Classification Algorithm Research Articles

EEG Signal Analysis for Numerical Digit Classification: Methodologies and Challenges

Electroencephalography (EEG) has existed since the early 20th century. It has proven to be a vital tool for electrophysiological studies of conditions like epilepsy. Recently, it has been revitalized as the field of machine learning has been developing, widening its usefulness among a plethora of neurological conditions and in brain–computer interface (BCI) applications. This study delves into the intricate process of classifying EEG signals elicited by the visual stimuli of subjects viewing the digits 0 and 1 and a blank screen. We focus on developing a comprehensive workflow for EEG preprocessing, as well as feature extraction and signal classification. We achieve strong differentiation capabilities between digit and non-digit values in all classification algorithms. However, our study also highlights the profound neurological challenges encountered in distinguishing between the digit values, as our model, inspired by the related bibliography, was unable to differentiate between digit values 0 and 1. These findings underscore the complexity of numerical processing in the brain, revealing critical insights into the limitations and potential of EEG-based digit classification and the need for clarity in the bioinformatics community.

Long-COVID incidence proportion in adults and children between 2020 and 2024.

Incidence estimates of post-acute sequelae of SARS-CoV-2 infection, also known as long-COVID, have varied across studies and changed over time. We estimated long-COVID incidence among adult and pediatric populations in three nationwide research networks of electronic health records (EHR) participating in the RECOVER Initiative using different classification algorithms (computable phenotypes). This EHR-based retrospective cohort study included adult and pediatric patients with documented acute SARS-CoV-2 infection and two control groups-- contemporary COVID-19 negative and historical patients (2019). We examined the proportion of individuals identified as having symptoms or conditions consistent with probable long-COVID within 30-180 days after COVID-19 infection (incidence proportion). Each network (the National COVID Cohort Collaborative (N3C), National Patient-Centered Clinical Research Network (PCORnet), and PEDSnet) implemented its own long-COVID definition. We introduced a harmonized definition for adults in a supplementary analysis. Overall, 4% of children and 10-26% of adults developed long-COVID, depending on computable phenotype used. Excess incidence among SARS-CoV-2 patients was 1.5% in children and ranged from 5-6% among adults, representing a lower-bound incidence estimation based on our control groups. Temporal patterns were consistent across networks, with peaks associated with introduction of new viral variants. Our findings indicate that preventing and mitigating long-COVID remains a public health priority. Examining temporal patterns and risk factors of long-COVID incidence informs our understanding of etiology and can improve prevention and management.

Class-aware multi-level attention learning for semi-supervised breast cancer diagnosis under imbalanced label distribution.

Breast cancer affects a significant number of patients worldwide, and early diagnosis is critical for improving cure rates and prognosis. Deep learning-based breast cancer classification algorithms have substantially alleviated the burden on medical personnel. However, existing breast cancer diagnosis models face notable limitations which are challenging to obtain in clinical settings, such as reliance on a large volume of labeled samples, an inability to comprehensively extract features from breast cancer images, and susceptibility to overfitting on account of imbalanced class distribution. Therefore, we propose the class-aware multi-level attention learning model focused on semi-supervised breast cancer diagnosis to effectively reduce the dependency on extensive data annotation. Additionally, we develop the multi-level fusion attention learning module, which integrates multiple mutual attention components across different layers, allowing the model to precisely identify critical regions for lesion categorization. Finally, we design the class-aware adaptive pseudo-labeling module which adaptively predicts category distribution in unlabeled data, and directs the model to focus on underrepresented categories, ensuring a balanced learning process. Experimental results on the BACH dataset demonstrate that our proposed model achieves an accuracy of 86.7% with only 40% labeled microscopic data, showcasing its outstanding contribution to semi-supervised breast cancer diagnosis.

High-Quality Classroom Dialogue Automatic Analysis System

Classroom dialogue analysis is crucial as it significantly impacts both knowledge transmission and teacher–student interactions. Since the inception of classroom analysis research, traditional methods such as manual transcription and coding have served as foundational tools for understanding these interactions. While precise and insightful, these methods are inherently time-consuming, labor-intensive, and susceptible to human bias. Moreover, they struggle to handle the scale and complexity of modern classroom data effectively. In contrast, many contemporary deep learning approaches focus primarily on dialogue classification, but often lack the capability to provide deeper interpretative insights. To address these challenges, this study introduces an automated dialogue analysis system that combines scalability, efficiency, and objectivity in evaluating teaching quality. We first collected a large dataset of classroom recordings from primary and secondary schools in China and manually annotated the dialogues using multiple encoding frameworks. Based on these data, we developed an automated analysis system featuring a novel dialogue classification algorithm that incorporates speaker role information for more accurate insights. Additionally, we implemented innovative visualization techniques to automatically generate comprehensive classroom analysis reports, effectively bridging the gap between traditional manual methods and modern automated approaches. Experimental results demonstrated the system’s high accuracy in distinguishing various types of classroom dialogue. Large-scale analysis revealed key patterns in classroom dynamics, showcasing the strong potential of our system to enhance teaching evaluation and provide valuable insights for improving education practices.

