Potential Of Machine Learning Research Articles

Machine learning algorithms to prevent fraudulent transactions in real-time

In the rapidly evolving landscape of digital finance, the proliferation of online transactions has been accompanied by a significant increase in fraudulent activities. This paper explores the application of machine learning algorithms to detect and prevent fraudulent transactions in real-time, thereby enhancing the security and reliability of financial systems. We investigate various machine learning techniques, including supervised learning models such as Random Forest, Logistic Regression, and Neural Networks, as well as unsupervised methods like anomaly detection. By leveraging these algorithms, we aim to identify patterns and anomalies indicative of fraudulent behavior. Our approach integrates robust preprocessing techniques and real-time data analysis to ensure high accuracy and efficiency. The results demonstrate that machine learning models can significantly reduce the incidence of fraud, providing financial institutions with a powerful tool to safeguard their customers’ assets. This study underscores the potential of machine learning to transform fraud detection, offering a scalable and adaptive solution to one of the most pressing challenges in the financial sector

Comparison of machine learning algorithms for dengue virus (DENV) classification

Dengue, transmitted by the Aedes aegypti mosquito, necessitates accurate case classification for effective management. The research focuses on leveraging machine learning algorithms to enhance diagnosis and streamline control strategies. Utilizing a dataset from a public hospital, encompassing 21,157 cases categorized by period, outcome, gender, age, symptoms, and origin (autochthonous or imported), the study conducted a comparative analysis of Support Vector Machine, Random Forest, and Artificial Neural Network algorithms. The dataset was divided into 70% (14,809 cases) for training and 30% (6,348 cases) for testing. Results unveiled the Artificial Neural Network as the frontrunner, exhibiting an impressive 86.47% accuracy and a robust 92.91% recall in the classification of dengue-related cases. This underscores the potential of machine learning in refining dengue diagnosis and facilitating more efficient control strategies, offering a promising avenue for combating this vector-borne disease.

Development of Machine Learning Atomistic Potential for Molecular Simulation of Hematite–Water Interfaces

A novel approach for constructing a machine-learned potential energy surface (MLP) from unlabeled training data is presented. Utilizing neural networks augmented with a pool-based active learning sampling method, a potential energy surface (PES) is developed for the accurate modeling of interfaces of hematite iron oxide and water, fitting the much more expensive density functional theory (DFT). Molecular dynamics simulations were performed using this DFT-based PES to characterize the structural and energetic properties of the system. By utilizing the developed machine learning potential (MLP), it was possible to simulate much larger systems for extended periods of time, which will be important for leveraging machine learning potentials as accurate and pragmatic simulation-led molecular design and prototyping tools whilst preserving the ab initio accuracy.

Identifying prognostic factors in patients with cardiogenic shock undergoing VA-ECMO treatment: insights from ECLS-SHOCK using machine learning

Abstract Background/Introduction Venoarterial extracorporeal membrane oxygenation (VA-ECMO) has not been shown to reduce short-term mortality in patients with acute myocardial infarction complicated by cardiogenic shock. However, whether some patients might benefit from VA-ECMO remains to be cleared. Purpose To identify the impact of VA-ECMO therapy on outcomes by leveraging the potential of machine learning. Methods The ECLS-SHOCK trial is a randomized controlled trial where patients with acute myocardial infarction complicated by cardiogenic shock for whom early revascularization was planned were randomly assigned to receive early VA-ECMO plus usual medical treatment or usual medical treatment alone. The primary outcome was death from any cause at 30 days. Extreme gradient boosting (XGBoost) was used to develop a model for the prediction of 30-day all-cause mortality and to assess the influence of VA-ECMO on outcomes. Results Overall, 209 patients were randomized to the VA-ECMO and 208 to control. Baseline characteristics between groups were well balanced (median age 63 years, 81% male, 67% presenting with ST-segment elevation myocardial infarction). At 30-days 100 patients in the VA-ECMO and 102 in the control group were dead. The final XGBoost model contained 27 variables and the 10 most influential variables are shown in Fig. 1. On derivation the model predicted mortality with an AUC of 1.00 and on validation with an AUC of 0.80 (95% CI 0.71 to 0.89), with a sensitivity of 95% (95% CI 88% to 100%) and a specificity of 55% (40% to 69%), resulting in a positive predictive value of 65% and a negative predictive value of 92% at a Youden-derived cut-off. Importantly, neither the VA-ECMO therapy nor its complications were among the most influential factors of the model with a relative importance of 1.1% for VA-ECMO therapy and 0.25% for peripheral vascular complications and bleeding complications even being discarded by the model. Conclusion In patients with acute myocardial infarction complicated by cardiogenic shock the influence of VA-ECMO therapy on 30-day mortality, even when investigated with complex machine learning models, is negligibly low.

