Classification Method Research Articles

Quantitative Assessment of Geosites and Mine Heritage as a Resource: The Case Study of Lungro Salt Mine (Calabria, Italy)

In this work, the old Lungro mining site was studied by applying two classification methods (Brilha and IELIG Method), considering the site-specific scientific, educational, touristic, and degradation aspects, to promote it as a geosite. The results were compared with those from other worldwide areas, and a potential re-evaluation in the tourism sector was suggested, considering historical data and geological issues. The methods used evaluate the Lungro site an average score from a scientific perspective, and from high to very high for the tourist-educational aspects. On the contrary, lower scores were found regarding the degradation of the site due to natural phenomena, led to a lower total score. The obtained results are comparable with other similar restored mining areas, located in various parts of the world which today represent important geotourists sites recognized internationally.Based on these results, the safest, most cost-effective, and straightforward way to preserve and restore the site is by creating a “Geotourism Route.“. This choice would increase awareness of the area, providing the general public, including the curious and “non-experts”, with an understanding of various geological processes and the extensive history of the mine. A campaign to promote and preserve the Lungro site as a geoheritage will be encouraged, with significant implications for tourism at the local, national, and international levels.

Tracking full posterior in online Bayesian classification learning: a particle filter approach

The rapid growth of data volume and velocity is challenging traditional methods of classification, making it impossible to store so much data in memory. Developing online classification methods is becoming increasingly important. Online classification approaches have been subject to several assumptions in recent years, such as an independent and consistent distribution of initial values. In this paper, we present a general online Bayesian classification algorithm that can be adapted to handle streaming data based on the Monte Carlo fusion method. Rather than storing the entire raw data, we only update the posterior with historical data and the current batch of data. In addition, the proposed method has the capability of being applied to a wide range of situations with a high degree of accuracy, even in the case of data imbalances and discrepancies between sub-posterior distributions. The proposed algorithm was validated and evaluated through a comprehensive simulation study.

A Classification Method for Incomplete Mixed Data Using Imputation and Feature Selection

A Classification Method for Incomplete Mixed Data Using Imputation and Feature Selection

Class-Irrelevant Feature Removal for Few-Shot Image Classification.

Most existing few-shot image classification methods employ global pooling to aggregate class-relevant local features in a data-drive manner. Due to the difficulty and inaccuracy in locating class-relevant regions in complex scenarios, as well as the large semantic diversity of local features, the class-irrelevant information could reduce the robustness of the representations obtained by performing global pooling. Meanwhile, the scarcity of labeled images exacerbates the difficulties of data-hungry deep models in identifying class-relevant regions. These issues severely limit deep models' few-shot learning ability. In this work, we propose to remove the class-irrelevant information by making local features class relevant, thus bypassing the big challenge of identifying which local features are class irrelevant. The resulting class-irrelevant feature removal (CIFR) method consists of three phases. First, we employ the masked image modeling strategy to build an understanding of images' internal structures that generalizes well. Second, we design a semantic-complementary feature propagation module to make local features class relevant. Third, we introduce a weighted dense-connected similarity measure, based on which a loss function is raised to fine-tune the entire pipeline, with the aim of further enhancing the semantic consistency of the class-relevant local features. Visualization results show that CIFR achieves the removal of class-irrelevant information by making local features related to classes. Comparison results on four benchmark datasets indicate that CIFR yields very promising performance.

The predictive role of lung clearance index on FEV<sub>1</sub> decline in cystic fibrosis

Background. The lung clearance index (LCI) is a sensitive lung function index that is used to detect early lung disease changes in children with cystic fibrosis (CF). This study aimed to define the predictive role of baseline LCI, along with other potential factors on the change in forced expiratory volume in one second (FEV1) during one-year follow-up in CF patients who had a percent predicted (pp) FEV1≥80. Methods. LCI was concurrently performed on 57 CF patients who had ppFEV1 ≥80 at month zero. The ppFEV1 decline was evaluated prospectively during the one year follow up. The primary outcome of ppFEV1 decline in the study group in one year was dichotomized according to the median value for the decline in ppFEV1, which was 3.7. The LCI value predicting ppFEV1 decline at the end of one year was calculated with receiver operating characteristic curve analysis. Regression analysis was performed. Furthermore, a decision tree was constructed using classification and regression tree methods to better define the potential effect of confounders on the ppFEV1 decline. Results. The LCI value for predicting ppFEV1 decline >3.7% at the end of one year was 8.2 (area under the curve: 0.80) Multivariable regression analysis showed that the absence of the F508del mutation in at least one allele, LCI >8.2 and initial FEV1 z-score were predictors of a ppFEV1 decline >3.7 (p<0.001). Factors altering ppFEV1 decline>3.7% at the end of one-year evaluated by decision trees were as follows: initial FEV1 z-score, type of CFTR mutation, LCI value and initial weight-for-age z-score. Conclusions. LCI is sensitive for predicting ppFEV1 decline in patients with ppFEV1 ≥80 along with the initial FEV1-z-score and type of CFTR mutation.

