Learning Classifiers Research Articles

Bank Customer Churn Prediction Using Machine Learning Framework

Abstract Using real customer data from a large community bank in the South of the US, this paper analyzes the customer churn prediction problem by constructing and comparing ten machine learning classification models with five sample techniques. Our results show that Random Forest, XG Boost, AdaBoost, and Bagging Meta classifiers dominate others in terms of overall accuracy, F-score, and AUC curve for the test observations. For the four classifiers, the overall accuracy ranges from 87% to 96% across five different sampling methods explored, while the AUC values range between 0.9 to 0.93. Considering overall accuracy and F-Score, AdaBoost with original and MTDF sampling technique dominates others; however, considering the AUC measure, XG Boost and Random Forest perform similarly to AdaBoost, which slightly dominate Bagging Meta across all sampling techniques; although the performance measures for these four classifiers are comparable across all sampling techniques. The paper further presents important features of customer churn behavior as predicted by the model. The diagnostic analysis also provides an insightful comparison between churned and non-churned customers. JEL classification numbers: C0, C5, C8, G21. Keywords: Machine learning, Big data, Sampling techniques, Customer churn, Customer retention, Financial services, Community bank.

Hydrogen-bond linking is crucial for growing ice VII embryos.

We use molecular dynamics simulations to examine the homogeneous nucleation of ice VII from metastable liquid water. An unsupervised machine learning classification identifies two distinct local structures composing Ice VII nuclei. The seeding method, combined with the classical nucleation theory (CNT), predicts the solid-liquid interfacial free energy, consistent with the value from the mold integration method. Meanwhile, the nucleation rates estimated from the CNT framework and brute force spontaneous nucleations are inconsistent, and we discuss the reasons for this discrepancy. Structural and dynamical heterogeneities suggest that the potential birthplace for an ice VII embryo is relatively ordered, although not necessarily relatively immobile. Moreover, we demonstrate that without the formation of hydrogen-bond links, ice VII embryos do not grow.

Deep learning classification of EEG-based BCI monitoring of the attempted arm and hand movements.

The primary objective of this research is to improve the average classification performance for specific movements in patients with cervical spinal cord injury (SCI). The study utilizes a low-frequency multi-class electroencephalography (EEG) dataset from Graz University of Technology. The research combines convolutional neural network (CNN) and long-short-term memory (LSTM) architectures to uncover neural correlations between temporal and spatial aspects of the EEG signals associated with attempted arm and hand movements. To achieve this, three different methods are used to select relevant features, and the proposed model's robustness against variations in the data is validated using 10-fold cross-validation (CV). The research also investigates subject-specific adaptation in an online paradigm, extending movement classification proof-of-concept. The combined CNN-LSTM model, enhanced by three feature selection methods, demonstrates robustness with a mean accuracy of 75.75 % and low standard deviation (+/- 0.74 %) in 10-fold cross-validation, confirming its reliability. In summary, this research aims to make valuable contributions to the field of neuro-technology by developing EEG-controlled assistive devices using a generalized brain-computer interface (BCI) and deep learning (DL) framework. The focus is on capturing high-level spatiotemporal features and latent dependencies to enhance the performance and usability of EEG-based assistive technologies.

Text mining and machine learning for crime classification: using unstructured narrative court documents in police academic

Text mining and machine learning for crime classification: using unstructured narrative court documents in police academic

THE EFFECT OF FEATURE EXTRACTION ON COVID-19 CLASSIFICATION

X-ray imaging stands as a prominent technique for diagnosing COVID-19, and it also serves as a crucial tool in the medical field for the analysis of various diseases. Numerous approaches are available to facilitate this analysis. Among these techniques, one involves the utilization of a Feature Extractor, which effectively captures pertinent characteristics from X-ray images. In a recent study, a comprehensive examination was conducted using 25 distinct feature extractors on X-ray images specific to COVID-19 cases. These images were categorized into two classes: COVID-19-positive and non-COVID-19. To enable a thorough evaluation, a sequence of machine learning classifiers was employed on these categorized images. The outcomes derived from this experimentation gauged the magnitude of impact that each individual feature exerted on COVID-19-related imagery. This assessment aimed to determine the efficacy levels of various feature extractors in terms of detection capability. Consequently, a distinction emerged between the more effective and less effective feature extractors, shedding light on their varying degrees of contribution to the detection process. Moreover, the comparative performance of different classifiers became evident, revealing the classifiers that exhibited superior performance when measured against their counterparts.

