The neutron star outer crust equation of state: a machine learning approach

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 aging and multimorbid population and health personnel shortages pose a substantial burden on primary health care. While predictive machine learning (ML) algorithms have the potential to address these challenges, concerns include transparency and insufficient reporting of model validation and effectiveness of the implementation in the clinical workflow. To systematically identify predictive ML algorithms implemented in primary care from peer-reviewed literature and US Food and Drug Administration (FDA) and Conformité Européene (CE) registration databases and to ascertain the public availability of evidence, including peer-reviewed literature, gray literature, and technical reports across the artificial intelligence (AI) life cycle. PubMed, Embase, Web of Science, Cochrane Library, Emcare, Academic Search Premier, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI.org (Association for the Advancement of Artificial Intelligence), arXiv, Epistemonikos, PsycINFO, and Google Scholar were searched for studies published between January 2000 and July 2023, with search terms that were related to AI, primary care, and implementation. The search extended to CE-marked or FDA-approved predictive ML algorithms obtained from relevant registration databases. Three reviewers gathered subsequent evidence involving strategies such as product searches, exploration of references, manufacturer website visits, and direct inquiries to authors and product owners. The extent to which the evidence for each predictive ML algorithm aligned with the Dutch AI predictive algorithm (AIPA) guideline requirements was assessed per AI life cycle phase, producing evidence availability scores. The systematic search identified 43 predictive ML algorithms, of which 25 were commercially available and CE-marked or FDA-approved. The predictive ML algorithms spanned multiple clinical domains, but most (27 [63%]) focused on cardiovascular diseases and diabetes. Most (35 [81%]) were published within the past 5 years. The availability of evidence varied across different phases of the predictive ML algorithm life cycle, with evidence being reported the least for phase 1 (preparation) and phase 5 (impact assessment) (19% and 30%, respectively). Twelve (28%) predictive ML algorithms achieved approximately half of their maximum individual evidence availability score. Overall, predictive ML algorithms from peer-reviewed literature showed higher evidence availability compared with those from FDA-approved or CE-marked databases (45% vs 29%). The findings indicate an urgent need to improve the availability of evidence regarding the predictive ML algorithms' quality criteria. Adopting the Dutch AIPA guideline could facilitate transparent and consistent reporting of the quality criteria that could foster trust among end users and facilitating large-scale implementation.

Identification of damage in its early stage can have a great contribution in decreasing the maintenance costs and prolonging the life of valuable structures. Although conventional damage detection techniques have a mature background, their widespread application in industrial practice is still missing. In recent years the application of Machine Learning (ML) algorithms have been more and more exploited in structural health monitoring systems (SHM). Because of the superior capabilities of ML approaches in recognizing and classifying available patterns in a dataset, they have demonstrated a significant improvement in traditional damage identification algorithms. This review study focuses on the use of machine learning (ML) approaches in Ultrasonic Guided Wave (UGW)-based SHM, in which a structure is continually monitored using permanent sensors. Accordingly, multiple steps required for performing damage detection through UGWs are stated. Moreover, it is outlined that the employment of ML techniques for UGW-based damage detection can be subtended into two main phases: (1) extracting features from the data set, and reducing the dimension of the data space, (2) processing the patterns for revealing patterns, and classification of instances. With this regard, the most frequent techniques for the realization of those two phases are elaborated. This study shows the great potential of ML algorithms to assist and enhance UGW-based damage detection algorithms.

The purpose of this analysis is to assess the use of machine learning (ML) algorithms in the prediction of postoperative outcomes, including complications, recurrence, and death in transsphenoidal surgery. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically reviewed all papers that used at least one ML algorithm to predict outcomes after transsphenoidal surgery. We searched Scopus, PubMed, and Web of Science databases for studies published prior to May 12, 2021. We identified 13 studies enrolling 5,048 patients. We extracted the general characteristics of each study; the sensitivity, specificity, area under the curve (AUC) of the ML models developed as well as the features identified as important by the ML models. We identified 12 studies with 5,048 patients that included ML algorithms for adenomas, three with 1807 patients specifically for acromegaly, and five with 2105 patients specifically for Cushing's disease. Nearly all were single-institution studies. The studies used a heterogeneous mix of ML algorithms and features to build predictive models. All papers reported an AUC greater than 0.7, which indicates clinical utility. ML algorithms have the potential to predict postoperative outcomes of transsphenoidal surgery and can improve patient care. Ensemble algorithms and neural networks were often top performers when compared with other ML algorithms. Biochemical and preoperative features were most likely to be selected as important by ML models. Inexplicability remains a challenge, but algorithms such as local interpretable model–agnostic explanation or Shapley value can increase explainability of ML algorithms. Our analysis shows that ML algorithms have the potential to greatly assist surgeons in clinical decision making.