Towards Predicting Basketball Player Positions with Transformative Insights

Basketball players are traditionally classified into five positions. This study examines the correlation between player performance, game statistics, and designated positions. It also explores how statistical contributions have evolved over time. Machine learning classifiers were used to identify key metrics that distinguish player positions and determine the most effective classification algorithms. Our findings confirm a correlation between game statistics and positions, reinforcing the relevance of traditional roles. However, results also show that modern players contribute across multiple positions, reflecting a shift toward versatility. Despite this flexibility, players maintain distinct roles and responsibilities. This classification approach highlights key performance metrics and lays the groundwork for future clustering and mapping analysis, offering deeper insights into player roles and team dynamics in contemporary basketball.

A paradoxical population structure of var DBLα types in Africa.

The var multigene family encodes Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1), central to host-parasite interactions. Genome structure studies have identified three major groups of var genes by specific upstream sequences (upsA, B, or C). Var with these ups groups have different chromosomal locations, transcriptional directions, and associations with disease severity. Here we explore temporal and spatial diversity of a region of var genes encoding the DBLα domain of PfEMP1 in Africa. By applying a novel ups classification algorithm (cUps) to publicly-available DBLα sequence datasets, we categorised DBLα according to association with the three ups groups, thereby avoiding the need to sequence complete genes. Data from deep sequencing of DBLα types in a local population in northern Ghana surveyed seven times from 2012 to 2017 found variants with rare-to-moderate-to-extreme frequencies, and the common variants were temporally stable in this local endemic area. Furthermore, we observed that every isolate repertoire, whether mono- or multiclonal, comprised DBLα types occurring with these frequency ranges implying a common genome structure. When comparing African countries of Ghana, Gabon, Malawi, and Uganda, we report that some DBLα types were consistently found at high frequencies in multiple African countries while others were common only at the country level. The implication of these local and pan-Africa population patterns is discussed in terms of advantage to the parasite with regards to within-host adaptation and resilience to malaria control.

Improving detection of Parkinson’s disease with acoustic feature optimization using particle swarm optimization and machine learning

Abstract Parkinson’s disease (PD), characterized by motor impairments and tremors, also presents early-stage vocal abnormalities that hold diagnostic potential. Leveraging voice analysis and classification techniques, numerous studies explore the feasibility of early PD detection through automated systems. While several databases offer acoustic features for this purpose, their effectiveness largely depends on the classification methodology employed. This study aims to refine PD detection systems by introducing customized weighting to acoustic features, adjusting their significance based on their correlation with the disease and the classification algorithm utilized. The particle swarm optimization algorithm is employed for this purpose, with the Oxford PD dataset serving as the source data for training and validation. Performance evaluation encompasses four classification algorithms: support vector machine, Gradient Boosting (GB), k-nearest neighbors (KNN), and Naïve Bayes. A 5-fold cross-validation technique was adopted to evaluate the effectiveness of our method for PD detection. The results show that our approach significantly improves performance regardless of the classifier used, demonstrating its generalization capability. The KNN classifier surpasses state-of-the-art results, achieving an accuracy of 97.44% and a sensitivity of 98.02%.

Fixed-focus laser triangulation simultaneously measures the morphology and color of object

Abstract Simultaneously measuring the surface morphology and color of a sample is crucial for full-color 3D printing and digitization of cultural artifacts. However, traditional methods are susceptible to the variations in the measurement angle and illumination condition, making it difficult to balance the color fidelity and spatial accuracy. Here, we propose a laser triangulation method that is implemented with a fixed focus, dark-field illumination, and data-driven spectral classification. With a fixed focus, this method measures the height of surface by analyzing the shape of reflected light spot. The dark-field illumination diminishes the background contributed by the specular reflection of white light, enabling an accurate spectral measurement. The data-driven spectral classification algorithm establishes color dataset for each sample, and ensures a precise color recognition through Principal component analysis (PCA) dimensionality reduction and K-nearest neighbor (KNN) classification. In our experiment, this method achieved an axial resolution of 5.1μm. The color classification accuracy was as high as 99.79% on ColorChecker cards and 96.03% on Pantone color match cards. Two samples were reconstructed and evaluated to validate this method, the Mean
Absolute Error (MAE) of the morphology measurements did not exceed 25μm, and the color difference (CIE2000) does not exceed 1.0, confirming its ability to accurately measure the surface morphology and color simultaneously.surface morphology and color simultaneously.

