Machine learning for geological mapping : algorithms and applications

ObjectiveEnhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice.MethodsResearches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized.ResultsBased on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics.ConclusionResearchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.

In Mild Cognitive Impairment (MCI), identifying a high risk of conversion to Alzheimer’s Disease Dementia (AD) is a primary goal for patient management. Machine Learning (ML) algorithms are widely employed to pursue data-driven diagnostic and prognostic goals. An agreement on the stability of these algorithms –when applied to different biomarkers and other conditions– is far from being reached. In this study, we compared the different prognostic performances of three supervised ML algorithms fed with multimodal biomarkers of MCI subjects obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Random Forest, Gradient Boosting, and eXtreme Gradient Boosting algorithms predict MCI conversion to AD. They can also be simultaneously employed –with the voting procedure– to improve predictivity. AD prediction accuracy is influenced by the nature of the data (i.e., neuropsychological test scores, cerebrospinal fluid AD-related proteins and APOE ε4, cerebral structural MRI (sMRI) data). In our study, independent of the applied ML algorithms, sMRI data showed the lowest accuracy (0.79) compared to other classes. Multimodal data were helpful in the algorithms’ performances by combining clinical and biological measures. Accordingly, using the three ML algorithms, the highest accuracy (0.90) was reached by employing neuropsychological and AD-related biomarkers. Finally, the feature selection procedure indicated that the most critical variables in the respective classes were the ADAS-Cog-13 scale, the medial temporal lobe and hippocampus atrophy, and the ratio between phosphorylated Tau and Aβ42 proteins. In conclusion, our data support the notion that using multiple ML algorithms and multimodal biomarkers helps make more accurate and solid predictions.

In this chapter, a brief overview of the role and applications of machine learning (ML) algorithms in future wireless cellular networks is presented, more specifically, in the context of self-organizing networks (SONs). SON is a promising and innovative concept, in which future networks are expected to analyze and use historical data in order to improve and adapt themselves to the network conditions. For this to be possible, however, algorithms that are capable of extracting patterns from data and learn from previous actions are necessary. This chapter highlights the utilization and possible applications of ML algorithms in future cellular networks. A brief introduction of ML and SON is presented, followed by an analysis of current state of the art solutions involving ML in SON. Lastly, guidelines on the utilization of intelligent algorithms in SON and future research trends in the area are highlighted and conclusions are drawn.

Fertility preferences significantly influence population dynamics and reproductive health outcomes, particularly in low-resource settings, such as Somalia, where high fertility rates and limited healthcare infrastructure pose significant challenges. Understanding the determinants of fertility preferences is critical for designing targeted interventions. This study leverages machine learning (ML) algorithms and Shapley Additive extensions (SHAP) to identify key predictors of fertility preferences among reproductive-aged women in Somalia. This cross-sectional study utilized data from the 2020 Somalia Demographic and Health Survey (SDHS), encompassing 8,951 women aged 15–49 years. The outcome variable, fertility preference, was dichotomized as either desire for more children or preference to cease childbearing. Predictor variables included sociodemographic factors, such as age, education, parity, wealth, residence, and distance to health facilities. Seven ML algorithms were evaluated for predictive performance, with Random Forest emerging as the optimal model based on metrics such as accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUROC). SHAP was employed to interpret the model by quantifying the feature contributions. The SHAP analysis identified the most influential predictors of fertility preferences as age group, region, number of births in the last five years, number of children born, marital status, wealth index, education level, residence, and distance to health facilities. Specifically, age group was the most significant feature, followed by region and number of births in the last five years. Women aged 45–49 years and those with higher parity were significantly more likely to prefer no additional children. Distance to health facilities has emerged as a critical barrier, with better access being associated with a greater likelihood of desiring more children. The Random Forest model demonstrated superior performance, achieving an accuracy of 81%, precision of 78%, recall of 85%, F1-score of 82%, and AUROC of 0.89. SHAP analysis provided interpretable insights, highlighting the nuanced interplay of sociodemographic factors. This study underscores the potential of ML algorithms and SHAP in advancing our understanding of fertility preferences in low-resource settings. By identifying critical sociodemographic determinants, such as age group, region, number of births in the last five years, number of children born, marital status, wealth index, education level, residence, distance to health facilities, and employment status, these findings offer actionable insights to inform evidence-based reproductive health interventions in Somalia. Future research should expand the application of ML to longitudinal data and incorporate additional cultural and psychosocial predictors to enhance the robustness and applicability of this model.