Slope stability prediction using integrated metaheuristic and machine learning approaches: A comparative study

Recent technological advances allowed an exponential expansion of biological sequence data, and the extraction of meaningful information through Machine Learning (ML) algorithms. This knowledge improved the understanding of the mechanisms related to several fatal diseases, e.g., Cancer and COVID-19, helping to develop innovative solutions, such as CRISPR-based gene editing, coronavirus vaccine, and precision medicine. These advances benefit our society and economy, directly impacting peoples lives in various areas, such as health care, drug discovery, forensic analysis, and food analysis. Nevertheless, ML approaches applied to biological data require representative, quantitative, and informative features. Necessarily, as many ML algorithms can handle only numerical data, sequences need to be translated into a feature vector. This process, known as feature extraction, is a fundamental step for the elaboration of high-quality ML-based models in bioinformatics, by allowing the feature engineering stage, with the design and selection of suitable features. Feature engineering, ML algorithm selection, and hyperparameter tuning are often manual and time-consuming processes, requiring extensive domain knowledge, and performed manually by a human expert. To deal with this problem, we developed a new package, BioAutoML, which automatically runs an end-to-end ML pipeline. BioAutoML extracts numerical and informative features from biological sequence databases, automating feature selection, recommendation of ML algorithm(s), and tuning of hyperparameters, using Automated ML (AutoML). BioAutoML has two components, divided into four modules, (1) automated feature engineering (feature extraction and selection modules) and (2) Metalearning (algorithm recommendation and hyperparameter tuning modules). Our experimental results, assessing the relevance of our proposal, indicate robust results for different problem domains, such as SARS-CoV-2, anticancer peptides, HIV sequences, and non-coding RNAs. According to our systematic review, our proposal is innovative compared to available studies in the literature, being the first study to propose automated feature engineering and metalearning for biological sequences. BioAutoML has a high potential to significantly reduce the expertise required to use ML pipelines, aiding researchers in combating diseases, particularly in low- and middle-income countries. This initiative can provide biologists, physicians, epidemiologists, and other stakeholders with an opportunity for widespread use of these techniques to enhance the health and well-being of their communities.

Hardware Trojans (HTs), maliciously inserted in an integrated circuit during untrusted design or fabrication process pose critical threat to the system security. With the ever increasing capabilities of an adversary to subvert the system during run-time, it is imperative to detect the manifested Trojans in order to reinforce the trust in hardware. In this regard, Machine Learning (ML) algorithms, with their intrinsic capability to execute feature engineering at high learning rates, are emerging as promising candidates to be utilized by system defenders. In this paper, we explore Trojan detection mechanisms that are based on ML, and thereby investigate the prowess of the ML algorithms in bolstering system security. Furthermore, we analyze the efficiency of each proposed Trojan detection strategy based on the underlying ML algorithm. Finally, we underline some problems with existing Trojan detection approaches and discuss future research in the interest of improved performance of the employed ML algorithms, thus aiding in enhancing the intended hardware security.

The integration of machine learning (ML) algorithms into audit processes represents a significant advancement in the field of auditing, offering substantial benefits in terms of efficiency, accuracy, and risk management. This review examines the transformative potential of ML in auditing, highlighting its key benefits and the challenges that must be addressed to fully leverage its capabilities. Machine learning algorithms, with their ability to analyze large datasets and identify patterns, enhance the accuracy and thoroughness of audits. Traditional auditing methods often rely on sampling and manual checks, which can miss anomalies and fraudulent activities. In contrast, ML algorithms can process entire datasets, uncovering subtle patterns and irregularities that may indicate fraud or errors. This comprehensive analysis reduces the risk of oversight and improves the reliability of audit findings. One of the primary benefits of ML in auditing is its capacity for anomaly detection. ML models can be trained on historical data to understand normal financial behavior and flag deviations that might signify irregularities. This ability to detect anomalies in real-time enables auditors to identify potential issues promptly, reducing the time lag between occurrence and detection of fraud. Predictive analytics, powered by ML, further enhances audit processes by forecasting future risks based on historical data. This proactive approach allows auditors to anticipate and mitigate risks before they materialize, contributing to more robust risk management strategies. Despite these advantages, integrating ML into audit processes presents several challenges. Ensuring data quality and integrity is crucial, as ML algorithms are only as good as the data they analyze. Poor-quality data can lead to inaccurate predictions and conclusions. Additionally, the "black box" nature of some ML algorithms can pose transparency issues, making it difficult for auditors to explain how specific conclusions were reached, which is critical for stakeholder trust and regulatory compliance. Another significant challenge is the potential for algorithmic bias. ML models can inadvertently perpetuate existing biases in the data, leading to unfair or skewed audit outcomes. Continuous monitoring and validation of ML algorithms are necessary to detect and mitigate such biases. In conclusion, while integrating machine learning algorithms into audit processes offers substantial benefits in terms of accuracy, efficiency, and risk management, it also necessitates careful attention to data quality, transparency, and bias mitigation. Addressing these challenges is essential to fully realize the potential of ML in enhancing audit practices. Keywords: Benefits, Challenges, Audit Processes, Algorithms, ML.

