Machine Learning Classifiers Research Articles

A stacking ensemble machine learning model for predicting postoperative axial pain intensity in patients with degenerative cervical myelopathy

Machine learning (ML) has been extensively utilized to predict complications associated with various diseases. This study aimed to develop ML-based classifiers employing a stacking ensemble strategy to forecast the intensity of postoperative axial pain (PAP) in patients diagnosed with degenerative cervical myelopathy (DCM). A total of 711 consecutive postoperative DCM patients were included between 2016 and 2024, and after excluding patients who did not meet the inclusion criteria and those who met the exclusion criteria, a total of 484 patients were ultimately included in this study. The intensity of PAP was assessed using a standardized Numerical Rating Scale (NRS) score one year following surgery. Participants were randomly allocated into training and testing sub-datasets in a ratio of 8:2. 91 initial ML classifiers were developed, from which the top three highest-performing classifiers were subsequently integrated into an ensemble model utilizing 13 different machine learning models. The area under the curve (AUC) served as the primary metric for evaluating the predictive performance of all classifiers. The classifiers EmbeddingLR-RF (AUC = 0.81), EmbeddingRF-MLP (AUC = 0.81), and RFE-SVM (AUC = 0.80) were recognized as the leading three models. By implementing an ensemble learning approach such as stacking, an enhancement in performance for the ML classifier was observed after amalgamating these three models, with SVM ensemble classifier performed the best (AUC = 0.91). Decision curve analysis underscored the advantages conferred by these ensemble classifiers; notably, prediction curves for PAP intensity among DCM patients exhibited significant variability across the top three initial classifiers. The ensemble classifiers effectively predicted PAP intensity in DCM patients, showcasing substantial potential to aid clinicians in managing DCM cases while optimizing medical resource utilization.

Vowel segmentation impact on machine learning classification for chronic obstructive pulmonary disease

Vowel-based voice analysis is gaining attention as a potential non-invasive tool for COPD classification, offering insights into phonatory function. The growing need for voice data has necessitated the adoption of various techniques, including segmentation, to augment existing datasets for training comprehensive Machine Learning (ML) modelsThis study aims to investigate the possible effects of segmentation of the utterance of vowel "a" on the performance of ML classifiers CatBoost (CB), Random Forest (RF), and Support Vector Machine (SVM). This research involves training individual ML models using three distinct dataset constructions: full-sequence, segment-wise, and group-wise, derived from the utterance of the vowel "a" which consists of 1058 recordings belonging to 48 participants. This approach comprehensively analyzes how each data categorization impacts the model's performance and results. A nested cross-validation (nCV) approach was implemented with grid search for hyperparameter optimization. This rigorous methodology was employed to minimize overfitting risks and maximize model performance. Compared to the full-sequence dataset, the findings indicate that the second segment yielded higher results within the four-segment category. Specifically, the CB model achieved superior accuracy, attaining 97.8% and 84.6% on the validation and test sets, respectively. The same category for the CB model also demonstrated the best balance regarding true positive rate (TPR) and true negative rate (TNR), making it the most clinically effective choice. These findings suggest that time-sensitive properties in vowel production are important for COPD classification and that segmentation can aid in capturing these properties. Despite these promising results, the dataset size and demographic homogeneity limit generalizability, highlighting areas for future research.Trial registration The study is registered on clinicaltrials.gov with ID: NCT06160674.

Post-Fire Burned Area Detection Using Machine Learning and Burn Severity Classification with Spectral Indices in İzmir: A SHAP-Driven XAI Approach

Post-Fire Burned Area Detection Using Machine Learning and Burn Severity Classification with Spectral Indices in İzmir: A SHAP-Driven XAI Approach

Empirical Comparison of Post-processing Debiasing Methods for Machine Learning Classifiers in Healthcare

Empirical Comparison of Post-processing Debiasing Methods for Machine Learning Classifiers in Healthcare

Computational Approaches for Predicting Drug Interactions with Human Organic Anion Transporter 4 (OAT4).