Remote Sensing Tools for Monitoring Marine Phanerogams:A Review of Sentinel and Landsat Applications

Seagrasses play a pivotal role in maintaining marine ecosystems, supporting biodiversity, and preventing sediment loss during storms. Their capacity for photosynthesis and growth is linked to light availability in the continental shelf waters. Satellite platforms such as Landsat (USGS) and Sentinel (ESA) provide accessible imagery for the monitoring of these submerged plants. This study employed the PRISMA methodology to conduct a systematic review of the literature, with the objective of identifying articles focused on these seagrasses and their detection via satellite imagery. The identified methodologies included the use of vegetation and water indices, which were validated through empirical observations, as well as supervised classification algorithms, such as Random Forest, Maximum Likelihood, and Support Vector Machine. These approaches were applied to Mediterranean and other coastal regions, revealing changes in seagrass cover due to anchor damage in tourist areas and trawling scars that resemble plough marks. Such tools are vital for informing management actions, such as the implementation of restrictions on anchoring and bottom trawling, in order to protect these vulnerable ecosystems. By enabling targeted interventions, this approach facilitates the preservation of seagrass meadows, which are also critical for carbon sequestration and the sustainability of marine habitats.

A novel fault identification method for rolling bearing

Abstract One-dimensional local binary pattern (1D-LBP) identifies the running state of bearings by describing local textural features of vibration signals. However, 1D-LBP algorithm is rather sensitive to noise and intrinsic periodic features of rotating machinery which is not included in consideration. This makes it difficult for 1D-LBP algorithm to comprehensively dig fault information of bearings. To solve this problem, a new method has been brought forward by combining the intrinsic periodicity of rotating machinery and extraction of textural features. Before extraction of local features of signals, vibration signals are subjected to first-order difference operation to highlight local impact features of bearing failure. Meanwhile, to reduce the noise and further strengthen periodic features, differential signals are segmented by a complete period, and the signals after segmentation are subjected to cross-correlation successively to acquire the corresponding cross-correlation functions. Secondly, 1D-LBP is employed on cross-correlation functions (not raw signals) to extract local features and local textural signals (LTS) are obtained. Thirdly, feature parameters and vectors have been built according to LTS and the running state of bearings has been described from the perspective of textural feature. At last, according to established feature vector and classification algorithm, running state of bearing was determined. A comparative analysis is given to proposed method and other methods through public data sets and data from an experimental equipment of bearing. The results of comparative analysis indicate that with the 2 kinds of data sets, the identification rate of proposed method for unknown samples is over 97% and it can judge the running state and the type of faults more precisely.

Utilizing MV-FLOW™ and multidimensional ultrasound characteristics for prognosticating FET outcomes in RIF patients: Study Protocol for a cross-sectional study.

Recurrent implantation failure (RIF) is a common issue in frozen-thawed embryo transfer (FET). Prior to transfer, uterine endometrial receptivity of FET patients can be assessed using multimodal transvaginal ultrasound indicators to predict the success rate of the current FET cycle. Endometrial blood flow is a crucial element in evaluating endometrial receptivity. MV-FLOW™ is an advanced two-dimensional superb microvascular imaging technology that can detect and display blood flow in micro-vessels. The data for this study were obtained from an ongoing cross-sectional study comprising 323 RIF patients and 323 first implantation (FI) patients, who underwent transvaginal ultrasound before FET. We collected basic clinical data and multimodal ultrasound data from these patients as predictive features, with clinical pregnancy as the predictive label, for model training. Based on the above, this study aims to establish and validate a clinical prediction model for FET outcomes using support vector classification (SVC) algorithms, based on MV-FLOW™ and multidimensional transvaginal ultrasound imaging features. The objective is to determine the predictive role of multimodal transvaginal ultrasound in embryo transfer outcomes and provide evidence for the clinical application of MV-FLOW™. Trial registration: Trial Registration: ChiCTR2400086401.

