from existing studies, MSLPP emphasizes retaining the effective inherent structure of data during the feature extraction process, thereby facilitating a richer semantic information extraction. Specifically, MSLPP captures and integrates four types of information: the distance and similarity information in the original feature space, and the distance and similarity information in the extracted feature space. Further, by adopting the graph embedding technique, it simultaneously preserves the intrinsic structure with multi-scale information through a constraint term. Moreover, taking into account the negative impact of the missing views on the model and the possible impact of missing views on the data inherent structure, we further propose a shielding strategy for missing views, which not only eliminates the negative effects of missing views on the model but also more accurately captures the inherent data structure. The experimental results on five widely recognized datasets indicate that the model performs better than many excellent methods.

Novel Antenna for Partial Discharge Detection and Classification: A Convolutional Neural Network-Based Deep Learning Approach

Novel Antenna for Partial Discharge Detection and Classification: A Convolutional Neural Network-Based Deep Learning Approach

Incomplete multi-view partial multi-label classification via deep semantic structure preservation

Recent advances in multi-view multi-label learning are often hampered by the prevalent challenges of incomplete views and missing labels, common in real-world data due to uncertainties in data collection and manual annotation. These challenges restrict the capacity of the model to fully utilize the diverse semantic information of each sample, posing significant barriers to effective learning. Despite substantial scholarly efforts, many existing methods inadequately capture the depth of semantic information, focusing primarily on shallow feature extractions that fail to maintain semantic consistency. To address these shortcomings, we propose a novel Deep semantic structure-preserving (SSP) model that effectively tackles both incomplete views and missing labels. SSP innovatively incorporates a graph constraint learning (GCL) scheme to ensure the preservation of semantic structure throughout the feature extraction process across different views. Additionally, the SSP integrates a pseudo-labeling self-paced learning (PSL) strategy to address the often-overlooked issue of missing labels, enhancing the classification accuracy while preserving the distribution structure of data. The SSP model creates a unified framework that synergistically employs GCL and PSL to maintain the integrity of semantic structural information during both feature extraction and classification phases. Extensive evaluations across five real datasets demonstrate that the SSP method outperforms existing approaches, including lrMMC, MVL-IV, MvEL, iMSF, iMvWL, NAIML, and DD-IMvMLC-net. It effectively mitigates the impacts of data incompleteness and enhances semantic representation fidelity.

Study on Deep-Learning Model for Phase Resolved Partial Discharge Pattern Classification Based on Convolutional Neural Network Algorithm

Study on Deep-Learning Model for Phase Resolved Partial Discharge Pattern Classification Based on Convolutional Neural Network Algorithm

Topological defects in K3 sigma models

We consider the topological defect lines commuting with the spectral flow and the N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{N} $$\\end{document} = (4, 4) superconformal symmetry in two dimensional non-linear sigma models on K3. By studying their fusion with boundary states, we derive a number of general results for the category of such defects. We argue that while for certain K3 models infinitely many simple defects, and even a continuum, can occur, at generic points in the moduli space the category is actually trivial, i.e. it is generated by the identity defect. Furthermore, we show that if a K3 model is at the attractor point for some BPS configuration of D-branes, then all topological defects have integral quantum dimension. We also conjecture that a continuum of topological defects arises if and only if the K3 model is a (possibly generalized) orbifold of a torus model. Finally, we test our general results in a couple of examples, where we provide a partial classification of the topological defects.

Frobenius Distributions of Low Dimensional Abelian Varieties Over Finite Fields

Abstract Given a $g$-dimensional abelian variety $A$ over a finite field $\mathbf{F}_{q}$, the Weil conjectures imply that the normalized Frobenius eigenvalues generate a multiplicative group of rank at most $g$. The Pontryagin dual of this group is a compact abelian Lie group that controls the distribution of high powers of the Frobenius endomorphism. This group, which we call the Serre–Frobenius group, encodes the possible multiplicative relations between the Frobenius eigenvalues. In this article, we classify all possible Serre–Frobenius groups that occur for $g \le 3$. We also give a partial classification for simple ordinary abelian varieties of prime dimension $g\geq 3$.

