Supervised Machine Learning Techniques Research Articles

Knowledge Integration in Steel Microstructure Analysis Using Unsupervised Image Segmentation and Supervised Machine Learning Techniques

This study presents a methodology for analyzing bainitic microstructures in steel using image segmentation techniques and machine learning methods. Images of steel microstructures were processed using a superpixel segmentation algorithm, which generated image segments based on grayscale intensity. The histograms of values in grayscale of those segments served as input for classification models such as decision trees, random forests, and the KNN algorithm. Experimental results demonstrated that this method enables effective microstructure classification, including the identification of segments containing bainite. A comparison of algorithms revealed the superiority of random forests in terms of stability and accuracy. Results also show that small segments provide better final results. The obtained results indicate the potential for further development of this method using more advanced neural networks for automated steel microstructure analysis.

Predicting the Risk of Maxillary Canine Impaction Based on Maxillary Measurements Using Supervised Machine Learning.

To predict palatally impacted maxillary canines based on maxilla measurements through supervised machine learning techniques. The maxilla images from 138 patients were analysed to investigate intermolar width, interpremolar width, interpterygoid width, maxillary length, maxillary width, nasal cavity width and nostril width, obtained through cone beam computed tomography scans. The predictive models were built using the following machine learning algorithms: Adaboost Classifier, Decision Tree, Gradient Boosting Classifier, K-Nearest Neighbours (KNN), Logistic Regression, Multilayer Perceptron Classifier (MLP), Random Forest Classifier and Support Vector Machine (SVM). A 5-fold cross-validation approach was employed to validate each model. Metrics such as area under the curve (AUC), accuracy, recall, precision and F1 Score were calculated for each model, and ROC curves were constructed. The predictive model included four variables (two dental and two skeletal measurements). The interpterygoid width and nostril width showed the largest effect sizes. The Gradient Boosting Classifier algorithm exhibited the best metrics, with AUC values ranging from 0.91 [CI95% = 0.74-0.98] for test data to 0.89 [CI95% = 0.86-0.94] for crossvalidation. The nostril width variable demonstrated the highest importance across all tested algorithms. The use of maxillary measurements, through supervised machine learning techniques, is a promising method for predicting palatally impacted maxillary canines. Among the models evaluated, both the Gradient Boosting Classifier and the Random Forest Classifier demonstrated the best performance metrics, with accuracy and AUC values exceeding 0.8, indicating strong predictive capability.

A Comparative Analysis Machine Learning Techniques for LULC Classification Using Landsat-8 Satellite Imagery

This study compares various categorization methods to assign land use and land cover (LULC) classes. Using Geographic Information Systems (GIS) and Remote Sensing (RS) to leverage the dynamic and complex area of LULC, this study examines the potential of different machine learning classification methods. Precise differentiation and classification of various land cover categories, such as green vegetation, urban areas, water bodies, dark green vegetation, and bare terrain, are made possible by the great spatial and spectral resolution of Landsat imagery. For efficient land management and planning, the integration of Landsat data with GIS and RS approaches provides insightful information about the distribution and temporal changes in LULC. This study uses four classifiers to explore the principles of supervised machine learning techniques and identify their drawbacks and advantages. Testing results show that the Support Vector Machine with four kernel linear-99.17%, radial basis (RBF)-99.11%, Sigmoid-99.11% and Polynomial-99.11% is a reliable option for LULC classification, outperforming than other classifiers in terms of accuracy, including the Minimum Distance Classifier (MD-93.47%), Maximum Likelihood Classifier (MLC- 98.98%), and Mahalanobis Distance Classifier (MH-97.83%). Among the tested classifiers, SVM with four kernels notably shows the highest accuracy. With their essential insights for well-informed decision-making towards sustainable development and resource utilization, our findings add to a thorough understanding of LULC dynamics. For accurate mapping and long-term monitoring of deviations in land cover (LC), the study emphasizes the value of using front-line classification systems in remote sensing applications