Human Organic Anion Transporter 4 (OAT4) is predominantly expressed in the kidneys, particularly in the apical membrane of the proximal tubule cells. This transporter is involved in the renal handling of endogenous and exogenous organic anions (OAs), making it an important transporter for drug-drug interactions (DDIs). To better understand OAT4-compound interactions, we generated single concentration (25 μM) in vitro inhibition data for over 1400 small molecules against the uptake of the fluorescent OA 6-carboxyfluorescein (6-CF) in Chinese hamster ovary (CHO) cells. Several drugs exhibiting higher than 50% inhibition in this initial screen were selected to determine IC50 values against three structurally distinct OAT4 substrates: estrone sulfate (ES), ochratoxin A (OTA), and 6-CF. These IC50 values were then compared to the drug plasma concentration as per the 2020 FDA drug-drug interaction (DDI) guidance. Several screened compounds, including some not previously reported, emerged as novel inhibitors of OAT4. These data were also used to build machine learning classification models to predict the activity of potential OAT4 inhibitors. We compared multiple machine learning algorithms and data cleaning techniques to model these screening data and investigated the utility of conformal predictors to predict OAT4 inhibition of a leave-out set. These experimental and computational approaches allowed us to model diverse and unbalanced data to enable predictions for DDIs mediated by this transporter.

Explainable TabNet ensemble model for identification of obfuscated URLs with features selection to ensure secure web browsing

Obfuscated and malicious URLs may lead to harmful content or actions to the system, such as downloading malware, phishing, scams, or adware. In the domain of cybersecurity, the identification of the obfuscated Uniform Resource Locator (URL) is a concerning facet. This study proposes a Robust unified TabNet ensemble model for the identification of Malicious URLs with feature extraction based on the computation of features’ importance for classification. A fine-tuned attention-based deep neural network TabNet is used to extract the features of the URL. The customized data with the most important features is generated, and a Machine Learning (ML) ensemble model is developed for the classification of the URLs. The evaluation parameters accuracy, Precision, Recall, and F1-score are measured to look at the performance of the TabNet ensemble model. Accuracy of 97.8%, precision of 0.978, recall of 0.976, and F1-score of 0.978 reflect the outperforming results of the proposed model while classifying the five URL classes. The model is further validated through statistical analysis by measuring the Kappa value, which comes up as 0.968 for the proposed model. With a 10-fold cross-validation model, we attained a mean accuracy of 97.27% and a confidence interval of 0.004. The Local Interpretable Model-agnostic Explanations (LIME) explainable AI model is used to validate the model to perceive the contributing features towards the classification model. The results are compared with the state-of-the-art ML classifiers and the previous studies, and the whole validation process favors the proposed model’s efficacy.

Unsupervised Machine Learning-Based Image Recognition of Raw Infrared Spectra: Toward Chemist-like Chemical Structural Classification and Beyond Numerical Data.

Recent advances in artificial intelligence have significantly improved spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR) spectral images, without relying on prior chemical knowledge. The potential of machine learning for chemical classification was demonstrated by extracting IR spectral images from the Spectral Database for Organic Compounds and converting them into 208,620-dimensional vector data. Hierarchical clustering of 230 compounds revealed distinct main clusters (A-G), each with specific subclusters exhibiting higher intracluster similarities. Despite the challenges, including sensitivity to spectral deviations and difficulty of distinguishing delicate chemical structures in spectra with low transparency in the fingerprint area, the proposed image recognition approach exhibits good potential. Both principal component analysis and k-means clustering produced similar results. Furthermore, the method demonstrated high robustness to noise. The Tanimoto coefficient was used to evaluate the molecular similarity, providing valuable insights. However, some results deviated from chemists' intuitions. The study also highlighted that the scaling composition formulas and molecular weights did not affect the classification results because high-dimensional features dominated the process. A comparison of the clustering results obtained from molecular fingerprints, using the adjusted Rand index as a metric, indicated that the image data provided better classification performance than numerical data of the same resolution. Overall, this study demonstrates the feasibility of using machine learning with IR spectral image data for chemical classification and offers a novel perspective that complements traditional methods, although the classifications may not always align with chemists' intuitions. This approach has broader implications for fields such as drug discovery, materials science, and automated spectral analysis, where handling large, raw spectral data sets is essential.