Deep learning or classical machine learning? An empirical study on line‐level software defect prediction

Deep learning or classical machine learning? An empirical study on line‐level software defect prediction

The Role of Last-Mile Delivery Quality and Satisfaction in Online Retail Experience: An Empirical Analysis

The rise of the e-commerce industry has markedly changed the global economy, providing customers with unparalleled access to goods and services. This study empirically examines online shoppers’ perceptions and preferences, focusing on their experiences with last-mile delivery (LMD) services and its impact on their shopping behaviour. This research employs machine learning classification and regression models for a large-scale analysis of customers’ responses, collected using an online survey in the main cities in Saudi Arabia, which is experiencing rapid e-commerce growth amidst a broader digital transformation. The findings highlight a strong consumer preference for timely LMD services, typically within a day of purchase, while noting dissatisfaction with exceedingly early delivery windows. The research emphasises the need to address customer dissatisfaction with delivery services to retain clientele, as many may switch retailers without informing the retailers. Additionally, a considerable trend towards preferring digital over cash-on-delivery payment methods was observed among online shoppers. Overall, this study provides valuable insights into the significant influence of LMD services on customer satisfaction and behaviour in the e-commerce sector. The use of robust machine learning models has revealed critical factors that can guide retailers and LMD providers in enhancing service delivery and customer experience, contributing to the broader discourse on e-commerce logistics efficiency and customer satisfaction.

Stacking classifier method for prediction of human body performance

<p>A healthy body is the capital of success and supports human activities. To maintain health, humans need to avoid disease. A healthy life is everyone’s dream and should start early. Busy activities often hinder a healthy lifestyle. Nonetheless, it is important for every individual to lead a healthy lifestyle. Human activities determine health and the implementation of a healthy life. One method that can perform classification with machine learning is extreme gradient boosting (XGBoost). XGBoost is one of the techniques in machine learning for regression analysis and classification based on gradient boosting decision tree (GBDT). By using gradient descent to minimize the error when creating a new model, the algorithm is called gradient boosting. In determining a classification starting from determining the model to the results, usually only using one algorithm method, and combining other methods together with the method is an algorithm called random forest classifier. Among these merging methods are, stacking classifier, voting classifier, and bagging classifier. The conclusion obtained from the results of this research is that the test results show that the stacking classifier achieves the highest accuracy of 76.07%, making it the best method in this research. And the stacking classifier has a precision of 76.96%, recall of 75.83%, and F1-score of 75.81%. This shows that the model has a good balance between the ability to provide true positive results and the ability to recover positive data.</p>

Variational Continuous Label Distribution Learning for Multi-Label Text Classification

Variational Continuous Label Distribution Learning for Multi-Label Text Classification

Optimal Machine Learning and Signal Processing Synergies for Low-Resource GNSS Interference Classification

Optimal Machine Learning and Signal Processing Synergies for Low-Resource GNSS Interference Classification

Enhancing mango quality control: A novel approach to spongy tissue inspection through image clustering and machine learning models via X‐ray imaging

AbstractThe globally renowned Alphonso mango is esteemed for its exceptional flavor, fragrance, texture, and extended shelf life. However, it faces a substantial challenge with spongy tissue (ST) disorder, affecting up to 30% of a single lot. This issue leads to the rejection of entire batches during export due to delayed detection, causing significant produce loss. Additionally, the current destructive assessment method is inadequate, as physically inspecting only a few mangoes within a batch does not ensure overall quality, emphasizing the need for a non‐destructive approach. To address these challenges, this study proposes a non‐destructive approach utilizing X‐ray imaging to identify ST in Alphonso mangoes, employing machine learning (ML) for binary classification. We captured and augmented 216 X‐ray images, utilizing various augmentation techniques. K‐means clustering effectively distinguished healthy and ST‐affected areas in mango fruits. For automated classification, ML models were fine‐tuned through grid search hyperparameter optimization within a 5‐fold cross‐validation framework. The CNN model achieved impressive accuracies of 99.20% and 96.82% at threshold values of .5 and .95, respectively, on the test dataset, while the SVM model attained overall accuracy rates of 95.23% and 87.35% for the same thresholds. Our study demonstrates the potential of machine learning in X‐ray imaging for detecting internal fruit issues, improving inspection for disease and damage identification, and enhancing quality control across the food industry. Practical implications include the implementation of non‐destructive inspection methods to reduce produce loss and ensure consistent quality in mango lots. Future research could focus on validating the model's efficacy with external datasets, evaluating its robustness in detecting various internal irregularities, and exploring advancements in X‐ray imaging technology for broader applications in agriculture.Practical applicationsThe suggested non‐destructive approach provides a practical solution for quality control, especially in export scenarios. The versatility of the ML‐powered X‐ray imaging technique to examine internal irregularities in various fruits beyond mangoes broadens its potential application in the fruit industry for sorting and grading purposes. This innovation contributes to heightened consumer satisfaction, providing a cost‐effective and scalable solution for widespread implementation. In summary, the findings of the study show promise for revolutionizing mango quality control, particularly for the esteemed Alphonso variety, and propelling advancements in fruit inspection methodologies across the industry.