Developing A Machine Learning Model to Predict Medication Adherence in Chronic Disease Management

In the healthcare industry, chronic disease prediction is crucial. It is crucial to diagnose the illness early. Large amounts of data are generated in computer science as a result of significant technological advancements. Many medical databases are created as clinical information networks advance. Data mining, the process of managing vast amounts of diverse data and extracting insights from it, has emerged as a crucial area of study. Early illness detection, patient treatment, and community services from huge data creation in the biomedical and healthcare communities are all benefited by the accurate analysis of medical data. Nowadays, one of the main areas of research is the management and extraction of knowledge from vast amounts of diverse data. Accurate processing of health data helps the biomedical and healthcare communities by improving patient care, early illness detection, and community services. However, analytical precision is reduced if medical data is not sufficiently consistent. For the domains of biomedical pattern recognition and master learning, the perception and diagnosis of chronic disease are guaranteed to be consistent. Additionally, the decision-making approach's goal is pushed. The study of high-dimensional, multi-modal biomedical data can be effectively addressed by machine learning. In computer science, chronic disease prediction is crucial. Early detection and prediction of chronic disease is crucial. The dataset for chronic obstructive pulmonary disease is used for analysis by the suggested model. Using supervised machine learning techniques such as Random Forest, Multiplayer Perceptron, Logistic Regression, Stochastic Gradient Descent, and XG boost, we provide a chronic obstructive pulmonary disease prediction system. Next, we examine classification techniques for predicting chronic diseases using a variety of criteria, such as accuracy, precision, sensitivity, ROC, and AUC.

The Application of Machine Learning Algorithms to Predict HIV Testing in Repeated Adult Population-Based Surveys in South Africa: Protocol for a Multiwave Cross-Sectional Analysis.

HIV testing is the cornerstone of HIV prevention and a pivotal step in realizing the Joint United Nations Program on HIV/AIDS (UNAIDS) goal of ending AIDS by 2030. Despite the availability of relevant survey data, there exists a research gap in using machine learning (ML) to analyze and predict HIV testing among adults in South Africa. Further investigation is needed to bridge this knowledge gap and inform evidence-based interventions to improve HIV testing. This study aims to determine consistent predictors of HIV testing by applying supervised ML algorithms in repeated adult population-based surveys in South Africa. A retrospective analysis of multiwave cross-sectional survey data will be conducted to determine the predictors of HIV testing among South African adults aged 18 years and older. A supervised ML technique will be applied across the five cycles of the South African National HIV Prevalence, Incidence, Behavior, and Communication Survey (SABSSM) surveys. The Human Science Research Council (HSRC) conducted the SABSSM surveys in 2002, 2005, 2008, 2012, and 2017. The available SABSSM datasets will be imported to RStudio (version 4.3.2; Posit Software, PBC) to clean and remove outliers. A chi-square test will be conducted to select important predictors of HIV testing. Each dataset will be split into 80% training and 20% test samples. Logistic regression, support vector machines, random forests, and decision trees will be used. A cross-validation technique will be used to divide the training sample into k-folds, including a validation set, and models will be trained on each fold. The models' performance will be evaluated on the validation set using evaluation metrics such as accuracy, precision, recall, F1-score, area under curve-receiver operating characteristics, and confusion matrix. The SABSSM datasets are open access datasets available on the HSRC database. Ethics approval for this study was obtained from the University of Johannesburg Research and Ethics Committee on April 23, 2024 (REC-2725-2024). The authors were given access to all five SABSSM datasets by the HSRC on August 20, 2024. The datasets were explored to identify the independent variables likely influencing HIV testing uptake. The findings of this study will determine consistent variables predicting HIV testing uptake among the South African adult population over the course of 20 years. Furthermore, this study will evaluate and compare the performance metrics of the 4 different ML algorithms, and the best model will be used to develop an HIV testing predictive model. This study will contribute to existing knowledge and deepen understanding of factors linked to HIV testing beyond traditional methods. Consequently, the findings would inform evidence-based policy recommendations that can guide policy makers to formulate more effective and targeted public health approaches toward strengthening HIV testing. DERR1-10.2196/59916.