Online Discourse and Trends Surrounding HPV Vaccination for Head and Neck Cancer Prevention.

In 2020, the Food and Drug Administration (FDA) approved Merck's GARDASIL 9 for prevention of certain human papillomavirus (HPV)-related head and neck cancer. This study characterizes the sentiment and themes of HPV vaccination online discourse surrounding the 2020 FDA recommendation. Retrospective cohort. Data from 2018 to 2022: Reddit, Teen VaxView (Centers for Disease Control and Prevention), and Google Trends. Online public discourse was assessed by searching Reddit for user submitted posts using relevant keywords. Reddit posts were categorized by a trained machine learning classifier. Natural language processing calculated positive/negative sentiment, polarity, and frequent nonstop words. Google Trends assessed relative search popularity, and TeenVaxView assessed HPV vaccinations among adolescents. Statistical relationships were assessed using linear and logarithmic regression models. Analysis of 2751 Reddit posts demonstrated a balanced overall positive/negative sentiment, with a substantial positive trend over the study period (P = .05). Most posts were personal stories/questions (n = 1997, 72.6%) and conspiracy posts made up a just a small minority (n = 186, 6.8%). Discussion including head and neck anatomical and cancer terms significantly increased (P = .01). Vaccination coverage trended upward significantly for males and females (P < .01). A significant relationship was found between positive sentiment trend and vaccination coverage (P = .02). Discourse surrounding the HPV vaccine has been increasingly positive in sentiment and changes in FDA guidelines correlated with increased discussion of HPV-related head and neck cancer. There was a concurrent increased adolescent vaccination rate.

A Machine Learning Model Based on Multi-Phase Contrast-enhanced CT for the Preoperative Prediction of the Muscle-Invasive Status of Bladder Cancer.

Muscle infiltration of bladder cancer has become the most important index to evaluate its prognosis. Machine learning is expected to accurately identify its muscle infiltration status on images. This study aimed to establish and validate a machine learning prediction model based on multi-phase contrast-enhanced CT (MCECT) for preoperatively evaluating the muscle-invasive status of bladder cancer. A retrospective study was conducted on bladder cancer patients who underwent surgical resection and pathological confirmation by MCECT scans. They were randomly divided into training and testing groups at a ratio of 8:2; we used an independent external testing set for verification. The radiomics features of lesions were extracted from MCECT images and radiomics signatures were established by dual sample T-test and least absolute shrinkage selection operator (LASSO) regression. Afterward, four machine learning classifier models were established. The receiver operating characteristic (ROC) curve, calibration, and decision curve analysis were employed to evaluate the efficiency of the model and analyze diagnostic performance using accuracy, precision, sensitivity, specificity, and F1-score. The best predictive model was found to have logic regression as the classifier. The AUC value was 0.89 (5-fold cross-validation range 0.83-0.96) in the training group, 0.80 in the test group, and 0.87 in the external testing group. In the testing and external testing group, the diagnostic accuracy, precision, sensitivity, specificity, and F1-score were 0.759, 0.826, 0.863, 0.729, 0.785, and 0.794, 0.755, 0.953, 0.720, and 0.809, respectively. The machine learning model showed good accuracy in predicting the muscle infiltration status of bladder cancer before surgery.