Spam detection is an evolving issue in line with the increasing volume of data and the evolution of spam techniques. In recent years, the application of machine learning (ML) algorithms has become an effective solution to enhance the accuracy and efficiency of spam detection systems. This study aims to analyze various machine learning algorithms applied in spam detection systems through a literature review. Several popular algorithms used in spam detection include Naive Bayes, Support Vector Machine (SVM), Neural Network, Recurrent Neural Network (RNN), and Transformer-based models. Each algorithm has its strengths and weaknesses that affect its performance in handling spam detection issues, depending on the characteristics of the data and the application requirements. Based on the data obtained, the Naive Bayes algorithm achieved 88% accuracy, 85% precision, 90% recall, and 87% F1-score. In contrast, SVM showed higher results with 93% accuracy, 92% precision, 94% recall, and 93% F1-score. Neural Network reached 96% accuracy, 95% precision, 97% recall, and 96% F1-score, while Recurrent Neural Network (RNN) achieved 95% accuracy, 94% precision, 96% recall, and 95% F1-score. Transformer-based models provided the best results with 97% accuracy, 96% precision, 98% recall, and 97% F1-score. This study adopts a literature analysis method by reviewing various articles and research that discuss the application of these algorithms in spam detection. In conclusion, the selection of the appropriate algorithm should be adjusted to the characteristics of the dataset, the complexity of the problem, and the availability of computational resources, as each algorithm has its own strengths and weaknesses in the context of spam detection.

With the evolution of fifth generation (5G) network technologies, network maintenance strategies have become increasingly complex, necessitating the use of predictive analysis enabled by Machine Learning (ML) algorithms. This paper emphasizes exploring how ML algorithms can further enhance predictive maintenance in 5G and future networks. It reviews the current literature on this interdisciplinary topic, identifying key ML models such as Decision Trees, Neural Networks, and Support Vector Machines, and discussing their benefits and limitations. Special attention is given to the methodologies in applying these models, handling of data stages, and the training process. Major challenges in implementing ML in the context of network maintenance, such as data privacy, data gathering, model training, and generalizability, are discussed. Furthermore, the research aims to go beyond predicting maintenance needs to introduce a proactive approach in improving overall network performance and pre-empting potential issues based on ML predictions. The paper also discusses possible future trends including advancements in ML algorithms, Automated Machine Learning (AutoML), Explainable AI, and others. The objective is to provide a comprehensive understanding of the current ML-based predictive maintenance field and outline possibilities for future research. The study finds that the application of ML algorithms continues to show promise in transforming the landscape of network management by improving predictive maintenance and proactive performance enhancement strategies. It remains a challenging yet important area in the context of 5G networks.

Application of machine learning in carbon capture and storage: An in-depth insight from the perspective of geoscience

Perfluorooctanoic acid (PFOA) is used in a variety of industries and is highly persistent in the environment, with potential human health risks. Photocatalysis has been extensively used for the decomposition of various organic pollutants, yet its simulation and modeling are challenging. This research aimed to establish different machine learning (ML) algorithms which can simulate and predict the photocatalytic degradation of PFOA. The published results were used to estimate and predict the photocatalytic degradation of PFOA. Statistical criteria including the coefficient of determination (R2 ), mean absolute error (MAE), and mean squared error (MSE) were considered in assessing the best method of modeling. Among the seven ML algorithms pre-screened, Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), and Random Forest (RF) showed the best performance and were chosen for deep modeling and analysis. Grid search was used to optimize the models developed by AdaBoost, GBM, and RF; and permutation variable importance (PVI) was used to analyze the relative importance of different variables. Based on the modeling results, GBM model (R2 = 0.878, MSE = 106.660, MAE = 6.009) and RF model (R2 = 0.867, MSE = 107.500, MAE = 6.796) showed superior performances compared with AdaBoost model (R2 = 0.574, MSE = 388.369, MAE = 16.480). Furthermore, the PVI results suggested that the GBM model provided the best outcome, with the light irradiation time, type of catalyst, dosage of catalyst, solution pH, irradiation intensity, initial PFOA concentration, oxidizing agents (peroxymonosulfate, ammonium persulfate, and sodium persulfate), irradiation wavelength, and solution temperature as the most important process variables in decreasing order.