A machine learning framework for short-term prediction of chronic obstructive pulmonary disease exacerbations using personal air quality monitors and lifestyle data

Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous disease with a variety of symptoms including, persistent coughing and mucus production, shortness of breath, wheezing, and chest tightness. As the disease advances, exacerbations, i.e. acute worsening of respiratory symptoms, may increase in frequency, leading to potentially life-threatening complications. Exposure to air pollutants may trigger COPD exacerbations. Literature predictive models for COPD exacerbations, while promising, may be constrained by their reliance on fixed air quality sensor data that may not fully capture individuals’ dynamic exposure to air pollution. To address this, we designed a machine learning (ML) framework that leverages data from personal air quality monitors, health records, lifestyle, and living condition information to build models that perform short-term prediction of COPD exacerbations. The framework employs (i) k-means clustering to uncover potentially distinct patient sub-types, (ii) supervised ML techniques (Logistic Regression, Random Forest, and eXtreme Gradient Boosting) to train and test predictive models for each patient sub-type and (iii) an explainable artificial intelligence technique (SHAP) to interpret the final models. The framework was tested on data collected in 101 COPD patients monitored for up to 6 months with occurrence of exacerbation in 10.7% of total samples. Two different patient sub-types have been identified, characterised by different disease severity. The best performing models were Random Forest in cluster 1, with area under the receiver operating characteristic curve (AUC) of 0.90, and area under the precision/recall curve (AUPRC) of 0.7; and Random Forest model in cluster 2, with AUC of 0.82 and AUPRC of 0.56. The model interpretability analysis identified previous symptoms and cumulative pollutant exposure as key predictors of exacerbations. The results of our study set a premise for a predictive framework in COPD exacerbations, particularly investigating the potential influence of environmental features. The SHAP analysis revealed that the contribution of environmental features is not uniform across all subjects. For instance, cumulative exposure to pollutants demonstrated greater predictive power in cluster 1. The SHAP analysis also shown that overall clinical factors and individual symptomatology play the most significant role in this setup to determine exacerbation risk.

Machine learning-based technique for gain prediction of mm-wave miniaturized 5G MIMO slotted antenna array with high isolation characteristics

This study presents the design and analysis of a compact 28 GHz MIMO antenna for 5G wireless networks, incorporating simulations, measurements, and machine learning (ML) techniques to optimize its performance. With dimensions of 3.19 λ₀ × 3.19 λ₀, the antenna offers a bandwidth of 5.1 GHz, a peak gain of 9.43 dBi, high isolation of 31.37 dB, and an efficiency of 99.6%. Simulations conducted in CST Studio were validated through prototype measurements, showing strong agreement between the measured and simulated results. To further validate the design, an equivalent RLC circuit model was developed and analyzed using ADS, with the reflection coefficient results closely matching those from CST. Additionally, supervised ML techniques were employed to predict the antenna’s gain, evaluating nine models using metrics such as R-squared, variance score, mean absolute error, and root mean squared error. Among the models, Random Forest Regression achieved the highest accuracy, delivering approximately 99% reliability in gain prediction. This integration of machine learning with antenna design underscores its potential to optimize performance and enhance design efficiency. With its compact size, high isolation, and exceptional efficiency, the proposed antenna is a promising candidate for 28 GHz 5G applications, offering innovative solutions for next-generation wireless communication.

