Enhanced Machine Learning Research Articles

A swelling ‎pressure prediction model based on boruta feature selection mila region

Swelling soils are problematic soil types that are prevalent across the globe. It ‎was noted that the costs ‎associated with damages caused by distended soils are ‎relatively high and this issue cannot be ignored. ‎Swelling pressure is a ‎fundamental parameter in the prediction of the swelling capacity of expansive ‎soils. In ‎machine learning, feature selection methods allow us to reduce computation time, enhance prediction accuracy, ‎and gain a deeper comprehension of the ‎data. In this paper, the Boruta algorithm is used to remove iteratively ‎the features ‎which are proved by a statistical test to be less relevant from 15 geotechnical ‎variables to predict ‎swelling pressure. The remaining variables are ‎inputs of a neural networks model (ANN). Results based on R ‎squared ‎determination coefficient, RMSE, MAPE, MSE, and RRSE show an ‎improvement of the neural model ‎by considering selected features by the Boruta ‎algorithm compared to the one without feature selection.‎ This approach highlights the effectiveness of feature selection in enhancing machine learning models for geotechnical applications.

Graph-based feature engineering for enhanced machine learning in rolling element bearing fault diagnosis

Abstract Fault diagnosis in rolling element bearings is critical for ensuring machinery reliability. This study improves machine learning techniques for predictive fault detection using the benchmark CWRU bearing dataset. Vibration signal data is preprocessed via balancing and graph-based feature engineering is performed to enable effective model training. Diverse classifiers including Random Forests, Support Vector Machines and Neural Networks are systematically evaluated through 10-fold cross-validation. Most of the models demonstrate exceptional performance, with top accuracies and AUC scores of 1.00. The research highlights the potential of hidden features that consider the implicit relations between the entities to improve predictive maintenance through data-driven bearing fault diagnosis.

Retraction Note: Enhanced machine learning approach with orthogonal frequency division multiplexing to avoid congestion in wireless communication system

Retraction Note: Enhanced machine learning approach with orthogonal frequency division multiplexing to avoid congestion in wireless communication system

Enhancing Cover Management Factor Classification Through Imbalanced Data Resolution

This study addresses the persistent challenge of class imbalance in land use and land cover (LULC) classification within the Shihmen Reservoir watershed in Taiwan, where LULC is used to map the Cover Management factor (C-factor). The dominance of forests in the LULC categories leads to an imbalanced dataset, resulting in poor prediction performance for minority classes when using machine learning techniques. To overcome this limitation, we applied the Synthetic Minority Over-sampling Technique (SMOTE) and the 90-model SMOTE-variants package in Python to balance the dataset. Due to the multi-class nature of the data and memory constraints, 42 models were successfully used to create a balanced dataset, which was then integrated with a Random Forest algorithm for C-factor classification. The results show a marked improvement in model accuracy across most SMOTE variants, with the Selected Synthetic Minority Over-sampling Technique (Selected_SMOTE) emerging as the best-performing method, achieving an overall accuracy of 0.9524 and a sensitivity of 0.6892. Importantly, the previously observed issue of poor minority class prediction was resolved using the balanced dataset. This study provides a robust solution to the class imbalance issue in C-factor classification, demonstrating the effectiveness of SMOTE variants and the Random Forest algorithm in improving model performance and addressing imbalanced class distributions. The success of Selected_SMOTE underscores the potential of balanced datasets in enhancing machine learning outcomes, particularly in datasets dominated by a majority class. Additionally, by addressing imbalance in LULC classification, this research contributes to Sustainable Development Goal 15, which focuses on the protection, restoration, and sustainable use of terrestrial ecosystems.