Three-dimensional (3D) printing has become a widely used manufacturing technology, but predicting the mechanical and physical properties of printed parts remains a critical challenge. In particular, tensile strength, hardness, and surface roughness are essential for assessing product quality and reliability. Addressing this gap requires advanced computational approaches. Machine Learning (ML) algorithms have the potential to enhance automation and provide accurate predictions of product quality in 3D printing. The primary objective of this study is to review, classify, and analyze recent research on the application of ML algorithms for predicting mechanical (tensile strength, hardness) and physical (surface roughness) properties in 3D-printed materials. This review aims to identify current research trends, highlight existing gaps in the literature, and propose potential directions for future investigations in this rapidly evolving interdisciplinary field. For this purpose, a literature review was conducted in the Web of Science database using keywords such as “3D printing”, “machine learning”, “tensile strength”, “hardness”, and “roughness. The review systematically examines the material types, ML algorithms, printing parameters, and testing methods reported in the literature, and the frequency of these parameters is visualized through graphs to illustrate research trends. The findings reveal that ML algorithms achieve high accuracy in predicting tensile strength, hardness, and surface roughness, providing valuable insights for process optimization. However, current research still shows limited evidence for direct improvements in efficiency and error reduction, indicating important directions for future work.

The screening and diagnosis of osteoporosis in patients with type 2 diabetes mellitus (T2DM) based on bone mineral density remains challenging because of the limited availability and accessibility of dual-energy X-ray absorptiometry. We aimed to develop and validate models to predict the risk of osteoporosis in postmenopausal women with T2DM based on machine learning (ML) algorithms. This retrospective study included 303 postmenopausal women with T2DM. To develop prediction models for osteoporosis, we applied nine ML algorithms combined with demographic, clinical, and laboratory data. The least absolute shrinkage and selection operator were used to perform feature selection. We used the bootstrap resampling technique for model training and validation. To test the performance of the models, we calculated indices including the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, calibration curve, and decision curve analysis. Furthermore, we conducted fivefold cross-validation for parameter optimization and model validation. Feature importance was assessed using the SHapley additive explanation (SHAP). We identified 10 independent predictors as the most valuable features. An AUROC of 0.616-1.000 was observed for nine ML algorithms. The extreme gradient boosting (XGBoost) model exhibited the best performance, outperforming conventional risk assessment tools and registering 0.993 in the training set, 0.798 in the validation set, and 0.786 in the test set for fivefold cross-validation. Using SHAP, we found that the explanatory variables contributed to the model and their relationship with osteoporosis occurrence. Furthermore, we developed a user-friendly tool for calculating the risk of osteoporosis. With the integration of demographic and clinical risk factors, ML algorithms can accurately predict osteoporosis. The XGBoost model showed ideal performance. With the incorporation of these models in the clinic, patients may benefit from early osteoporosis diagnosis and treatment.

Predictive modelling of mineral prospectivity using satellite remote sensing and machine learning algorithms

Remotely sensed air temperature data from NASA POWER are widely used in regions with scarce climatic observations, particularly for agricultural applications such as calculating crop water requirements. This study employed a suite of machine learning (ML) algorithms to correct biases in NASA POWER air temperature outputs, including multiple support vector regression (SVR) variants—Linear SVR, Quadratic SVR, Cubic SVR, Fine Gaussian SVR, Medium Gaussian SVR, Coarse Gaussian SVR—and ensemble decision tree models: bagged trees (BGT) and boosted trees (BT). The objective of this study was to assess the ability of different ML algorithms to reduce biases in NASA POWER air temperature data, with the broader goal of identifying the most suitable ML method for air temperature bias correction in Nigeria. For this analysis, we used daily air temperature records from seven meteorological stations across diverse regions of Nigeria. The performance of NASA POWER minimum and maximum air temperature datasets was evaluated using standard error metrics. Subsequent application of ML algorithms significantly improved data accuracy: the normalized root mean square error (NRMSE) of the corrected outputs was mostly below 10%, indicating excellent predictive performance when ML was integrated. Among the SVR variants tested, Fine Gaussian SVR consistently yielded the best prediction results. This finding suggests that Fine Gaussian SVR is a robust tool for enhancing the reliability of air temperature data—critical for improving the accuracy of crop water requirement calculations in regions where in-situ air temperature observations are limited.