Symmetric 2‐(36,15,6) $(36,15,6)$ designs with an automorphism of order two

AbstractBouyukliev, Fack and Winne classified all 2‐ designs that admit an automorphism of odd prime order, and gave a partial classification of such designs that admit an automorphism of order 2. In this paper, we give the classification of all symmetric 2‐ designs that admit an automorphism of order two. It is shown that there are exactly nonisomorphic such designs, of which are self‐dual designs. The ternary linear codes spanned by the incidence matrices of these designs are computed. Among these codes, there are near‐extremal self‐dual codes with previously unknown weight distributions.

A Comparison of Mixed and Partial Membership Diagnostic Classification Models with Multidimensional Item Response Models

Diagnostic classification models (DCM) are latent structure models with discrete multivariate latent variables. Recently, extensions of DCMs to mixed membership have been proposed. In this article, ordinary DCMs, mixed and partial membership models, and multidimensional item response theory (IRT) models are compared through analytical derivations, three example datasets, and a simulation study. It is concluded that partial membership DCMs are similar, if not structurally equivalent, to sufficiently complex multidimensional IRT models.

Achieving optimal trade-off for student dropout prediction with multi-objective reinforcement learning

Student dropout prediction (SDP) in educational research has gained prominence for its role in analyzing student learning behaviors through time series models. Traditional methods often focus singularly on either prediction accuracy or earliness, leading to sub-optimal interventions for at-risk students. This issue underlines the necessity for methods that effectively manage the trade-off between accuracy and earliness. Recognizing the limitations of existing methods, this study introduces a novel approach leveraging multi-objective reinforcement learning (MORL) to optimize the trade-off between prediction accuracy and earliness in SDP tasks. By framing SDP as a partial sequence classification problem, we model it through a multiple-objective Markov decision process (MOMDP), incorporating a vectorized reward function that maintains the distinctiveness of each objective, thereby preventing information loss and enabling more nuanced optimization strategies. Furthermore, we introduce an advanced envelope Q-learning technique to foster a comprehensive exploration of the solution space, aiming to identify Pareto-optimal strategies that accommodate a broader spectrum of preferences. The efficacy of our model has been rigorously validated through comprehensive evaluations on real-world MOOC datasets. These evaluations have demonstrated our model’s superiority, outperforming existing methods in achieving optimal trade-off between accuracy and earliness, thus marking a significant advancement in the field of SDP.

Application of computer data model in the characteristics and evolution of ceramic decorative painting

With the rapid development of computer technology, computer information technology has made intelligent transformation to all industries. It is particularly important to optimize the computerization of ceramic painting. This paper analyzes the development of ceramic decorative painting and the basic principle of computer data model, determines the method of combining ceramic decorative painting with computer data model, and establishes a computer data model for feature extraction of ceramic decorative painting. And 1015 images of self-made ceramic decorative paintings are applied to carry out simulation analysis. The results show that compared with the traditional classification algorithm based on non deep learning and the partial classification algorithm based on deep learning, the average F1 value of the hybrid neural network model is 96.4%, which is 1.5% higher than the best traditional model. This shows that the designed computer data model for feature extraction of ceramic decorative painting has a good effect on the classification and feature recognition of ceramic painting. This has laid a certain foundation for the computerized optimization of ceramic decorative painting and provided a good support for the healthy development of ceramic decorative painting in the information age.

Attention-Induced Embedding Imputation for Incomplete Multi-View Partial Multi-Label Classification

As a combination of emerging multi-view learning methods and traditional multi-label classification tasks, multi-view multi-label classification has shown broad application prospects. The diverse semantic information contained in heterogeneous data effectively enables the further development of multi-label classification. However, the widespread incompleteness problem on multi-view features and labels greatly hinders the practical application of multi-view multi-label classification. Therefore, in this paper, we propose an attention-induced missing instances imputation technique to enhance the generalization ability of the model. Different from existing incomplete multi-view completion methods, we attempt to approximate the latent features of missing instances in embedding space according to cross-view joint attention, instead of recovering missing views in kernel space or original feature space. Accordingly, multi-view completed features are dynamically weighted by the confidence derived from joint attention in the late fusion phase. In addition, we propose a multi-view multi-label classification framework based on label-semantic feature learning, utilizing the statistical weak label correlation matrix and graph attention network to guide the learning process of label-specific features. Finally, our model is compatible with missing multi-view and partial multi-label data simultaneously and extensive experiments on five datasets confirm the advancement and effectiveness of our embedding imputation method and multi-view multi-label classification model.