Subband Cascaded CSP-Based Deep Transfer Learning for Cross-Subject Lower Limb Motor Imagery Classification

Subband Cascaded CSP-Based Deep Transfer Learning for Cross-Subject Lower Limb Motor Imagery Classification

Chronic Obstructive Pulmonary Disease Diagnosis with Bagging Ensemble Learning and ANN Classifiers

Chronic Obstructive Pulmonary Disease Diagnosis with Bagging Ensemble Learning and ANN Classifiers

Discriminating subtypes in advanced breast cancer with ctDNA methylation profiling.

1013 Background: Immunohistochemical subtyping of advanced breast cancer is critical to direct appropriate therapy. Yet, biopsy of recurrent cancer may not be technically possible, single-site tumour biopsies sample only a single metastasis and subtype may change through therapy. We developed a non-invasive blood test to subtype tumours based on circulating tumor DNA (ctDNA) methylation profiling. Methods: We interrogated TCGA-BRCA (level 3) methylation data from 783 tumours to identify regions with aberrant CpG methylation that differed between breast cancers and normal tissues, and between different breast cancer subtypes. A capture based enzymatic ctDNA based methylation profile was developed targeting 1257 differentially methylated regions (TWIST Methyl custom panel, TWIST Bioscience, panel size 0.4Mb). Plasma samples from patients with breast cancer, and healthy donors, were sequenced with the capture assay. A machine learning (ML) classifier was built to detect the presence of breast cancer, and a second classifier was built to discriminate ER positive v ER negative, and HER2 positive v HER2 negative from ctDNA methylation profiles. A 10-fold stratified nested cross-validation was used to assess classifier characteristics. Results: Plasma samples from 427 people (126 health donor, 27 early breast cancer patients and 291 metastatic breast cancer patients), along with 133 tumor tissue samples, were sequenced and a ML classifier built to detect the presence of breast cancer. In cross-validation, a random forest classifier yielded a median AUC of 0.996 (SD 0.020), with sensitivity of 94.9% at 99% specificity. A total of 231 ER positive and 63 ER negative plasma samples from patients were used for IHC ER status differentiation. In cross-validation a LGBM classifier yielded a median AUC of 0.928 (SD 0.051). Median sensitivity was 76.7% at specificity 86-99%. A total of 11% (25/231) ER positive and 8% (5/63) ER negative samples were misclassified. A total of 41 HER2 positive and 253 HER2 negative plasma samples were used for IHC HER2 status differentiation. In cross-validation a LGBM classifier yielded a median AUC of 0.934 (SD 0.075). Median sensitivity was 77.5% at specificity 85-92%, A total of 7% (3/41) HER-2 positive and 13% (33/253) HER2 negative samples were misclassified. In both IHC classifiers, subtype misclassifications were likely due to multiple concurrent BC tumours of differing IHC subtypes, biopsy proven subtype transitions and low purity. Effects of tumor purity on IHC-based classifiers will also be presented. Conclusions: Non-invasivemethylation based ctDNA subtyping can detect the presence of breast cancer, and discriminate IHC subtypes, with high accuracy. Further assessment is warranted to assess whether methylation-based subtyping has the potential to report IHC subtypes when repeat biopsy is not possible, detect subtype transitions and direct advanced breast cancer treatment.