Intent aware data augmentation by leveraging generative AI for stress detection in social media texts

Stress is a major issue in modern society. Researchers focus on identifying stress in individuals, linking language with mental health, and often utilizing social media posts. However, stress classification systems encounter data scarcity issues, necessitating data augmentation. Approaches like Back-Translation (BT), Easy Data Augmentation (EDA), and An Easier Data Augmentation (AEDA) are common. But, recent studies show the potential of generative AI, notably ChatGPT. This article centers on stress identification using the DREADDIT dataset and A Robustly Optimized BERT Pretraining Approach (RoBERTa) transformer, emphasizing the use of generative AI for augmentation. We propose two ChatGPT prompting techniques: same-intent and opposite-intent 1-shot intent-aware data augmentation. Same-intent prompts yield posts with similar topics and sentiments, while opposite-intent prompts produce posts with contrasting sentiments. Results show a 2% and 3% performance increase for opposing and same sentiments, respectively. This study pioneers intent-based data augmentation for stress detection and explores advanced mental health text classification methods with generative AI. It concludes that data augmentation has limited benefits and highlights the importance of diverse Reddit data and further research in this field.

Updating Correlation-Enhanced Feature Learning for Multi-Label Classification

In the domain of multi-label classification, label correlations play a crucial role in enhancing prediction precision. However, traditional methods heavily depend on ground-truth label sets, which can be incompletely tagged due to the diverse backgrounds of annotators and the significant cost associated with procuring extensive labeled datasets. To address these challenges, this paper introduces a novel multi-label classification method called updating Correlation-enhanced Feature Learning (uCeFL), which extracts label correlations directly from the data instances, circumventing the dependency on potentially incomplete label sets. uCeFL initially computes a revised label matrix by multiplying the incomplete label matrix with the label correlations extracted from the data matrix. This revised matrix is then utilized to enrich the original data features, enabling a neural network to learn correlation-enhanced representations that capture intricate relationships between data features, labels, and their interactions. Notably, label correlations are not static; they are dynamically updated during the neural network’s training process. Extensive experiments carried out on various datasets emphasize the effectiveness of the proposed approach. By leveraging label correlations within data instances, along with the hierarchical learning capabilities of neural networks, it offers a significant improvement in multi-label classification, even in scenarios with incomplete labels.

Evaluation of the Protection of Historical Buildings in Universities Based on RCM-AHP-FCE

The accumulation of years imbues historical buildings within universities with a profound sense of heritage, evident not only in the temporal imprints within their internal spaces but also in the evolution of their external surroundings. This cultural legacy subtly enriches students’ spatial awareness of history and fosters a collective memory of campus context. Current scholarly inquiry into university historical buildings primarily revolves around comprehensive considerations encompassing the preservation of these edifices, the overarching planning of academic institutions, and the safeguarding of the distinctive features inherent to historical structures. However, the predominant focus lies on qualitative analyses, leaving a pressing need for quantitative assessments and the establishment of an evaluation framework to gauge the efficacy of historical building preservation in academia. Addressing this gap, this study employs the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE) to formulate the University Historical Building Protection Evaluation Framework (UHBPEF). Drawing from the examination and categorization of the primary instructional edifices within the Yujiatou campus of Wuhan University of Technology through the Research Classification Method (RCM), along with the consolidation of insights from experts and academic stakeholders, this study underscores the imperative of enhancing the scientific precision and pertinence of the university’s strategy for preserving historical buildings. By employing both qualitative and quantitative methodologies, this study offers innovative insights into the challenges facing historical building preservation in university settings, therefore propounding effective preservation strategies and offering a roadmap for future endeavors in this domain.