Prediction of outcome in malignant hypertension patients using machine learning: a report from the West Birmingham malignant hypertension registry

Abstract Objectives Malignant hypertension (MHT) is a rare, yet severe condition characterized by high morbidity and mortality rates. This study aimed to assess the potential of machine learning (ML) algorithms in forecasting prognostic outcomes in MHT patients. Methods The dataset from the West Birmingham MHT Registry was used. We evaluated the efficacy of 9 ML algorithms, CatBoost, Decision Tree (DT), Light-Gradient Boosting Machine (LightGBM), K-Nearest Neighbors (KNN), Logistic Regression (LR), Multi-Layer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM) and XGBoost, in predicting a composite outcome of all-cause mortality or dialysis. Evaluation metrics included the area under the receiver operating characteristic curve (AUC) and the F1 score. The SHapley Additive exPlanations values were employed to quantify the importance of each feature in the models. Results The study cohort comprised 385 individuals diagnosed with MHT (mean age of 48±13 years, 66% male). During a median follow-up period of 11 years (interquartile range: 3-18 years), 282 patients (73%) experienced the composite outcome. From 24 demographic and clinical variables, 16 were selected into the ML models. Among 9 models, the SVM and LR models exhibited robust predictive performance, achieving AUCs of 0.86 (95% CI: 0.76-0.93) and 0.81 (95% CI: 0.72-0.93), respectively. Furthermore, these models demonstrated high F1 scores (SVM: 0.89, LR: 0.85) and sensitivity (SVM: 0.89, LR: 0.83) (Table). Age, smoking history, systolic blood pressure at follow-up and baseline creatinine levels were commonly identified as primary prognostic features in both SVM and LR models (Figure). Conclusions The application of ML algorithms facilitates effective prediction of prognostic outcomes in patients with MHT, illustrating their potential utility in clinical decision-making through more targeted risk stratification and individualized patient care in these high-risk patients.

Standardization of transthoracic impedance values for estimating heart failure and its utility

Abstract Background The use of intrathoracic impedance for examining pleural effusion in heart failure patients has been explored previously. However, due to concerns about its accuracy and complexity, transthoracic impedance values have had limitations in quantifying extravascular lung water. Our dual objectives were to clarify the relationship between intrathoracic conditions and percutaneous transthoracic impedance values, and to establish a basic model for a new machine learning-based estimation system for assessing intrathoracic conditions in patients with heart failure. Methods First, we developed a live porcine congested lung model that induced pleural effusion by substantial fluid loading, and then longitudinally evaluated its correlation with percutaneous transthoracic impedance values. Second, we conducted multi-frequency bioelectrical impedance analysis to simultaneously collect electrical, physical, and hematological data from 63 hospitalized heart failure patients and 82 healthy volunteers Results In the porcine model, pleural effusion correlated with a concurrent decrease in SpO2 and a gradual decrease in impedance. We indexed and generated features from the measured values and developed an intrathoracic estimation model based on electrical measurements and clinical findings using a decision tree-based machine learning approach. Among the 286 features collected per individual, the model used 16 features. Notably, the model demonstrated high accuracy in discriminating patients with pleural effusion, achieving an AUC of 0.905 (95% CI: 0.870-0.940) in cross-validation, significantly outperforming the conventional frequency-based method. Conclusions Transthoracic impedance values, when indexed, reflect intrathoracic conditions and improve estimation. Our results highlighted the potential of machine learning and thoracic impedance measurement for accurate estimation of pleural effusion. This approach provides an effective means of assessing intrathoracic conditions. Figure: The upper figure shows the relationship between impedance values and pleural effusion in the congested lung model. On these results, human data were collected to construct the estimation model. The lower illustrates the accuracy of the model and provides a comprehensive view of the system.