Infinitesimally Moebius bendable hypersurfaces

AbstractLi, Ma and Wang have provided in [13] a partial classification of the so-called Moebius deformable hypersurfaces, that is, the umbilic-free Euclidean hypersurfaces $f\colon M^n\to \mathbb{R}^{n+1}$ that admit non-trivial deformations preserving the Moebius metric. For $n\geq 5$, the classification was completed by the authors in [12]. In this article we obtain an infinitesimal version of that classification. Namely, we introduce the notion of an infinitesimal Moebius variation of an umbilic-free immersion $f\colon M^n\to \mathbb{R}^m$ into Euclidean space as a one-parameter family of immersions $f_t\colon M^n\to \mathbb{R}^m$, with $t\in (-\epsilon, \epsilon)$ and $f_0=f$, such that the Moebius metrics determined by ft coincide up to the first order. Then we characterize isometric immersions $f\colon M^n\to \mathbb{R}^m$ of arbitrary codimension that admit a non-trivial infinitesimal Moebius variation among those that admit a non-trivial conformal infinitesimal variation, and use such characterization to classify the umbilic-free Euclidean hypersurfaces of dimension $n\geq 5$ that admit non-trivial infinitesimal Moebius variations.

Effectiveness of Wavelet Scalogram on Partial Discharge Pattern Classification of XLPE Cable Insulation

Effectiveness of Wavelet Scalogram on Partial Discharge Pattern Classification of XLPE Cable Insulation

Predição do índice de maturidade da manga cultivar ‘Ataulfo’ usando o sensor espectral AS7262

Abstract ‘Ataulfo’ mango is highly produced in Mexico, being harvested when it reaches its physiological maturity. This process takes at least another month for the fruit to reach consumption maturity. Warehouses and markets present important losses as the ready-to-eat status is unknown. Maturity status is determined by measuring slow and destructive physicochemical variables. An optical device based on the AS7262 spectral sensor was connected to the ESP32 microcontroller and measurements were correlated with soluble solids content (SSC), dry matter (DM) and firmness of mangoes obtained from the local market. Data analysis was carried out by partial least squares (PLS) regression, classification, regression tree (CART) and random forest (RF) models. With PLS, SST and firmness were predicted with R2 of 0.61 and 0.76, respectively. The root mean squared error of prediction (RMSEP) was 0.91 for SSC and 0.67 for firmness. With the CART model, classification accuracy was 90% for SSC and 87% for firmness of intact mango fruits.

Characteristic Analysis and Short-Impending Prediction of Aircraft Bumpiness over Airport Approach Areas and Flight Routes

Based on the Quick Access Recorder (QAR) data covering over 9000 routes in China, the monthly and intra-day distribution characteristics of aircraft bumpiness at different levels were analyzed, and the relationships between the eddy dissipation rate (EDR) and other aircraft flight status elements during bumpiness occurrence were also analyzed. Afterward, aircraft bumpiness routes were constructed using 19 machine learning models. The analyses show that (1) aircraft bumpiness was mainly concentrated between 0:00 a.m. and 17:00 p.m. Severe aircraft bumpiness occurred more frequently in the early morning in January, especially between 5:00 a.m. and 6:00 a.m., and moderate bumpiness always occurred from 3:00 a.m. to 11:00 a.m. (2) The relationship between the left and right attack angles and aircraft bumpiness on the routes was more symmetrical, with a center at 0 degrees, unlike in the approach area where the hotspots were mainly concentrated in the range of −5 to 0 degrees. In the approach area, the larger the Mach number, the more severe the bumpiness. (3) The performances of the Automatic Relevance Determination Regression (ARD), Partial Least Squares Regression (PLS), Elastic-Net Regression (ENR), Classification and Regression Tree (CART), Passive Aggressive Regression (PAR), Random Forest (RF), Stochastic Gradient Descent Regression (SGD), and Tweedie Regression (TWD) based models were relatively good, while the performances of the Huber Regression (HUB), Least Angle Regression (LAR), Polynomial Regression (PLN), and Ridge Regressor (RR) based models were very poor. The aircraft bumpiness prediction models performed best over the approach area of ZBDT (airport in Datong), ZULS (airport in Lhasa), ZPPP (airport in Kunming), and ZLQY (airport in Qingyang). The model performed best in predicting the ZLLL-ZBDT air route (flight routes for Lanzhou to Datong) with different prediction times.