Explainable artificial intelligence for LDL cholesterol prediction and classification

IntroductionMonitoring LDL-C levels is essential in clinical practice because there is a direct relation between low-density lipoprotein cholesterol (LDL-C) levels and atherosclerotic heart disease risk. Therefore, measurement or estimate of LDL-C is critical. The present study aims to evaluate Artificial Intelligence (AI) and Explainable AI (XAI) methodologies in predicting LDL-C levels while emphasizing the interpretability of these predictions. Materials and methodsWe retrospectively reviewed data from the Laboratory Information System (LIS) of Ankara Etlik City Hospital (AECH). We included 60.217 patients with standard lipid profiles (total cholesterol [TC], high-density lipoprotein cholesterol, and triglycerides) paired with same-day direct LDL-C results. AI methodologies, such as Gradient Boosting (GB), Random Forests (RF), Support Vector Machines (SVM), and Decision Trees (DT), were used to predict LDL-C and compared directly measured and calculated LDL-C with formulas. XAI techniques such as Shapley additive annotation (SHAP) and locally interpretable model-agnostic explanation (LIME) were used to interpret AI models and improve their explainability. ResultsPredicted LDL-C values using AI, especially RF or GB, showed a stronger correlation with direct measurement LDL-C values than calculated LDL-C values with formulas. TC was shown to be the most influential factor in LDL-C prediction using SHAP and LIME. The agreement between the treatment groups based on NCEP ATPIII guidelines according to measured LDL-C and the LDL-C groups obtained with AI was higher than that obtained with formulas. ConclusionsIt can be concluded that AI is not only a reliable method but also an explainable method for LDL-C estimation and classification.

PND-Net: plant nutrition deficiency and disease classification using graph convolutional network

Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. Hence, continuous health monitoring of plant is very crucial for handling plant stress. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN). Sometimes, a global feature descriptor might fail to capture the vital region of a diseased leaf, which causes inaccurate classification of disease. To address this issue, regional feature learning is crucial for a holistic feature aggregation. In this work, region-based feature summarization at multi-scales is explored using spatial pyramidal pooling for discriminative feature representation. Furthermore, a GCN is developed to capacitate learning of finer details for classifying plant diseases and insufficiency of nutrients. The proposed method, called Plant Nutrition Deficiency and Disease Network (PND-Net), has been evaluated on two public datasets for nutrition deficiency, and two for disease classification using four backbone CNNs. The best classification performances of the proposed PND-Net are as follows: (a) 90.00% Banana and 90.54% Coffee nutrition deficiency; and (b) 96.18% Potato diseases and 84.30% on PlantDoc datasets using Xception backbone. Furthermore, additional experiments have been carried out for generalization, and the proposed method has achieved state-of-the-art performances on two public datasets, namely the Breast Cancer Histopathology Image Classification (BreakHis 40×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document}: 95.50%, and BreakHis 100×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes $$\\end{document}: 96.79% accuracy) and Single cells in Pap smear images for cervical cancer classification (SIPaKMeD: 99.18% accuracy). Also, the proposed method has been evaluated using five-fold cross validation and achieved improved performances on these datasets. Clearly, the proposed PND-Net effectively boosts the performances of automated health analysis of various plants in real and intricate field environments, implying PND-Net’s aptness for agricultural growth as well as human cancer classification.

Fault classification and localization of multi-machine-based ieee benchmark test case power transmission lines using optimizable weighted extreme learning machine

Accurate fault diagnosis in transmission lines is crucial to ensure the reliability and stability of power grids. Conventional approaches often rely on expert knowledge or complex feature extraction methods, which are subjective and time-consuming. Additionally, many existing approaches use separate sub-algorithms for fault classification and localization. These are operating independently and sequentially. This research work proposes an innovative method for fault classification and localization in transmission lines using phasor measurement unit (PMU) data. The proposed method employs a Weighted Extreme Learning Machine (WELM) algorithm, which uses the variable data distribution across different fault classes through a weighted approach. The PMU data is generated by simulation using an IEEE 9-bus test system in the MATLAB simulation environment. The Maximal Overlap Discrete Wavelet Transform-based feature extraction technique is applied to derive input feature data to facilitate fault classification and localization. The WELM classifier is also optimized using the Grey Wolf Optimization (GWO) algorithm. The resulting GWO-optimized WELM (GWO-WELM) model, when trained on PMU data, achieves a remarkable fault classification accuracy of 99.83 % and fault localization accuracy of 95.48 %, respectively. These results demonstrate that the GWO-WELM model outperforms commonly used classifiers. Moreover, the proposed model shows robustness by accurately classifying the noisy data with a signal-to-noise ratio (SNR) of 10 dB (achieving 91.5 % accuracy in classification and 88.1 % accuracy in localization, respectively).