Evaluasi Kinerja Algoritma Klasifikasi dalam Studi Kasus Prediksi Kelulusan di Universitas XYZ

This study aims to evaluate the performance of three classification algorithms—Decision Tree, K-Nearest Neighbors (KNN), and Naive Bayes—in a case study of predicting student graduation outcomes at XYZ University. Utilizing a dataset that includes a diverse range of academic attributes of students, this research conducts an in-depth analysis of each algorithm's capability to predict students' graduation status. The findings reveal that the Decision Tree provides the best performance with an accuracy rate of 97%, while KNN also shows very good results with similar accuracy, and Naive Bayes records an accuracy of 91%. The advantage of the Decision Tree lies in its ease of interpretation and high decision transparency, making it highly suitable for educational system implementations. Meanwhile, KNN excels in handling complex datasets due to its adaptability to data variability, and Naive Bayes offers rapid analysis speeds ideal for processing large data volumes. These insights provide valuable guidance for educational institutions in developing more effective graduation prediction systems and supporting strategic decisions in academic management. The study also opens avenues for further research in exploring these algorithms' applications in broader educational contexts, as well as fine-tuning modeling techniques to enhance prediction accuracy and efficiency.

Dual-Modality Transformer with Time Series Imaging for Robust Epileptic Seizure Prediction

Automated EEG classification algorithms for seizures can facilitate the clinical diagnosis of epilepsy, enabling more expedient and precise classification. However, existing EEG signal preprocessing methods oriented towards artifact removal and signal enhancement have demonstrated suboptimal accuracy and robustness. In response to this challenge, we propose an Adaptive Dual-Modality Learning Model (ADML) for epileptic seizure prediction by combining time series imaging with Transformer-based architecture. Our approach effectively captures both temporal dependencies and spatial relationships in EEG signals through a specialized attention mechanism. Evaluated on the CHB-MIT and Bonn datasets, our method achieves 98.7% and 99.2% accuracy, respectively, significantly outperforming existing approaches. The model demonstrates strong generalization capability across datasets while maintaining computational efficiency. Cross-dataset validation confirms the robustness of our approach, with consistent performance above 96% accuracy. These results suggest that our dual-modality approach provides a reliable and practical solution for clinical epileptic seizure prediction.

Integration of Deep Learning with Fox Optimization Algorithm for Early Detection and Classification of Tomato Leaf and Fruit Diseases

Tomato is a common vegetable crop extensively cultivated in the farming lands in India. The hot climate of India is perfect for its development, but particular weather conditions along with many other aspects affect the growing of tomato plants. Apart from these natural disasters and weather conditions, plant diseases consist a major issue in crop production. Precisely classifying leaf and fruit diseases in tomato plants is a vital step toward computerizing processes. Traditional disease detection models for tomato crops often fall short in predictability. To address this, Machine Learning (ML) and Deep Learning (DL) models have been developed, presenting advanced classification capabilities and the ability to manage the vast variability in agricultural data that conventional computer vision models struggle with. This work presents an Integration of DL with Fox Optimization Algorithm (FOA) for the Recognition and Classification of Tomato Leaf and Fruit Diseases (IDLFOA-DCTLFD). The major objective of the proposed IDLFOA-DCTLFD model is to enhance the detection and classification outcomes of tomato leaf and fruit diseases. At the initial stage, the Median Filter (MF) model is used for pre-processing and the Efficient Channel Attention-SqueezeNet (ECA-SqueezeNet) model is employed for feature extraction. For the hyperparameter tuning process, the proposed IDLFOA-DCTLFD technique implements the FOA. Finally, a Wasserstein Generative Adversarial Network (WGAN) is utilized for the detection of tomato leaf and fruit diseases. The IDLFOA-DCTLFD method is experimentally examined in a tomato leaf and fruit dataset. The experimental validation of the IDLFOA-DCTLFD methodology portrayed a superior accuracy value of 98.02%, surpassing the existing techniques.