6 - Machine learning and deep learning algorithms for fault diagnosis of photovoltaic systems

Applications of Machine Learning (ML) algorithms in Structural Health Monitoring (SHM) have become of great interest in recent years owing to their superior ability to detect damage and deficiencies in civil engineering structures. With the advent of the Internet of Things, big data and the colossal and complex backlog of aging civil infrastructure assets, such applications will increase very rapidly. ML can efficiently perform several analyses of clustering, regression and classification of damage in diverse structures, including bridges, buildings, dams, tunnels, wind turbines, etc. In this systematic review, the diverse ML algorithms used in this domain have been classified into two major subfields: vibration-based SHM and image-based SHM. The efficacy of deploying ML algorithms in SHM has been discussed and detailed critical analysis of ML applications in SHM has been provided. Accordingly, practical recommendations have been made and current knowledge gaps and future research needs have been outlined.

Deep learning and its application in geochemical mapping

Predicting intraurban PM2.5 concentrations using enhanced machine learning approaches and incorporating human activity patterns

This study presents a comparative analysis of the classification performance of facial and emotion recognition systems using Machine Learning (ML) and Deep Learning (DL) algorithms. The primary objective of this work is to evaluate the applicability of emotion recognition in fields such as psychotherapy and crime analysis, using the FER-2013 dataset.The study was conducted with ML algorithms such as Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Decision Trees, and Gradient Boosting, as well as the DL algorithm, Convolutional Neural Network (CNN). Supported by the application of feature selection, preprocessing, and feature extraction techniques, the model’s performance was measured using standard metrics such as accuracy, precision, F1-score, and AUC-ROC.The experimental results showed that the highest classification accuracy (46.61%) was achieved with the CNN model. While the ML models generally offered lower accuracy, they provided advantages in terms of computational efficiency in specific scenarios.This study aims to contribute to the literature by providing a comparative analysis of ML and DL algorithms and by highlighting the effect of data preprocessing on performance. The findings set targets for future work, such as real-time system integration and hybrid model development.

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.

Satellite-based aerosol optical property retrieval over land, especially size-related parameters, is challenging. This study proposed a novel two-stage machine learning (ML) algorithm for retrieving aerosol optical depth (AOD), Ångström exponent (AE), fine mode fraction (FMF), and fine mode AOD (FAOD)) over land using MODIS observed reflectance. The new ML algorithm consists of three steps: (1) first, all samples extracted from AERONET measurements were used to train the ML model, (2) then, to reduce the extreme estimation bias of the model, divided low-value and high-value samples were used to train low-value and high-value ML models, respectively, and (3) finally, the three ML models were integrated into the final retrieval based on the weight interpolation. Independent site network validation results show that the new ML algorithm has a Pearson correlation coefficient (R) of 0.894 (0.638, 0.661, 0.865) and root mean square error (RMSE) of 0.146 (0.258, 0.245, 0.153) for the AOD (AE, FMF, FAOD) retrieval, which significantly outperforms the validation metrics of MODIS operational products, with AOD (AE, FMF, FAOD) RMSE of 0.130-0.156 (0.536-0.569, 0.313, 0.191). The inter-comparison of aerosol products shows that the spatial patterns of AOD, AE, FMF, and FAOD of the new ML algorithm are in good agreement with those of the MODIS and POLDER products. These results illustrate that the new ML algorithm has good performance and transferability and indicate the ability of ML methods to be applied to multispectral instruments (such as MODIS) to retrieve multiple aerosol properties.