A miRNA-based epigenetic molecular clock for biological skin-age prediction

Skin aging is one of the visible characteristics of the aging process in humans. In recent years, different biological clocks have been generated based on protein or epigenetic markers, but few have focused on biological age in the skin. Arrest the aging process or even being able to restore an organism from an older to a younger stage is one of the main challenges in the last 20 years in biomedical research. We have implemented several machine learning models, including regression and classification algorithms, in order to create an epigenetic molecular clock based on miRNA expression profiles of healthy subjects to predict biological age-related to skin. Our best models are capable of classifying skin samples according to age groups (18–28; 29–39; 40–50; 51–60 or 61–83 years old) with an accuracy of 80% or predict age with a mean absolute error of 10.89 years using the expression levels of 1856 unique miRNAs. Our results suggest that this kind of epigenetic clocks arises as a promising tool with several applications in the pharmaco-cosmetic industry.

AI-based approach to enable proactive identification of early lung cancer: A retrospective population health study and economic model.

10536 Background: Approximately 48,500 new cases of lung cancer are diagnosed each year in the UK with a five-year survival rate which lags behind other European countries. Most patients are diagnosed at stage III or IV disease, which has a significantly worse prognosis. The integration of real-world data and AI techniques presents a promising avenue for improvement, using novel solutions not achievable through traditional care models. This study explores proactive patient identification at scale, aligning with the "left shift" paradigm in population health. The approach uses innovative technologies and novel data sources to identify patients with early-stage disease, enhancing both clinical and economic outcomes. Importantly, these interventions should be feasible on a large scale without overburdening already stretched clinical services. Methods: A retrospective study of real-world data for an urban population with high socioeconomic disadvantage and health needs was undertaken. Two endpoints were determined: detection potential and health economic impact. For detection potential (DP), patients aged 40 and above who underwent chest X-rays (CXR) at a large academic medical centre between 2016-2022 were included. Natural Language Processing (NLP) analysed unstructured free-text data from Electronic Health Records, combined with ICD-10 codes, for predictive model (PM) development using Machine Learning (ML) classification techniques. The PM's performance was assessed using the area under the receiver operating characteristic curve (AUCROC). Health economic impact was evaluated through a theoretical extrapolation of DP. Assuming a 9% left shift from late (stage III or IV) to early (stage I or II) disease, a health economic model (HEM) compared the impact of the current care model to a potential future state facilitated by the digital medicine care model (DMCM). Results: 75,342 patients were included, with 755 lung cancer diagnoses occurring within 6 months of CXR. The PM achieved a peak AUROC of 0.75, exceeding the performance of currently used risk prediction models (Q Cancer Risk, Risk Assessment Tools). A 9% left shift equated to 4.5 years mean increase in survival. The DMCM was associated with a net healthcare cost benefit of £6k per patient diagnosed with lung cancer, improved quality-adjusted life years of £21M for the year 1 cohort over 10 years and increased economic productivity (from additional years in workforce) of £2.4M. Conclusions: The study demonstrates the practical utility of AI-supported early lung cancer detection at population scale. Real-world evidence is needed to prospectively validate the model as cost-effective, scalable, and efficient. Future work will seek to improve the model's performance against a broader at-risk patient population and explore its applicability to other late-presenting tumours.

A multimodal machine learning approach to generate news articles from geo-tagged images

Classical machine learning algorithms typically operate on unimodal data and hence it can analyze and make predictions based on data from a single source (modality). Whereas multimodal machine learning algorithm, learns from information across multiple modalities, such as text, images, audio, and sensor data. The paper leverages the functionalities of multimodal machine learning (ML) application for generating text from images. The proposed work presents an innovative multimodal algorithm that automates the creation of news articles from geo-tagged images by leveraging cutting-edge developments in machine learning, image captioning, and advanced text generation technologies. Employing a multimodal approach that integrates machine learning and transformer algorithms, such as visual geometry group network16 (VGGNet16), convolutional neural network (CNN) and a long short-term memory (LSTM) based system, the algorithm initiates by extracting the location from exchangeable image file format (Exif) data from the image. The features are extracted from the image and corresponding news headline is generated. The headlines are used for generating a comprehensive article with contemporary large language model (LLM). Further, the algorithm generates the news article big-science large open-science open-access multilingual language model (BLOOM). The algorithm was tested on real time photographs as well as images from the internet. In both the cases the news articles generated were validated with ROUGE and BULE score. The proposed work is found to be successful attempt in journalism field.

