Classification Accuracy Performance Research Articles

Analysis of Sparse Trajectory Features Based on Mobile Device Location for User Group Classification Using Gaussian Mixture Model

Location data collected from mobile devices via global positioning system often lack semantic information and can form sparse trajectories in space and time. This study investigates whether user age groups can be accurately classified solely from such sparse spatial–temporal trajectories. We propose a feature extraction method based on a Gaussian mixture model (GMM), which assigns representative points (RPs) by clustering the location data and aggregating user trajectories into these RPs. We then construct three machine learning (ML) models—support vector classifier (SVC), random forest (RF), and deep neural network (DNN)—using the GMM-based features and compare their performance with that of the improved DNN (IDNN), which is an existing feature extraction approach. In our experiments, we introduced a missing value ratio θth to quantify trajectory sparsity and analyzed the effect of trajectory sparsity on the classification accuracy and generalizability performance of the ML models. The results indicate that GMM-based features outperform IDNN-based features in both classification accuracy and generalization performance. Notably, the RF model achieved the highest accuracy, whereas the SVC model displayed stable generalizability. As the missing value ratio θth increases, the IDNN becomes more susceptible to overfitting, whereas the GMM-based approach preserves accuracy and robustness. These findings suggest that sparse trajectories can still offer meaningful classification performance with appropriate feature design and model selection even without semantic information. This approach holds promise for domains where large-scale, sparse trajectory data are common, including urban planning, marketing analysis, and public policy.

SEMG Signal-Based Human Lower-Limb Intention Recognition Algorithm Using Improved Extreme Learning Machine

To address the difficulty in identifying human lower-limb movement intentions, low accuracy of classification models, and weak generalization ability, this study proposes a motion intention recognition method that combines an improved pelican optimization algorithm (IPOA) and a hybrid kernel extreme learning machine (HKELM). First, we collect the surface electromyography (sEMG) signals of subjects in six motion modes and perform feature parameter extraction under non-ideal conditions. On this basis, we establish a dataset of the relationships between the feature parameters and gait movements. Second, we build a motion intention classification model based on relational data using the HKELM to solve the problems of low modeling accuracy and weak generalizability. Third, the IPOA is used to optimize the parameters related to the HKELM, and a differential evolution algorithm is introduced to improve the population quality and prevent the algorithm from falling into a local optimal solution. The experimental results show that the IPOA exhibits better optimization accuracy and convergence speed for four classical benchmark functions. Its average classification accuracy, average classification recall, and average F-value are 94.45%, 94.47%, and 94.46%, respectively, which are significantly higher than those of other intention recognition algorithms. Therefore, the proposed method has high classification accuracy and generalization performance.

Generalized Design for Additive Manufacturing (DfAM) Expert System Using Compliance and Design Rules

Additive manufacturing (AM) has revolutionized the design and production of complex geometries by offering unprecedented creative freedom over traditional manufacturing. Despite its growing prominence, AM lacks automated and standardized design rules tailored to specific AM processes, resulting in time-consuming and expert-dependent manual verification. To address these limitations, this research introduces a novel design for additive manufacturing (DfAM) framework consisting of two complementary models designed to automate the design process. The first model, based on a decision tree algorithm, evaluates part compliance with established AM design rules. A modified J48 classifier was implemented to enhance data mining accuracy by achieving a 91.25% classification performance accuracy. This model systematically assesses whether input part characteristics meet AM processing standards, thereby providing a robust tool for verifying design rules. The second model features an AM design rule engine developed with a Python-based graphical user interface (GUI). This engine generates specific recommendations for design adjustments based on part characteristics and machine compatibility, offering a user-friendly approach for identifying potential design issues and ensuring DfAM compliance. By linking part specifications to various AM techniques, this model supports both researchers and engineers in anticipating and mitigating design flaws. Overall, this research establishes a foundation for a comprehensive DfAM expert system.

RanBALL: An Ensemble Random Projection Model for Identifying Subtypes of B-Cell Acute Lymphoblastic Leukemia.