PVEMLPTS: design of an efficient psoriasis and vitiligo detection model through enhanced machine learning and personalized treatment strategies

PVEMLPTS: design of an efficient psoriasis and vitiligo detection model through enhanced machine learning and personalized treatment strategies

Enhancing machine learning recommendation systems with CNN architectures: Applications and innovations in computer vision and speech recognition

Abstract. This paper provides an in-depth analysis of enhancing machine learning recommendation systems using Convolutional Neural Network (CNN) architectures, with a focus on their applications in computer vision and speech recognition. Traditional recommendation systems often struggle with scalability and the complexity of high-dimensional data. By integrating CNNs, these systems can utilize advanced feature extraction and representation learning to more effectively process and analyze diverse data sources. Our study demonstrates significant improvements in recommendation accuracy and personalization through CNN-enhanced systems. We discuss the architecture, design principles, and advantages of CNNs, supported by case studies across various domains. Our findings illustrate the potential of CNNs to revolutionize recommendation systems by addressing existing limitations and offering innovative solutions for real-time, high-quality recommendations. This research emphasizes the importance of advanced machine learning techniques in creating robust and scalable recommendation systems, paving the way for future advancements and applications in multiple fields.

An enhanced machine learning algorithm for type 2 diabetes prognosis with a detailed examination of Key correlates

This study aimed to construct a high-performance prediction and diagnosis model for type 2 diabetic retinopathy (DR) and identify key correlates of DR. This study utilized a cross-sectional dataset of 3,000 patients from the People’s Liberation Army General Hospital in 2021. Logistic regression was used as the baseline model to compare the prediction performance of the machine learning model and the related factors. The recursive feature elimination cross-validation (RFECV) algorithm was used to select features. Four machine learning models, support vector machine (SVM), decision tree (DT), random forest (RF), and gradient boost decision tree (GBDT), were developed to predict DR. The models were optimized using grid search to determine hyperparameters, and the model with superior performance was selected. Shapley-additive explanations (SHAP) were used to analyze the important correlation factors of DR. Among the four machine learning models, the optimal model was GBDT, with predicted accuracy, precision, recall, F1-measure, and AUC values of 0.7883, 0.8299, 0.7539, 0.7901, and 0.8672, respectively. Six key correlates of DR were identified, including rapid micronutrient protein/creatinine measurement, 24-h micronutrient protein, fasting C-peptide, glycosylated hemoglobin, blood urea, and creatinine. The logistic model had 27 risk factors, with an AUC value of 0.8341. A superior prediction model was constructed that identified easily explainable key factors. The number of correlation factors was significantly lower compared to traditional statistical methods, leading to a more accurate prediction performance than the latter.

Enhanced Diabetes Detection and Blood Glucose Prediction Using TinyML-Integrated E-Nose and Breath Analysis: A Novel Approach Combining Synthetic and Real-World Data.

Diabetes mellitus, a chronic condition affecting millions worldwide, necessitates continuous monitoring of blood glucose level (BGL). The increasing prevalence of diabetes has driven the development of non-invasive methods, such as electronic noses (e-noses), for analyzing exhaled breath and detecting biomarkers in volatile organic compounds (VOCs). Effective machine learning models require extensive patient data to ensure accurate BGL predictions, but previous studies have been limited by small sample sizes. This study addresses this limitation by employing conditional generative adversarial networks (CTGAN) to generate synthetic data from real-world tests involving 29 healthy and 29 diabetic participants, resulting in over 14,000 new synthetic samples. These data were used to validate machine learning models for diabetes detection and BGL prediction, integrated into a Tiny Machine Learning (TinyML) e-nose system for real-time analysis. The proposed models achieved an 86% accuracy in BGL identification using LightGBM (Light Gradient Boosting Machine) and a 94.14% accuracy in diabetes detection using Random Forest. These results demonstrate the efficacy of enhancing machine learning models with both real and synthetic data, particularly in non-invasive systems integrating e-noses with TinyML. This study signifies a major advancement in non-invasive diabetes monitoring, underscoring the transformative potential of TinyML-powered e-nose systems in healthcare applications.

Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging

Predicting survival outcomes in critical accidents has been a focal point in machine learning research. This study addresses several limitations of existing methods, including insufficient management of data imbalance, lack of emphasis on hyperparameter tuning, and proneness to overfitting. Many existing models struggle to generalize effectively on imbalanced datasets or depend on default hyperparameter settings, resulting in biased predictions. By integrating Principal Component Analysis (PCA), hyperparameter optimization, and resampling methods, as well as combining Edited Nearest Neighbors (ENN) with the Synthetic Minority Oversampling Technique (SMOTE), the model significantly improves predictive accuracy and model generalization. An ensemble model combining seven machine learning algorithms—Logistic Regression, Support Vector Machine, KNN, Random Forest, XGBoost, LightGBM, and CatBoost—was applied to predict survival outcomes. Stochastic Weighted Averaging (SWA) was applied to mitigate overfitting and enhance generalization. The accuracy increased from 91.97% to 94.89% after SWA was applied in this specific scenario. The combination of PCA-based dimensionality reduction, hyperparameter tuning, and resampling techniques (ENN + SMOTE) ensured the model handled data imbalance and optimized predictive accuracy. The final model demonstrated excellent performance, with Area Under the Curve (AUC) and Average Precision (AP) values both reaching 0.98, indicating high accuracy and precision. These improvements were validated using the Titanic dataset in a binary classification problem of predicting passenger survival. The results emphasize that ensemble learning, enhanced by SWA, offers a powerful framework for handling imbalanced and complex datasets, providing significant advancements in predictive modeling accuracy. This study provides insights into how machine learning techniques can be effectively combined to solve classification challenges in real-world scenarios.

Optimizing Heart Disease Diagnosis: Feature Selection Techniques for Enhanced Machine Learning Model Performance

Heart disease is a growing global concern, affecting people across various age groups and genders. Detecting heart failure early is crucial, and ongoing research leverages advancements in healthcare technology, machine learning, imaging techniques, and data science to analyze vast datasets for this purpose. However, not all data attributes contribute equally to diagnosing heart disease, and the inclusion of irrelevant features can increase resource demands and potentially lead to inaccurate predictions with fatal consequences. This study focuses on feature extraction and reduction techniques to identify the most critical attributes for heart disease diagnosis, balancing resource efficiency with diagnostic accuracy. Using a dataset from the UCI repository, which includes both continuous and categorical features, we standardize the data and split it into training and testing sets in an 80:20 ratio. We then apply feature selection techniques to machine learning models such as K-nearest neighbor, decision tree classifier, SVM, logistic regression, and random forest. The models' predictive performance is evaluated using confusion matrices and ROC curves, demonstrating the impact of feature selection on diagnostic accuracy.

Enhancing machine learning multi-class fault detection in electric motors through entropy-based analysis

Abstract In the field of electric motor maintenance, this study introduces a transformative approach by integrating entropy-based algorithms with machine learning for enhanced multi-class fault detection. Employing Shannon, Renyi, and Tsallis entropy algorithms on standard fault detection measurements, the research significantly advances predictive maintenance strategies through a robust, early-indication, system-agnostic analysis. Detailed examination is conducted, comparing results derived from datasets that include statistical features (excluding entropy) against the proposed entropy-based datasets, when applied to a multi-layer perceptron classifier (MLPC). Optimization of the MLPC and all compared algorithms’ hyperparameters is done using the state-of-the-art Optuna tool to dynamically explore each search space, ensuring that each methodology performs adequately in a timely fashion while allowing for adaptation. The results showcase significant enhancement in classification accuracy of diverse electric motor operational states, facilitating the differentiation between healthy and various levels of fault conditions under assorted load scenarios. Computational analyses reveal favorable results related to execution time and memory overhead, thereby supporting the practicality in operations constrained by memory resources. Validation of the approach is achieved through laboratory experiments on a purpose-built test bench. Versatility of entropy-based measures through their proposed utilization in diverse fault indications is achieved by a demonstration in the field of mechanical fault detection with a focus on bearing faults through well-respected datasets.

