Ensemble Classifier Research Articles

A novel stacked ensemble classifier for Parkinson’s disease classification using voice features

A novel stacked ensemble classifier for Parkinson’s disease classification using voice features

Developing a peripheral blood RNA-seq based NETseq ensemble classifier: A potential novel tool for non-invasive detection and treatment response assessment in neuroendocrine tumor patients receiving 177Lu-DOTATATE PRRT.

Neuroendocrine tumors (NETs) are presented with metastases due to delayed diagnosis. We aimed to identify NET-related biomarkers from peripheral blood. The development and validation of a multi-gene NETseq ensemble classifier using peripheral blood RNA-Seq is reported. RNA-Seq was performed on peripheral blood samples from 178 NET patients and 73 healthy donors. Distinguishing gene features were identified from a learning cohort (59 PRRT-naïve GEP-NET patients and 38 healthy donors). Ensemble classifier combining the output of five machine learning algorithms viz. Random Forest (RF), Extreme Gradient Boosting (XGBOOST), Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Logistic Regression (LR) were trained and independently validated in the evaluation cohort (n = 106). The response to PRRT was evaluated in the PRRT cohort (n = 46) and the PRRT response monitoring cohort (n = 16). The response to 177Lu-DOTATATE PRRT was assessed using RECIST 1.1 criteria. The Ensemble classifier trained on 61 gene features, distinguished NET from healthy samples with 100% accuracy in the learning cohort. In an evaluation cohort, the classifier achieved 93% sensitivity (95% CI: 87.8%-98.03%) and 91.4% specificity (95% CI: 82.1%-100%) for PRRT-naïve GEP-NETs (AUROC = 95.4%). The classifier returned >87.5% sensitivity across different tumor characteristics and outperformed serum Chromogranin A sensitivity (χ2 = 21.89, p = 4.161e-6). In the PRRT cohort, RECIST 1.1 responders showed significantly lower NETseq prediction scores after 177Lu-DOTATATE PRRT, in comparison to the non-responders. In an independent response monitoring cohort, paired samples (before PRRT and after 2nd or 3rd cycle of PRRT) were analyzed. The NETseq prediction score significantly decreased in partial responders (p = .002) and marginally reduced in stable disease (p = .068). The NETseq ensemble classifier identified PRRT-naïve GEP-NETs with high accuracy (≥92%) and demonstrated a potential role in early treatment response monitoring in the PRRT setting. This blood-based, non-invasive, multi-analyte molecular method could be developed as a valuable adjunct to conventional methods in the detection and treatment response assessment in NET patients.

Integrative ensemble modelling of cetuximab sensitivity in colorectal cancer patient-derived xenografts.

Patient-derived xenografts (PDXs) are tumour fragments engrafted into mice for preclinical studies. PDXs offer clear advantages over simpler in vitro cancer models - such as cancer cell lines (CCLs) and organoids - in terms of structural complexity, heterogeneity, and stromal interactions. Here, we characterise 231 colorectal cancer PDXs at the genomic, transcriptomic, and epigenetic levels, along with their response to cetuximab, an EGFR inhibitor used clinically for metastatic colorectal cancer. After evaluating the PDXs' quality, stability, and molecular concordance with publicly available patient cohorts, we present results from training, interpreting, and validating the integrative ensemble classifier CeSta. This model takes in input the PDXs' multi-omic characterisation and predicts their sensitivity to cetuximab treatment, achieving an area under the receiver operating characteristics curve > 0.88. Our study demonstrates that large PDX collections can be leveraged to train accurate, interpretable drug sensitivity models that: (1) better capture patient-derived therapeutic biomarkers compared to models trained on CCL data, (2) can be robustly validated across independent PDX cohorts, and (3) could contribute to the development of future therapeutic biomarkers.

Ensemble Pretrained Convolutional Neural Networks for the Classification of Insulator Surface Conditions

Overhead transmission line insulators are non-conductive materials that separate conductors from grounded transmission towers. Once in operation, they frequently experience environmental pollution and electrical or mechanical stress. Since adverse operational conditions can lead to insulation failure, regular inspections are essential to prevent power outages. To this end, this paper proposes a novel technique based on deep convolutional neural networks (CNNs) to classify high-voltage insulator surface conditions based on their image. Successful applications of CNNs in computer vision have led to several pretrained architectures for image classification. To use these pretrained models, a practitioner typically fine-tunes and selects one final model via a model selection stage and discards all other models. In contrast with many existing studies that use such a “winner-takes-all” approach, here, we identify the best subset of seven popular pretrained CNN architectures that are combined by soft voting to form an ensemble classifier. From a machine learning (ML) perspective, this focus is warranted because the convolutional base of each pretrained architecture operates as a feature extractor and an ensemble of them works as a combination of various feature extraction rules. Our numerical experiments demonstrate the advantage of the identified ensemble model to individual pretrained architectures.