Learning shared and non-redundant label-specific features for partial multi-label classification

Partial multi-label learning (PML) is designed to address the challenge of having both ground-truth labels and noisy labels in the label set of training instances. In real-world applications, there are often noisy features in addition to noisy labels, but existing PML methods fail to filter out these noisy features or account for feature correlations, label correlations, and feature-label correlations together in the feature learning process. These oversights lead to poor classification accuracy, as the comprehensive exploration of feature and label information in PML can contribute significantly to disambiguation. To address this issue, we propose a novel PML framework that disambiguates candidate labels and learns label-specific features while taking into account all three types of correlations between features and labels. First, we capture high-order label correlations by employing the low-rank representation to obtain a complementary label matrix induced from the partial label matrix, which accounts for the presence of noisy labels. Then, we learn a shared and non-redundant label-specific data representation by incorporating intrinsic feature correlations and the learned label correlations to mitigate the impact of noisy features. Finally, we build a classifier by mapping the label-specific feature representation to the complementary label matrix to model the feature-label correlations. Various experiments validate the superiority of our method.

Classification of low-rank odd-dimensional modular categories

We prove that any odd-dimensional modular category of rank at most 23 is pointed. We also show that an odd-dimensional modular category of rank 25 is either pointed, perfect, or equivalent to Rep(Dω(Z7⋊Z3)). Finally, we give partial classification results for modular categories of rank up to 73.

Relating the One-Parameter Logistic Diagnostic Classification Model to the Rasch Model and One-Parameter Logistic Mixed, Partial, and Probabilistic Membership Diagnostic Classification Models

Diagnostic classification models (DCMs) are statistical models with discrete latent variables (so-called skills) to analyze multiple binary variables (i.e., items). The one-parameter logistic diagnostic classification model (1PLDCM) is a DCM with one skill and shares desirable measurement properties with the Rasch model. This article shows that the 1PLDCM is indeed a latent class Rasch model. Furthermore, the relationship of the 1PLDCM to extensions of the DCM to mixed, partial, and probabilistic memberships is treated. It is argued that the partial and probabilistic membership models are also equivalent to the Rasch model. The fit of the different models was empirically investigated using six datasets. It turned out for these datasets that the 1PLDCM always had a worse fit than the Rasch model and mixed and partial membership extensions of the DCM.

Unraveling the relationship between key aroma components and sensory properties of fragrant peanut oils based on flavoromics and machine learning

Key aroma components of 33 fragrant peanut oils with different aroma types were screened by combined using flavoromics and machine learning. A total of 108 volatile compounds were identified and 100 kinds of them were accurately quantified, and 38 compounds out of them were with odorant activity value ≥1. The 33 peanut oils presented varied intensity of ‘fresh peanuts’, ‘roasted nut’, ‘burnt’, ‘over-burnt’, ‘sweet’, ‘peanut butter-like’, ‘puffed food’ and ‘exotic flavor’, and could be classified into four aroma types, namely raw, light, thick and salty. Partial least squares regression analysis, random forest and classification regression tree revealed that 2-acetyl pyrazine had a negative effect on ‘fresh peanuts’ and could distinguish raw flavor samples well; 2-methylbutanal and 4-vinylguaiacol were key compounds of ‘roasted nut’ and had significant differences (P < 0.0001) in thick and raw flavor samples; furfural contributed to the ‘puffed food’ as well as key compound of salty flavor.