A note on the generalized Hessian of the least squares associated with systems of linear inequalities

The goal of this note is to point out an erroneous formula for the generalized Hessian of the least squares associated with a system of linear inequalities, that was given in the paper ‘A finite Newton method for classification’ by Mangasarian (Optim. Methods Softw. 17 (2002), pp. 913–929) and reproduced multiple times in other publications. We also provide sufficient contiditions for the validity of Mangasarian's formula and show that Slater's condition guarantees that some particular elements from the set defined by Mangasarian belong to the generalized Hessian of the corresponding function.

Duplicate image detection using deep learning modified SVM and k-NN classification method for multimedia application

Duplicate image detection using deep learning modified SVM and k-NN classification method for multimedia application

An appearance quality classification method for Auricularia auricula based on deep learning

The intelligent appearance quality classification method for Auricularia auricula is of great significance to promote this industry. This paper proposes an appearance quality classification method for Auricularia auricula based on the improved Faster Region-based Convolutional Neural Networks (improved Faster RCNN) framework. The original Faster RCNN is improved by establishing a multiscale feature fusion detection model to improve the accuracy and real-time performance of the model. The multiscale feature fusion detection model makes full use of shallow feature information to complete target detection. It fuses shallow features with rich detailed information with deep features rich in strong semantic information. Since the fusion algorithm directly uses the existing information of the feature extraction network, there is no additional calculation. The fused features contain more original detailed feature information. Therefore, the improved Faster RCNN can improve the final detection rate without sacrificing speed. By comparing with the original Faster RCNN model, the mean average precision (mAP) of the improved Faster RCNN is increased by 2.13%. The average precision (AP) of the first-level Auricularia auricula is almost unchanged at a high level. The AP of the second-level Auricularia auricula is increased by nearly 5%. And the third-level Auricularia auricula AP is increased by 1%. The improved Faster RCNN improves the frames per second from 6.81 of the original Faster RCNN to 13.5. Meanwhile, the influence of complex environment and image resolution on the Auricularia auricula detection is explored.

Analysis of the distribution of authorship by gender in scientific output: A global perspective

This study presents a thorough examination of gendered scholarly contributions and impact from 2003 to 2023, encompassing details on 212,631,585 authorships indexed in Scopus. The analysis unveils promising advancements towards gender equity, demonstrating an increase in contributions from both genders, which indicates the trend towards a progressive and inclusive environment. These findings challenge an initial perception of male prolificacy. The positive trends extend to female-led research teams, highlighting a correlation between gender balance and leadership. This evolving landscape is reflected in the convergence of male and female authorship participation over time. A decline in citable papers suggests a narrowing of the productivity gap, which challenges gender disparities in impact metrics and emphasizes the multifaceted nature of scholarly excellence across genders. Our data and gender classification method also enables us to look into the country level in order to characterize gender distribution locally. Contrary to conventional assumptions, developing countries are exhibiting a pronounced evolution in female authorship rates. In summary, the study underscores the positive trends towards gender equity, advocating for sustained efforts to promote diversity and foster nuanced understanding in academia.

A new method for predicting precipitation δ18O distribution based on deep learning and spatio-temporal clustering

ABSTRACT Predicting precipitation δ18O accurately is crucial for understanding water cycles, paleoclimates, and hydrological applications. Yet, forecasting its spatio-temporal distribution remains challenging due to complex climate interactions and extreme events. We developed a method combining spatio-temporal clustering and deep learning neural networks to improve multi-site, multi-year precipitation δ18O predictions. Using a comprehensive dataset from 33 German sites (1978-2012), our model considers precipitation δ18O and its controlling factors, including precipitation and temperature distribution. We applied the K-means++ method for classification and divided data into training and prediction sets. The CNN (Convolutional Neural Network) model extracted spatial features, while the Bi-LSTM (Bi-directional Long Short-Term Memory) model focused on temporal features. Spatio-temporal clustering using K-means++ improved forecast accuracy and reduced errors. This study highlights the potential of deep learning and clustering techniques for forecasting complex spatio-temporal data and offers insights for future research on isotope distributions.