Enhancing machine learning models with audio augmentation techniques

This paper investigates how audio augmentation techniques improve the classification accuracy of guitar chords. By applying methods such as noise addition, speed modification, and time shifting, a CNN trained on an augmented dataset achieved better results compared to non-augmented data. The findings highlight the importance of task-specific augmentation techniques in enhancing audio analysis

Enhanced Machine Learning Based Network Traffic Detection Model for IoT Network

Enhanced Machine Learning Based Network Traffic Detection Model for IoT Network

Optimizing Hadoop Data Locality: Performance Enhancement Strategies in Heterogeneous Computing Environments

As organizations increasingly harness big data for analytics and decision-making, the efficient processing of massive datasets becomes paramount. Hadoop, a widely adopted distributed computing framework, excels in processing large-scale data. However, its performance is contingent on effective data locality, which becomes challenging in heterogeneous computing environments comprising diverse hardware resources. This research addresses the imperative of enhancing Hadoop’s data locality performance in heterogeneous computing environments. The study explores strategies to optimize data placement and task scheduling, considering the diverse characteristics of nodes within the infrastructure. Through a comprehensive analysis of Hadoop’s data locality algorithms and their impact on performance, this work proposes novel approaches to mitigate challenges associated with disparate hardware capabilities. Weighted Extreme Learning Machine Technique (Weighted ELM) with the Firefly Algorithm (WELM-FF) is used in the proposed work. The integration of Weighted Extreme Learning Machine (WELM) with the Firefly Algorithm holds promise for enhancing machine learning models in the context of large-scale data processing. The research employs a combination of theoretical analysis and practical experiments to evaluate the effectiveness of the proposed enhancements. Factors such as network latency, disk I/O, and CPU capabilities are taken into account to develop a holistic framework for improving data locality and, consequently, overall Hadoop performance. The findings presented in this study contribute valuable insights to the field of distributed computing, offering practical recommendations for organizations seeking to maximize the efficiency of their Hadoop deployments in heterogeneous computing environments. By addressing the intricacies of data locality, this research strives to enhance the scalability and performance of Hadoop clusters, thereby facilitating more effective utilization of big data resources.

A review of feature selection strategies utilizing graph data structures and Knowledge Graphs.

Feature selection in Knowledge Graphs (KGs) is increasingly utilized in diverse domains, including biomedical research, Natural Language Processing (NLP), and personalized recommendation systems. This paper delves into the methodologies for feature selection (FS) within KGs, emphasizing their roles in enhancing machine learning (ML) model efficacy, hypothesis generation, and interpretability. Through this comprehensive review, we aim to catalyze further innovation in FS for KGs, paving the way for more insightful, efficient, and interpretable analytical models across various domains. Our exploration reveals the critical importance of scalability, accuracy, and interpretability in FS techniques, advocating for the integration of domain knowledge to refine the selection process. We highlight the burgeoning potential of multi-objective optimization and interdisciplinary collaboration in advancing KG FS, underscoring the transformative impact of such methodologies on precision medicine, among other fields. The paper concludes by charting future directions, including the development of scalable, dynamic FS algorithms and the integration of explainable AI principles to foster transparency and trust in KG-driven models.

Regulating Modality Utilization within Multimodal Fusion Networks.

Multimodal fusion networks play a pivotal role in leveraging diverse sources of information for enhanced machine learning applications in aerial imagery. However, current approaches often suffer from a bias towards certain modalities, diminishing the potential benefits of multimodal data. This paper addresses this issue by proposing a novel modality utilization-based training method for multimodal fusion networks. The method aims to guide the network's utilization on its input modalities, ensuring a balanced integration of complementary information streams, effectively mitigating the overutilization of dominant modalities. The method is validated on multimodal aerial imagery classification and image segmentation tasks, effectively maintaining modality utilization within ±10% of the user-defined target utilization and demonstrating the versatility and efficacy of the proposed method across various applications. Furthermore, the study explores the robustness of the fusion networks against noise in input modalities, a crucial aspect in real-world scenarios. The method showcases better noise robustness by maintaining performance amidst environmental changes affecting different aerial imagery sensing modalities. The network trained with 75.0% EO utilization achieves significantly better accuracy (81.4%) in noisy conditions (noise variance = 0.12) compared to traditional training methods with 99.59% EO utilization (73.7%). Additionally, it maintains an average accuracy of 85.0% across different noise levels, outperforming the traditional method's average accuracy of 81.9%. Overall, the proposed approach presents a significant step towards harnessing the full potential of multimodal data fusion in diverse machine learning applications such as robotics, healthcare, satellite imagery, and defense applications.