VIOLET: Visual Analytics for Explainable Quantum Neural Networks.

With the rapid development of Quantum Machine Learning, quantum neural networks (QNN) have experienced great advancement in the past few years, harnessing the advantages of quantum computing to significantly speed up classical machine learning tasks. Despite their increasing popularity, the quantum neural network is quite counter-intuitive and difficult to understand, due to their unique quantum-specific layers (e.g., data encoding and measurement) in their architecture. It prevents QNN users and researchers from effectively understanding its inner workings and exploring the model training status. To fill the research gap, we propose VIOLET, a novel visual analytics approach to improve the explainability of quantum neural networks. Guided by the design requirements distilled from the interviews with domain experts and the literature survey, we developed three visualization views: the Encoder View unveils the process of converting classical input data into quantum states, the Ansatz View reveals the temporal evolution of quantum states in the training process, and the Feature View displays the features a QNN has learned after the training process. Two novel visual designs, i.e., satellite chart and augmented heatmap, are proposed to visually explain the variational parameters and quantum circuit measurements respectively. We evaluate VIOLET through two case studies and in-depth interviews with 12 domain experts. The results demonstrate the effectiveness and usability of VIOLET in helping QNN users and developers intuitively understand and explore quantum neural networks.

A comprehensive evaluation method for frailty based on semi-supervised learning and transfer-learning

Frailty evaluation is of great significance for the specific population, which can speed up the treatment process and reduce the adverse effects after treatment. In this article, in order to make up for the shortcomings of the traditional evaluation methods, a comprehensive intelligent evaluation method that integrates physiological data from multiple perspectives, such as mobility, muscle level, and body composition data has been proposed. The multi-source physiological data of 174 participants has been collected, and a series of features related to frailty have been extracted. A recurrent stepping semi-supervised learning (RSSSL) framework is proposed to transmit the knowledge from the annotated data to the unannotated gait sequence utilizing various networks with varying architectures. Moreover, in order to enhance the frailty evaluation’s performance, an approach based on subspace transfer learning integrating with heterogeneous features (STL-IHF) is proposed. The mean segmentation accuracy on the unannotated gait sequence reaches over 97.10% across recruited subjects with distinct gait patterns, which is significantly superior when compared with classical deep learning methods. And the recognition accuracy of the STL-IHF method for frailty evaluation reaches over 92.9%. The overall experimental results demonstrate that the proposed methods are effective in frailty evaluation tasks in clinics.

Identifying miRNA as biomarker for breast cancer subtyping using association rule

— This paper presents a comprehensive study focused on breast cancer subtyping, utilizing a multifaceted approach that integrates feature selection, machine learning classifiers, and miRNA regulatory networks. The feature selection process begins with the CFS algorithm, followed by the Apriori algorithm for association rule generation, resulting in the identification of significant features tailored to Luminal A, Luminal B, HER-2 enriched, and Basal-like subtypes. The subsequent application of Random Forest (RF) and Support Vector Machine (SVM) classifiers yielded promising results, with the SVM model achieving an overall accuracy of 76.60 % and the RF model demonstrating robust performance at 80.85 %. Detailed accuracy metrics revealed strengths and areas for refinement, emphasizing the potential for optimizing subtype-specific recall. To explore the regulatory landscape in depth, an analysis of selected miRNAs was conducted using MIENTURNET, a tool for visualizing miRNA-target interactions. While FDR analysis raised concerns for HER-2 and Basal-like subtypes, Luminal A and Luminal B subtypes showcased significant miRNA-gene interactions. Functional enrichment analysis for Luminal A highlighted the role of Ovarian steroidogenesis, implicating specific miRNAs such as hsa-let-7c-5p and hsa-miR-125b-5p as potential diagnostic biomarkers and regulators of Luminal A breast cancer. Luminal B analysis uncovered associations with the MAPK signaling pathway, with miRNAs like hsa-miR-203a-3p and hsa-miR-19a-3p exhibiting potential diagnostic and therapeutic significance. In conclusion, this integrative approach combines machine learning techniques with miRNA analysis to provide a holistic understanding of breast cancer subtypes. The identified miRNAs and associated pathways offer insights into potential diagnostic biomarkers and therapeutic targets, contributing to the ongoing efforts to improve breast cancer diagnostics and personalized treatment strategies.