CLPr_in_ML: Cleft Lip and Palate Reconstructed Features with Machine Learning

Background: Cleft lip and palate are two of the most common craniofacial congenital malformations in humans. It influences tens of millions of patients worldwide. The hazards of this disease are multifaceted, extending beyond the obvious facial malformation to encompass physiological functions, oral health, psychological well-being, and social aspects. Objective: The primary objective of our study is to demonstrate the importance of imaging in detecting cleft lip and palate. By observing the morphological and structural abnormalities involving the lip and palate through imaging methods, this study aims to establish imaging as the primary diagnostic approach for this disease. Methods: In this work, we proposed a novel model to analyze unilateral complete cleft lip and palate after velopharyngeal closure and non-left lip and palate patients from the Department of Stomatology of Xuzhou First People's Hospital, Conical Beam CT (CBCT) images in silicon. In order to demonstrate the generalization, the simulated dataset was constructed using the random disturbance factor, which is from the actual dataset. We extracted several raw features from CBCT images in detail. Then, we proposed a novel feature reconstruction method, including six types of reconstructed factors, to reconstruct the existing features. Then, the reconstructed features weretrained with machine learning algorithms. Finally, the testing and independent data model was utilized to analyze the performance of this work. Results: By comparing different operator features, the min operator, max operator, average operator, and all operators can achieve good performances in both the testing set and the independent set. Conclusion: With the different operator features, the majority of classification models, including Gradient Boosting, Hist Gradient Boosting, Multilayer Perceptron, lightGBM, and broadened learning, classification algorithms can get the well-performances in the selected reconstructed feature operators.

Probabilistic mapping of imbalanced data for groundwater contamination using classification algorithms: Performance and reliability

Probabilistic mapping of imbalanced data for groundwater contamination using classification algorithms: Performance and reliability

Dataset of aerial photographs acquired with UAV using a multispectral (green, red and near-infrared) camera for cherry tomato (Solanum lycopersicum var. cerasiforme) monitoring.

A dataset of aerial photographs acquired with an Unmanned Aerial Vehicle (UAV) DJI Phantom 4 Pro is presented for monitoring a cherry tomato (Solanum lycopersicum var. cerasiforme) crop in Navolato, Mexico. Seven photogrammetric flights were carried out to assess the plant growth using a Mapir Survey 3W multispectral camera. Multispectral images with an approximate spatial resolution of 1.83 cm/px were obtained in each photogrammetric flight. These images were acquired every 15 days starting on October 15, 2021, and ending on January 23, 2022. The dataset contains the radiometrically calibrated images of the tomato crop divided into 2 open field parcels. The dataset also includes the processed photogrammetric products (ortho-mosaics) using a binary mask to exclude the soil from the plant area. The dataset was originally acquired to assess plant growth, stress levels, and overall crop health. However, this multispectral imagery dataset can also have various uses, such as creating training datasets with accurate labels or classes which can then be used to develop, train, and/or validate machine learning algorithms for image classification, object detection tasks, or change detection analysis.

Robust Direction-of-Arrival Estimation using improved Coprime Array for Wireless Communication Applications

In wireless communication systems, robust and accurate Direction-of-Arrival (DOA) estimation is essential for tasks such as beamforming and interference suppression. This research presents advancements in the Multiple Signal Classification (MUSIC) algorithm leveraging enhanced coprime sensor arrays for DOA estimation. Coprime arrays, characterized by their non-uniform spacing derived from coprime integers, offer superior angular resolution compared to traditional uniform arrays. By exploiting this unique array geometry, the proposed method enhances the spatial localization capabilities of the MUSIC algorithm, thereby improving signal detection and mitigation of interference. Experimental validation demonstrates the efficacy of the approach in various signal environments, highlighting its potential to enhance the performance and reliability of wireless communication systems.

Using classification algorithms to model nighttime Surface Urban Heat Island (SUHI), with an emphasis on the role of urban trees

Using classification algorithms to model nighttime Surface Urban Heat Island (SUHI), with an emphasis on the role of urban trees

Comparison of deep transfer learning models for classification of cervical cancer from pap smear images

Cervical cancer is one of the most commonly diagnosed cancers worldwide, and it is particularly prevalent among women living in developing countries. Traditional classification algorithms often require segmentation and feature extraction techniques to detect cervical cancer. In contrast, convolutional neural networks (CNN) models require large datasets to reduce overfitting and poor generalization. Based on limited datasets, transfer learning was applied directly to pap smear images to perform a classification task. A comprehensive comparison of 16 pre-trained models (VGG16, VGG19, ResNet50, ResNet50V2, ResNet101, ResNet101V2, ResNet152, ResNet152V2, DenseNet121, DenseNet169, DenseNet201, MobileNet, XceptionNet, InceptionV3, and InceptionResNetV2) were carried out for cervical cancer classification by relying on the Herlev dataset and Sipakmed dataset. A comparison of the results revealed that ResNet50 achieved 95% accuracy both for 2-class classification and for 7-class classification using the Herlev dataset. Based on the Sipakmed dataset, VGG16 obtained an accuracy of 99.95% for 2-class and 5-class classification, DenseNet121 achieved an accuracy of 97.65% for 3-class classification. Our findings indicate that DTL models are suitable for automating cervical cancer screening, providing more accurate and efficient results than manual screening.