Manipulating attention facilitates cooperation

Cooperation is essential for human societies, but not all individuals cooperate to the same degree. This is typically attributed to individual motives - for example, to be prosocial or to avoid risks. Here, we investigate whether cooperative behavior can, in addition, reflect what people pay attention to and whether cooperation may therefore be influenced by manipulations that direct attention. We first analyze the attentional patterns of participants playing one-shot Prisoner’s Dilemma games and find that choices indeed relate systematically to attention to specific social outcomes, as well as to individual eye movement patterns reflecting attentional strategies. To test for the causal impact of attention independently of participants’ prosocial and risk attitudes, we manipulate the task display and find that cooperation is enhanced when displays facilitate attention to others’ outcomes. Machine learning classifiers trained on these attentional patterns confirm that attentional strategies measured using eye-tracking can accurately predict cooperation out-of-sample. Our findings demonstrate that theories of cooperation can benefit from incorporating attention and that attentional interventions can improve cooperative outcomes.

A Framework for Comparison and Interpretation of Machine Learning Classifiers to Predict Autism on the ABIDE Dataset.

Autism is a neurodevelopmental condition affecting ~1% of the population. Recently, machine learning models have been trained to classify participants with autism using their neuroimaging features, though the performance of these models varies in the literature. Differences in experimental setup hamper the direct comparison of different machine-learning approaches. In this paper, five of the most widely used and best-performing machine learning models in the field were trained to classify participants with autism and typically developing (TD) participants, using functional connectivity matrices, structural volumetric measures, and phenotypic information from the Autism Brain Imaging Data Exchange (ABIDE) dataset. Their performance was compared under the same evaluation standard. The models implemented included: graph convolutional networks (GCN), edge-variational graph convolutional networks (EV-GCN), fully connected networks (FCN), autoencoder followed by a fully connected network (AE-FCN) and support vector machine (SVM). Our results show that all models performed similarly, achieving a classification accuracy around 70%. Our results suggest that different inclusion criteria, data modalities, and evaluation pipelines rather than different machine learning models may explain variations in accuracy in the published literature. The highest accuracy in our framework was obtained when using ensemble models (p < 0.001), leading to an accuracy of 72.2% and AUC = 0.77 using GCN classifiers. However, an SVM classifier performed with an accuracy of 70.1% and AUC = 0.77, just marginally below GCN, and significant differences were not found when comparing different algorithms under the same testing conditions (p > 0.05). Furthermore, we also investigated the stability of features identified by the different machine learning models using the SmoothGrad interpretation method. The FCN model demonstrated the highest stability in selecting relevant features contributing to model decision making. The code is available at https://github.com/YilanDong19/Machine-learning-with-ABIDE.

Abnormal heart sound recognition using SVM and LSTM models in real-time mode

Cardiovascular diseases are non-communicable diseases that are considered the leading cause of death worldwide accounting for 17.9 million fatalities. Auscultation of heart sounds is the most common and valuable way of diagnosing heart diseases. Normal heart sounds have a special rhythmic pattern as an indicator of heart integrity. Many experts concentrate on diagnosing the heart by automatic digital auscultation systems which find various distinguishable characteristics for heart sound classifications. This can decrease the mortality rate for cardiovascular diseases and enhance the patient’s quality of life. This study aims to propose a real-time heart sound recognition system to classify both normal and abnormal phonocardiograms with the ability to define the abnormality type if existed. Digital signal processing methods, by applying the fast Fourier transform, filtering techniques, and the dual-tree complex wavelet transform, with machine learning classification algorithms are employed to segment the input phonocardiogram signal, extract meaningful features, and find the appropriate class for the input signal. We utilized three datasets, the PhysioNet of 1395, the GitHub of 800, and the PASCAL of 100 files segmented into three cardiac cycles. The proposed solution relies on the support vector machine and the long-short term memory neural network to distinguish between normal and abnormal heartbeat sounds and to recognize the type of abnormality (in the case distinguished) respectively. The results show that the proposed approach for normal/abnormal classification achieves an overall accuracy of 96.0 and 98.1%, sensitivity of 94.4 and 84.2%, and specificity of 64.9 and 98.4% for two and one support vector machines respectively among the state-of-the-art solutions. The long short-term memory model is also a well-known efficient classifier for temporal data, and the results show the accuracy of 99.2, 99.5, 98.6, and 99.4% for four (aortic stenosis (AS), mitral regurgitation (MR), mitral stenosis (MS), and mitral valve prolapse (MVP)), five (AS, MR, MS, MVP, and normal), six (AS, MR, MS, MVP, extrahls, and extrasystole), and seven classes (AS, MR, MS, MVP, extrahls, extrasystole, and normal). Furthermore, we found an efficient automatic segmentation method that was tested with the PASCAL database achieving a total error of 867,525.6 and 23,590.3 ms for datasets A and B respectively, with a computational time of 0.04 s to segment one cardiac cycle.