Tree-based Ensemble Machine Learning for Phishing Website Detection

Phishing remains a prevalent and perilous cyber threat in the digital age, exploiting human vulnerabilities to deceive individuals into disclosing sensitive information. This paper presents a method to achieve high accuracy in phishing website detection using Tree-based Ensemble Machine Learning techniques. Through rigorous experimentation and evaluation, we identified RandomForest and ExtraTrees as the top-performing models, achieving accuracy, precision, recall, and F1 scores all exceeding 98%. Additionally, our study highlights the significance of feature selection techniques in enhancing model performance, with thresholding methods proving effective in retaining relevant features for classification. By addressing imbalanced datasets and optimizing hyperparameters, our models demonstrate robust detection capabilities against phishing attacks. These findings contribute to the advancement of cybersecurity measures and underscore the potential of ensemble machine learning in combatting online threats, ultimately enhancing internet user security.

Theoretical Catalyst Screening of Multielement Alloy Catalysts for Ammonia Synthesis Using Machine Learning Potential and Generative Artificial Intelligence

Theoretical Catalyst Screening of Multielement Alloy Catalysts for Ammonia Synthesis Using Machine Learning Potential and Generative Artificial Intelligence

A Non-Invasive Gait-Based Screening Approach for Parkinson’s Disease

Parkinson's disease (PD) presents a significant global health challenge, characterized by the progressive degeneration of dopamine-producing neurons in the brain, resulting in both motor and non-motor symptoms that severely impact quality of life. This study addresses the complexities of PD, highlighting the critical need for early diagnosis to slow disease progression. This research addresses the challenges of early diagnosis, such as the use of unreliable diagnostic techniques and limited healthcare resources. It uses the MMU Parkinson Disease Dataset and applies camera-based data collection to analyze gait patterns that can identify a risk of Parkinson's Disease. The study utilizes computer vision and the AlphaPose framework to analyze video data and detect body key points. By employing machine learning algorithms, including Support Vector Machines (SVM) and CatBoost, showing highly effective in identifying temporal dependencies in gait patterns. The algorithms achieved a high accuracy of 83.33% on the MMU dataset. This method enhances the accuracy of PD detection and enables immediate detection and control of the disease. The combination of advanced data analysis methods and medical knowledge offers new possibilities to develop targeted treatments that improve patient outcomes, demonstrating the potential of machine learning in effectively managing and treating Parkinson's disease. To enhance the generalizability of models, future research should collect extensive and diverse datasets covering various backgrounds and different stages of Parkinson's disease and utilize advanced techniques for extracting features to improve the accuracy of gait analysis.

Leveraging Machine Learning for Photometric Redshift Estimation of JWST Galaxies

With the launch of JWST, the volume and complexity of astronomical data are increasing, a trend that will continue with future instruments such as SKA and Euclid. It is inevitable that data-driven methods will become more prominent alongside model-driven analysis. This research utilises machine learning, specifically a kernelised local linear regression model, in photometric redshift predictions of galaxies observed with JWST. With a spectroscopic dataset of 4605 galaxies, we trained the model and achieved a deviation of σdz = 0.018 and a catastrophic outlier rate of foutlier = 3.5%. These results demonstrate high accuracy and computational efficiency, highlighting the potential of machine learning for astronomical data analysis, in particular for large-scale surveys.