Comprehensive Morphometric Analysis to Identify Key Neuroimaging Biomarkers for the Diagnosis of Adult Hydrocephalus Using Artificial Intelligence.

Hydrocephalus involves abnormal cerebrospinal fluid accumulation in brain ventricles. Early and accurate diagnosis is crucial for timely intervention and preventing progressive neurological deterioration. The aim of this study was to identify key neuroimaging biomarkers for the diagnosis of hydrocephalus using artificial intelligence to develop practical and accurate diagnostic tools for neurosurgeons. Fifteen 1-dimensional (1-D) neuroimaging parameters and ventricular volume of adult patients with non-normal pressure hydrocephalus and healthy subjects were measured using manual image processing, and 10 morphometric indices were also calculated. The data set was analyzed using 8 machine, ensemble, and deep learning classifiers to predict hydrocephalus. SHapley Additive exPlanations (SHAP) feature importance analysis identified key neuroimaging diagnostic biomarkers. Gradient Boosting achieved the highest performance, with an accuracy of 0.94 and an area under the curve of 0.97. SHAP analysis identified ventricular volume as the most important parameter. Given the challenges of measuring volume for clinicians, we identified key 1-D morphometric biomarkers that are easily measurable yet provide similar classifier performance. The results showed that the frontal-temporal horn ratio, modified Evan index, modified cella media index, sagittal maximum lateral ventricle height, and coronal posterior callosal angle are key 1-D diagnostic biomarkers. Notably, higher modified Evan index, modified cella media index, and sagittal maximum lateral ventricle height, and lower frontal-temporal horn ratio and coronal posterior callosal angle values were associated with hydrocephalus prediction. The results also elucidated the relationships between these key 1-D morphometric parameters and ventricular volume, providing potential diagnostic insights. This study highlights the importance of a multifaceted diagnostic approach incorporating 5 easily measurable 1-D neuroimaging biomarkers for neurosurgeons to differentiate non-normal pressure hydrocephalus from healthy subjects. Incorporating our artificial intelligence model, interpreted through SHAP analysis, into routine clinical workflows may transform the diagnostic landscape for hydrocephalus by standardizing diagnosis and overcoming the limitations of visual evaluations, particularly in early stages and challenging cases.

Prediction of Strength and Failure Modes in Reinforced Concrete Beam–Column Joints using Ensemble Machine Learning Techniques

The beam-column joint is one of the important structural elements that control the structural behaviour during seismic excitation. Moreover, failure of any of these components may lead to the partial or overall collapse of the entire structure. In view of the safety concern, there is a need to evaluate the response properties, such as failure modes and ultimate shear capacity of the beam-column joints. Conventional methods to evaluate the shear capacity of beam-column joints are usually time-consuming. Therefore, in this study, an attempt has been made to evaluate the joint shear capacity and mode of failure of an exterior beam-column joint using ensembled machine learning (ML) approaches like Decision Tree, Random Forest, AdaBoost, CatBoost, and LightGBM. The present study highlights the potential use of the CatBoost model as a useful tool that can assist in predicting the shear strength and failure mode. The results of the study reveal that this model has performed well in predicting the load-carrying capacity and shear strength with high correlation coefficients of 0.9836 and 0.9978, respectively. Moreover, it has been observed that the prediction model provided by the CatBoost algorithm exhibits the highest accuracy for classification in the failure mode of beam-column joints.

A Cross Community Comparison of Muting in Conversations of Gendered Violence on Reddit