As the most common pediatric malignancy, B-cell acute lymphoblastic leukemia (B-ALL) has multiple distinct subtypes characterized by recurrent and sporadic somatic and germline genetic alterations. Identifying B-ALL subtypes can facilitate risk stratification and enable tailored therapeutic design. Existing methods for B-ALL subtyping primarily depend on immunophenotyping, cytogenetic tests and genomic profiling, which would be costly, complicated, and laborious. To overcome these challenges, we present RanBALL (an ensemble Ran dom projection-based model for identifying B - ALL subtypes), an accurate and cost-effective model for B-ALL subtype identification. By leveraging random projection (RP) and ensemble learning, RanBALL can preserve patient-to-patient distances after dimension reduction and yield robustly accurate classification performance for B-ALL subtyping. Benchmarking results based on >1700 B-ALL patients demonstrated that RanBALL achieved remarkable performance (accuracy: 0.93, F1-score: 0.93, and Matthews correlation coefficient: 0.93), significantly outperforming state-of-the-art methods like ALLSorts in terms of all performance metrics. In addition, RanBALL performs better than tSNE in terms of visualizing B-ALL subtype information. We believe RanBALL will facilitate the discovery of B-ALL subtype-specific marker genes and therapeutic targets to have consequential positive impacts on downstream risk stratification and tailored treatment design. To extend its applicability and impacts, a Python-based RanBALL package is available at https://github.com/wan-mlab/RanBALL .

Clustering and classification for dry bean feature imbalanced data

The traditional machine learning methods such as decision tree (DT), random forest (RF), and support vector machine (SVM) have low classification performance. This paper proposes an algorithm for the dry bean dataset and obesity levels dataset that can balance the minority class and the majority class and has a clustering function to improve the traditional machine learning classification accuracy and various performance indicators such as precision, recall, f1-score, and area under curve (AUC) for imbalanced data. The key idea is to use the advantages of borderline-synthetic minority oversampling technique (BLSMOTE) to generate new samples using samples on the boundary of minority class samples to reduce the impact of noise on model building, and the advantages of K-means clustering to divide data into different groups according to similarities or common features. The results show that the proposed algorithm BLSMOTE + K-means + SVM is superior to other traditional machine learning methods in classification and various performance indicators. The BLSMOTE + K-means + DT generates decision rules for the dry bean dataset and the the obesity levels dataset, and the BLSMOTE + K-means + RF ranks the importance of explanatory variables. These experimental results can provide scientific evidence for decision-makers.

Probabilistic Attention Map: A Probabilistic Attention Mechanism for Convolutional Neural Networks.

The attention mechanism is essential to convolutional neural network (CNN) vision backbones used for sensing and imaging systems. Conventional attention modules are designed heuristically, relying heavily on empirical tuning. To tackle the challenge of designing attention mechanisms, this paper proposes a novel probabilistic attention mechanism. The key idea is to estimate the probabilistic distribution of activation maps within CNNs and construct probabilistic attention maps based on the correlation between attention weights and the estimated probabilistic distribution. The proposed approach consists of two main components: (i) the calculation of the probabilistic attention map and (ii) its integration into existing CNN architectures. In the first stage, the activation values generated at each CNN layer are modeled by using a Laplace distribution, which assigns probability values to each activation, representing its relative importance. Next, the probabilistic attention map is applied to the feature maps via element-wise multiplication and is seamlessly integrated as a plug-and-play module into existing CNN architectures. The experimental results show that the proposed probabilistic attention mechanism effectively boosts image classification accuracy performance across various CNN backbone models, outperforming both baseline and other attention mechanisms.

New parameterized quantum gates design and efficient gradient solving based on variational quantum classification algorithm

Abstract Currently, variational quantum classification algorithms (VQCAs) generally rely on traditional optimization techniques such as Powell and SLSQP in the parameter optimization session. However, the performance of these methods shows limitations in practical applications. Although the parameter-shift rule can efficiently compute the parameter gradient with quantum circuits, it needs to run the quantum circuit twice repeatedly, which significantly reduces the computation efficiency. In order to overcome this challenge, this paper innovatively integrates the principle of unitary operation in quantum mechanics with the technical characteristics of superconducting quantum chips and elaborately designs some new parameterized quantum gates (PQGs). These PQGs strictly follow the rules of unitary operation, which ensures the stability and accuracy of quantum state evolution while realizing an efficient solution to the parameter gradient. Especially for the gradient calculation of a single qubit and single-angle PQGs, the new method can be completed with only a single quantum circuit run, which greatly improves the computation efficiency. Experimental validation on benchmark datasets such as breast cancer and iris shows that the method proposed in this paper exhibits excellent performance on quantum classification tasks. Compared with the parameter-shift rule, the computation efficiency of the new method is improved by 40%. And the classification accuracy, precision, and other key performance metrics are improved by an average of 5% in comparison with traditional optimization algorithms. This work not only enriches the methodology of quantum machine learning theoretically but also demonstrates its remarkable superiority in practical applications, which indicates that the method has great potential in scientific research and industrial applications.