An Efficient Method for Detecting Cyberbullying Using Supervised Machine Learning Techniques

An Efficient Method for Detecting Cyberbullying Using Supervised Machine Learning Techniques

Exploring the process—structure–property relationship of nylon aramid 3D printed composites and parameter optimization using supervised machine learning techniques

The main goals of this research are to identify the significant input parameters using supervised machine learning methods and investigate the relationship between the process, structure, and properties of components created using fused deposition modeling utilizing nylon aramid composite filaments. To develop an experimental layout using Taguchi’s L18 orthogonal array, six different FDM parameters such as infill pattern, infill density, layer thickness, component orientation, print temperature, and raster angle have been taken into consideration.Using an Ultimaker FDM printer, rectangular samples were created, and the values of face hardness, thickness regions, printing time, and component weight were assessed. ANOVA and the signal to noise ratio method are two techniques that have been used to find the significant influencing parameter and the ideal combination of parameters. When comparing thickly layered samples with a suitable increase in time and weight, thin-layered samples are shown to have greater hardness values at both tested areas. With a 50.09% contribution to face hardness and a 30.11% contribution to hardness at the thickness area, raster angle is found to be significant over hardness.Layer thickness is an important element that contributes 81.95% to printing time and 42.09% to part weight, respectively. In an 80:20 train-test split, the decision tree approach outperformed the k-nearest neighbor algorithm for all four output responses, with classification accuracy ranging from 83.33% to 100%. Infill density is recommended by the decision tree method to be extremely significant over face hardness and component weight, and layer thickness is similarly recommended to be highly significant over printing time and hardness at the thickness region. The presence of surface pores, interior voids, and layer abnormalities is confirmed by FESEM images.

Analyzing and predicting global happiness index via integrated multilayer clustering and machine learning models.

This study addresses the research objective of predicting global happiness and identifying its key drivers. We propose a novel predictive framework that integrates unsupervised and supervised machine learning techniques to uncover the complex patterns underlying happiness scores across nations. Initially, we apply K-Means clustering to group countries based on similarities in their happiness patterns. For the first time, these cluster assignments are subsequently incorporated as additional features into ensemble learning models-specifically, Random Forests and XGBoost-to enhance the prediction of happiness scores. This hierarchical analysis approach yields a significant improvement in predictive performance, with an approximate 12% increase in R² compared to models that do not include clustering information. Using data from the World Happiness Report, our analysis reveals that global happiness can be categorized into three distinct groups (high, medium, and low). Among the various determinants examined, social support and GDP emerge as the most influential factors contributing to the happiness index. These findings not only advance the methodological framework for predicting happiness but also provide robust evidence for policymakers seeking to implement targeted interventions aimed at improving public well-being and promoting social progress.

Analyzing Sentiments in Twitter Data with a Support Vector Machine

Analyzing public sentiments through various tweets, particularly on platforms like Twitter, has emerged as a valuable tool for measuring opinions on multiple topics, including politics, elections, government policies, and known figures. These tweets convey a wide range of sentiments, from supportive to critical viewpoints. In this study, the tweets are categorized into negative, neutral, and positive sentiments, represented by -1, 0, and 1, respectively. We harnessed a support vector machine, a robust supervised machine learning technique recognized for its efficacy in sentiment analysis tasks. Proposed methodology encompassed stages, such as data collection and pre-processing, which involved tasks like text cleaning, tokenisation, and TF-IDF vectorization. Subsequently, we trained the SVM model and evaluated its performance visually using bar charts, confusion matrices, and a classification report, capturing key metrics such as accuracy, precision, recall, and F1-score. This study underscores the significance of social media-driven political sentiment analysis and its relevance in understanding public opinion and political discourse. Furthermore, the results underscore the efficiency of SVM in analyzing tweet sentiments, achieving an accuracy rate of 94%, a weighted average rate F1-score of 94%, and recall rates of 87% for negative sentiment, 97% for neutral sentiment, and 95% for positive sentiment. The proposed strategy achieves classification accuracy up to 94% on the standard datasets (i.e., Kaggle).