Gender-based violence (GBV) is an ongoing public health issue. Prevention practice and research on GBV contend with an incomplete understanding of its public health burden due to gender-exclusive definitions of GBV and inhibited survivor disclosure. Prior work in CSCW and HCI has explored sensitive disclosures and GBV conversations but excludes examination of conversation dynamics in surfacing knowledge of GBV. We used a mixed-methods approach to understand the phenomenon characterized by Muted Group Theory as a mechanism inhibiting disclosure in the context of GBV discussions on Reddit. Using an iterative process informed by literature of GBV research on cis-gender women, girls, trans, and non-binary populations, we developed comprehensive keywords to obtain, annotate, and analyze 298 posts and 10,369 comments about GBV across 7 subreddits. We found that muting faced by survivors offline, precluding reporting, is replicated on the Reddit platform. This study surfaced 5 categories of muting in discussions of GBV situated by variations in communication norms between dominant and non-dominant groups. These findings were supported by analysis of linguistic attributes that inform an Ensemble Classifier's detection of muting in conversations of GBV. The results offer that muting is a harmful occurrence in online disclosures of GBV that is mediated by existing moderation practices. This work contributes an expanded understanding of GBV conversations online, muting as a feature of those conversations, and an initial foray into detection to inform muting prevention.

TATPat based explainable EEG model for neonatal seizure detection

The most cost-effective data collection method is electroencephalography (EEG) to obtain meaningful information about the brain. Therefore, EEG signal processing is very important for neuroscience and machine learning (ML). The primary objective of this research is to detect neonatal seizures and explain these seizures using the new version of Directed Lobish. This research uses a publicly available neonatal EEG signal dataset to get comparative results. In order to classify these EEG signals, an explainable feature engineering (EFE) model has been proposed. In this EFE model, there are four essential phases and these phases: (i) automaton and transformer-based feature extraction, (ii) feature selection deploying cumulative weight-based neighborhood component analysis (CWNCA), (iii) the Directed Lobish (DLob) and Causal Connectome Theory (CCT)-based explainable result generation and (iv) classification deploying t algorithm-based support vector machine (tSVM). In the first phase, we have used a channel transformer to get channel numbers and these values have been divided into three levels and these levels are named (1) high, (2) medium and (3) low. By utilizing these levels, we have created an automaton and this automaton has three nodes (each node defines each level). In the feature extraction phase, transition tables of these nodes has been extracted. Therefore, the proposed feature extraction function is termed Triple Nodes Automaton-based Transition table Pattern (TATPat). The used EEG signal dataset contains 19 channels and there are 9 (= 32) connection in the defined automaton. Thus, the presented TATPat extracts 3249 (= 19 × 19 × 9) features from each EEG segment. To choose the most informative features of these 3249 features, a new feature selector which is CWNCA has been applied. By cooperating findings of this feature selector and the presented DLob, the explainable results have been obtained. The last phase is the classification phase and to get high classification performance from this phase, an ensemble classifier (tSVM) has been presented and the classification results have been obtained using two validation techniques which are 10-fold cross-validation (CV) and leave-one subject-out (LOSO) CV. The proposed EFE model generates a DLob string and by using this string, the explainable results have been obtained. Moreover, the presented EFE model attained 99.15% and 76.37% classification accuracy deploying 10-fold and LOSO CVs respectively. According to the classification performances, the recommended TATPat-based EFE is a good model at EEG signal classification. Also, the presented TATPat-based EFE model is a good model for explainable artificial intelligence (XAI) since TTPat-based EFE is cooperating by the DLob.

A Resilient Intrusion Detection System for IoT Environment Based on a Modified Stacking Ensemble Classifier

A Resilient Intrusion Detection System for IoT Environment Based on a Modified Stacking Ensemble Classifier

Classification of faults in friction stir processed composites using a machine learning and ensemble learning approach

Abstract Aluminium alloy based surface composites with hard reinforcement particles have wide scope in aerospace and automobile manufacturing industries. In this paper, the aluminium composites, manufactured by friction stir processing (FSP) with varying parameters are investigated for the faults occurred during fabrication process. It explores a machine-learning approach to detect defects of surface hybrid composites with an Al6061 alloy matrix, reinforced with copper and graphene nano-powders, using friction stir processing and a tungsten carbide tool on a milling machine. Multi-sensor time series data (vibration, force, and current) collected during fabrication, is preprocessed and labelled with normal and defective categories (e.g., pin break, brazing break, rough surface, no composite) using visual inspection. The important time domain and frequency domain features are extracted using different libraries in python. Thenafter, various types of feature selection techniques, viz filter, wrapper and embedded methods are implemented to select most relevant features. The selected subset of features from all selection methods used, are applied to different machine learning and ensemble learning classifiers and their performances are evaluated. The optimal combinations of the type of feature selection method and classifier used, are obtained for efficient classification of surface defects in composited formed by FSP. The real time monitoring and defect detection system can be developed in future for the composites developed by FSP using the developed models.