Geological type recognition for shield machine using a semi-supervised variational auto-encoder-based adversarial method

In the process of tunneling, accurate and timely recognition of the geological type is significant to optimize the control parameters of the tunneling machine, improving tunneling efficiency and avoiding accidents. The shield machine operator in shield tunneling machine cannot directly observe the geological environment due to the closed working environment, so the soft method that can indirectly recognize the geological type by the machine parameters has become a research hotspot. However, most current soft methods use only a small amount of labeled data for supervised learning, and large amounts of unlabeled data is wasted. In order to use all data to improve the recognition performance of the classifier, a semi-supervised variational auto-encoder-based adversarial method (VAE-EMGAN) is proposed. Firstly, 50 parameters associated with geological types are selected and pre-processed, then the Variational Auto-Encoder (VAE) is trained by unlabeled data, and the generated part of VAE is added to the structure of Enhanced Multi-Classification Adversarial Generative Network (EMGAN) as a generator. Finally, the recognition accuracy of classifier is improved through adversarial training with labeled data, unlabeled data and generated data. We used data from upper and lower tunnels in Singapore to create two tasks to verify the validity and generalization performance of VEVE-EMGAN. The results show that the proposed model not only achieves high accuracy of all test sets on both tasks, but also has much better generalization performance than other models. Mean accuracy is 10.82%, 17.68%, 11.05%, 17.72%, 17.45%, 12.68% and 5.27% higher than SVM, KNN, RF, XGBoost, MLP, DNN and CNN respectively of test set 2 on task A; Mean accuracy is 13.06%, 12.80%, 7.64%, 18.31%, 8.74%, 7.94% and 4.05% higher than SVM, KNN, RF, XGBoost, MLP, DNN and CNN respectively of test set 2 on task B. In particular, the performance of the adversarial trained classifier is better than that has the same structure but separately trained classifier. Therefore, this method can use unlabeled data for adversarial training to improve the classification accuracy and generalization performance of the classifier, which has important implications for engineering practice.

VaxLLM: Leveraging Fine-tuned Large Language Model for automated annotation of Brucella Vaccines.

Vaccines play a vital role in enhancing immune defense and preventing the hosts against a wide range of diseases. However, research relating to vaccine annotation remains a labor-intensive task due to the ever-increasing volume of scientific literature. This study explores the application of Large Language Models (LLMs) to automate the classification and annotation of scientific literature on vaccines as exemplified on Brucella vaccines. We developed an automatic pipeline to automatically perform the classification and annotation of Brucella vaccine-related articles, using abstract and title. The pipeline includes VaxLLM (Vaccine Large Language Model), which is a fine-tuned Llama 3 model. VaxLLM systematically classifies articles by identifying the presence of vaccine formulations and extracts the key information about vaccines, including vaccine antigen, vaccine formulation, vaccine platform, host species used as animal models, and experiments used to investigate the vaccine. The model demonstrated high performance in classification (Precision: 0.90, Recall: 1.0, F1-Score: 0.95) and annotation accuracy (97.9%), significantly outperforming a corresponding non-fine-tuned Llama 3 model. The outputs from VaxLLM are presented in a structured format to facilitate the integration into databases such as the VIOLIN vaccine knowledgebase. To further enhance the accuracy and depth of the Brucella vaccine data annotations, the pipeline also incorporates PubTator, enabling cross comparison with VaxLLM annotations and supporting downstream analyses like gene enrichment. VaxLLM rapidly and accurately extracted detailed itemized vaccine information from publications, significantly outperforming traditional annotation methods in both speed and precision. VaxLLM also shows great potential in automating knowledge extraction in the domain of vaccine research. All data is available at https://github.com/xingxianli/VaxLLM , and the model was also uploaded to HuggingFace ( https://huggingface.co/Xingxian123/VaxLLM ).