Enhanced QSVM with elitist non‐dominated sorting genetic optimisation algorithm for breast cancer diagnosis

AbstractMachine learning has emerged as a promising method for predicting breast cancer using quantum computation techniques. Quantum machine learning algorithms, such as quantum support vector machines (QSVMs), are demonstrating superior efficiency and economy in tackling complex problems compared to traditional machine learning methods. When compared with classical support vector machine, the quantum machine produces remarkably accurate results. The suggested quantum SVM model in this study effectively resolved the binary classification problem for diagnosing malignant breast cancer. This work introduces an enhanced approach to breast cancer diagnosis by integrating QSVM with elitist non‐dominated sorting genetic optimization (ENSGA), leveraging the strengths of both techniques to achieve more accurate and efficient classification results. ENSGA plays a crucial role in optimising QSVM parameters, ensuring that the model attains the best possible classification accuracy while considering multiple objectives simultaneously. Moreover, the quantum kernel estimation method demonstrated exceptional performance by achieving high accuracy within an impressive execution time of 0.14 in the IBM QSVM simulator. The seamless integration of quantum computation techniques with optimisation strategies such as ENSGA highlights the potential of quantum machine learning in revolutionising the field of healthcare, particularly in the domain of breast cancer diagnosis.

Convergent reductive evolution in bee-associated lactic acid bacteria.

Distantly related organisms may evolve similar traits when exposed to similar environments or engaging in certain lifestyles. Several members of the Lactobacillaceae [lactic acid bacteria (LAB)] family are frequently isolated from the floral niche, mostly from bees and flowers. In some floral LAB species (henceforth referred to as bee-associated LAB), distinctive genomic (e.g., genome reduction) and phenotypic (e.g., preference for fructose over glucose or fructophily) features were recently documented. These features are found across distantly related species, raising the hypothesis that specific genomic and phenotypic traits evolved convergently during adaptation to the floral environment. To test this hypothesis, we examined representative genomes of 369 species of bee-associated and non-bee-associated LAB. Phylogenomic analysis unveiled seven independent ecological shifts toward the bee environment in LAB. In these species, we observed significant reductions of genome size, gene repertoire, and GC content. Using machine leaning, we could distinguish bee-associated from non-bee-associated species with 94% accuracy, based on the absence of genes involved in metabolism, osmotic stress, or DNA repair. Moreover, we found that the most important genes for the machine learning classifier were seemingly lost, independently, in multiple bee-associated lineages. One of these genes, acetaldehyde-alcohol dehydrogenase (adhE), encodes a bifunctional aldehyde-alcohol dehydrogenase which has been associated with the evolution of fructophily, a rare phenotypic trait that is pervasive across bee-associated LAB species. These results suggest that the independent evolution of distinctive phenotypes in bee-associated LAB has been largely driven by independent losses of the same sets of genes.IMPORTANCESeveral LAB species are intimately associated with bees and exhibit unique biochemical properties with potential for food applications and honeybee health. Using a machine learning-based approach, our study shows that adaptation of LAB to the bee environment was accompanied by a distinctive genomic trajectory deeply shaped by gene loss. Several of these gene losses occurred independently in distantly related species and are linked to some of their unique biotechnologically relevant traits, such as the preference for fructose over glucose (fructophily). This study underscores the potential of machine learning in identifying fingerprints of adaptation and detecting instances of convergent evolution. Furthermore, it sheds light onto the genomic and phenotypic particularities of bee-associated bacteria, thereby deepening the understanding of their positive impact on honeybee health.

Enhancing Network Threat Detection with Random Forest-Based NIDS and Permutation Feature Importance

Network Intrusion Detection Systems (NIDS) are critical for protecting computer networks from unauthorized activities. Traditional NIDS rely on rule-based signatures, which can be limiting in detecting emerging threats. This study investigates the effectiveness of the random forest classifier in advancing NIDS capabilities through machine learning. Using the CICIDS-2017 dataset, the data are preprocessed to enhance their quality by removing redundancies. feature selection and permutation importance were employed to identify the most relevant features. The methodology involves rigorous testing and analysis of the random forest classifier’s performance, focusing on f1-score rates compared to other machine learning models. Results demonstrate that by optimizing class weights, applying a custom prediction function and leveraging 26 key features, the random forest classifier achieves an outstanding 99.8% in the weighted f1-score and 93.31% in the macro f1-score in various attack types. This research highlights the potential of machine learning to significantly enhance NIDS effectiveness, offering a robust defense mechanism against evolving cybersecurity threats in modern networks.