Exploring the Frontiers of Quantum Machine Learning: A New Era in AI and Computation

In the realm of artificial intelligence (AI), the integration of quantum computing with machine learning (ML) has sparked a significant shift in how we approach problems that were once deemed computationally infeasible. Quantum Machine Learning (QML) represents an exciting intersection of quantum mechanics and machine learning, potentially revolutionizing areas such as optimization, pattern recognition, and data analysis. Although quantum computing itself is still in its infancy, the synergy between quantum theory and classical machine learning algorithms promises to unlock new capabilities that classical computing could never achieve. This paper delves into the latest advancements in Quantum Machine Learning, its theoretical underpinnings, practical applications, and the challenges that researchers face as they explore this rapidly growing field.

Twitter Sentiment Analysis

Sentiment analysis deals with relating and classifying opinions or sentiments expressed in source textbook. A significant quantum of sentiment-rich data is being produced via social media in the form of tweets, status updates, blog entries, and other content. Understanding the opinions of the millions can be greatly served from sentiment analysis of this stoner- generated data. Twitter sentiment analysis is more grueling than general sentiment analysis because of the frequence of misspellings and shoptalk expressions. Twitter allows a character count of over to 140 characters. The two approaches employed to assay sentiments from the textbook are the knowledge base fashion and the machine literacy approach. Analysing sentiments help in understanding how people are allowing emotionally and classifying it as negative, positive or neutral. The dataset used is a collection of tweets related to the brand apple. Two different machine learning classifiers are used then, so that a person's sentiment can be linked. These classifiers are applied and also the stylish classifier with the stylish result will be chosen in order to prognosticate people's feelings. Professionals will be better suitable to assess people's feelings and fete early signs of torture through this analysis.

Vision Transformer-Based Approach: A Novel Method for Object Recognition

This paper proposes a hybrid method to improve object recognition applications on inefficient and imbalanced datasets. The proposed method aims to enhance object recognition performance using the Vision Transformer (ViT) deep learning model and various classical machine learning classifiers (LightGBM, AdaBoost, ExtraTrees, and Logistic Regression). The Caltech-101 dataset used in the study is a low-resolution and noisy image dataset with class imbalance problems. Our method achieves better results by combining the feature extraction capabilities of the Vision Transformer model and the robust classification performance of classical machine learning classifiers. Experiments conducted on the Caltech-101 dataset demonstrate that the proposed method achieves a precision of 92.3%, a recall of 89.7%, and an accuracy of 95.5%, highlighting its effectiveness in addressing the challenges of object recognition in imbalanced datasets.

Stable and discriminating OCT-derived radiomics features for predicting anti-VEGF treatment response in diabetic macular edema.