Improving LIBS-based mineral identification with Raman imaging and spectral knowledge distillation

Combining data from different sensing modalities has been a promising research topic for building better and more reliable data-driven models. In particular, it is known that multimodal spectral imaging can improve the analytical capabilities of standalone spectroscopy techniques through fusion, hyphenation, or knowledge distillation techniques. In this manuscript, we focus on the latter, exploring how one can increase the performance of a Laser-induced Breakdown Spectroscopy system for mineral classification problems using additional spectral imaging techniques. Specifically, focusing on a scenario where Raman spectroscopy delivers accurate mineral classification performance, we show how to deploy a knowledge distillation pipeline where Raman spectroscopy may act as an autonomous supervisor for LIBS. For a case study concerning a challenging Li-bearing mineral identification of spodumene and petalite, our results demonstrate the advantages of this method in improving the performance of a single-technique system. LIBS trained with labels obtained by Raman presents an enhanced classification performance. Furthermore, leveraging the interpretability of the model deployed, the workflow opens opportunities for the deployment of assisted feature discovery pipelines, which may impact future academic and industrial applications.

Cleft Lip and Palate Classification Through Vision Transformers and Siamese Neural Networks.

This study introduces a novel approach for the diagnosis of Cleft Lip and/or Palate (CL/P) by integrating Vision Transformers (ViTs) and Siamese Neural Networks. Our study is the first to employ this integration specifically for CL/P classification, leveraging the strengths of both models to handle complex, multimodal data and few-shot learning scenarios. Unlike previous studies that rely on single-modality data or traditional machine learning models, we uniquely fuse anatomical data from ultrasound images with functional data from speech spectrograms. This multimodal approach captures both structural and acoustic features critical for accurate CL/P classification. Employing Siamese Neural Networks enables effective learning from a small number of labeled examples, enhancing the model's generalization capabilities in medical imaging contexts where data scarcity is a significant challenge. The models were tested on the UltraSuite CLEFT dataset, which includes ultrasound video sequences and synchronized speech data, across three cleft types: Bilateral, Unilateral, and Palate-only clefts. The two-stage model demonstrated superior performance in classification accuracy (82.76%), F1-score (80.00-86.00%), precision, and recall, particularly distinguishing Bilateral and Unilateral Cleft Lip and Palate with high efficacy. This research underscores the significant potential of advanced AI techniques in medical diagnostics, offering valuable insights into their application for improving clinical outcomes in patients with CL/P.

Developing a machine learning-based evaluation system for the recruitment of maritime professionals

The maritime sector predominantly relies on subjective evaluations of seafarers' skills and experience in conventional recruiting procedures. Nevertheless, subjective evaluation methods are highly susceptible to biases and inconsistencies. This study proposes a novel recruitment process within the maritime industry by merging psychological tests and machine learning methods in the recruitment process. Using psychological tests such as MMPI-I as features in machine learning methods for the recruitment process represents a step-change approach within the industry to promote more objective assessments of maritime professionals during recruitment processes and identify suitable candidates based on data from same-rank maritime professionals. This new methodology contributes innovatively to traditional maritime sector recruitment methods and potentially addresses a significant gap in the existing literature. The proposed methodology aims to predict future values by analysing existing data sets. Data were collected from 183 volunteer cadets from different backgrounds using an application form and the MMPI-I Personality Inventory. The dataset was classified using several machine learning algorithms, and their performance metrics were compared. The top five classification algorithms (Decision tree, PNN, random forest, gradient boost trees, and naive Bayes) with the best performance were evaluated, and the most accurate classification performance was achieved with the GBT algorithm. The results show that the highest values are for Gradient Boosted Trees (86%) and Random Forest (80%). The GBT algorithm has scored higher values for other metrics as well. The findings of this study indicate that it is possible to train algorithms that can adequately forecast the credentials and appropriateness of marine recruits. Through more development, these data-driven solutions could enhance existing subjective recruitment practices. This approach could improve the objectivity and prediction accuracy of marine recruiting processes, hence facilitating the selection of highly qualified individuals.

Multi-resolution visual Mamba with multi-directional selective mechanism for retinal disease detection.