Retraction notice: Diagnosis of COVID-19 through blood sample using ensemble genetic algorithms and machine learning classifier

Retraction notice: Diagnosis of COVID-19 through blood sample using ensemble genetic algorithms and machine learning classifier

Topological embedding and directional feature importance in ensemble classifiers for multi-class classification

Cancer is the second leading cause of disease-related death worldwide, and machine learning-based identification of novel biomarkers is crucial for improving early detection and treatment of various cancers. A key challenge in applying machine learning to high-dimensional data is deriving important features in an interpretable manner to provide meaningful insights into the underlying biological mechanismsWe developed a class-based directional feature importance (CLIFI) metric for decision tree methods and demonstrated its use for the The Cancer Genome Atlas proteomics data. The CLIFI metric was incorporated into four algorithms, Random Forest (RF), LAtent VAriable Stochastic Ensemble of Trees (LAVASET), and Gradient Boosted Decision Trees (GBDTs), and a new extension incorporating the LAVA step into GBDTs (LAVABOOST). Both LAVA methods incorporate topological information from protein interactions into the decision function.The different models' performance in classifying 28 cancers resulted in F1-scores of 92.6% (RF), 92.0% (LAVASET), 89.3% (LAVABOOST) and 85.7% (GBDT), with no method outperforming all others for individual cancer type prediction. The CLIFI metric enables visualisation of the model's decision-making functions. The resulting CLIFI value distributions indicated heterogeneity in the expression of several proteins (MYH11, ERα, BCL2) across different cancer types (including brain glioma, breast, kidney, thyroid and prostate cancer) aligning with the original raw expression data.In conclusion, we have developed an integrated, directional feature importance metric for multi-class decision tree-based classification models that facilitates interpretable feature importance assessment. The CLIFI metric can be combined with incorporating topological information into the decision functions of models to introduce inductive bias, enhancing interpretability.

Optimizing SMS Spam Detection Using Machine Learning: A Comparative Analysis of Ensemble and Traditional Classifiers

With the rapid rise of mobile communication, Short Message Service (SMS) has become an essential platform for transmitting information. However, the growing volume of unsolicited and harmful spam messages presents significant challenges for both users and mobile network operators. This study explores the effectiveness of various machine learning models, including Random Forest, Gradient Boosting, AdaBoost, Support Vector Machine (SVM), Logistic Regression, and an Ensemble Voting Classifier, in detecting SMS spam. A dataset containing 5,572 SMS messages, labeled as either spam or ham (legitimate), was used to evaluate these models. Hyperparameter tuning was performed on each model to optimize accuracy, and the models were assessed using metrics such as precision, recall, F1-score, and accuracy. The results indicated that the SVM and Ensemble Voting Classifier achieved the highest performance, with accuracies of 0.9857 and 0.9848, respectively. Both models demonstrated superior recall for spam messages, making them highly effective for real-world spam detection systems. While Random Forest, Gradient Boosting, and AdaBoost also performed well, their slightly lower recall for spam suggests that they may misclassify some spam as legitimate messages. The study highlights the effectiveness of machine learning models in addressing the SMS spam problem, particularly when using ensemble methods. Future research should focus on addressing class imbalance and exploring deep learning approaches to further enhance model performance. These findings offer valuable insights for developing more accurate and scalable SMS spam detection systems.

Parkinson’s Disease Interstage Prognostic Biomarkers for Early Detection through Hybrid Machine Learning Model

Background: Parkinson’s Disease (PD) is a neurodegenerative condition that takes 15 to 20 years to exhibit the symptoms. However, during the initial stages of the disease, there are microlevel changes in the body that are typically undetected. The pathogenesis of Parkinson’s disease requires understanding of these microlevel changes. Objectives: The objective of the present study is to develop a hybrid ensembled machine learning pipeline model for identifying inter-stage Parkinson’s Disease (PD) biomarkers for understanding disease progression as well as etiology. Methods: The proposed work was carried out on the dataset GSE202667 from Gene Expression Omnibus, containing time-resolved RNA signatures of CD4+ T cells at various stages. Differentiating genes were identified in different interstage groups. Two types of unsupervised learning methods- distance-based (K-means, Agglomerative, Density-Based Spatial Clustering of Application with Noise) and probability-based (Hidden Markov Model, Gaussian Mixture Model Latent Dirichlet Allocation) were applied. The best algorithms were selected and applied to optimize clusters. Enrichment analysis was conducted on the top 10 PD biomarkers in each category. Findings: The top 10 PD biomarkers in each category are identified and gene set enrichment analysis resulted into their enrichment in three KEGG Pathways and depletion in two GO molecular functions. These biomarkers’ depletion is observed in 215 Reactome pathways and enrichment in 18 Reactome pathways. Twelve GO-Cellular components had an enrichment whereas 111 GO-Cellular components had a depletion in the gene set. A total of 25 GO-Biological process components were enriched, while 339 GO-Biological process components were depleted. Novelty: The proposed hybrid ensembled machine learning pipeline model works as a tool to identify Parkinson’s Disease biomarkers from omics data. The model contributes to identify implicit patterns in omics data in order to unveil Parkinson’s disease progress mechanism through biomarkers discovery. Keywords: Gaussian Mixture Model clustering, Early Detection, k-means clustering, Machine learning, Parkinson’s Disease Interstage Biomarkers