MiRNA in Machine-Learning-Based Diagnostics of Oral Cancer.

MicroRNAs (miRNAs) are crucial regulators of gene expression, playing significant roles in various cellular processes, including cancer pathogenesis. Traditional cancer diagnostic methods, such as biopsies and histopathological analyses, while effective, are invasive, costly, and require specialized skills. With the rising global incidence of cancer, there is a pressing need for more accessible and less invasive diagnostic alternatives. This research investigates the potential of machine-learning (ML) models based on miRNA attributes as non-invasive diagnostic tools for oral cancer. Methods and Tools: We utilized a comprehensive methodological framework involving the generation of miRNA attributes, including sequence characteristics, target gene associations, and cancer-specific signaling pathways. The miRNAs were classified using various ML algorithms, with the BayesNet classifier demonstrating superior performance, achieving an accuracy of 95% and an area under receiver operating characteristic curve (AUC) of 0.98 during cross-validation. The model's effectiveness was further validated using independent datasets, confirming its potential clinical utility. Our findings highlight the promise of miRNA-based ML models in enhancing early cancer detection, reducing healthcare burdens, and potentially saving lives. This study paves the way for future research into miRNA biomarkers, offering a scalable and adaptable diagnostic approach for various cancers.

Efficient Parametrization of Transferable Atomic Cluster Expansion for Water.

We present a highly accurate and transferable parametrization of water using the atomic cluster expansion (ACE). To efficiently sample liquid water, we propose a novel approach that involves sampling static calculations of various ice phases and utilizing the active learning (AL) feature of the ACE-based D-optimality algorithm to select relevant liquid water configurations, bypassing computationally intensive ab initio molecular dynamics simulations. Our results demonstrate that the ACE descriptors enable a potential initially fitted solely on ice structures, which is later upfitted with few configurations of liquid, identified with AL to provide an excellent description of liquid water. The developed potential exhibits remarkable agreement with first-principles reference, accurately capturing various properties of liquid water, including structural characteristics such as pair correlation functions, covalent bonding profiles, and hydrogen bonding profiles, as well as dynamic properties like the vibrational density of states, diffusion coefficient, and thermodynamic properties such as the melting point of the ice Ih. Our research introduces a new and efficient sampling technique for machine learning potentials in water simulations while also presenting a transferable interatomic potential for water that reveals the accuracy of first-principles reference. This advancement not only enhances our understanding of the relationship between ice and liquid water at the atomic level but also opens up new avenues for studying complex aqueous systems.

Evaluating Higher Education Performance via Machine Learning During Disruptive Times: A Case of Applied Education in Türkiye

ABSTRACTIn response to the COVID‐19 pandemic, an abrupt wave of digitisation and online migration swept the higher education institutions around the globe. In the aftermath of this digital transformation which endures as the legacy of the pandemic, what lacks in knowledge is how effective the anti‐COVID measures were in maintaining quality education. Using machine learning to analyse student grades as a proxy for educational standards, this study investigates and demonstrates the evaluative potential of machine learning (vs. traditional statistics) with respect to not only crisis responses in education but also applied studies such as Information Systems and Tourism. Main implication of this study is the analytical utility of machine learning even when educational data are irregular and small. However, incorporating accurate and meaningful data points into the existing online educational systems is crucial to leverage this utility of machine learning.