Retinal diseases significantly impact patients' quality of life and increase social medical costs. Optical coherence tomography (OCT) offers high-resolution imaging for precise detection and monitoring of these conditions. While deep learning techniques have been employed to extract features from OCT images for classification, convolutional neural networks (CNNs) often fail to capture global context due to their focus on local receptive fields. Transformer-based methods, on the other hand, suffer from quadratic complexity when handling long-range dependencies. To overcome these limitations, we introduce the Multi-Resolution Visual Mamba (MRVM) model, which addresses long-range dependencies with linear computational complexity for OCT image classification. The MRVM model initially employs convolution to extract local features and subsequently utilizes the retinal Mamba to capture global dependencies. By integrating multi-scale global features, the MRVM enhances classification accuracy and overall performance. Additionally, the multi-directional selection mechanism (MSM) within the retinal Mamba improves feature extraction by concentrating on various directions, thereby better capturing complex, orientation-specific retinal patterns. Experimental results demonstrate that the MRVM model excels in differentiating retinal images with various lesions, achieving superior detection accuracy compared to traditional methods, with overall accuracies of 98.98\% and 96.21\% on two public datasets, respectively. This approach offers a novel perspective for accurately identifying retinal diseases and could contribute to the development of more robust artificial intelligence algorithms and recognition systems for medical image-assisted diagnosis.

Predictive Analysis of Breast Cancer from Full-Field Digital Mammography Images using Residual Network

Breast cancer has been a significant contributor to cancer-related mortality, but advancements in early detection through regular mammography and improvements in treatment modalities have contributed to declining mortality rates in several regions. This study presents a novel approach to cancer diagnosis utilizing Full-Field Digital Mammography images through predictive analysis methods. By using predictive analytic techniques and mammography images, this study offers a novel way to cancer detection. The research involves the application of deep learning techniques to extract valuable insights from cancer images captured by mammography devices. The CBIS-DDSM (Curated Breast Imaging Subset of Digital Database for Screening Mammography) dataset including images from patients with varying types and stages of cancer, is collected and pre-processed to ensure uniformity and quality. Relevant features, including color, texture, and shape characteristics, are extracted, and a rigorous feature selection process is employed to identify discriminative markers. The Residual Network (ResNet) model is selected and trained on the dataset, with a focus on classification accuracy and robust predictive performance. Validation metrics, such as accuracy, IoU (Intersection over Union) score, dice score, and ROC (Receiver Operating Characteristic) curve are employed to evaluate the model’s efficiency. After analysis, the proposed method had the best degree of mass lesion detection accuracy, at 99.24%. This research contributes to the advancement of non-invasive and efficient diagnostic tools, potentially enhancing early detection and intervention in cancer patients. The proposed method not only demonstrates promising results in terms of diagnostic accuracy but also emphasizes interpretability, seamless integration into clinical workflows, and adherence to ethical standards.

Deep Learning-Based Classification of Powerlifting Movements Using Mediapipe

The analysis of sports movements is of great importance for optimizing sports performance, minimizing injury risks, and ensuring that athletes work with correct techniques. Powerlifting is a power sport consisting of fundamental movements such as bench press, deadlift, and squat. These movements are inherently complex and challenging to execute. Therefore, it is of great importance to perform these movements with the correct technique and safely. The aim of this study was to classify these movements using deep learning methods to ensure that the basic movements in powerlifting sports (bench press, deadlift, and squat) are applied with correct techniques and to minimize the risk of injury. In this study, feature extraction was performed on powerlifting movements using the deep learning-based SqueezeNet model, followed by classification using machine learning methods. The dataset was compiled from 876 images of bench press, deadlift, and squat movements sourced from various online platforms. Additionally, the dataset was expanded through data augmentation techniques, and key points of posture estimation were added to the images using the Mediapipe library. The obtained datasets were classified using Neural Network, Logistic Regression, Support Vector Machine and Random Forest algorithms and model performances were evaluated using various metrics. The findings revealed that the Neural Network model demonstrated superior performance, achieving the highest accuracy (0.989). Additionally, the integration of pose estimation and data augmentation techniques significantly enhanced classification accuracy and overall model performance. The findings of this study show that deep learning methods are powerful tools in sports movement analysis and can make significant contributions to the evaluation of athletes' performance.