Statistical Relationship between Satellite observed Land Surface Temperature and in-situ measured Surface Air Temperature over the Indian Region: An Exploratory Study

This paper presents an investigation of spatio-temporal estimation of daily minimum (Tmin) and maximum (Tmax) surface air temperature using satellite (INSAT and MODIS) derived Land surface temperature and in-situ observational data over the Indian region. Least Absolute Shrinkage and Selection Operator (LASSO) regression technique is used to identify influencing neighboring stations. To capture spatial and temporal variability of surface air temperature of a particular IMD station, Linear Regression and Auto-Regressive Integrated Moving Average, which is known as ARIMA, are used respectively. These models are statistically ensembled using stack generalization. Obtained station-by-station relationship is validated on an independent test data using Root Mean Squared Error (RMSE) to check the validity of the model under consideration. Results show ensembled model outperform (has lowest RMSE) the traditional methods for prediction of Tmin and Tmax with RMSE within a range of [1, 2] range for most of the regions and seasons.

OB-TSASA Guided Soft Voting Ensemble Classifier for Breast Cancer Classification Using Thermal Infrared Images

Nowadays, the use of thermal infrared images has become more popular in breast cancer detection. Thermal image analysis with machine learning algorithms allows physicians to make accurate decisions on cancer diagnostics. Hence, this paper introduces a novel automated breast cancer classifier using thermal infrared images. Initially, the features from the DMR – IR dataset are extracted by an integrated approach of Kernel Principle Fast Independent-Component Analysis (KPFICA) and deep object-centric pooling convolution neural network (DOCP-CNN). Additionally, the DOCP-CNN hyperparameters are tuned by the Harris Hawks Optimization (HHO) algorithm. Next, the proposed oppositional based tunicate swarm algorithm with simulated annealing (OB-TSASA) optimization is used to select the relevant features from the extracted features. Finally, the OB-TSASA guided soft voting ensemble classifier is proposed to detect and classify cancerous patients. The experiment is conducted on the DMR – IR dataset to estimate the effectiveness of the proposed method. Furthermore, the proposed method produces outstanding classification results in terms of AUC, accuracy, F1-score, sensitivity, MCC, and kappa statistics. It is observed that the classification accuracy is 97.73% which is superior to other classifiers and the error rate is 2.27% which is very low compared to other methods.

Pemanfaatan Chi Square dan Ensemble Tree Classifier pada Model SVM, KNN dan C4.5 dalam Penjualan Online

This research aims to assist MSMEs in overcoming problems in online sales. Currently, sellers only prepare stock without knowing how well the products are sold in their market segment. In the city of Tangerang alone, there are 222,602 MSMEs with various product categories. Therefore, besides utilizing offline sales, business actors should also engage in online sales. This research conducts feature selection using the Chi-Square method and Ensemble Tree Classifier to select the top 6 and 10 features. The SVM, KNN, and C4.5 algorithms are used to build prediction models based on the selected features. Using feature selection, it was found that the influential features are Estimated Shipping Cost, Shipping Cost Paid by Buyer, Total Product Price, and Estimated Shipping Cost Discount. The evaluation results using the three algorithms, SVM, KNN, and C4.5, indicate that the highest accuracy value is obtained when using the C4.5 model with data from the ensemble tree classifier with 6 features at 0.86%, followed by the C4.5 model with 10 features, KNN with 6 features, and KNN with 10 features, all of which source data from the ensemble tree classifier with an accuracy value of 0.85%.