From processing to properties: Enhancing machine learning models with microstructural information in polymer nanocomposites

For polymers and their composites, processing conditions and the resultant microstructures are crucial in determining their properties. Traditional machine learning (ML) methods typically focus on establishing direct relationships between processing parameters and material properties, often overlooking the critical intermediate step of how processing influences microstructure, limiting the predictive accuracy. In this study, we introduce an approach that first establishes a detailed relationship between processing parameters and the resultant microstructure, and then uses transfer learning and feature fusion to integrate this relationship into the prediction of material properties. Using carbon black-reinforced rubber composites (CRC) as an example, we compared ML models in predicting mechanical properties from processing data. A multi-task deep neural network performed best achieving an R2 of 0.763 with only processing data as input. When incorporating transfer learning and feature fusion, the R2 improved to 0.852 and 0.878, respectively. Shapley explanation analysis validated our approach, highlighting the importance of integrating processing, microstructure, and properties in ML models. This method emphasizes the critical effect of comprehensively considering processing–(micro)structure–property (P–S–P) relationships, leading to more accurate predictions in polymer composite research.

Enhancing General Aviation Safety: The Integration of Generative AI in Preflight Weather Planning

Artificial Intelligence (AI) permeates daily life and industries through enhanced machine learning, natural language processing, and computer vision. Generative AI, a significant advancement, mimics human-like text and makes data-driven decisions, particularly in generative AI applications. Exploring novel AI applications is crucial, such as using generative AI to aid General Aviation (GA) pilots in preflight weather planning, with the aim to enhance pilots’ awareness and reduce weather-related accidents. However, the rapid evolution of generative AI raises many concerns like volatility, security risks, and decision-making biases. Preflight weather planning is vital in aviation, with weather-related incidents comprising a significant portion of accidents. Despite advancements, GA pilots’ interpretation of weather information remains subpar. This paper examines how generative AI may have potential to assist GA pilots to perform preflight weather planning, while also addressing risks and suggesting ethical research directions.

Enhanced machine learning models for predicting one-year mortality in individuals suffering from type A aortic dissection

ObjectiveThe study objective was to develop and validate an interpretable machine learning model to predict 1-year mortality in patients with type A aortic dissection, improving risk classification and aiding clinical decision-making. MethodsWe enrolled 289 patients with type A aortic dissection, dividing them into a training cohort (202 patients) and a validation cohort (87 patients). The Least Absolute Shrinkage and Selection Operator method with 10-fold cross-validation identified 8 key factors related to 1-year mortality. The Treebag model's performance was assessed using accuracy, F1-Score, Brier score, area under the curve, and area under the precision-recall curve with calibration and clinical utility evaluated through decision curves. Shapley Additive Explanations analysis determined the most influential predictors. ResultsThe Treebag model outperformed others, achieving a Brier score of 0.128 and an area under the curve of 0.91. Key risk factors included older age and elevated white blood cell count, whereas higher systolic blood pressure, lymphocyte, carbon dioxide combining power, eosinophil, β-receptor blocker use, and surgical intervention were protective. A web-based application, TAAD One-Year Prognostic Risk Assessment Web, was developed for clinical use (available at https://taad-1year-mortality-predictor.streamlit.app/). This platform allows for the prediction of 1-year mortality in patients with type A aortic dissection based on the identified predictive factors, facilitating clinical decision-making and patient management. ConclusionsThe Treebag machine learning model effectively predicts 1-year mortality in patients with type A aortic dissection, stratifying risk profiles. Key factors for enhancing survival include surgical intervention, β-blocker administration, and management of systolic blood pressure, lymphocyte, carbon dioxide combining power, eosinophil, and white blood cell levels, offering a valuable tool for improving patient outcomes.

Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance Machine Learning Robustness

Introducing Adaptive Continuous Adversarial Training (ACAT) to Enhance Machine Learning Robustness