Radiomics-based characterization of fluid and retinal tissue compartments of spectral-domain optical coherence tomography (SD-OCT) scans has shown promise to predict anti-VEGF therapy treatment response in diabetic macular edema (DME). Radiomics features are sensitive to different image acquisition parameters of OCT scanners such as axial resolution, A-scan rate, and voxel size; consequently, the predictive capability of the radiomics features might be impacted by inter-site and inter-scanner variations. The main objective of this study was (1) to develop a more generalized classifier by identifying the OCT-derived texture-based radiomics features that are both stable (across multiple scanners) as well as discriminative of therapeutic response in DME and (2) to identify the relative stability of individual radiomic features that are associated with specific spatial compartments (e/g. fluid or tissue) within the eye. A combination of 151 optimal responders and rebounders of anti-VEGF therapy in DME were included from the PERMEATE (imaged using Cirrus HD-OCT scanner) and VISTA clinical trials (imaged using Cirrus HD-OCT and Spectralis scanners). For each patient within the study, a set of 494 texture-based radiomics features were extracted from the fluid and the retinal tissue compartment of OCT images. The training set ( ) included 76 patients and the independent test set ) comprised of 75 patients. Features were ranked based on (i) only discriminability criteria, that is, maximizing area under the receiver operating characteristic curve (AUC) and (ii) both stability and discriminability criteria. The subset of radiomic features for which the feature expression remained relatively consistent between the two datasets, as assessed by Wilcoxon rank-sum test, were considered to be stable. Different machine learning (ML) classifiers (such as k-nearest neighbors, Random Forest, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Support Vector Machine using linear and radial basis kernel, Naive Bayes) were trained using the features selected based on both the stability and discriminability criteria on and then subsequently validated on . The ML classifier ( ) that yielded maximum AUC on was considered to be more generalized and stable for distinguishing anti-VEGF therapy treatment response as well as less sensitive to the effect of inter-site and inter-scanner variability. The model (based on both stability and discriminability criteria) achieved higher AUC compared to the criteria based off feature discrimination alone on (maximum AUCs of 0.9 versus 0.81; p-value=0.048). The texture-based radiomic features pertaining to the retinal tissue compartment were found to be more stable compared to the fluid related features across the two datasets. Our study suggests that incorporating both stable and discriminatory texture-based radiomic features extracted from fluid and retinal tissue compartments of OCT scans, a more generalized radiomic classifier can be developed to predict therapeutic response in DME. Also, the feature stability was found to be a function of the spatial location within the eye from where the features were extracted.

Semi-Automated Multi-Label Classification of Autistic Mannerisms by Machine Learning on Post Hoc Skeletal Tracking.

Mannerisms describe repetitive or unconventional body movements like arm flapping. These movements are early markers of restricted and repetitive behaviors (RRBs) in autism spectrum disorder (ASD). However, assessing mannerisms reliably is challenging. Even after extensive training in behavioral observations, inter-rater agreements for mannerism items remain insufficient. The current study used machine learning (ML) to classify mannerisms from videotaped behavioral observations in children with ASD. We developed a classification scheme for mannerisms as ground truth and applied it to videotaped behavioral observations from an early intervention study. ML was used in two steps: First, the OpenPose algorithm post hoc extracted features based on body movements in the videos. Second, a long short-term memory (LSTM) neural network classified the features in a multi-label approach to distinguish between the absence of mannerisms, flapping, jumping, and both flapping + jumping. The trained models achieved 70.2% accuracy (F1 score: 31.8%) using nested cross-validation. The analysis improves on previous videotaped ML classification studies by splitting training and test data subject-wise, highlighting its clinical applicability. The LSTM models are made publicly available for use with other video datasets. Our results show that ML-based classification of mannerisms is a promising tool for enhancing objective diagnostic methods of behavioral observations.

Social and economic predictors of under-five stunting in Mexico: a comprehensive approach through the XGB model.