Machine Learning Approach for Rock Mass Classification with Imbalanced Database of TBM Tunnelling in Himalayan Geology

AbstractThe geological condition of the Himalayan region is very complex and challenging. So far, empirical and analytical approaches for rock mass characterization have been a common practice in the Himalayas. Due to the limitations of input parameters and governing equations in design practices, rock mass characterization in tunnel boring machine (TBM) excavated tunnels is crucial. This research introduces robust machine learning (ML) approaches to predict rock mass quality conditions in complex geological environments, leveraging a large database of TBM parameters and rock mass rating (RMR) values. To do so, a total of 6879 stable phase TBM cycle data were collected from 12 km long tunnel in Nepal. The pre-processed parameters were randomly split into a training set (80%) and a testing set (20%). Seven individual classifiers consisting of logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), k-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and bagging, and stacking ensemble classifier were exploited with optimal hyperparameters. The comprehensive assessment carried out has shown that the ensemble classifier gave highest overall accuracy as compared to other individual classifiers. More importantly, the synthetic minority over-sampling technique (SMOTE) performs better to handle the imbalanced database, while the RF and stacking classifier demonstrated the best prediction performance with accuracy of 92%. Moreover, for the minority rock mass class, the RF shows better performance compared to stacking classifier. The authors emphasize that the effective application of ML-based data-driven approach shows substantial potential for rock mass characterization in TBM tunnelling.

A Comprehensive Analysis of a Framework for Rebalancing Imbalanced Medical Data Using an Ensemble-based Classifier

In medical data, addressing imbalanced datasets is paramount for accurate predictive modeling. This paper delves into exploring a well-established rebalancing framework proposed in previous research. While acknowledged for its effectiveness, the adaptability of this framework across diverse medical datasets remains unexplored. We conduct a comprehensive investigation to bridge this gap by integrating an ensemble-based classifier into the existing framework. By leveraging seven imbalanced medical binary datasets, our study comprises three distinct experiments: utilizing standard baseline classifiers from the framework (original), incorporating the baseline with an ensemble-based classifier, and introducing our novel ensemble-based classifier with the self-paced ensemble (SPE) algorithm. Our novel ensemble, composed of decision tree (DT), radial support vector machine (R.SVM), and extreme gradient boosting (XGB) classifiers, serves as the foundation for the SPE. Our primary objective is to demonstrate the potential improvement of the existing framework’s overall performance through the integration of an ensemble. Experimental results reveal significant enhancements, with our proposed ensemble classifier outperforming the original by 4.96%, 5.89%, 5.68%, 7.85%, and 6.84% in terms of accuracy, precision, recall, F-score, and G-mean, respectively. This study contributes valuable insights into the adaptability and performance augmentation achievable through ensemble methods in addressing class imbalances within the medical domain.

Prediction of human O-linked glycosylation sites using stacked generalization and embeddings from pre-trained protein language model.

O-linked glycosylation, an essential post-translational modification process in Homo sapiens, involves attaching sugar moieties to the oxygen atoms of serine and/or threonine residues. It influences various biological and cellular functions. While threonine or serine residues within protein sequences are potential sites for O-linked glycosylation, not all serine and/or threonine residues undergo this modification, underscoring the importance of characterizing its occurrence. This study presents a novel approach for predicting intracellular and extracellular O-linked glycosylation events on proteins, which are crucial for comprehending cellular processes. Two base multi-layer perceptron models were trained by leveraging a stacked generalization framework. These base models respectively employ ProtT5 and Ankh O-linked glycosylation site-specific embeddings whose combined predictions are used to train the meta-multi-layer perceptron model. Trained on extensive O-linked glycosylation datasets, the stacked-generalization model demonstrated high predictive performance on independent test datasets. Furthermore, the study emphasizes the distinction between nucleocytoplasmic and extracellular O-linked glycosylation, offering insights into their functional implications that were overlooked in previous studies. By integrating the protein language model's embedding with stacked generalization techniques, this approach enhances predictive accuracy of O-linked glycosylation events and illuminates the intricate roles of O-linked glycosylation in proteomics, potentially accelerating the discovery of novel glycosylation sites. Stack-OglyPred-PLM produces Sensitivity, Specificity, Matthews Correlation Coefficient, and Accuracy of 90.50%, 89.60%, 0.464, and 89.70%, respectively on a benchmark NetOGlyc-4.0 independent test dataset. These results demonstrate that Stack-OglyPred-PLM is a robust computational tool to predict O-linked glycosylation sites in proteins. The developed tool, programs, training, and test dataset are available at https://github.com/PakhrinLab/Stack-OglyPred-PLM. Supplementary data are available at Bioinformatics online.