Comprehensive application of AI algorithms with TCR NGS data for glioma diagnosis

T-cell receptor (TCR) detection can examine the extent of T-cell immune responses. Therefore, the article analyzed characteristic data of glioma obtained by DNA-based TCR high-throughput sequencing, to predict the disease with fewer biomarkers and higher accuracy. We downloaded data online and obtained six TCR-related diversity indices to establish a multidimensional classification system. By comparing actual presence of the 602 correlated sequences, we obtained two-dimensional and multidimensional datasets. Multiple classification methods were utilized for both datasets with the classification accuracy of multidimensional data slightly less to two-dimensional datasets. This study reduced the TCR β sequences through feature selection methods like RFECV (Recursive Feature Elimination with Cross-Validation). Consequently, using only the presence of these three sequences, the classification AUC value of 96.67% can be achieved. The combination of the three correlated TCR clones obtained at a source data threshold of 0.1 is: CASSLGGNTEAFF_TRBV12_TRBJ1-1, CASSYSDTGELFF_TRBV6_TRBJ2-2, and CASSLTGNTEAFF_TRBV12_TRBJ1-1. At 0.001, the combination is: CASSLGETQYF_TRBV12_TRBJ2-5, CASSLGGNQPQHF_TRBV12_TRBJ1-5, and CASSLSGNTIYF_TRBV12_TRBJ1-3. This method can serve as a potential diagnostic and therapeutic tool, facilitating diagnosis and treatment of glioma and other cancers.

X-ray computed tomographyFatigue damage evolution and residual strength analysis of 3D5D braided composites using X-ray tomography, acoustic emission, and digital image correlation

The accumulation of fatigue damage leads to continuous degradation of the residual strength of three-dimensional braided composites, directly determining the fatigue life. However, the inability to continuously collect residual strength and multiple damage modes pose a challenge to study the residual strength degradation mechanisms of three-dimensional braided composites. In this work, digital image correlation and acoustic emission techniques are employed to assess the strain field and damage events of the specimens during the residual strength tests. X-ray computed tomography facilitates the visualization and quantification of the fatigue damage evolution in three-dimensional braided composites. Moreover, the “two-step” damage classification method is applied to isolate the damage mechanisms. The Pearson correlation analysis is preformed between the quantitative results of three non-destructive techniques and residual strength degradation of 3D braided composites. The integration of the outcomes provides a comprehensive depiction of the residual strength degradation mechanisms.

Online awareness is associated with superior performance on assessments of functional cognition

BackgroundIntact awareness facilitates an individual’s adoption of strategies to support community living skills. However, most studies have not examined awareness during ongoing complex task performance. Objective: To examine whether community-dwelling adult’s Accuracy and Strategy use on the Weekly Calendar Planning Activity 17-item version (WCPA-17), Total Cues on the Performance Assessment of Self-care Skills Checkbook Balancing and Shopping Task (PCST), and scores on the self-report Alzheimer’s Disease Cooperative Study – Activities of Daily Living Scale (ADCS-ADL) differ between groups who do and do not demonstrate awareness of performance difficulties on the WCPA-17.MethodsUsing data collected as part of a larger study we performed a cross-sectional analysis of 274 community-dwelling adults aged 55 to 93 years. Two methods classified participants into groups aware or unaware of their performance. Independent sample t-tests examined group differences on four dependent variables: Accuracy and Strategy use on the WCPA-17, PCST Total Cues, and score on the ADCS-ADL.ResultsUsing one classification method, aware individuals showed superior Accuracy (p < 0.001), used more Strategies (p = 0.002), needed fewer PCST Total Cues (p < 0.001), and reported greater independence on the ADCS-ADL (p < 0.004), similar trends were observed with the other method in Accuracy (p < 0.001) and PCST Total Cues (p < 0.001) but Strategy use and ADCS-ADL differences failed to reach significance after Bonferroni correction.ConclusionGroups categorized as aware performed better on all measures. Intact awareness is critical to performance on complex everyday activities and can be evaluated with functional cognition assessments.

Depth-based statistical analysis in the spike train space

Metric-based summary statistics such as mean and covariance have been introduced in neural spike train space. They can properly describe template and variability in spike train data, but are often sensitive to outliers and expensive to compute. Recent studies also examine outlier detection and classification methods on point processes. These tools provide reasonable result, whereas the accuracy remains at a low level in certain cases. In this study, we propose to adopt a well-established notion of statistical depth to the spike train space. This framework can naturally define the median in a set of spike trains, which provides a robust description of the ‘template’ of the observations. It also provides a principled method to identify ‘outliers’ and classify data from different categories. We systematically compare the new median, outlier detection and classification tools with state-of-the-art competing methods. The result shows the median has superior description for template than the mean. Moreover, the proposed outlier detection and classification perform more accurately than previous methods.