Model Assessment of Supervised Machine Learning Techniques for Predicting Heart Disease

Abstract: Stroke is a leading cause of death and permanent disability, making it a serious global health concern. Cell death is the result of impaired blood flow to the brain. Patient outcomes are ultimately impacted by prompt and precise stroke type identification, which is essential for efficient management and treatment. The potential of machine learning algorithms to categorize stroke subtypes and forecast the probability of stroke occurrence is examined in this project. A thorough dataset that included clinical features, medical imaging results, and patient demographics was put together. To guarantee compatibility with machine learning algorithms, this dataset was preprocessed, fixing missing values and transforming categorical variables into a numerical format. To find the most pertinent variables for prediction, feature selection was done. Four machine learning algorithms were used: Random Forest (an ensemble learning technique), k-NN (a nearest-neighbor method), J48 (a decision tree algorithm), and Naive Bayes. A 10-fold cross-validation technique was used to thoroughly assess the model's performance, guaranteeing solid and trustworthy outcomes. The Random Forest algorithm proved to be effective in predicting stroke, as evidenced by its highest accuracy. The potential of machine learning to help medical professionals prevent, diagnose, and treat strokes is highlighted by this finding. The knowledge gathered from this research could improve patient care and guide the creation of individualized treatment programs. This project highlights the wider application of machine learning in healthcare beyond stroke. Machine learning has the potential to revolutionize healthcare delivery by utilizing data analysis and predictive modeling, which could result in better patient outcomes, more individualized treatments, and better diagnostics.

Aspect-based Sentiment Analysis with an Ensemble Learning Framework for Requirements Elicitation from App Reviews

In the past three years, numerous studies have demonstrated the effective performance of aspect-based sentiment analysis (ABSA) in eliciting requirements from APP reviews. However, in aspect category detection (ACD) and aspect category polarity (ACP), traditional supervised machine learning techniques still dominate the latest research. Inherent limitations, such as poor generalization ability, low robustness, and high dependence on feature engineering, often constrain the methods. Additionally, while most research continues to center on the efficacy of singular models or strategies, the effective amalgamation of traditional and contemporary techniques has yet to be adequately explored. Given these challenges, this study proposes an ensemble learning framework based on XGBoost and Stacking. Compared to baseline, the framework achieves a performance improvement of 22.9%-28.4% in ACD and 9.3%-13.2% in ACP tasks based on different feature engineering. Overall, the preliminary attempt in this study to apply ensemble learning in ABSA for requirements elicitation from APP reviews, providing a feasible new direction for overcoming the inherent limitations of traditional techniques in fine-grained sentiment modeling in this field.

A Comparative Study of Supervised Machine Learning Techniques for Water Leakage Prediction in Drill and Blast Excavated Water Tunnels in the Himalayan Region

In the Himalayan region, drill and blast (DB) method excavated water tunnels often pass through complex geological rock mass conditions which were formed with frequent tectonic movements. Due to these tectonic movements, the rock mass conditions in the Himalayas are highly faulted, folded, jointed, sheared, and fractured. The geological formations are the pathway for the water ingress and leakage out in the water tunnel. This water leakage in the tunnel causes a complicated geological hazard, which significantly increases tunnel instability and can lead to a delay in completion time and finally increase the cost of the tunnel project. Therefore, an efficient water ingress/leakage prediction model is essential to mitigate these challenges. In this research, various field data such as rock mass properties, topography, and permeability data sets were collected from the Nilgiri-II Hydroelectric project water tunnel. These real-time field datasets have been used for comprehensive assessment and to predict the water ingress/leakage in the water tunnel by using four supervised machine learning (ML) approaches such as Support Vector Regression (SVR), Decision Tree (DT) regression, K-Nearest Neighbors (KNN) and Random Forest (RF) regression models. It was observed that the KNN shows the best regression performance of 93% followed by RF of 92%, DT of 86%, and SVR of 66%. Therefore, all these machine learning approaches show good performance in predicting water ingress/leakage based on real field data except the SVR model.