Structure prediction of stable sodium germanides at 0 and 10 GPa

In this paper we used random structure searching (AIRSS) to carry out a systematic search for crystalline Na-Ge materials at both 0 and 10 GPa. The high-throughput structural relaxations were accelerated using a machine-learned interatomic potential (MLIP) fit to density-functional theory (DFT) reference data, allowing ∼1.5 million structures to be relaxed. At ambient conditions we predict three new Zintl phases, Na3Ge2,Na2Ge, and Na9Ge4, to be stable and a number of Ge-rich layered structures to lie in close proximity to the convex hull. The known NaδGe34 clathrate and Na4Ge13 host-guest structures are found to be relatively stabilized at higher temperature by vibrational contributions to the free energy. Overall, the low-energy phases exhibit exceptional structural diversity, with the expected mixture of covalent and ionic bonding confirmed using the electron-localization function (ELF). The local Ge structural motifs present at each composition were determined using smooth overlap of atomic positions (SOAP) descriptors and the Ge-K edge was simulated for representatives of each motif, providing a direct link to experimental x-ray absorption spectroscopy (XAS). Two Ge-rich phases are predicted to be stable at 10 GPa; NaGe3 and NaGe2 have simple kagome and simple hexagonal Ge lattices respectively with Na contained in the pores. NaGe3 is isostructural with the MgB3 and MgSi3 family of kagome superconductors and remains dynamically stable at 0 GPa. Removing the Na from NaGe2 results in the hexagonal lonsdalite Ge allotrope, which has a direct band gap. Published by the American Physical Society 2024

Van der Waals semiconductor InSe plastifies by martensitic transformation.

Inorganic semiconductor materials are crucial for modern technologies, but their brittleness and limited processability hinder the development of flexible, wearable, and miniaturized electronics. The recent discovery of room-temperature plasticity in some inorganic semiconductors offers a promising solution, but the deformation mechanisms remain controversial. Here, we investigate the deformation of indium selenide, a two-dimensional van der Waals semiconductor with substantial plasticity. By developing a machine-learned deep potential, we perform atomistic simulations that capture the deformation features of hexagonal indium selenide upon out-of-plane compression. Unexpectedly, we find that indium selenide plastifies through a martensitic transformation; that is, the layered hexagonal structure is converted to a tetragonal lattice with specific orientation relationship. This observation is corroborated by high-resolution experimental observations and theory. It suggests a change of paradigm, where the design of new plastically deformable inorganic semiconductors can focus on compositions and structures that facilitate phase transformations, going beyond the conventional dislocation slip.

Building Scalable MLOps: Optimizing Machine Learning Deployment and Operations

As machine learning (ML) models become increasingly integrated into mission-critical applications and production systems, the need for robust and scalable MLOps (Machine Learning Operations) practices has grown significantly. This paper explores key strategies and best practices for building scalable MLOps pipelines to optimize the deployment and operation of machine learning models at an enterprise scale. It delves into the importance of automating the end-to-end lifecycle of ML models, from data ingestion and model training to testing, deployment, and monitoring. Approaches for implementing continuous integration and continuous deployment (CI/CD) pipelines tailored for ML workflows are discussed, enabling efficient and repeatable model updates and deployments. The paper emphasizes the criticality of implementing comprehensive monitoring and observability mechanisms to track model performance, detect drift, and ensure the reliability and trustworthiness of deployed models. The paper also addresses the challenges of managing model versioning and governance at scale, including techniques for maintaining a centralized model registry, enforcing access controls, and ensuring compliance with regulatory requirements. The paper aims to provide a comprehensive guide for organizations seeking to establish scalable and robust MLOps practices, enabling them to unlock the full potential of machine learning while mitigating risks and ensuring responsible AI deployment. Keywords—Machine Learning Operations (MLOps), Scalable AI Deployment, Continuous Integration and Continuous Deployment (CI/CD) for ML, ML Monitoring and Observability, Model Reproducibility, Model Versioning and Governance, Centralized Model Registry, Responsible AI Deployment, Ethical AI Practices, Enterprise MLOps

Improving earthquake prediction accuracy in Los Angeles with machine learning

This research breaks new ground in earthquake prediction for Los Angeles, California, by leveraging advanced machine learning and neural network models. We meticulously constructed a comprehensive feature matrix to maximize predictive accuracy. By synthesizing existing research and integrating novel predictive features, we developed a robust subset capable of estimating the maximum potential earthquake magnitude. Our standout achievement is the creation of a feature set that, when applied with the Random Forest machine learning model, achieves a high accuracy in predicting the maximum earthquake category within the next 30 days. Among sixteen evaluated machine learning algorithms, Random Forest proved to be the most effective. Our findings underscore the transformative potential of machine learning and neural networks in enhancing earthquake prediction accuracy, offering significant advancements in seismic risk management and preparedness for Los Angeles.