The multifaceted issue of childhood stunting in low- and middle-income countries has a profound and enduring impact on children's well-being, cognitive development, and future earning potential. Childhood stunting arises from a complex interplay of genetic, environmental, and socio-cultural factors. It requires a comprehensive approach across nutrition, education, healthcare, and poverty reduction sectors to mitigate its prevalence and short- and long-term effects. The Mexican case presents a distinct challenge, as the country has experienced the recent dissolution of social health security programmes, rising poverty rates, and reduced government expenditures for childhood well-being. We propose a machine learning approach to understand the contribution of social and economic determinants to childhood stunting risk in Mexico. Using data from the 2006-2018 population-based Mexican National Health and Nutrition Surveys, six different machine learning classification algorithms were used to model and identify the most important predictors of childhood stunting. Among the six classification algorithms tested, Extreme Gradient Boosting (XGB) obtained the highest Youden Index value, effectively balancing the correct classification of children with and without stunting. In the XGB model, the most important predictor for classifying childhood stunting is the household's socioeconomic status, followed by the state of residence, the child's age, indigenous population status, the household's portion of children under five years old, and the local area's deprivation level. This paper contributes to understanding the structural determinants of stunting in children, emphasising the importance of implementing tailored interventions and policies, especially given our findings that highlight indigenous status and local deprivation as key predictors. In the context of diminishing health initiatives, this underscores the urgent need for specific, targeted, and sustainable actions to prevent and address a potential rise in stunting in similar settings. social and economic deprivation, stunting, children, machine learning, XGB model, Mexico.

Surface-Induced Unfolding Reveals Unique Structural Features and Enhances Machine Learning Classification Models.

Native ion mobility-mass spectrometry combined with collision-induced unfolding (CIU) is a powerful analytical method for protein characterization, offering insights into structural stability and enabling the differentiation of analytes with similar mass and mobility. A surface-induced dissociation (SID) device was recently commercialized, enabling broader adoption of SID measurements and surface-induced unfolding (SIU). This study evaluates SIU, benchmarking its reproducibility and performance against CIU on a Waters CyclicIMS ion mobility-mass spectrometer. Reproducibility studies were conducted on model proteins, including β-lactoglobulin (β-lac), bovine serum albumin (BSA), and immunoglobulin G1 kappa (IgG1κ). SIU and CIU exhibited comparable reproducibility, with root-mean-square deviation (RMSD) values averaging less than 4% across multiple charge states. Notably, SIU achieved unfolding transitions at lower lab-frame energies, enhancing sensitivity to subtle structural differences and providing additional analytical information, such as unique high arrival time unfolding features and additional unfolding transitions. Furthermore, the differentiation of closely related protein subclasses, such as IgG1κ and IgG4κ, was improved with SIU, as evidenced by the enhancement of supervised machine learning models for IgG subclass classifications. SIU-trained models outperformed or matched CIU-trained models, achieving high cross-validation accuracies (>90%) and robust classifications of biotherapeutics Adalimumab and Nivolumab. This work establishes SIU as a complementary and efficient alternative to CIU, offering improved sensitivity and analytical depth without loss in reproducibility. This work highlights the benefits of including SIU in protein characterization workflows, particularly in high-throughput and machine learning-guided applications.

A Graph Neural Network Model for Financial Fraud Prevention

Financial fraud prevention is a critical challenge for banks, payment processors, and online financial services. Traditional fraud detection models, including rule-based systems and machine learning classifiers, often struggle with adaptive fraud tactics, requiring frequent retraining to remain effective. Recent advancements in graph neural networks (GNNs) have enabled fraud detection models to leverage relational transaction data, capturing multi-hop fraud patterns and collusive fraud schemes that are difficult to detect with conventional approaches. This study proposes a GNN-based fraud prevention framework that models financial transactions as a heterogeneous graph, where nodes represent users and transactions, while edges encode financial relationships such as payment frequency, transaction amount similarity, and shared device usage. The GNN model learns fraud indicators by aggregating information from neighboring transactions, allowing it to detect complex fraud networks and coordinated money laundering activities. The proposed system was evaluated on large-scale transaction datasets, demonstrating higher fraud detection accuracy and lower false positive rates compared to traditional fraud detection models. The results confirm that graph-based fraud detection improves scalability and adaptability, making it a more effective approach for modern financial institutions seeking real-time fraud prevention solutions.