Machine Learning Side Effect Trend Predictions and the SIDER Database

In the Pharmaceutical and Healthcare industries, understanding medications is key in the treatment of patients. Worldwide, there are hundreds of thousands of medications available, classified in categories related to medication therapy and the remediation that they provide. With so many different types of medication, medical doctors and pharmacists need to determine what kinds of drugs to provide to patients with specific medical needs. New medication studies necessitate careful analysis of available medication data during clinical trials, prior to production of new medications, and through the course of prescribed medication therapy. he use of medication therapy is not justified if the number of side effects outweighs the remedial benefits. Therefore, not all medications are deemed medically safe for all patients. Supervised machine learning techniques assist scientists with predicting side effects of medications that are under development. Prediction techniques aid future development of medications based on the properties of current medication data models.

Data anonymization through supervised Machine Learning

In the digital era, protecting personal data has become increasingly important as organizations collect vast amounts of sensitive information. Data anonymization techniques, such as k-anonymity, aim to strike a balance between privacy preservation and data utility. In this study, we applied supervised machine learning techniques to assess the impact of anonymization on data utility and privacy. Specifically, we evaluated k-anonymity and l-diversity models using four supervised learning methods: Naive Bayes, K-nearest neighbors, Decision Trees, and Random Forest. By measuring classification accuracy, our results highlight Naive Bayes as the most effective method for handling anonymized data, achieving high accuracy. This work provides valuable insights into the trade-offs between privacy protection and data utility, using advanced machine learning techniques to evaluate anonymization models.

A whole-brain functional connectivity model of Alzheimer's disease pathology.

A whole-brain functional connectivity model of Alzheimer's disease pathology.

Diabetes Detection Using Supervised Machine Learning Classifiers

Diabetes mellitus is a chronic metabolic disease that has grown to be a major global health issue. Effective treatment and the avoidance of serious consequences depend on an early and precise diagnosis. A promising method for automated and precise diabetes detection in recent years has been machine learning (ML) approaches. The use of supervised machine learning techniques, such as Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Random Forest, for diabetes detection is examined in this study. A popular benchmark dataset in the field of diabetes research, the Pima Indian Diabetes Dataset, is used to assess the efficacy of these algorithms. With Logistic Regression obtaining the best accuracy, experimental results demonstrate the effectiveness of machine learning algorithms to predict diabetes. The study's conclusions demonstrate ML's potential as a valuable tool for early detection and prevention of diabetes. Key Words: Machine learning, Diabetes detection, Support vector machine, Logistic regression, Random forest

Detecting DDoS Attacks in Network Traffic Based on Supervised Machine Learning Techniques

Detecting DDoS Attacks in Network Traffic Based on Supervised Machine Learning Techniques

Use and Comparison of Machine Learning Techniques to Discern the Protein Patterns of Autoantibodies Present in Women with and without Breast Pathology.

Breast cancer (BC) has become a global health problem, ranking first in incidence and fifth in mortality in women around the world. Although there are some diagnostic methods for the disease, these are not sufficiently effective and are invasive. In this work, we discriminated between patients without breast pathology (BP), with benign BP, and with BC based on the band patterns obtained from Western blot strip images of the autoantibody response to antigens of the T47D tumor line using and comparing supervised machine learning techniques to have a sensitive and accurate method. When comparing the aforementioned machine learning techniques, it was found that by obtaining a convolutional neural network architecture from a neuroevolution algorithm, it is possible to automatically discriminate with a classification accuracy of 90.67% between patients with cancer and with/without BP. In the case of discrimination between patients with cancer and without BP, a classification accuracy of 96.67% was obtained with the K-NN algorithm and 95.13% with the convolutional neural network obtained using a neuroevolution algorithm, although these results are not statistically significant. It is concluded that the convolutional neural network obtained by neuroevolution is the method with the best performance with respect to those evaluated in this work.