The rapid expansion of urban populations, coupled with growing epidemiological evidence that associates extreme temperature events with adverse health outcomes and elevated mortality rates underscores the critical role of Urban Green Areas (UGAs) in delivering ecosystem services that enhance human well-being. Among these services, the air temperature cooling potential (ΔT°C) driven by ecosystem evapotranspiration (ET) represents a key mechanism for mitigating heat-related health risks.This study investigates the capacity of various Machine Learning (ML) algorithms to predict the ΔT°C of UGAs, thereby supporting thermal regulation through ET and highlighting their importance in sustainable urban planning and climate adaptation strategies. We used multiple years of experimental Eddy Covariance (EC) observations of the ET the to train and validate a series of ML algorithms with the objective to simulate the cooling effect of the urban vegetation. A preliminary analysis of predictor variables was conducted to identify and rank their importance using the mean absolute Shapley (Sh) values. Results indicate that incoming shortwave solar radiation (Rg) was the most influential predictor (Sh = 0.45), followed by vapor pressure deficit (VPD, Sh = 0.20), relative humidity (RH, Sh = 0.075), air temperature (AirT, Sh = 0.065), friction velocity (u*, Sh = 0.02), and wind speed (WS, Sh = 0.01). The application of ML algorithms revealed that Bootstrap Aggregation (Bagging) and Least-Squares Boosting (LSBoost) performed best, achieving R² values of 0.89 and 0.83, respectively, during the training phase compared to observed data. Other algorithms, including Neural Networks (NN), Gaussian Process Regression (GPR), and Support Vector Machines (SVM), showed also similar, but slightly lower r2 , with values ranging from 0.80 (NN) to 0.79 (SVM). Ten-fold cross-validation confirmed robust generalization, as model performance remained consistent regardless of the data subset used to compute R² between modeled and observed values. Further evaluation using Taylor diagrams showed that the average normalized standard deviation (σn) and Pearson correlation coefficient of the models were 0.89 (±0.02) and 0.90 (±0.02), respectively, closely matching the observed data.During the testing phase we observed, as expected, a clear reduction of the ML performance compared to the training phase: however, over the three years of the testing phase, RG bagging and LSBOOST have confirmed their superiority, compared to the other algorithms, with an average r2 between observed and simulated data of 0.66 and 0.67 respectively. Discrepancies between predicted and observed ΔT°C during testing were most evident during midday hours, with an average overestimation of 0.31°C (±0.2).Overall, the investigated UGAs demonstrated an average capacity to reduce ambient air temperature during summer by approximately 2°C to 4°C.

BackgroundDespite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explored. ML models are capable of algorithmically learning from large, complex datasets and can offer advantages in predicting time-to-event data. We reviewed the recent applications of ML for survival analysis using RWD in healthcare.MethodsPUBMED and EMBASE were searched from database inception through March 2023 to identify peer-reviewed English-language studies of ML models for predicting time-to-event outcomes using the RWD. Two reviewers extracted information on the data source, patient population, survival outcome, ML algorithms, and the Area Under the Curve (AUC).ResultsOf 257 citations, 28 publications were included. Random survival forests (N = 16, 57%) and neural networks (N = 11, 39%) were the most popular ML algorithms. There was variability across AUC for these ML models (median 0.789, range 0.6–0.950). ML algorithms were predominately considered for predicting overall survival in oncology (N = 12, 43%). ML survival models were often used to predict disease prognosis or clinical events (N = 27, 96%) in the oncology, while less were used for treatment outcomes (N = 1, 4%).ConclusionsThe ML algorithms, random survival forests and neural networks, are mainly used for RWD to predict survival outcomes such as disease prognosis or clinical events in the oncology. This review shows that more opportunities remain to apply these ML algorithms to inform treatment decision-making in clinical practice. More methodological work is also needed to ensure the utility and applicability of ML models in survival outcomes.

The disease popularly known as Alzheimer’s disease (AD), which causes cognitive impairment, is extensively distributed throughout the world. The lack of a cure or strategy to stop the growth of the disease notwithstanding, it is one of the most extensively studied illnesses in medicine and healthcare. However, patients with AD have a variety of treatment options to select from, including pharmaceuticals or non-drug alternatives, to help them maintain their quality of life. Over time, patients will need to be treated differently based on how far into the disease process they are. In this regard, early detection and classification of the illness stages can have a substantial impact on the treatment of symptoms. Early detection of Alzheimer’s disease is critical for the analysis of patient medications and the selection of appropriate specialists. The goal of this project is to provide a comprehensive comparison of the techniques used in the detection and classification of Alzheimer’s disease. In medical research, machine learning algorithms can be used to uncover facts, particularly in disease prediction. Support vector machines (SVM), decision trees, Bayes classifiers, K-nearest neighbors (KNN), gradient boosting, and other machine learning algorithms are used to diagnose a variety of diseases and conditions. The application of machine learning algorithms can result in the quick and highly accurate prediction and classification of this disease. This project helps in analyses of different machine learning techniques and algorithms that are used to predict and classify and provides an extensive accuracy comparison of the machine learning techniques used for the prediction and classification of Alzheimer’s disease.