Real-Time Fire Classification Models Based on Deep Learning for Building an Intelligent Multi-Sensor System

Fire detection systems are critical for mitigating the damage caused by fires, which can result in significant annual property losses and fatalities. This paper presents a deep learning-based fire classification model for an intelligent multi-sensor system aimed at early and reliable fire detection. The model processes data from multiple sensors that detect various parameters, such as temperature, humidity, and gas concentrations. Several deep learning architectures were evaluated, including LSTM, GRU, Bi-LSTM, LSTM-FCN, InceptionTime, and Transformer. The models were trained on data collected from controlled fire scenarios and validated for classification accuracy, loss, and real-time performance. The results indicated that the LSTM-based models (particularly Bi-LSTM and LSTM) could achieve high classification accuracy and low false alarm rates, demonstrating their effectiveness for real-time fire detection. The findings highlight the potential of advanced deep-learning models to enhance the reliability of sensor-based fire detection systems.

The Evaluation of Machine Learning Techniques for Isotope Identification Contextualized by Training and Testing Spectral Similarity

Precise gamma-ray spectral analysis is crucial in high-stakes applications, such as nuclear security. Research efforts toward implementing machine learning (ML) approaches for accurate analysis are limited by the resemblance of the training data to the testing scenarios. The underlying spectral shape of synthetic data may not perfectly reflect measured configurations, and measurement campaigns may be limited by resource constraints. Consequently, ML algorithms for isotope identification must maintain accurate classification performance under domain shifts between the training and testing data. To this end, four different classifiers (Ridge, Random Forest, Extreme Gradient Boosting, and Multilayer Perceptron) were trained on the same dataset and evaluated on twelve other datasets with varying standoff distances, shielding, and background configurations. A tailored statistical approach was introduced to quantify the similarity between the training and testing configurations, which was then related to the predictive performance. Wilcoxon signed-rank tests revealed that the OVR-wrapped XGB significantly outperformed the other algorithms, with confidence levels of 99.0% or above for the 133Ba, 60Co, 137Cs, and 152Eu sources. The findings from this work are significant as they outline techniques to promote the development of robust ML-based approaches for isotope identification.

NTS-CAM classification model with channel attention mechanism for grading In-Vitro Fertilization (IVF) blastocyst quality

An automated-based intelligence approaches have widely used for quantifying In-Vitro Fertilisation (IVF) blastocyst image features that offer automation in morphology assessment as well as embryo selection to improve embryo implantation. Since the IVF blastocyst co-existed three main features of Zona Pellucida (ZP), Trophectoderm (TE) and Inner Cell Mass (ICM), this has made it crucial to consider the informative regions of all features in image morphology assessment. Although the implementation of Navigator-Teacher-Scrutinizer Network (NTS-net) has been detected most informative regions under the guidance of the Teacher network, there still limitation on calculation of the feature extraction process of different blastocyst features that led to poor classification performance. Therefore, this study proposes a new classification model namely NTS-CAM to improve extracted blastocyst features by assigning weights to channel features in channel attention mechanism (CAM) while extracting informative regions of each blastocyst feature. The benchmarking dataset showed significant performance of classification accuracy for ZP, TE, and ICM features with 80.5 %, 67.4 %, and 76.3 %, and the clinical dataset showed 74.1 %, 71.8 %, and 63.5 %, respectively. In conclusion, the proposed NTS-CAM model to predict grade of IVF blastocyst quality has improved the performance compared to classic NTS model. Furthermore, the improved model can be used for clinical decision making as well as for quality control in IVF procedure.

Generalized visible curvature: An indicator for bubble identification and price trend prediction in cryptocurrencies

We propose a novel curvature-based indicator constructed on log-price time series that captures an interplay between trend, acceleration, and volatility found relevant to quantify risks and improve trading strategies. We apply it to diagnose explosive bubble-like behaviors in cryptocurrency price time series and provide early warning signals of impending market shifts or increased volatility. This improves significantly on standard statistical tests such as the Generalized Supremum Augmented Dickey–Fuller (GSADF) and the Backward SADF tests. Furthermore, the incorporation of our curvature-based indicator as a feature into the Light Gradient Boosting Machine enhances its predictive capabilities, as measured by classification accuracy and trading performance.

Building and validating trend-based multiple sclerosis case definitions: a population-based cohort study for Manitoba, Canada

ObjectiveThis study aims to (1) build and validate model-based case definitions for multiple sclerosis (MS) that use trends (ie, trend-based case definitions) and (2) to apply dynamic classification to identify...